Simple_Mix_Linear_Regression.ipynb

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      "name": "Simple/Mix Linear Regression.ipynb",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/ygeszvain/projects/blob/master/Simple_Mix_Linear_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "metadata": {
        "id": "--2OqVVQ2gy1",
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      "cell_type": "code",
      "source": [
        "# import packages for analysis and modeling\n",
        "import pandas as pd  # data frame operations\n",
        "import numpy as np  # arrays and math functions\n",
        "from scipy.stats import uniform  # for training-and-test split\n",
        "import statsmodels.api as sm  # statistical models (including regression)\n",
        "import statsmodels.formula.api as smf  # R-like model specification\n",
        "import matplotlib.pyplot as plt  # 2D plotting\n",
        "import seaborn as sns  # PROVIDES TRELLIS AND SMALL MULTIPLE PLOTTING\n",
        "#from pyvttbl import DataFrame # Anova\n",
        "from scipy import stats\n",
        "from statsmodels.formula.api import ols\n",
        "import scipy.stats as stats\n",
        "from sklearn import linear_model\n",
        "from shapely.geometry import Point, Polygon\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import r2_score\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "vYJVmZsdhCKm",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Data Scource and Clean Up"
      ]
    },
    {
      "metadata": {
        "id": "vymycAA32gy7",
        "colab_type": "code",
        "outputId": "5ec8b927-1d55-4c8d-f784-f8a5b608ea35",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "# coaches salary data\n",
        "coaches = pd.read_csv(\"https://raw.githubusercontent.com/ygeszvain/BigDataAnalytics/master/coaches9.csv\")\n",
        "coaches.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>School</th>\n",
              "      <th>Conference</th>\n",
              "      <th>Coach</th>\n",
              "      <th>SchoolPay</th>\n",
              "      <th>TotalPay</th>\n",
              "      <th>Bonus</th>\n",
              "      <th>BonusPaid</th>\n",
              "      <th>AssistantPay</th>\n",
              "      <th>Buyout</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Air Force</td>\n",
              "      <td>Mt. West</td>\n",
              "      <td>Troy Calhoun</td>\n",
              "      <td>885000</td>\n",
              "      <td>885000</td>\n",
              "      <td>247000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Akron</td>\n",
              "      <td>MAC</td>\n",
              "      <td>Terry Bowden</td>\n",
              "      <td>411000</td>\n",
              "      <td>412500</td>\n",
              "      <td>225000</td>\n",
              "      <td>50000</td>\n",
              "      <td>0</td>\n",
              "      <td>688500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Alabama</td>\n",
              "      <td>SEC</td>\n",
              "      <td>Nick Saban</td>\n",
              "      <td>8307000</td>\n",
              "      <td>8307000</td>\n",
              "      <td>1100000</td>\n",
              "      <td>500000</td>\n",
              "      <td>0</td>\n",
              "      <td>33600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Alabama at Birmingham</td>\n",
              "      <td>C-USA</td>\n",
              "      <td>Bill Clark</td>\n",
              "      <td>900000</td>\n",
              "      <td>900000</td>\n",
              "      <td>950000</td>\n",
              "      <td>165471</td>\n",
              "      <td>0</td>\n",
              "      <td>3847500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Appalachian State</td>\n",
              "      <td>Sun Belt</td>\n",
              "      <td>Scott Satterfield</td>\n",
              "      <td>712500</td>\n",
              "      <td>712500</td>\n",
              "      <td>295000</td>\n",
              "      <td>145000</td>\n",
              "      <td>0</td>\n",
              "      <td>2160417</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            "text/plain": [
              "                  School Conference              Coach  SchoolPay  TotalPay  \\\n",
              "0              Air Force   Mt. West       Troy Calhoun     885000    885000   \n",
              "1                  Akron        MAC       Terry Bowden     411000    412500   \n",
              "2                Alabama        SEC         Nick Saban    8307000   8307000   \n",
              "3  Alabama at Birmingham      C-USA         Bill Clark     900000    900000   \n",
              "4      Appalachian State   Sun Belt  Scott Satterfield     712500    712500   \n",
              "\n",
              "     Bonus  BonusPaid  AssistantPay    Buyout  \n",
              "0   247000          0             0         0  \n",
              "1   225000      50000             0    688500  \n",
              "2  1100000     500000             0  33600000  \n",
              "3   950000     165471             0   3847500  \n",
              "4   295000     145000             0   2160417  "
            ]
          },
          "metadata": {
            "tags": []
          },
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        }
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      },
      "cell_type": "code",
      "source": [
        "# graduation success rate data\n",
        "gs = pd.read_csv(\"https://raw.githubusercontent.com/ygeszvain/BigDataAnalytics/master/NCAAFootballGSR2006.csv\")\n",
        "gs.head()\n",
        "\n",
        "# The NCAA Graduation Success Rate (GSR) is designed to show the proportion of student-athletes on any given team who earn a college degree. \n",
        "# The NCAA has imposed a new set of academic standards that seeks to hold teams and institutions accountable for how well a student-athlete \n",
        "# progresses toward a degree.\n",
        "\n",
        "# The Federal Graduation Rate (FGR) is compiled by the U.S. Department of Education and is used as an indicator of academic success for \n",
        "# college student-athletes. FGR measures the percentage of first-time, full-time freshman who graduate within six years of entering their \n",
        "# original four-year institution."
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Cohort Year</th>\n",
              "      <th>School</th>\n",
              "      <th>Conference</th>\n",
              "      <th>Sport</th>\n",
              "      <th>State</th>\n",
              "      <th>GSR</th>\n",
              "      <th>FGR</th>\n",
              "      <th>ConfAbbr</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2006</td>\n",
              "      <td>Alabama A&amp;M University</td>\n",
              "      <td>Southwestern Athletic Conf.</td>\n",
              "      <td>Football</td>\n",
              "      <td>AL</td>\n",
              "      <td>40</td>\n",
              "      <td>45.0</td>\n",
              "      <td>SAC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2006</td>\n",
              "      <td>Alabama State University</td>\n",
              "      <td>Southwestern Athletic Conf.</td>\n",
              "      <td>Football</td>\n",
              "      <td>AL</td>\n",
              "      <td>81</td>\n",
              "      <td>59.0</td>\n",
              "      <td>SAC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2006</td>\n",
              "      <td>Alcorn State University</td>\n",
              "      <td>Southwestern Athletic Conf.</td>\n",
              "      <td>Football</td>\n",
              "      <td>MS</td>\n",
              "      <td>57</td>\n",
              "      <td>39.0</td>\n",
              "      <td>SAC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2006</td>\n",
              "      <td>Appalachian State University</td>\n",
              "      <td>Southern Conference</td>\n",
              "      <td>Football</td>\n",
              "      <td>NC</td>\n",
              "      <td>68</td>\n",
              "      <td>56.0</td>\n",
              "      <td>Sco</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2006</td>\n",
              "      <td>Arizona State University</td>\n",
              "      <td>Pac-12 Conference</td>\n",
              "      <td>Football</td>\n",
              "      <td>AZ</td>\n",
              "      <td>67</td>\n",
              "      <td>52.0</td>\n",
              "      <td>Pac-12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Cohort Year                        School                   Conference  \\\n",
              "0         2006        Alabama A&M University  Southwestern Athletic Conf.   \n",
              "1         2006      Alabama State University  Southwestern Athletic Conf.   \n",
              "2         2006       Alcorn State University  Southwestern Athletic Conf.   \n",
              "3         2006  Appalachian State University          Southern Conference   \n",
              "4         2006      Arizona State University            Pac-12 Conference   \n",
              "\n",
              "      Sport State  GSR   FGR ConfAbbr  \n",
              "0  Football    AL   40  45.0      SAC  \n",
              "1  Football    AL   81  59.0      SAC  \n",
              "2  Football    MS   57  39.0      SAC  \n",
              "3  Football    NC   68  56.0      Sco  \n",
              "4  Football    AZ   67  52.0   Pac-12  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
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      ]
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        "id": "cpz87uE5Gbvj",
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      "source": [
        "# Algorithm to match strings\n",
        "import numpy as np\n",
        "def levenshtein_ratio_and_distance(s, t, ratio_calc = False):\n",
        "    \"\"\" levenshtein_ratio_and_distance:\n",
        "        Calculates levenshtein distance between two strings.\n",
        "        If ratio_calc = True, the function computes the\n",
        "        levenshtein distance ratio of similarity between two strings\n",
        "        For all i and j, distance[i,j] will contain the Levenshtein\n",
        "        distance between the first i characters of s and the\n",
        "        first j characters of t\n",
        "    \"\"\"\n",
        "    # Initialize matrix of zeros\n",
        "    rows = len(s)+1\n",
        "    cols = len(t)+1\n",
        "    distance = np.zeros((rows,cols),dtype = int)\n",
        "\n",
        "    # Populate matrix of zeros with the indeces of each character of both strings\n",
        "    for i in range(1, rows):\n",
        "        for k in range(1,cols):\n",
        "            distance[i][0] = i\n",
        "            distance[0][k] = k\n",
        "\n",
        "    # Iterate over the matrix to compute the cost of deletions,insertions and/or substitutions    \n",
        "    for col in range(1, cols):\n",
        "        for row in range(1, rows):\n",
        "            if s[row-1] == t[col-1]:\n",
        "                cost = 0 # If the characters are the same in the two strings in a given position [i,j] then the cost is 0\n",
        "            else:\n",
        "                # In order to align the results with those of the Python Levenshtein package, if we choose to calculate the ratio\n",
        "                # the cost of a substitution is 2. If we calculate just distance, then the cost of a substitution is 1.\n",
        "                if ratio_calc == True:\n",
        "                    cost = 2\n",
        "                else:\n",
        "                    cost = 1\n",
        "            distance[row][col] = min(distance[row-1][col] + 1,      # Cost of deletions\n",
        "                                 distance[row][col-1] + 1,          # Cost of insertions\n",
        "                                 distance[row-1][col-1] + cost)     # Cost of substitutions\n",
        "    if ratio_calc == True:\n",
        "        # Computation of the Levenshtein Distance Ratio\n",
        "        Ratio = ((len(s)+len(t)) - distance[row][col]) / (len(s)+len(t))\n",
        "        return Ratio\n",
        "    else:\n",
        "        # print(distance) # Uncomment if you want to see the matrix showing how the algorithm computes the cost of deletions,\n",
        "        # insertions and/or substitutions\n",
        "        # This is the minimum number of edits needed to convert string a to string b\n",
        "        #return \"The strings are {} edits away\".format(distance[row][col])\n",
        "        return distance[row][col]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "NqzyACla2-qt",
        "colab_type": "code",
        "outputId": "5410e21c-897a-4aa9-e425-962980411ca2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "cell_type": "code",
      "source": [
        "# create a dictionary base on string matching algorithm\n",
        "matches1 = {}\n",
        "coaches_school = coaches['School'].values\n",
        "print(len(coaches_school))\n",
        "\n",
        "gs_school = gs['School'].values\n",
        "print(len(gs_school))\n",
        "\n",
        "for elm in gs_school:\n",
        "    for el in coaches_school:\n",
        "        if levenshtein_ratio_and_distance(elm, el, ratio_calc=True) > 0.71:\n",
        "            matches1[elm] = el\n",
        "\n",
        "print(len(matches1))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "129\n",
            "242\n",
            "29\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "gZEDpbeVsOYV",
        "colab_type": "code",
        "outputId": "b60272a5-3d87-4514-c8d0-c526d4e114b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "cell_type": "code",
      "source": [
        "# review the dictionary\n",
        "print(matches1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'Appalachian State University': 'Appalachian State', 'Arkansas State University': 'Arkansas State', 'Boston College': 'Boston College', 'Central Michigan University': 'Central Michigan', 'Coastal Carolina University': 'Coastal Carolina', 'Colorado State University': 'Colorado State', 'Davidson College': 'Boston College', 'Eastern Michigan University': 'Eastern Michigan', 'Florida Atlantic University': 'Florida Atlantic', 'Florida International University': 'Florida International', 'Georgia Southern University': 'Georgia Southern', 'Louisiana Tech University': 'Louisiana Tech', 'Marist College': 'Boston College', 'Michigan State University': 'Michigan State', 'Mississippi State University': 'Mississippi State', 'New Mexico State University': 'New Mexico State', 'North Carolina A&T State University': 'North Carolina State', 'North Carolina State University': 'North Carolina State', 'Northern Illinois University': 'Northern Illinois', 'Oklahoma State University': 'Oklahoma State', 'San Diego State University': 'San Diego State', 'San Jose State University': 'San Jose State', 'Southern Methodist University': 'Southern Methodist', 'Texas Christian University': 'Texas Christian', 'University of Alabama at Birmingham': 'Alabama at Birmingham', 'University of Southern California': 'Southern California', 'Washington State University': 'Washington State', 'Western Kentucky University': 'Western Kentucky', 'Western Michigan University': 'Western Michigan'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "x20ywNe-GgRm",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# match up the strings\n",
        "gs[\"School\"] = gs[\"School\"].map(matches1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "D6ONseOoHSev",
        "colab_type": "code",
        "outputId": "a5f0b50e-484b-4866-d6c3-2418e196c5fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "# review the data after string matching\n",
        "gs.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>School</th>\n",
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              "      <th>Sport</th>\n",
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              "      <th>0</th>\n",
              "      <td>2006</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southwestern Athletic Conf.</td>\n",
              "      <td>Football</td>\n",
              "      <td>AL</td>\n",
              "      <td>40</td>\n",
              "      <td>45.0</td>\n",
              "      <td>SAC</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2006</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southwestern Athletic Conf.</td>\n",
              "      <td>Football</td>\n",
              "      <td>AL</td>\n",
              "      <td>81</td>\n",
              "      <td>59.0</td>\n",
              "      <td>SAC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2006</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southwestern Athletic Conf.</td>\n",
              "      <td>Football</td>\n",
              "      <td>MS</td>\n",
              "      <td>57</td>\n",
              "      <td>39.0</td>\n",
              "      <td>SAC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2006</td>\n",
              "      <td>Appalachian State</td>\n",
              "      <td>Southern Conference</td>\n",
              "      <td>Football</td>\n",
              "      <td>NC</td>\n",
              "      <td>68</td>\n",
              "      <td>56.0</td>\n",
              "      <td>Sco</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2006</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Pac-12 Conference</td>\n",
              "      <td>Football</td>\n",
              "      <td>AZ</td>\n",
              "      <td>67</td>\n",
              "      <td>52.0</td>\n",
              "      <td>Pac-12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Cohort Year             School                   Conference     Sport  \\\n",
              "0         2006                NaN  Southwestern Athletic Conf.  Football   \n",
              "1         2006                NaN  Southwestern Athletic Conf.  Football   \n",
              "2         2006                NaN  Southwestern Athletic Conf.  Football   \n",
              "3         2006  Appalachian State          Southern Conference  Football   \n",
              "4         2006                NaN            Pac-12 Conference  Football   \n",
              "\n",
              "  State  GSR   FGR ConfAbbr  \n",
              "0    AL   40  45.0      SAC  \n",
              "1    AL   81  59.0      SAC  \n",
              "2    MS   57  39.0      SAC  \n",
              "3    NC   68  56.0      Sco  \n",
              "4    AZ   67  52.0   Pac-12  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "metadata": {
        "id": "B3Co3tG5H403",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Combine the coaches salary data and graduation success data\n",
        "data1 = coaches.merge(gs, \n",
        "                        left_on='School', \n",
        "                        right_on='School', \n",
        "                        how='outer', \n",
        "                        suffixes=[\"\",\"_2\"] #since they are the same name, we need a suffix\n",
        "                       )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "bb3uBFLIYXKJ",
        "colab_type": "code",
        "outputId": "fc0bb0c1-3744-4117-f6f9-0009d7e5d72c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3060
        }
      },
      "cell_type": "code",
      "source": [
        "data1"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>School</th>\n",
              "      <th>Conference</th>\n",
              "      <th>Coach</th>\n",
              "      <th>SchoolPay</th>\n",
              "      <th>TotalPay</th>\n",
              "      <th>Bonus</th>\n",
              "      <th>BonusPaid</th>\n",
              "      <th>AssistantPay</th>\n",
              "      <th>Buyout</th>\n",
              "      <th>Cohort Year</th>\n",
              "      <th>Conference_2</th>\n",
              "      <th>Sport</th>\n",
              "      <th>State</th>\n",
              "      <th>GSR</th>\n",
              "      <th>FGR</th>\n",
              "      <th>ConfAbbr</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Air Force</td>\n",
              "      <td>Mt. West</td>\n",
              "      <td>Troy Calhoun</td>\n",
              "      <td>885000.0</td>\n",
              "      <td>885000.0</td>\n",
              "      <td>247000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Akron</td>\n",
              "      <td>MAC</td>\n",
              "      <td>Terry Bowden</td>\n",
              "      <td>411000.0</td>\n",
              "      <td>412500.0</td>\n",
              "      <td>225000.0</td>\n",
              "      <td>50000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>688500.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Alabama</td>\n",
              "      <td>SEC</td>\n",
              "      <td>Nick Saban</td>\n",
              "      <td>8307000.0</td>\n",
              "      <td>8307000.0</td>\n",
              "      <td>1100000.0</td>\n",
              "      <td>500000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>33600000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Alabama at Birmingham</td>\n",
              "      <td>C-USA</td>\n",
              "      <td>Bill Clark</td>\n",
              "      <td>900000.0</td>\n",
              "      <td>900000.0</td>\n",
              "      <td>950000.0</td>\n",
              "      <td>165471.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3847500.0</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Conference USA</td>\n",
              "      <td>Football</td>\n",
              "      <td>AL</td>\n",
              "      <td>60.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>C-USA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Appalachian State</td>\n",
              "      <td>Sun Belt</td>\n",
              "      <td>Scott Satterfield</td>\n",
              "      <td>712500.0</td>\n",
              "      <td>712500.0</td>\n",
              "      <td>295000.0</td>\n",
              "      <td>145000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2160417.0</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Southern Conference</td>\n",
              "      <td>Football</td>\n",
              "      <td>NC</td>\n",
              "      <td>68.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>Sco</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Arizona</td>\n",
              "      <td>Pac-12</td>\n",
              "      <td>Kevin Sumlin</td>\n",
              "      <td>1600000.0</td>\n",
              "      <td>2000000.0</td>\n",
              "      <td>2025000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10000000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Arizona State</td>\n",
              "      <td>Pac-12</td>\n",
              "      <td>Herm Edwards</td>\n",
              "      <td>2000000.0</td>\n",
              "      <td>2000000.0</td>\n",
              "      <td>3010000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8166667.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Arkansas</td>\n",
              "      <td>SEC</td>\n",
              "      <td>Chad Morris</td>\n",
              "      <td>3500000.0</td>\n",
              "      <td>3500000.0</td>\n",
              "      <td>1000000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12500000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Arkansas State</td>\n",
              "      <td>Sun Belt</td>\n",
              "      <td>Blake Anderson</td>\n",
              "      <td>825000.0</td>\n",
              "      <td>825000.0</td>\n",
              "      <td>185000.0</td>\n",
              "      <td>25000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>300000.0</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Sun Belt Conference</td>\n",
              "      <td>Football</td>\n",
              "      <td>AR</td>\n",
              "      <td>68.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>Sun Belt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Army</td>\n",
              "      <td>Ind.</td>\n",
              "      <td>Jeff Monken</td>\n",
              "      <td>932521.0</td>\n",
              "      <td>932521.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Auburn</td>\n",
              "      <td>SEC</td>\n",
              "      <td>Gus Malzahn</td>\n",
              "      <td>6700000.0</td>\n",
              "      <td>6705656.0</td>\n",
              "      <td>1400000.0</td>\n",
              "      <td>375000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>32143750.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Ball State</td>\n",
              "      <td>MAC</td>\n",
              "      <td>Mike Neu</td>\n",
              "      <td>435689.0</td>\n",
              "      <td>435689.0</td>\n",
              "      <td>380000.0</td>\n",
              "      <td>30000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>980300.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Baylor</td>\n",
              "      <td>Big 12</td>\n",
              "      <td>Matt Rhule</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Boise State</td>\n",
              "      <td>Mt. West</td>\n",
              "      <td>Bryan Harsin</td>\n",
              "      <td>1650010.0</td>\n",
              "      <td>1650010.0</td>\n",
              "      <td>475000.0</td>\n",
              "      <td>145000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7784038.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Boston College</td>\n",
              "      <td>ACC</td>\n",
              "      <td>Steve Addazio</td>\n",
              "      <td>2514859.0</td>\n",
              "      <td>2514859.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Atlantic Coast Conference</td>\n",
              "      <td>Football</td>\n",
              "      <td>MA</td>\n",
              "      <td>94.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>ACC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Boston College</td>\n",
              "      <td>ACC</td>\n",
              "      <td>Steve Addazio</td>\n",
              "      <td>2514859.0</td>\n",
              "      <td>2514859.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Pioneer Football League</td>\n",
              "      <td>Football</td>\n",
              "      <td>NC</td>\n",
              "      <td>98.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>PFL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Boston College</td>\n",
              "      <td>ACC</td>\n",
              "      <td>Steve Addazio</td>\n",
              "      <td>2514859.0</td>\n",
              "      <td>2514859.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Pioneer Football League</td>\n",
              "      <td>Football</td>\n",
              "      <td>NY</td>\n",
              "      <td>77.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>PFL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Bowling Green</td>\n",
              "      <td>MAC</td>\n",
              "      <td>Mike Jinks</td>\n",
              "      <td>437228.0</td>\n",
              "      <td>437228.0</td>\n",
              "      <td>245000.0</td>\n",
              "      <td>81250.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>874456.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Brigham Young</td>\n",
              "      <td>Ind.</td>\n",
              "      <td>Kalani Sitake</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Buffalo</td>\n",
              "      <td>MAC</td>\n",
              "      <td>Lance Leipold</td>\n",
              "      <td>455500.0</td>\n",
              "      <td>455500.0</td>\n",
              "      <td>381000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020833.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>California</td>\n",
              "      <td>Pac-12</td>\n",
              "      <td>Justin Wilcox</td>\n",
              "      <td>1500000.0</td>\n",
              "      <td>1500000.0</td>\n",
              "      <td>900000.0</td>\n",
              "      <td>75000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7208500.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Central Florida</td>\n",
              "      <td>AAC</td>\n",
              "      <td>Josh Heupel</td>\n",
              "      <td>1700000.0</td>\n",
              "      <td>1700000.0</td>\n",
              "      <td>250000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3587500.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Central Michigan</td>\n",
              "      <td>MAC</td>\n",
              "      <td>John Bonamego</td>\n",
              "      <td>655000.0</td>\n",
              "      <td>655000.0</td>\n",
              "      <td>415000.0</td>\n",
              "      <td>45000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1125000.0</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Mid-American Conference</td>\n",
              "      <td>Football</td>\n",
              "      <td>MI</td>\n",
              "      <td>51.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>MAC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Charlotte</td>\n",
              "      <td>C-USA</td>\n",
              "      <td>Brad Lambert</td>\n",
              "      <td>625000.0</td>\n",
              "      <td>625000.0</td>\n",
              "      <td>120000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>556389.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Cincinnati</td>\n",
              "      <td>AAC</td>\n",
              "      <td>Luke Fickell</td>\n",
              "      <td>2000000.0</td>\n",
              "      <td>2000000.0</td>\n",
              "      <td>625000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7100000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Clemson</td>\n",
              "      <td>ACC</td>\n",
              "      <td>Dabo Swinney</td>\n",
              "      <td>6205000.0</td>\n",
              "      <td>6543350.0</td>\n",
              "      <td>1125000.0</td>\n",
              "      <td>500000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>35000000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Coastal Carolina</td>\n",
              "      <td>Sun Belt</td>\n",
              "      <td>Joe Moglia</td>\n",
              "      <td>400000.0</td>\n",
              "      <td>400000.0</td>\n",
              "      <td>800000.0</td>\n",
              "      <td>25000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>516667.0</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Big South Conference</td>\n",
              "      <td>Football</td>\n",
              "      <td>SC</td>\n",
              "      <td>66.0</td>\n",
              "      <td>57.0</td>\n",
              "      <td>BSC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Colorado</td>\n",
              "      <td>Pac-12</td>\n",
              "      <td>Mike MacIntyre</td>\n",
              "      <td>2878500.0</td>\n",
              "      <td>2878500.0</td>\n",
              "      <td>2150000.0</td>\n",
              "      <td>297000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10314583.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Colorado State</td>\n",
              "      <td>Mt. West</td>\n",
              "      <td>Mike Bobo</td>\n",
              "      <td>1800000.0</td>\n",
              "      <td>1800000.0</td>\n",
              "      <td>905000.0</td>\n",
              "      <td>50000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8000000.0</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Mountain West Conference</td>\n",
              "      <td>Football</td>\n",
              "      <td>CO</td>\n",
              "      <td>78.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>Mt. West</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Connecticut</td>\n",
              "      <td>AAC</td>\n",
              "      <td>Randy Edsall</td>\n",
              "      <td>1100000.0</td>\n",
              "      <td>1100000.0</td>\n",
              "      <td>650000.0</td>\n",
              "      <td>129000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1000000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>315</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Pioneer Football League</td>\n",
              "      <td>Football</td>\n",
              "      <td>CA</td>\n",
              "      <td>83.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>PFL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>316</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Southeastern Conference</td>\n",
              "      <td>Football</td>\n",
              "      <td>SC</td>\n",
              "      <td>65.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>SEC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>317</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Missouri Valley Football Conference</td>\n",
              "      <td>Football</td>\n",
              "      <td>SD</td>\n",
              "      <td>60.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>MVF</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>318</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>American Athletic Conference</td>\n",
              "      <td>Football</td>\n",
              "      <td>FL</td>\n",
              "      <td>57.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>AAC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>319</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Southern Conference</td>\n",
              "      <td>Football</td>\n",
              "      <td>TN</td>\n",
              "      <td>46.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>Sco</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>320</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Ohio Valley Conference</td>\n",
              "      <td>Football</td>\n",
              "      <td>TN</td>\n",
              "      <td>62.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>OVC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>321</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Southeastern Conference</td>\n",
              "      <td>Football</td>\n",
              "      <td>TN</td>\n",
              "      <td>64.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>SEC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>322</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Big 12 Conference</td>\n",
              "      <td>Football</td>\n",
              "      <td>TX</td>\n",
              "      <td>56.0</td>\n",
              "      <td>57.0</td>\n",
              "      <td>Big 12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>323</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Conference USA</td>\n",
              "      <td>Football</td>\n",
              "      <td>TX</td>\n",
              "      <td>66.0</td>\n",
              "      <td>59.0</td>\n",
              "      <td>C-USA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>324</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Mid-American Conference</td>\n",
              "      <td>Football</td>\n",
              "      <td>OH</td>\n",
              "      <td>67.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>MAC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>325</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Pac-12 Conference</td>\n",
              "      <td>Football</td>\n",
              "      <td>UT</td>\n",
              "      <td>73.0</td>\n",
              "      <td>57.0</td>\n",
              "      <td>Pac-12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>326</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Atlantic Coast Conference</td>\n",
              "      <td>Football</td>\n",
              "      <td>VA</td>\n",
              "      <td>74.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>ACC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>327</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Pac-12 Conference</td>\n",
              "      <td>Football</td>\n",
              "      <td>WA</td>\n",
              "      <td>74.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>Pac-12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>328</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Big Ten Conference</td>\n",
              "      <td>Football</td>\n",
              "      <td>WI</td>\n",
              "      <td>72.0</td>\n",
              "      <td>57.0</td>\n",
              "      <td>Big Ten</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>329</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Mountain West Conference</td>\n",
              "      <td>Football</td>\n",
              "      <td>WY</td>\n",
              "      <td>65.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>Mt. West</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>330</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Western Athletic Conference</td>\n",
              "      <td>Football</td>\n",
              "      <td>UT</td>\n",
              "      <td>84.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>WAC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>331</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Pioneer Football League</td>\n",
              "      <td>Football</td>\n",
              "      <td>IN</td>\n",
              "      <td>90.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>PFL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>332</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Southeastern Conference</td>\n",
              "      <td>Football</td>\n",
              "      <td>TN</td>\n",
              "      <td>82.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>SEC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>333</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Colonial Athletic Association</td>\n",
              "      <td>Football</td>\n",
              "      <td>PA</td>\n",
              "      <td>84.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>CAA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>334</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Big South Conference</td>\n",
              "      <td>Football</td>\n",
              "      <td>VA</td>\n",
              "      <td>58.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>BSC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>335</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Atlantic Coast Conference</td>\n",
              "      <td>Football</td>\n",
              "      <td>VA</td>\n",
              "      <td>78.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>ACC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>336</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Northeast Conference</td>\n",
              "      <td>Football</td>\n",
              "      <td>NY</td>\n",
              "      <td>82.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>NEC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>337</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Atlantic Coast Conference</td>\n",
              "      <td>Football</td>\n",
              "      <td>NC</td>\n",
              "      <td>86.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>ACC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>338</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Big Sky Conference</td>\n",
              "      <td>Football</td>\n",
              "      <td>UT</td>\n",
              "      <td>47.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>BSkyC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>339</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Big 12 Conference</td>\n",
              "      <td>Football</td>\n",
              "      <td>WV</td>\n",
              "      <td>80.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>Big 12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>340</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Southern Conference</td>\n",
              "      <td>Football</td>\n",
              "      <td>NC</td>\n",
              "      <td>62.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>Sco</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>341</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Missouri Valley Football Conference</td>\n",
              "      <td>Football</td>\n",
              "      <td>IL</td>\n",
              "      <td>67.0</td>\n",
              "      <td>59.0</td>\n",
              "      <td>MVF</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>342</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Southern Conference</td>\n",
              "      <td>Football</td>\n",
              "      <td>SC</td>\n",
              "      <td>88.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>Sco</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>343</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>The Ivy League</td>\n",
              "      <td>Football</td>\n",
              "      <td>CT</td>\n",
              "      <td>97.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>TIL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>344</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Missouri Valley Football Conference</td>\n",
              "      <td>Football</td>\n",
              "      <td>OH</td>\n",
              "      <td>52.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>MVF</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>345 rows × 16 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                    School Conference              Coach  SchoolPay  \\\n",
              "0                Air Force   Mt. West       Troy Calhoun   885000.0   \n",
              "1                    Akron        MAC       Terry Bowden   411000.0   \n",
              "2                  Alabama        SEC         Nick Saban  8307000.0   \n",
              "3    Alabama at Birmingham      C-USA         Bill Clark   900000.0   \n",
              "4        Appalachian State   Sun Belt  Scott Satterfield   712500.0   \n",
              "5                  Arizona     Pac-12       Kevin Sumlin  1600000.0   \n",
              "6            Arizona State     Pac-12       Herm Edwards  2000000.0   \n",
              "7                 Arkansas        SEC        Chad Morris  3500000.0   \n",
              "8           Arkansas State   Sun Belt     Blake Anderson   825000.0   \n",
              "9                     Army       Ind.        Jeff Monken   932521.0   \n",
              "10                  Auburn        SEC        Gus Malzahn  6700000.0   \n",
              "11              Ball State        MAC           Mike Neu   435689.0   \n",
              "12                  Baylor     Big 12         Matt Rhule        0.0   \n",
              "13             Boise State   Mt. West       Bryan Harsin  1650010.0   \n",
              "14          Boston College        ACC      Steve Addazio  2514859.0   \n",
              "15          Boston College        ACC      Steve Addazio  2514859.0   \n",
              "16          Boston College        ACC      Steve Addazio  2514859.0   \n",
              "17           Bowling Green        MAC         Mike Jinks   437228.0   \n",
              "18           Brigham Young       Ind.      Kalani Sitake        0.0   \n",
              "19                 Buffalo        MAC      Lance Leipold   455500.0   \n",
              "20              California     Pac-12      Justin Wilcox  1500000.0   \n",
              "21         Central Florida        AAC        Josh Heupel  1700000.0   \n",
              "22        Central Michigan        MAC      John Bonamego   655000.0   \n",
              "23               Charlotte      C-USA       Brad Lambert   625000.0   \n",
              "24              Cincinnati        AAC       Luke Fickell  2000000.0   \n",
              "25                 Clemson        ACC       Dabo Swinney  6205000.0   \n",
              "26        Coastal Carolina   Sun Belt         Joe Moglia   400000.0   \n",
              "27                Colorado     Pac-12     Mike MacIntyre  2878500.0   \n",
              "28          Colorado State   Mt. West          Mike Bobo  1800000.0   \n",
              "29             Connecticut        AAC       Randy Edsall  1100000.0   \n",
              "..                     ...        ...                ...        ...   \n",
              "315                    NaN        NaN                NaN        NaN   \n",
              "316                    NaN        NaN                NaN        NaN   \n",
              "317                    NaN        NaN                NaN        NaN   \n",
              "318                    NaN        NaN                NaN        NaN   \n",
              "319                    NaN        NaN                NaN        NaN   \n",
              "320                    NaN        NaN                NaN        NaN   \n",
              "321                    NaN        NaN                NaN        NaN   \n",
              "322                    NaN        NaN                NaN        NaN   \n",
              "323                    NaN        NaN                NaN        NaN   \n",
              "324                    NaN        NaN                NaN        NaN   \n",
              "325                    NaN        NaN                NaN        NaN   \n",
              "326                    NaN        NaN                NaN        NaN   \n",
              "327                    NaN        NaN                NaN        NaN   \n",
              "328                    NaN        NaN                NaN        NaN   \n",
              "329                    NaN        NaN                NaN        NaN   \n",
              "330                    NaN        NaN                NaN        NaN   \n",
              "331                    NaN        NaN                NaN        NaN   \n",
              "332                    NaN        NaN                NaN        NaN   \n",
              "333                    NaN        NaN                NaN        NaN   \n",
              "334                    NaN        NaN                NaN        NaN   \n",
              "335                    NaN        NaN                NaN        NaN   \n",
              "336                    NaN        NaN                NaN        NaN   \n",
              "337                    NaN        NaN                NaN        NaN   \n",
              "338                    NaN        NaN                NaN        NaN   \n",
              "339                    NaN        NaN                NaN        NaN   \n",
              "340                    NaN        NaN                NaN        NaN   \n",
              "341                    NaN        NaN                NaN        NaN   \n",
              "342                    NaN        NaN                NaN        NaN   \n",
              "343                    NaN        NaN                NaN        NaN   \n",
              "344                    NaN        NaN                NaN        NaN   \n",
              "\n",
              "      TotalPay      Bonus  BonusPaid  AssistantPay      Buyout  Cohort Year  \\\n",
              "0     885000.0   247000.0        0.0           0.0         0.0          NaN   \n",
              "1     412500.0   225000.0    50000.0           0.0    688500.0          NaN   \n",
              "2    8307000.0  1100000.0   500000.0           0.0  33600000.0          NaN   \n",
              "3     900000.0   950000.0   165471.0           0.0   3847500.0       2006.0   \n",
              "4     712500.0   295000.0   145000.0           0.0   2160417.0       2006.0   \n",
              "5    2000000.0  2025000.0        0.0           0.0  10000000.0          NaN   \n",
              "6    2000000.0  3010000.0        0.0           0.0   8166667.0          NaN   \n",
              "7    3500000.0  1000000.0        0.0           0.0  12500000.0          NaN   \n",
              "8     825000.0   185000.0    25000.0           0.0    300000.0       2006.0   \n",
              "9     932521.0        0.0        0.0           0.0         0.0          NaN   \n",
              "10   6705656.0  1400000.0   375000.0           0.0  32143750.0          NaN   \n",
              "11    435689.0   380000.0    30000.0           0.0    980300.0          NaN   \n",
              "12         0.0        0.0        0.0           0.0         0.0          NaN   \n",
              "13   1650010.0   475000.0   145000.0           0.0   7784038.0          NaN   \n",
              "14   2514859.0        0.0        0.0           0.0         0.0       2006.0   \n",
              "15   2514859.0        0.0        0.0           0.0         0.0       2006.0   \n",
              "16   2514859.0        0.0        0.0           0.0         0.0       2006.0   \n",
              "17    437228.0   245000.0    81250.0           0.0    874456.0          NaN   \n",
              "18         0.0        0.0        0.0           0.0         0.0          NaN   \n",
              "19    455500.0   381000.0        0.0           0.0   1020833.0          NaN   \n",
              "20   1500000.0   900000.0    75000.0           0.0   7208500.0          NaN   \n",
              "21   1700000.0   250000.0        0.0           0.0   3587500.0          NaN   \n",
              "22    655000.0   415000.0    45000.0           0.0   1125000.0       2006.0   \n",
              "23    625000.0   120000.0        0.0           0.0    556389.0          NaN   \n",
              "24   2000000.0   625000.0        0.0           0.0   7100000.0          NaN   \n",
              "25   6543350.0  1125000.0   500000.0           0.0  35000000.0          NaN   \n",
              "26    400000.0   800000.0    25000.0           0.0    516667.0       2006.0   \n",
              "27   2878500.0  2150000.0   297000.0           0.0  10314583.0          NaN   \n",
              "28   1800000.0   905000.0    50000.0           0.0   8000000.0       2006.0   \n",
              "29   1100000.0   650000.0   129000.0           0.0   1000000.0          NaN   \n",
              "..         ...        ...        ...           ...         ...          ...   \n",
              "315        NaN        NaN        NaN           NaN         NaN       2006.0   \n",
              "316        NaN        NaN        NaN           NaN         NaN       2006.0   \n",
              "317        NaN        NaN        NaN           NaN         NaN       2006.0   \n",
              "318        NaN        NaN        NaN           NaN         NaN       2006.0   \n",
              "319        NaN        NaN        NaN           NaN         NaN       2006.0   \n",
              "320        NaN        NaN        NaN           NaN         NaN       2006.0   \n",
              "321        NaN        NaN        NaN           NaN         NaN       2006.0   \n",
              "322        NaN        NaN        NaN           NaN         NaN       2006.0   \n",
              "323        NaN        NaN        NaN           NaN         NaN       2006.0   \n",
              "324        NaN        NaN        NaN           NaN         NaN       2006.0   \n",
              "325        NaN        NaN        NaN           NaN         NaN       2006.0   \n",
              "326        NaN        NaN        NaN           NaN         NaN       2006.0   \n",
              "327        NaN        NaN        NaN           NaN         NaN       2006.0   \n",
              "328        NaN        NaN        NaN           NaN         NaN       2006.0   \n",
              "329        NaN        NaN        NaN           NaN         NaN       2006.0   \n",
              "330        NaN        NaN        NaN           NaN         NaN       2006.0   \n",
              "331        NaN        NaN        NaN           NaN         NaN       2006.0   \n",
              "332        NaN        NaN        NaN           NaN         NaN       2006.0   \n",
              "333        NaN        NaN        NaN           NaN         NaN       2006.0   \n",
              "334        NaN        NaN        NaN           NaN         NaN       2006.0   \n",
              "335        NaN        NaN        NaN           NaN         NaN       2006.0   \n",
              "336        NaN        NaN        NaN           NaN         NaN       2006.0   \n",
              "337        NaN        NaN        NaN           NaN         NaN       2006.0   \n",
              "338        NaN        NaN        NaN           NaN         NaN       2006.0   \n",
              "339        NaN        NaN        NaN           NaN         NaN       2006.0   \n",
              "340        NaN        NaN        NaN           NaN         NaN       2006.0   \n",
              "341        NaN        NaN        NaN           NaN         NaN       2006.0   \n",
              "342        NaN        NaN        NaN           NaN         NaN       2006.0   \n",
              "343        NaN        NaN        NaN           NaN         NaN       2006.0   \n",
              "344        NaN        NaN        NaN           NaN         NaN       2006.0   \n",
              "\n",
              "                            Conference_2     Sport State   GSR   FGR  ConfAbbr  \n",
              "0                                    NaN       NaN   NaN   NaN   NaN       NaN  \n",
              "1                                    NaN       NaN   NaN   NaN   NaN       NaN  \n",
              "2                                    NaN       NaN   NaN   NaN   NaN       NaN  \n",
              "3                         Conference USA  Football    AL  60.0  55.0     C-USA  \n",
              "4                    Southern Conference  Football    NC  68.0  56.0       Sco  \n",
              "5                                    NaN       NaN   NaN   NaN   NaN       NaN  \n",
              "6                                    NaN       NaN   NaN   NaN   NaN       NaN  \n",
              "7                                    NaN       NaN   NaN   NaN   NaN       NaN  \n",
              "8                    Sun Belt Conference  Football    AR  68.0  60.0  Sun Belt  \n",
              "9                                    NaN       NaN   NaN   NaN   NaN       NaN  \n",
              "10                                   NaN       NaN   NaN   NaN   NaN       NaN  \n",
              "11                                   NaN       NaN   NaN   NaN   NaN       NaN  \n",
              "12                                   NaN       NaN   NaN   NaN   NaN       NaN  \n",
              "13                                   NaN       NaN   NaN   NaN   NaN       NaN  \n",
              "14             Atlantic Coast Conference  Football    MA  94.0  86.0       ACC  \n",
              "15               Pioneer Football League  Football    NC  98.0   NaN       PFL  \n",
              "16               Pioneer Football League  Football    NY  77.0   NaN       PFL  \n",
              "17                                   NaN       NaN   NaN   NaN   NaN       NaN  \n",
              "18                                   NaN       NaN   NaN   NaN   NaN       NaN  \n",
              "19                                   NaN       NaN   NaN   NaN   NaN       NaN  \n",
              "20                                   NaN       NaN   NaN   NaN   NaN       NaN  \n",
              "21                                   NaN       NaN   NaN   NaN   NaN       NaN  \n",
              "22               Mid-American Conference  Football    MI  51.0  37.0       MAC  \n",
              "23                                   NaN       NaN   NaN   NaN   NaN       NaN  \n",
              "24                                   NaN       NaN   NaN   NaN   NaN       NaN  \n",
              "25                                   NaN       NaN   NaN   NaN   NaN       NaN  \n",
              "26                  Big South Conference  Football    SC  66.0  57.0       BSC  \n",
              "27                                   NaN       NaN   NaN   NaN   NaN       NaN  \n",
              "28              Mountain West Conference  Football    CO  78.0  68.0  Mt. West  \n",
              "29                                   NaN       NaN   NaN   NaN   NaN       NaN  \n",
              "..                                   ...       ...   ...   ...   ...       ...  \n",
              "315              Pioneer Football League  Football    CA  83.0   NaN       PFL  \n",
              "316              Southeastern Conference  Football    SC  65.0  51.0       SEC  \n",
              "317  Missouri Valley Football Conference  Football    SD  60.0  55.0       MVF  \n",
              "318         American Athletic Conference  Football    FL  57.0  39.0       AAC  \n",
              "319                  Southern Conference  Football    TN  46.0  35.0       Sco  \n",
              "320               Ohio Valley Conference  Football    TN  62.0  43.0       OVC  \n",
              "321              Southeastern Conference  Football    TN  64.0  61.0       SEC  \n",
              "322                    Big 12 Conference  Football    TX  56.0  57.0    Big 12  \n",
              "323                       Conference USA  Football    TX  66.0  59.0     C-USA  \n",
              "324              Mid-American Conference  Football    OH  67.0  60.0       MAC  \n",
              "325                    Pac-12 Conference  Football    UT  73.0  57.0    Pac-12  \n",
              "326            Atlantic Coast Conference  Football    VA  74.0  69.0       ACC  \n",
              "327                    Pac-12 Conference  Football    WA  74.0  66.0    Pac-12  \n",
              "328                   Big Ten Conference  Football    WI  72.0  57.0   Big Ten  \n",
              "329             Mountain West Conference  Football    WY  65.0  48.0  Mt. West  \n",
              "330          Western Athletic Conference  Football    UT  84.0  56.0       WAC  \n",
              "331              Pioneer Football League  Football    IN  90.0   NaN       PFL  \n",
              "332              Southeastern Conference  Football    TN  82.0  68.0       SEC  \n",
              "333        Colonial Athletic Association  Football    PA  84.0  79.0       CAA  \n",
              "334                 Big South Conference  Football    VA  58.0  47.0       BSC  \n",
              "335            Atlantic Coast Conference  Football    VA  78.0  62.0       ACC  \n",
              "336                 Northeast Conference  Football    NY  82.0  68.0       NEC  \n",
              "337            Atlantic Coast Conference  Football    NC  86.0  77.0       ACC  \n",
              "338                   Big Sky Conference  Football    UT  47.0  43.0     BSkyC  \n",
              "339                    Big 12 Conference  Football    WV  80.0  61.0    Big 12  \n",
              "340                  Southern Conference  Football    NC  62.0  46.0       Sco  \n",
              "341  Missouri Valley Football Conference  Football    IL  67.0  59.0       MVF  \n",
              "342                  Southern Conference  Football    SC  88.0  78.0       Sco  \n",
              "343                       The Ivy League  Football    CT  97.0   NaN       TIL  \n",
              "344  Missouri Valley Football Conference  Football    OH  52.0  49.0       MVF  \n",
              "\n",
              "[345 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "metadata": {
        "id": "Y-sj_cmuIIw7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# remove rows if null values in school column\n",
        "data1 = data1[pd.notnull(data1['School'])]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "UCYqeaHKQTUm",
        "colab_type": "code",
        "outputId": "a2bb45a3-672e-4dfc-f433-030f4ce06d42",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        }
      },
      "cell_type": "code",
      "source": [
        "# review dataset\n",
        "data1.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>School</th>\n",
              "      <th>Conference</th>\n",
              "      <th>Coach</th>\n",
              "      <th>SchoolPay</th>\n",
              "      <th>TotalPay</th>\n",
              "      <th>Bonus</th>\n",
              "      <th>BonusPaid</th>\n",
              "      <th>AssistantPay</th>\n",
              "      <th>Buyout</th>\n",
              "      <th>Cohort Year</th>\n",
              "      <th>Conference_2</th>\n",
              "      <th>Sport</th>\n",
              "      <th>State</th>\n",
              "      <th>GSR</th>\n",
              "      <th>FGR</th>\n",
              "      <th>ConfAbbr</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Air Force</td>\n",
              "      <td>Mt. West</td>\n",
              "      <td>Troy Calhoun</td>\n",
              "      <td>885000.0</td>\n",
              "      <td>885000.0</td>\n",
              "      <td>247000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Akron</td>\n",
              "      <td>MAC</td>\n",
              "      <td>Terry Bowden</td>\n",
              "      <td>411000.0</td>\n",
              "      <td>412500.0</td>\n",
              "      <td>225000.0</td>\n",
              "      <td>50000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>688500.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Alabama</td>\n",
              "      <td>SEC</td>\n",
              "      <td>Nick Saban</td>\n",
              "      <td>8307000.0</td>\n",
              "      <td>8307000.0</td>\n",
              "      <td>1100000.0</td>\n",
              "      <td>500000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>33600000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Alabama at Birmingham</td>\n",
              "      <td>C-USA</td>\n",
              "      <td>Bill Clark</td>\n",
              "      <td>900000.0</td>\n",
              "      <td>900000.0</td>\n",
              "      <td>950000.0</td>\n",
              "      <td>165471.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3847500.0</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Conference USA</td>\n",
              "      <td>Football</td>\n",
              "      <td>AL</td>\n",
              "      <td>60.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>C-USA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Appalachian State</td>\n",
              "      <td>Sun Belt</td>\n",
              "      <td>Scott Satterfield</td>\n",
              "      <td>712500.0</td>\n",
              "      <td>712500.0</td>\n",
              "      <td>295000.0</td>\n",
              "      <td>145000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2160417.0</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Southern Conference</td>\n",
              "      <td>Football</td>\n",
              "      <td>NC</td>\n",
              "      <td>68.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>Sco</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  School Conference              Coach  SchoolPay   TotalPay  \\\n",
              "0              Air Force   Mt. West       Troy Calhoun   885000.0   885000.0   \n",
              "1                  Akron        MAC       Terry Bowden   411000.0   412500.0   \n",
              "2                Alabama        SEC         Nick Saban  8307000.0  8307000.0   \n",
              "3  Alabama at Birmingham      C-USA         Bill Clark   900000.0   900000.0   \n",
              "4      Appalachian State   Sun Belt  Scott Satterfield   712500.0   712500.0   \n",
              "\n",
              "       Bonus  BonusPaid  AssistantPay      Buyout  Cohort Year  \\\n",
              "0   247000.0        0.0           0.0         0.0          NaN   \n",
              "1   225000.0    50000.0           0.0    688500.0          NaN   \n",
              "2  1100000.0   500000.0           0.0  33600000.0          NaN   \n",
              "3   950000.0   165471.0           0.0   3847500.0       2006.0   \n",
              "4   295000.0   145000.0           0.0   2160417.0       2006.0   \n",
              "\n",
              "          Conference_2     Sport State   GSR   FGR ConfAbbr  \n",
              "0                  NaN       NaN   NaN   NaN   NaN      NaN  \n",
              "1                  NaN       NaN   NaN   NaN   NaN      NaN  \n",
              "2                  NaN       NaN   NaN   NaN   NaN      NaN  \n",
              "3       Conference USA  Football    AL  60.0  55.0    C-USA  \n",
              "4  Southern Conference  Football    NC  68.0  56.0      Sco  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "metadata": {
        "id": "u8rA3mlq2gzA",
        "colab_type": "code",
        "outputId": "ccdd9ca6-948f-4c19-f527-76c6641303ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "cell_type": "code",
      "source": [
        "# stadium size data set\n",
        "stadiums = pd.read_html(\"http://en.wikipedia.org/wiki/List_of_American_football_stadiums_by_capacity\",\n",
        "                         index_col=0, attrs={\"class\":\"wikitable\"}, header=0)[0]\n",
        "stadiums.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Stadium</th>\n",
              "      <th>Capacity</th>\n",
              "      <th>City</th>\n",
              "      <th>State/Province</th>\n",
              "      <th>Home teams</th>\n",
              "      <th>Refs</th>\n",
              "      <th>Unnamed: 7</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Image</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>NaN</th>\n",
              "      <td>Michigan Stadium</td>\n",
              "      <td>107601</td>\n",
              "      <td>Ann Arbor</td>\n",
              "      <td>Michigan</td>\n",
              "      <td>Michigan Wolverines</td>\n",
              "      <td>[1]</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NaN</th>\n",
              "      <td>Beaver Stadium</td>\n",
              "      <td>106572</td>\n",
              "      <td>University Park</td>\n",
              "      <td>Pennsylvania</td>\n",
              "      <td>Penn State Nittany Lions</td>\n",
              "      <td>[2]</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NaN</th>\n",
              "      <td>Kyle Field</td>\n",
              "      <td>102733</td>\n",
              "      <td>College Station</td>\n",
              "      <td>Texas</td>\n",
              "      <td>Texas A&amp;M Aggies</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NaN</th>\n",
              "      <td>Neyland Stadium</td>\n",
              "      <td>102455</td>\n",
              "      <td>Knoxville</td>\n",
              "      <td>Tennessee</td>\n",
              "      <td>Tennessee Volunteers</td>\n",
              "      <td>[3]</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NaN</th>\n",
              "      <td>Tiger Stadium</td>\n",
              "      <td>102321</td>\n",
              "      <td>Baton Rouge</td>\n",
              "      <td>Louisiana</td>\n",
              "      <td>LSU Tigers</td>\n",
              "      <td>[4]</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Stadium Capacity             City State/Province  \\\n",
              "Image                                                              \n",
              "NaN    Michigan Stadium   107601        Ann Arbor       Michigan   \n",
              "NaN      Beaver Stadium   106572  University Park   Pennsylvania   \n",
              "NaN          Kyle Field   102733  College Station          Texas   \n",
              "NaN     Neyland Stadium   102455        Knoxville      Tennessee   \n",
              "NaN       Tiger Stadium   102321      Baton Rouge      Louisiana   \n",
              "\n",
              "                     Home teams Refs Unnamed: 7  \n",
              "Image                                            \n",
              "NaN         Michigan Wolverines  [1]        NaN  \n",
              "NaN    Penn State Nittany Lions  [2]        NaN  \n",
              "NaN            Texas A&M Aggies  NaN        NaN  \n",
              "NaN        Tennessee Volunteers  [3]        NaN  \n",
              "NaN                  LSU Tigers  [4]        NaN  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "metadata": {
        "id": "43UXeyRKMotX",
        "colab_type": "code",
        "outputId": "c4571657-530b-4399-deb7-a2b5c147f96e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "cell_type": "code",
      "source": [
        "# create a dictionary base on string matching algorithm\n",
        "matches2 = {}\n",
        "data1_school = data1['School'].values\n",
        "print(len(data1_school))\n",
        "\n",
        "stadiums_hteam = stadiums['Home teams'].values\n",
        "print(len(stadiums_hteam))\n",
        "\n",
        "for elm in stadiums_hteam:\n",
        "    for el in data1_school:\n",
        "        if levenshtein_ratio_and_distance(elm, el, ratio_calc=True) > 0.72:\n",
        "            matches2[elm] = el\n",
        "\n",
        "print(len(matches2))    \n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "132\n",
            "150\n",
            "30\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "xVIsNZdmipU2",
        "colab_type": "code",
        "outputId": "a6e094b4-3bfa-4b1d-90ec-1d62e6632913",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "cell_type": "code",
      "source": [
        "# review the dictionary\n",
        "print(matches2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'Florida Gators': 'Florida State', 'Oklahoma Sooners': 'Oklahoma State', 'South Carolina Gamecocks': 'South Carolina', 'Florida State Seminoles': 'Florida State', 'Michigan State Spartans': 'Michigan State', 'Virginia Tech Hokies': 'Virginia Tech', 'North Carolina Tar Heels': 'North Carolina State', 'Mississippi State Bulldogs': 'Mississippi State', 'Oklahoma State Cowboys': 'Oklahoma State', 'Kansas State Wildcats': 'Kansas State', 'East Carolina Pirates': 'East Carolina', 'Northwestern Wildcats': 'Northwestern', 'Oregon State Beavers': 'Oregon State', 'Utah Utes': 'Utah State', 'Boston College Eagles': 'Boston College', 'Fresno State Bulldogs': 'Fresno State', 'Colorado State Rams.': 'Colorado State', 'Connecticut Huskies': 'Connecticut', \"Louisiana-Lafayette Ragin' Cajuns\": 'Louisiana-Lafayette', 'Washington State Cougars': 'Washington State', 'Northern Illinois Huskies': 'Northern Illinois', 'San Jose State Spartans': 'San Jose State', 'Louisiana-Monroe Warhawks': 'Louisiana-Monroe', 'New Mexico State Aggies': 'New Mexico State', 'Eastern Michigan Eagles': 'Eastern Michigan', 'Central Michigan Chippewas': 'Central Michigan', 'Western Michigan Broncos': 'Western Michigan', 'Florida Atlantic Owls': 'Florida Atlantic', 'Texas State Bobcats': 'Texas State', 'Louisiana Tech Bulldogs': 'Louisiana Tech'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "XjjlpyOJN_Lw",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# match up the strings\n",
        "stadiums[\"School\"] = stadiums['Home teams'].map(matches2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "lr5SmqbyOnNa",
        "colab_type": "code",
        "outputId": "c9c55cd4-0d5b-47c1-e66c-fe423d9aadc7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "cell_type": "code",
      "source": [
        "# review the data after string matching\n",
        "stadiums.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Stadium</th>\n",
              "      <th>Capacity</th>\n",
              "      <th>City</th>\n",
              "      <th>State/Province</th>\n",
              "      <th>Home teams</th>\n",
              "      <th>Refs</th>\n",
              "      <th>Unnamed: 7</th>\n",
              "      <th>School</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Image</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>NaN</th>\n",
              "      <td>Michigan Stadium</td>\n",
              "      <td>107601</td>\n",
              "      <td>Ann Arbor</td>\n",
              "      <td>Michigan</td>\n",
              "      <td>Michigan Wolverines</td>\n",
              "      <td>[1]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NaN</th>\n",
              "      <td>Beaver Stadium</td>\n",
              "      <td>106572</td>\n",
              "      <td>University Park</td>\n",
              "      <td>Pennsylvania</td>\n",
              "      <td>Penn State Nittany Lions</td>\n",
              "      <td>[2]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NaN</th>\n",
              "      <td>Kyle Field</td>\n",
              "      <td>102733</td>\n",
              "      <td>College Station</td>\n",
              "      <td>Texas</td>\n",
              "      <td>Texas A&amp;M Aggies</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NaN</th>\n",
              "      <td>Neyland Stadium</td>\n",
              "      <td>102455</td>\n",
              "      <td>Knoxville</td>\n",
              "      <td>Tennessee</td>\n",
              "      <td>Tennessee Volunteers</td>\n",
              "      <td>[3]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NaN</th>\n",
              "      <td>Tiger Stadium</td>\n",
              "      <td>102321</td>\n",
              "      <td>Baton Rouge</td>\n",
              "      <td>Louisiana</td>\n",
              "      <td>LSU Tigers</td>\n",
              "      <td>[4]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Stadium Capacity             City State/Province  \\\n",
              "Image                                                              \n",
              "NaN    Michigan Stadium   107601        Ann Arbor       Michigan   \n",
              "NaN      Beaver Stadium   106572  University Park   Pennsylvania   \n",
              "NaN          Kyle Field   102733  College Station          Texas   \n",
              "NaN     Neyland Stadium   102455        Knoxville      Tennessee   \n",
              "NaN       Tiger Stadium   102321      Baton Rouge      Louisiana   \n",
              "\n",
              "                     Home teams Refs Unnamed: 7 School  \n",
              "Image                                                   \n",
              "NaN         Michigan Wolverines  [1]        NaN    NaN  \n",
              "NaN    Penn State Nittany Lions  [2]        NaN    NaN  \n",
              "NaN            Texas A&M Aggies  NaN        NaN    NaN  \n",
              "NaN        Tennessee Volunteers  [3]        NaN    NaN  \n",
              "NaN                  LSU Tigers  [4]        NaN    NaN  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "metadata": {
        "id": "lOnSjT9hPmGf",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Combine datasets\n",
        "data2 = data1.merge(stadiums, \n",
        "                        left_on='School', \n",
        "                        right_on='School', \n",
        "                        how='outer', \n",
        "                        suffixes=[\"\",\"_3\"] #since they are the same name, we need a suffix\n",
        "                       )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "c11qpAKiPubH",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# remove rows if null values in school column\n",
        "data2 = data2[pd.notnull(data2['School'])]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "7X6VCEAYRUCH",
        "colab_type": "code",
        "outputId": "093a06c2-610d-4aae-dd49-891f9566ec6b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        }
      },
      "cell_type": "code",
      "source": [
        "# drop invalid columns and review data\n",
        "data2 = data2.drop(['Refs','Unnamed: 7','Conference_2'], axis = 1)\n",
        "data2.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>School</th>\n",
              "      <th>Conference</th>\n",
              "      <th>Coach</th>\n",
              "      <th>SchoolPay</th>\n",
              "      <th>TotalPay</th>\n",
              "      <th>Bonus</th>\n",
              "      <th>BonusPaid</th>\n",
              "      <th>AssistantPay</th>\n",
              "      <th>Buyout</th>\n",
              "      <th>Cohort Year</th>\n",
              "      <th>Sport</th>\n",
              "      <th>State</th>\n",
              "      <th>GSR</th>\n",
              "      <th>FGR</th>\n",
              "      <th>ConfAbbr</th>\n",
              "      <th>Stadium</th>\n",
              "      <th>Capacity</th>\n",
              "      <th>City</th>\n",
              "      <th>State/Province</th>\n",
              "      <th>Home teams</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Air Force</td>\n",
              "      <td>Mt. West</td>\n",
              "      <td>Troy Calhoun</td>\n",
              "      <td>885000.0</td>\n",
              "      <td>885000.0</td>\n",
              "      <td>247000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Akron</td>\n",
              "      <td>MAC</td>\n",
              "      <td>Terry Bowden</td>\n",
              "      <td>411000.0</td>\n",
              "      <td>412500.0</td>\n",
              "      <td>225000.0</td>\n",
              "      <td>50000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>688500.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Alabama</td>\n",
              "      <td>SEC</td>\n",
              "      <td>Nick Saban</td>\n",
              "      <td>8307000.0</td>\n",
              "      <td>8307000.0</td>\n",
              "      <td>1100000.0</td>\n",
              "      <td>500000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>33600000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Alabama at Birmingham</td>\n",
              "      <td>C-USA</td>\n",
              "      <td>Bill Clark</td>\n",
              "      <td>900000.0</td>\n",
              "      <td>900000.0</td>\n",
              "      <td>950000.0</td>\n",
              "      <td>165471.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3847500.0</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Football</td>\n",
              "      <td>AL</td>\n",
              "      <td>60.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>C-USA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Appalachian State</td>\n",
              "      <td>Sun Belt</td>\n",
              "      <td>Scott Satterfield</td>\n",
              "      <td>712500.0</td>\n",
              "      <td>712500.0</td>\n",
              "      <td>295000.0</td>\n",
              "      <td>145000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2160417.0</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Football</td>\n",
              "      <td>NC</td>\n",
              "      <td>68.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>Sco</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  School Conference              Coach  SchoolPay   TotalPay  \\\n",
              "0              Air Force   Mt. West       Troy Calhoun   885000.0   885000.0   \n",
              "1                  Akron        MAC       Terry Bowden   411000.0   412500.0   \n",
              "2                Alabama        SEC         Nick Saban  8307000.0  8307000.0   \n",
              "3  Alabama at Birmingham      C-USA         Bill Clark   900000.0   900000.0   \n",
              "4      Appalachian State   Sun Belt  Scott Satterfield   712500.0   712500.0   \n",
              "\n",
              "       Bonus  BonusPaid  AssistantPay      Buyout  Cohort Year     Sport  \\\n",
              "0   247000.0        0.0           0.0         0.0          NaN       NaN   \n",
              "1   225000.0    50000.0           0.0    688500.0          NaN       NaN   \n",
              "2  1100000.0   500000.0           0.0  33600000.0          NaN       NaN   \n",
              "3   950000.0   165471.0           0.0   3847500.0       2006.0  Football   \n",
              "4   295000.0   145000.0           0.0   2160417.0       2006.0  Football   \n",
              "\n",
              "  State   GSR   FGR ConfAbbr Stadium Capacity City State/Province Home teams  \n",
              "0   NaN   NaN   NaN      NaN     NaN      NaN  NaN            NaN        NaN  \n",
              "1   NaN   NaN   NaN      NaN     NaN      NaN  NaN            NaN        NaN  \n",
              "2   NaN   NaN   NaN      NaN     NaN      NaN  NaN            NaN        NaN  \n",
              "3    AL  60.0  55.0    C-USA     NaN      NaN  NaN            NaN        NaN  \n",
              "4    NC  68.0  56.0      Sco     NaN      NaN  NaN            NaN        NaN  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "metadata": {
        "id": "MIU-cMw8kEBO",
        "colab_type": "code",
        "outputId": "db20ec2f-3fe2-474b-ca58-fc9b5e11bf03",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "cell_type": "code",
      "source": [
        "# find null values\n",
        "\n",
        "data2.apply(lambda x: sum(x.isnull()),axis=0)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "School              0\n",
              "Conference          0\n",
              "Coach               0\n",
              "SchoolPay           0\n",
              "TotalPay            0\n",
              "Bonus               0\n",
              "BonusPaid           0\n",
              "AssistantPay        0\n",
              "Buyout              0\n",
              "Cohort Year       104\n",
              "Sport             104\n",
              "State             104\n",
              "GSR               104\n",
              "FGR               106\n",
              "ConfAbbr          104\n",
              "Stadium           101\n",
              "Capacity          101\n",
              "City              101\n",
              "State/Province    101\n",
              "Home teams        101\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "metadata": {
        "id": "IMALMmGi2WmC",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Data Transformation"
      ]
    },
    {
      "metadata": {
        "id": "ZF3icsDPjqYp",
        "colab_type": "code",
        "outputId": "5cde3c2a-10de-4c07-ae0b-c6f0b73ff224",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "cell_type": "code",
      "source": [
        "# review data types for each column\n",
        "data2.dtypes"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "School             object\n",
              "Conference         object\n",
              "Coach              object\n",
              "SchoolPay         float64\n",
              "TotalPay          float64\n",
              "Bonus             float64\n",
              "BonusPaid         float64\n",
              "AssistantPay      float64\n",
              "Buyout            float64\n",
              "Cohort Year       float64\n",
              "Sport              object\n",
              "State              object\n",
              "GSR               float64\n",
              "FGR               float64\n",
              "ConfAbbr           object\n",
              "Stadium            object\n",
              "Capacity           object\n",
              "City               object\n",
              "State/Province     object\n",
              "Home teams         object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "metadata": {
        "id": "7Rm9VSiv4Dal",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# clean up the value\n",
        "data2.loc[data2['Capacity'] == '30,255 [33]', 'Capacity'] = 30255"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "k2x37aW-2Vbr",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# convert the data type for capacity\n",
        "data2[\"Capacity\"] = data2.Capacity.astype(float)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "dxEnh-Lf9ZZV",
        "colab_type": "code",
        "outputId": "bf463089-4c68-4f23-e30e-8fdf6802866c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "cell_type": "code",
      "source": [
        "# review (again) data types for each column\n",
        "data2.dtypes"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "School             object\n",
              "Conference         object\n",
              "Coach              object\n",
              "SchoolPay         float64\n",
              "TotalPay          float64\n",
              "Bonus             float64\n",
              "BonusPaid         float64\n",
              "AssistantPay      float64\n",
              "Buyout            float64\n",
              "Cohort Year       float64\n",
              "Sport              object\n",
              "State              object\n",
              "GSR               float64\n",
              "FGR               float64\n",
              "ConfAbbr           object\n",
              "Stadium            object\n",
              "Capacity          float64\n",
              "City               object\n",
              "State/Province     object\n",
              "Home teams         object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "metadata": {
        "id": "vL_6WNkZUREA",
        "colab_type": "code",
        "outputId": "066ac2d5-b537-46c0-eb0f-3f434c03c965",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "# Label encoding ConfAbbr\n",
        "\n",
        "lb_make = LabelEncoder()\n",
        "data2[\"Conference_code\"] = lb_make.fit_transform(data2[\"Conference\"].astype(str))\n",
        "data2[[\"Conference\", \"Conference_code\"]].head(11)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Conference</th>\n",
              "      <th>Conference_code</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Mt. West</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>MAC</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>SEC</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>C-USA</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Sun Belt</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Pac-12</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Pac-12</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>SEC</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Sun Belt</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Ind.</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>SEC</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Conference  Conference_code\n",
              "0    Mt. West                7\n",
              "1         MAC                6\n",
              "2         SEC                9\n",
              "3       C-USA                4\n",
              "4    Sun Belt               10\n",
              "5      Pac-12                8\n",
              "6      Pac-12                8\n",
              "7         SEC                9\n",
              "8    Sun Belt               10\n",
              "9        Ind.                5\n",
              "10        SEC                9"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "metadata": {
        "id": "jNjA6QJIjNBh",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Data Exploration"
      ]
    },
    {
      "metadata": {
        "id": "Y3ROaHUhjxXU",
        "colab_type": "code",
        "outputId": "95e44c35-6db2-4dbb-94a6-b9582d372f89",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# review the size of the dataset\n",
        "data2.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(134, 21)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "metadata": {
        "id": "KrrUpSaJj3Gs",
        "colab_type": "code",
        "outputId": "11a9abc0-dd0f-4d4b-ea6b-e9e51882e159",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        }
      },
      "cell_type": "code",
      "source": [
        "# Statistical Summary of Numeric Variables\n",
        "data2.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SchoolPay</th>\n",
              "      <th>TotalPay</th>\n",
              "      <th>Bonus</th>\n",
              "      <th>BonusPaid</th>\n",
              "      <th>AssistantPay</th>\n",
              "      <th>Buyout</th>\n",
              "      <th>Cohort Year</th>\n",
              "      <th>GSR</th>\n",
              "      <th>FGR</th>\n",
              "      <th>Capacity</th>\n",
              "      <th>Conference_code</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.340000e+02</td>\n",
              "      <td>1.340000e+02</td>\n",
              "      <td>1.340000e+02</td>\n",
              "      <td>1.340000e+02</td>\n",
              "      <td>134.0</td>\n",
              "      <td>1.340000e+02</td>\n",
              "      <td>30.0</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>33.000000</td>\n",
              "      <td>134.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.382965e+06</td>\n",
              "      <td>2.389271e+06</td>\n",
              "      <td>7.258363e+05</td>\n",
              "      <td>1.007137e+05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.814013e+06</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>65.533333</td>\n",
              "      <td>54.321429</td>\n",
              "      <td>48315.545455</td>\n",
              "      <td>4.820896</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.891470e+06</td>\n",
              "      <td>1.895620e+06</td>\n",
              "      <td>6.642366e+05</td>\n",
              "      <td>2.060597e+05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.968399e+06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14.170716</td>\n",
              "      <td>11.919649</td>\n",
              "      <td>18211.886529</td>\n",
              "      <td>3.209129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>28562.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>8.000000e+05</td>\n",
              "      <td>8.007510e+05</td>\n",
              "      <td>2.125000e+05</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.180752e+05</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>44.750000</td>\n",
              "      <td>30456.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.865794e+06</td>\n",
              "      <td>1.950004e+06</td>\n",
              "      <td>6.250000e+05</td>\n",
              "      <td>2.000000e+04</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.760938e+06</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>65.000000</td>\n",
              "      <td>54.500000</td>\n",
              "      <td>44500.000000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.591250e+06</td>\n",
              "      <td>3.600625e+06</td>\n",
              "      <td>1.043750e+06</td>\n",
              "      <td>9.875000e+04</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.187500e+06</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>74.250000</td>\n",
              "      <td>59.250000</td>\n",
              "      <td>61337.000000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>8.307000e+06</td>\n",
              "      <td>8.307000e+06</td>\n",
              "      <td>3.100000e+06</td>\n",
              "      <td>1.350000e+06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.812500e+07</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>98.000000</td>\n",
              "      <td>86.000000</td>\n",
              "      <td>88548.000000</td>\n",
              "      <td>10.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          SchoolPay      TotalPay         Bonus     BonusPaid  AssistantPay  \\\n",
              "count  1.340000e+02  1.340000e+02  1.340000e+02  1.340000e+02         134.0   \n",
              "mean   2.382965e+06  2.389271e+06  7.258363e+05  1.007137e+05           0.0   \n",
              "std    1.891470e+06  1.895620e+06  6.642366e+05  2.060597e+05           0.0   \n",
              "min    0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00           0.0   \n",
              "25%    8.000000e+05  8.007510e+05  2.125000e+05  0.000000e+00           0.0   \n",
              "50%    1.865794e+06  1.950004e+06  6.250000e+05  2.000000e+04           0.0   \n",
              "75%    3.591250e+06  3.600625e+06  1.043750e+06  9.875000e+04           0.0   \n",
              "max    8.307000e+06  8.307000e+06  3.100000e+06  1.350000e+06           0.0   \n",
              "\n",
              "             Buyout  Cohort Year        GSR        FGR      Capacity  \\\n",
              "count  1.340000e+02         30.0  30.000000  28.000000     33.000000   \n",
              "mean   6.814013e+06       2006.0  65.533333  54.321429  48315.545455   \n",
              "std    9.968399e+06          0.0  14.170716  11.919649  18211.886529   \n",
              "min    0.000000e+00       2006.0  37.000000  37.000000  28562.000000   \n",
              "25%    5.180752e+05       2006.0  56.000000  44.750000  30456.000000   \n",
              "50%    2.760938e+06       2006.0  65.000000  54.500000  44500.000000   \n",
              "75%    9.187500e+06       2006.0  74.250000  59.250000  61337.000000   \n",
              "max    6.812500e+07       2006.0  98.000000  86.000000  88548.000000   \n",
              "\n",
              "       Conference_code  \n",
              "count       134.000000  \n",
              "mean          4.820896  \n",
              "std           3.209129  \n",
              "min           0.000000  \n",
              "25%           2.000000  \n",
              "50%           4.000000  \n",
              "75%           8.000000  \n",
              "max          10.000000  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "metadata": {
        "id": "JpZsK8q8whFN",
        "colab_type": "code",
        "outputId": "70579312-d46f-4d5e-f576-0adc29341f9d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "cell_type": "code",
      "source": [
        "# find max amounts\n",
        "print(\"Heighest of SchoolPay:       \", data2['SchoolPay'].max())\n",
        "print(\"Heighest of TotalPay:       \", data2['TotalPay'].max())\n",
        "print(\"Heighest of Bonus:       \", data2['Bonus'].max())\n",
        "print(\"Heighest of BonusPaid:       \", data2['BonusPaid'].max())\n",
        "print(\"Heighest of AssistantPay:       \", data2['AssistantPay'].max())\n",
        "print(\"Heighest of Buyout:       \", data2['Buyout'].max())\n",
        "print(\"Heighest of Capacity:       \", data2['Capacity'].max())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Heighest of SchoolPay:        8307000.0\n",
            "Heighest of TotalPay:        8307000.0\n",
            "Heighest of Bonus:        3100000.0\n",
            "Heighest of BonusPaid:        1350000.0\n",
            "Heighest of AssistantPay:        0.0\n",
            "Heighest of Buyout:        68125000.0\n",
            "Heighest of Capacity:        88548.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "TcQKoVKGbYh1",
        "colab_type": "code",
        "outputId": "97f94cae-90f6-42f4-afe4-8742781fe51a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        }
      },
      "cell_type": "code",
      "source": [
        "# Boxplot for all\n",
        "sns.boxplot(data=data2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f361902d278>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ffz5bt27lvPPOa3QoZmZAnb/gtqsPUin/raJKhvoto0aVW6vVq1fz8MMPA/DQQw+xdu3a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\nzMwKnBjMzKzAicHMzAqcGMzMrOD/A6c10lPh19z9AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "apFRjpDlbiG8",
        "colab_type": "code",
        "outputId": "f3d2dbf0-aa9d-48d5-a5d6-9a86428939a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "cell_type": "code",
      "source": [
        "# Boxplot for Buyout\n",
        "sns.boxplot(x=data2['Buyout'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3616e37d30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "rgiUTnWobwR_",
        "colab_type": "code",
        "outputId": "8a6edd2b-fc0c-4f63-9d2d-b1c3a318c15c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        }
      },
      "cell_type": "code",
      "source": [
        "# identify Z score for buyout\n",
        "from scipy import stats\n",
        "import numpy as np\n",
        "z = np.abs(stats.zscore(data2.Buyout))\n",
        "print(z)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.7545344  0.67276286 3.23605928 0.29757579 0.49794671 0.43314229\n",
            " 0.21540161 0.73006146 0.7189041  0.7545344  3.06310386 0.63810645\n",
            " 0.7545344  0.16995765 0.7545344  0.7545344  0.7545344  0.6506773\n",
            " 0.7545344  0.63329244 0.10160234 0.32845539 0.62092077 0.68845337\n",
            " 0.08871605 3.40233402 0.69317106 0.47050458 0.19560695 0.63576673\n",
            " 0.7545344  0.60805431 0.64170511 0.67067763 0.41604654 0.37618502\n",
            " 1.85340563 1.85340563 0.10843828 2.561162   0.74463713 0.5763829\n",
            " 0.27946372 0.7189041  0.5036377  0.74633157 0.51699906 1.90541601\n",
            " 1.53669192 0.39823139 0.39823139 0.61795158 1.10121043 0.7545344\n",
            " 0.45662553 0.43720203 0.73375006 0.91489365 0.12605544 0.52273459\n",
            " 0.14927322 0.7189041  0.12810737 0.7545344  0.69250205 1.27771225\n",
            " 0.07683928 0.03334348 0.34901523 0.7545344  0.7545344  0.52739123\n",
            " 0.7545344  2.2641439  0.6476435  0.64170511 0.59974521 0.5724885\n",
            " 0.69628691 0.02708243 0.02708243 0.22898746 0.53803068 0.7545344\n",
            " 0.7545344  0.68822938 3.7655733  1.719792   1.17544022 1.17544022\n",
            " 0.6120132  0.13595283 0.27500792 1.42782152 0.7545344  1.80094999\n",
            " 0.7545344  0.47471098 0.70139632 0.57922619 0.64546613 1.46048263\n",
            " 0.73169443 0.7545344  0.7545344  0.56648563 0.7545344  0.7545344\n",
            " 0.64454874 1.07646721 0.7545344  0.66261321 0.2519987  0.39922108\n",
            " 0.33142458 0.33191948 0.4087225  0.7545344  0.7545344  0.31437462\n",
            " 0.34406654 0.50240049 0.7545344  1.40629488 1.02698064 0.7545344\n",
            " 2.37548847 0.17257282 0.09465444 0.6120132  0.65952026 0.04192838\n",
            " 0.19758645]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "VhjLDTmOb7WD",
        "colab_type": "code",
        "outputId": "20da3cc3-08aa-4039-a8b3-9448264d1dff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# identify array for z score greater than 3 (outlier)\n",
        "threshold = 3\n",
        "print(np.where(z > 3))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(array([ 2, 10, 25, 86]),)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "FrBAbNTacrpN",
        "colab_type": "code",
        "outputId": "b69d2594-1cd7-492f-a722-246e549ab859",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "cell_type": "code",
      "source": [
        "# locate the data\n",
        "data2.iloc[110,]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "School                Texas A&M\n",
              "Conference                  SEC\n",
              "Coach              Jimbo Fisher\n",
              "SchoolPay               7.5e+06\n",
              "TotalPay                7.5e+06\n",
              "Bonus                  1.35e+06\n",
              "BonusPaid                     0\n",
              "AssistantPay                  0\n",
              "Buyout               6.8125e+07\n",
              "Cohort Year                 NaN\n",
              "Sport                       NaN\n",
              "State                       NaN\n",
              "GSR                         NaN\n",
              "FGR                         NaN\n",
              "ConfAbbr                    NaN\n",
              "Stadium                     NaN\n",
              "Capacity                    NaN\n",
              "City                        NaN\n",
              "State/Province              NaN\n",
              "Home teams                  NaN\n",
              "Conference_code               9\n",
              "Name: 110, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "metadata": {
        "id": "OVOYXPSGew3D",
        "colab_type": "code",
        "outputId": "f5344773-285f-4982-b044-9388af6b89b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163
        }
      },
      "cell_type": "code",
      "source": [
        "# locate the data\n",
        "data2.loc[data2['School'] == 'Texas A&M']"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>School</th>\n",
              "      <th>Conference</th>\n",
              "      <th>Coach</th>\n",
              "      <th>SchoolPay</th>\n",
              "      <th>TotalPay</th>\n",
              "      <th>Bonus</th>\n",
              "      <th>BonusPaid</th>\n",
              "      <th>AssistantPay</th>\n",
              "      <th>Buyout</th>\n",
              "      <th>Cohort Year</th>\n",
              "      <th>...</th>\n",
              "      <th>State</th>\n",
              "      <th>GSR</th>\n",
              "      <th>FGR</th>\n",
              "      <th>ConfAbbr</th>\n",
              "      <th>Stadium</th>\n",
              "      <th>Capacity</th>\n",
              "      <th>City</th>\n",
              "      <th>State/Province</th>\n",
              "      <th>Home teams</th>\n",
              "      <th>Conference_code</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>110</th>\n",
              "      <td>Texas A&amp;M</td>\n",
              "      <td>SEC</td>\n",
              "      <td>Jimbo Fisher</td>\n",
              "      <td>7500000.0</td>\n",
              "      <td>7500000.0</td>\n",
              "      <td>1350000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>68125000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1 rows × 21 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        School Conference         Coach  SchoolPay   TotalPay      Bonus  \\\n",
              "110  Texas A&M        SEC  Jimbo Fisher  7500000.0  7500000.0  1350000.0   \n",
              "\n",
              "     BonusPaid  AssistantPay      Buyout  Cohort Year  ... State GSR  FGR  \\\n",
              "110        0.0           0.0  68125000.0          NaN  ...   NaN NaN  NaN   \n",
              "\n",
              "     ConfAbbr Stadium Capacity  City State/Province Home teams Conference_code  \n",
              "110       NaN     NaN      NaN   NaN            NaN        NaN               9  \n",
              "\n",
              "[1 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "metadata": {
        "id": "JXQPdsi1f2i1",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Remove outlier\n",
        "data2 = data2.drop([110])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "aRa1uVEPiRxn",
        "colab_type": "code",
        "outputId": "30c7570b-9e35-464e-8b8c-b30bb21b5c87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "cell_type": "code",
      "source": [
        "# Review Boxplot for Buyout after the outlier is removed\n",
        "sns.boxplot(x=data2['Buyout'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f36107af7f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lSb9ge3O5oc4Nkr5YHvuO7bfY3iDpfSuGfF7Fx08BL1NuEPaApDfbftL2+yX9sqT3235E\n0qP60U9l2iHprn5tcMUaNDJYXoOWin+121mu333T9qclHZH0DUkPNV3vTklbIuIxqfhIKRcfBHx/\n2c8/R8Tny7YflHSPpGOSZiWdXf78Lkl/bXuXpOsj4msDuYVYlyLihhaHmvfJXm7/R/0cn/+Dxrpl\n+zZJD0XEx+uuBRgEAhrrku1Dkl6UtG3FJ2gAZxQCGgCS4kVCAEiKgAaApAhoAEiKgAaApAhoAEiK\ngAaApP4Pifk8FuhZvYoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "uz7lpkRjkh8N",
        "colab_type": "code",
        "outputId": "f1861b10-7474-4421-a7c9-da8bc915ad23",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        }
      },
      "cell_type": "code",
      "source": [
        "# TotalPay by conference\n",
        "data2.boxplot(column='TotalPay', by = 'Conference')\n",
        "plt.ylim(0, 9000000)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 9000000)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jwDuAK/L8ZcAJeXp+fk6eP0epm8d84NKIeCkiHiTdTvvI/FgXEQ9ExK+BS4H5eZlmY5iZ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\n+kTK+mEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "xT-Cdgb1voal",
        "colab_type": "code",
        "outputId": "d7964fd4-49ad-4ec0-8625-073978dfb193",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        }
      },
      "cell_type": "code",
      "source": [
        "# Calculate correlations\n",
        "corr = data2.corr()\n",
        " \n",
        "# Heatmap\n",
        "sns.heatmap(corr)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f36169b35f8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SKXfloBctuNpsCNzdxbGzge/kLsufSFqn8wm5H3ln4IoGXmMIIfRayV2UTwCr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\n3K8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "lmSvikDMkRBT",
        "colab_type": "code",
        "outputId": "8465160f-fc2b-4469-f183-5fb59d009908",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2101
        }
      },
      "cell_type": "code",
      "source": [
        "# pairplots showing the relationship and distribution\n",
        "sns.pairplot(data2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/numpy/lib/histograms.py:824: RuntimeWarning: invalid value encountered in greater_equal\n",
            "  keep = (tmp_a >= first_edge)\n",
            "/usr/local/lib/python3.6/dist-packages/numpy/lib/histograms.py:825: RuntimeWarning: invalid value encountered in less_equal\n",
            "  keep &= (tmp_a <= last_edge)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f3616de5f28>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PaZw6QyDUuw5Rizw7DCh3UC/cYwgj4hr1QmwB/sX9GQ5cR+mlsYmibb+XxiY8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\ng2vPkrVWQceoLRJgoBw1V2jW/ZxQQJ9+8PmcJVV7B2K69cnDdxUcNbd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HHm8jz0nPBi5baDWL7lBazcdhA9J7wcFJqEU74AHtj/Fqa6HUnX8rLmBnj9Gk6NBKMD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\n+ob9xT60rGQb15net9nujV6ObViuLC8Sv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BekkUWOoy4dM9HuEtpfKol3MdA/P9qVTM1YqXYpdYbX61/I3dmh3dCnHYTJ+xmjio5vNPJZ1M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\nv8LBQP4cHGQ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Zu4i/bf3KivekZ4zJeo1uzPx/n6gsL5FYzNWfdu/ThYlu+y74b4folr99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\n/pt3afWPH1dLuC6Vb3Z3hukaD6i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6oru0y4TmItN+ckVR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0Dy63FF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\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3Fq66/xVs3NuFWz83HU0BD5oCHvzDZZllCdY99hoUFTg2MILewXBCu3raPn4ijEdumJu3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\nyplorHOjKejT/Uw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EREREREQUa9o/LFdKfUZEvgzghaj+Ifa3KqXORdwsIiIiIiIiIiIiIiIi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\nSZIkSZIkSZIkSZIkSZJ0O/4PO8IFlpyyGAoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1980x1980 with 132 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "WHZUR8g3kNdh",
        "colab_type": "code",
        "outputId": "701f3b28-bc14-4d67-94c2-5dbcee732279",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379
        }
      },
      "cell_type": "code",
      "source": [
        "# plots showing distribution between TotalPay and SchoolPay\n",
        "sns.lmplot(x='TotalPay', y='SchoolPay', data=data2)\n",
        "plt.ylim(0, 9000000)\n",
        "plt.xlim(0, 9000000)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 9000000)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MfRIi0iUs31DGPYvXkx03Eglnc0UUHOOH9lVwdEE6bCUiXcLcFZvJjhvVtQ28X1kDQF5W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\nwxQeIiLSYQoPERHpMIWHiIh02P8POcCMZmUNJzEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "BGj9h-gNxgqZ",
        "colab_type": "code",
        "outputId": "ab32b1fb-6ef0-4404-f6a1-dddab9d0e358",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        }
      },
      "cell_type": "code",
      "source": [
        "# plots showing distribution between TotalPay and Bonus\n",
        "sns.lmplot(x='TotalPay', y='Bonus', data=data2)\n",
        "plt.ylim(0, 4000000)\n",
        "plt.xlim(0, 9000000)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 9000000)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZsKV7fzTJTw44QfcXR/rJ7RBZ3FwzMm1sdWvjtK811VkWleAToTbopzbosz3h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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "FMRlvJVyx8LD",
        "colab_type": "code",
        "outputId": "0f86c886-de78-4933-f066-f95ad9186daf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        }
      },
      "cell_type": "code",
      "source": [
        "# plots showing distribution between TotalPay and BonusPaid\n",
        "sns.lmplot(x='TotalPay', y='BonusPaid', data=data2)\n",
        "plt.ylim(0, 1500000)\n",
        "plt.xlim(0, 3000000)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 3000000)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IyCRVO2b+HeAtwFNAPhQ7oDAXEWkA1fbMu4EzvNInWYiISN1Uezv/s8DJtWyIiIhMXbU9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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "0TVQ0c-nB1dY",
        "colab_type": "code",
        "outputId": "09842226-f150-4c63-87c9-dff8fea5e04d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        }
      },
      "cell_type": "code",
      "source": [
        "# plots showing distribution between TotalPay and Capacity\n",
        "sns.lmplot(x='TotalPay', y='Capacity', data=data2)\n",
        "plt.ylim(0, 90000)\n",
        "plt.xlim(0, 1500000)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 1500000)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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T2BSmAe4FPhPCehOw3d17gRfMrIUkdAFa3H0/gJltD/OOGbT55PqaZgZm16An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\nkcgUtCIikf1/fNNixEU///UAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "dJpNAESuir77",
        "colab_type": "code",
        "outputId": "e4c46e8d-5a23-486f-8240-d83bd8238a83",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379
        }
      },
      "cell_type": "code",
      "source": [
        "# plots showing distribution between TotalPay and Buyout\n",
        "sns.lmplot(x='TotalPay', y='Buyout', data=data2)\n",
        "plt.ylim(0, 3000000)\n",
        "plt.xlim(0, 1500000)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 1500000)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2bwB/NsYsAP7s/gxwPbDA/VoNPADOmzzwLeBSYCnwrb43eveY29Me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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "-aKvRhSQzc7L",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Data Predictive Model"
      ]
    },
    {
      "metadata": {
        "id": "zLFyXHYs1rEr",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Model1 - Multiple Linear Regression**"
      ]
    },
    {
      "metadata": {
        "id": "AF1cNboNzhV1",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# create training and test datasets\n",
        "np.random.seed(1234)\n",
        "data2['runiform'] = uniform.rvs(loc = 0, scale = 1, size = len(data2))\n",
        "data2_train = data2[data2['runiform'] >= 0.33]\n",
        "data2_test = data2[data2['runiform'] < 0.33]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "5uuD8Hn_CfSB",
        "colab_type": "code",
        "outputId": "30c4adf9-c89f-4910-8d49-0ab2f554f0c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        }
      },
      "cell_type": "code",
      "source": [
        "# check training data frame\n",
        "print('\\SusItems_train data frame (rows, columns): ',data2_train.shape)\n",
        "print(data2_train.head())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\\SusItems_train data frame (rows, columns):  (94, 22)\n",
            "                  School Conference              Coach  SchoolPay   TotalPay  \\\n",
            "1                  Akron        MAC       Terry Bowden   411000.0   412500.0   \n",
            "2                Alabama        SEC         Nick Saban  8307000.0  8307000.0   \n",
            "3  Alabama at Birmingham      C-USA         Bill Clark   900000.0   900000.0   \n",
            "4      Appalachian State   Sun Belt  Scott Satterfield   712500.0   712500.0   \n",
            "7               Arkansas        SEC        Chad Morris  3500000.0  3500000.0   \n",
            "\n",
            "       Bonus  BonusPaid  AssistantPay      Buyout  Cohort Year  ...   GSR  \\\n",
            "1   225000.0    50000.0           0.0    688500.0          NaN  ...   NaN   \n",
            "2  1100000.0   500000.0           0.0  33600000.0          NaN  ...   NaN   \n",
            "3   950000.0   165471.0           0.0   3847500.0       2006.0  ...  60.0   \n",
            "4   295000.0   145000.0           0.0   2160417.0       2006.0  ...  68.0   \n",
            "7  1000000.0        0.0           0.0  12500000.0          NaN  ...   NaN   \n",
            "\n",
            "    FGR  ConfAbbr  Stadium Capacity City  State/Province Home teams  \\\n",
            "1   NaN       NaN      NaN      NaN  NaN             NaN        NaN   \n",
            "2   NaN       NaN      NaN      NaN  NaN             NaN        NaN   \n",
            "3  55.0     C-USA      NaN      NaN  NaN             NaN        NaN   \n",
            "4  56.0       Sco      NaN      NaN  NaN             NaN        NaN   \n",
            "7   NaN       NaN      NaN      NaN  NaN             NaN        NaN   \n",
            "\n",
            "  Conference_code  runiform  \n",
            "1               6  0.622109  \n",
            "2               9  0.437728  \n",
            "3               4  0.785359  \n",
            "4              10  0.779976  \n",
            "7               9  0.801872  \n",
            "\n",
            "[5 rows x 22 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "kkt1WB7ICmeO",
        "colab_type": "code",
        "outputId": "26864655-8a29-4515-b7b0-99d902c10f39",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        }
      },
      "cell_type": "code",
      "source": [
        "# check test data frame\n",
        "print('\\SusItems_test data frame (rows, columns): ',data2_test.shape)\n",
        "print(data2_test.head())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\\SusItems_test data frame (rows, columns):  (39, 22)\n",
            "              School Conference          Coach  SchoolPay   TotalPay  \\\n",
            "0          Air Force   Mt. West   Troy Calhoun   885000.0   885000.0   \n",
            "5            Arizona     Pac-12   Kevin Sumlin  1600000.0  2000000.0   \n",
            "6      Arizona State     Pac-12   Herm Edwards  2000000.0  2000000.0   \n",
            "17     Bowling Green        MAC     Mike Jinks   437228.0   437228.0   \n",
            "22  Central Michigan        MAC  John Bonamego   655000.0   655000.0   \n",
            "\n",
            "        Bonus  BonusPaid  AssistantPay      Buyout  Cohort Year  ...   GSR  \\\n",
            "0    247000.0        0.0           0.0         0.0          NaN  ...   NaN   \n",
            "5   2025000.0        0.0           0.0  10000000.0          NaN  ...   NaN   \n",
            "6   3010000.0        0.0           0.0   8166667.0          NaN  ...   NaN   \n",
            "17   245000.0    81250.0           0.0    874456.0          NaN  ...   NaN   \n",
            "22   415000.0    45000.0           0.0   1125000.0       2006.0  ...  51.0   \n",
            "\n",
            "     FGR  ConfAbbr               Stadium Capacity            City  \\\n",
            "0    NaN       NaN                   NaN      NaN             NaN   \n",
            "5    NaN       NaN                   NaN      NaN             NaN   \n",
            "6    NaN       NaN                   NaN      NaN             NaN   \n",
            "17   NaN       NaN                   NaN      NaN             NaN   \n",
            "22  37.0       MAC  Kelly/Shorts Stadium  30255.0  Mount Pleasant   \n",
            "\n",
            "    State/Province                  Home teams Conference_code  runiform  \n",
            "0              NaN                         NaN               7  0.191519  \n",
            "5              NaN                         NaN               8  0.272593  \n",
            "6              NaN                         NaN               8  0.276464  \n",
            "17             NaN                         NaN               6  0.013768  \n",
            "22        Michigan  Central Michigan Chippewas               6  0.075381  \n",
            "\n",
            "[5 rows x 22 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "tN1pPYZjCqz9",
        "colab_type": "code",
        "outputId": "71eef178-6473-48b7-d205-676c2b10a811",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 4151
        }
      },
      "cell_type": "code",
      "source": [
        "# review results for each row\n",
        "# specify a simple model\n",
        "my_model = str('TotalPay ~ C(SchoolPay) + C(BonusPaid) + C(Capacity) + C(Buyout)')\n",
        "\n",
        "# fit the model to the training set\n",
        "train_model_fit = smf.ols(my_model, data = data2_train).fit()\n",
        "# summary of model fit to the training set\n",
        "print(train_model_fit.summary())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:               TotalPay   R-squared:                       1.000\n",
            "Model:                            OLS   Adj. R-squared:                  1.000\n",
            "Method:                 Least Squares   F-statistic:                 6.413e+27\n",
            "Date:                Sat, 27 Apr 2019   Prob (F-statistic):           1.56e-28\n",
            "Time:                        22:56:15   Log-Likelihood:                 438.16\n",
            "No. Observations:                  25   AIC:                            -830.3\n",
            "Df Residuals:                       2   BIC:                            -802.3\n",
            "Df Model:                          22                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================================\n",
            "                                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "----------------------------------------------------------------------------------------------\n",
            "Intercept                   1.772e+06    3.9e-09   4.54e+14      0.000    1.77e+06    1.77e+06\n",
            "C(SchoolPay)[T.390000.0]   -4.608e+05    6.8e-09  -6.78e+13      0.000   -4.61e+05   -4.61e+05\n",
            "C(SchoolPay)[T.400000.0]    1.023e-10   1.51e-24   6.78e+13      0.000    1.02e-10    1.02e-10\n",
            "C(SchoolPay)[T.411000.0]    1.142e-74   4.67e-88   2.45e+13      0.000    1.14e-74    1.14e-74\n",
            "C(SchoolPay)[T.419640.0]   -3.382e+05   5.16e-09  -6.56e+13      0.000   -3.38e+05   -3.38e+05\n",
            "C(SchoolPay)[T.425004.0]    1.484e-58   1.55e-72   9.58e+13      0.000    1.48e-58    1.48e-58\n",
            "C(SchoolPay)[T.435689.0]    1.713e-74   4.75e-88   3.61e+13      0.000    1.71e-74    1.71e-74\n",
            "C(SchoolPay)[T.440000.0]    9.976e-11   8.52e-25   1.17e+14      0.000    9.98e-11    9.98e-11\n",
            "C(SchoolPay)[T.455500.0]    5.861e-75   1.19e-87   4.92e+12      0.000    5.86e-75    5.86e-75\n",
            "C(SchoolPay)[T.460000.0]   -3.281e+05   5.16e-09  -6.36e+13      0.000   -3.28e+05   -3.28e+05\n",
            "C(SchoolPay)[T.500000.0]   -3.681e-58   5.26e-72  -6.99e+13      0.000   -3.68e-58   -3.68e-58\n",
            "C(SchoolPay)[T.524826.0]   -2.206e-57   3.44e-71  -6.41e+13      0.000   -2.21e-57   -2.21e-57\n",
            "C(SchoolPay)[T.590424.0]    -3.94e+05    6.8e-09   -5.8e+13      0.000   -3.94e+05   -3.94e+05\n",
            "C(SchoolPay)[T.600000.0]   -1.718e-73   1.56e-87   -1.1e+14      0.000   -1.72e-73   -1.72e-73\n",
            "C(SchoolPay)[T.625000.0]    1.837e-74   6.96e-88   2.64e+13      0.000    1.84e-74    1.84e-74\n",
            "C(SchoolPay)[T.633460.0]   -2.847e+05   5.16e-09  -5.52e+13      0.000   -2.85e+05   -2.85e+05\n",
            "C(SchoolPay)[T.700000.0]   -5.362e+05   9.97e-09  -5.38e+13      0.000   -5.36e+05   -5.36e+05\n",
            "C(SchoolPay)[T.712500.0]   -1.461e-73   1.42e-87  -1.03e+14      0.000   -1.46e-73   -1.46e-73\n",
            "C(SchoolPay)[T.762570.0]    -4.26e-11   1.88e-24  -2.26e+13      0.000   -4.26e-11   -4.26e-11\n",
            "C(SchoolPay)[T.801504.0]    -1.12e-73   1.54e-87  -7.26e+13      0.000   -1.12e-73   -1.12e-73\n",
            "C(SchoolPay)[T.810000.0]     1.19e-73   2.34e-87   5.09e+13      0.000    1.19e-73    1.19e-73\n",
            "C(SchoolPay)[T.822690.0]    2.137e-73   2.26e-87   9.47e+13      0.000    2.14e-73    2.14e-73\n",
            "C(SchoolPay)[T.825000.0]  -3.482e-123  1.17e-136  -2.98e+13      0.000  -3.48e-123  -3.48e-123\n",
            "C(SchoolPay)[T.850000.0]   -3.075e+05    6.8e-09  -4.52e+13      0.000   -3.07e+05   -3.07e+05\n",
            "C(SchoolPay)[T.900000.0]   -2.181e+05   5.16e-09  -4.23e+13      0.000   -2.18e+05   -2.18e+05\n",
            "C(SchoolPay)[T.932521.0]            0          0        nan        nan           0           0\n",
            "C(SchoolPay)[T.945000.0]            0          0        nan        nan           0           0\n",
            "C(SchoolPay)[T.1000000.0]  -1.931e+05   5.16e-09  -3.75e+13      0.000   -1.93e+05   -1.93e+05\n",
            "C(SchoolPay)[T.1100000.0]  -1.681e+05   5.16e-09  -3.26e+13      0.000   -1.68e+05   -1.68e+05\n",
            "C(SchoolPay)[T.1102500.0]  -2.233e+05    6.8e-09  -3.28e+13      0.000   -2.23e+05   -2.23e+05\n",
            "C(SchoolPay)[T.1412000.0]           0          0        nan        nan           0           0\n",
            "C(SchoolPay)[T.1425000.0]           0          0        nan        nan           0           0\n",
            "C(SchoolPay)[T.1500000.0]           0          0        nan        nan           0           0\n",
            "C(SchoolPay)[T.1518177.0]           0          0        nan        nan           0           0\n",
            "C(SchoolPay)[T.1550000.0]  -5.561e+04   5.16e-09  -1.08e+13      0.000   -5.56e+04   -5.56e+04\n",
            "C(SchoolPay)[T.1650010.0]           0          0        nan        nan           0           0\n",
            "C(SchoolPay)[T.1700000.0]           0          0        nan        nan           0           0\n",
            "C(SchoolPay)[T.1701109.0]           0          0        nan        nan           0           0\n",
            "C(SchoolPay)[T.1750000.0]           0          0        nan        nan           0           0\n",
            "C(SchoolPay)[T.1900008.0]   4.253e+04    6.8e-09   6.26e+12      0.000    4.25e+04    4.25e+04\n",
            "C(SchoolPay)[T.2000000.0]           0          0        nan        nan           0           0\n",
            "C(SchoolPay)[T.2129638.0]           0          0        nan        nan           0           0\n",
            "C(SchoolPay)[T.2163000.0]           0          0        nan        nan           0           0\n",
            "C(SchoolPay)[T.2200000.0]           0          0        nan        nan           0           0\n",
            "C(SchoolPay)[T.2350000.0]           0          0        nan        nan           0           0\n",
            "C(SchoolPay)[T.2401206.0]           0          0        nan        nan           0           0\n",
            "C(SchoolPay)[T.2500000.0]           0          0        nan        nan           0           0\n",
            "C(SchoolPay)[T.2514859.0]   3.712e+05   5.97e-09   6.22e+13      0.000    3.71e+05    3.71e+05\n",
            "C(SchoolPay)[T.2540928.0]           0          0        nan        nan           0           0\n",
            "C(SchoolPay)[T.2600000.0]   4.138e+05   9.97e-09   4.15e+13      0.000    4.14e+05    4.14e+05\n",
            "C(SchoolPay)[T.2625965.0]           0          0        nan        nan           0           0\n",
            "C(SchoolPay)[T.2812523.0]           0          0        nan        nan           0           0\n",
            "C(SchoolPay)[T.2878500.0]           0          0        nan        nan           0           0\n",
            "C(SchoolPay)[T.3000000.0]   3.069e+05   5.16e-09   5.95e+13      0.000    3.07e+05    3.07e+05\n",
            "C(SchoolPay)[T.3017718.0]           0          0        nan        nan           0           0\n",
            "C(SchoolPay)[T.3300000.0]           0          0        nan        nan           0           0\n",
            "C(SchoolPay)[T.3500000.0]   4.319e+05   5.16e-09   8.38e+13      0.000    4.32e+05    4.32e+05\n",
            "C(SchoolPay)[T.3550000.0]           0          0        nan        nan           0           0\n",
            "C(SchoolPay)[T.3619775.0]   9.237e+05   9.97e-09   9.27e+13      0.000    9.24e+05    9.24e+05\n",
            "C(SchoolPay)[T.3703975.0]           0          0        nan        nan           0           0\n",
            "C(SchoolPay)[T.3750000.0]           0          0        nan        nan           0           0\n",
            "C(SchoolPay)[T.3787917.0]           0          0        nan        nan           0           0\n",
            "C(SchoolPay)[T.3800000.0]           0          0        nan        nan           0           0\n",
            "C(SchoolPay)[T.3980434.0]           0          0        nan        nan           0           0\n",
            "C(SchoolPay)[T.4000000.0]           0          0        nan        nan           0           0\n",
            "C(SchoolPay)[T.4200000.0]   6.069e+05   5.16e-09   1.18e+14      0.000    6.07e+05    6.07e+05\n",
            "C(SchoolPay)[T.4375000.0]           0          0        nan        nan           0           0\n",
            "C(SchoolPay)[T.4390417.0]   6.545e+05   5.16e-09   1.27e+14      0.000    6.54e+05    6.54e+05\n",
            "C(SchoolPay)[T.4700000.0]           0          0        nan        nan           0           0\n",
            "C(SchoolPay)[T.4800000.0]           0          0        nan        nan           0           0\n",
            "C(SchoolPay)[T.5000000.0]   1.529e+06   6.22e-09   2.46e+14      0.000    1.53e+06    1.53e+06\n",
            "C(SchoolPay)[T.5500000.0]           0          0        nan        nan           0           0\n",
            "C(SchoolPay)[T.6205000.0]           0          0        nan        nan           0           0\n",
            "C(SchoolPay)[T.6603600.0]           0          0        nan        nan           0           0\n",
            "C(SchoolPay)[T.6700000.0]           0          0        nan        nan           0           0\n",
            "C(SchoolPay)[T.7600000.0]           0          0        nan        nan           0           0\n",
            "C(SchoolPay)[T.8307000.0]           0          0        nan        nan           0           0\n",
            "C(BonusPaid)[T.10000.0]    -3.281e+05   5.16e-09  -6.36e+13      0.000   -3.28e+05   -3.28e+05\n",
            "C(BonusPaid)[T.12669.0]             0          0        nan        nan           0           0\n",
            "C(BonusPaid)[T.17500.0]             0          0        nan        nan           0           0\n",
            "C(BonusPaid)[T.20000.0]    -3.382e+05   5.16e-09  -6.56e+13      0.000   -3.38e+05   -3.38e+05\n",
            "C(BonusPaid)[T.25000.0]             0          0        nan        nan           0           0\n",
            "C(BonusPaid)[T.30000.0]             0          0        nan        nan           0           0\n",
            "C(BonusPaid)[T.35000.0]             0          0        nan        nan           0           0\n",
            "C(BonusPaid)[T.40000.0]    -1.931e+05   5.16e-09  -3.75e+13      0.000   -1.93e+05   -1.93e+05\n",
            "C(BonusPaid)[T.42500.0]    -2.847e+05   5.16e-09  -5.52e+13      0.000   -2.85e+05   -2.85e+05\n",
            "C(BonusPaid)[T.50000.0]             0          0        nan        nan           0           0\n",
            "C(BonusPaid)[T.55000.0]             0          0        nan        nan           0           0\n",
            "C(BonusPaid)[T.62500.0]     6.795e+05   5.22e-09    1.3e+14      0.000    6.79e+05    6.79e+05\n",
            "C(BonusPaid)[T.65000.0]    -2.181e+05   5.16e-09  -4.23e+13      0.000   -2.18e+05   -2.18e+05\n",
            "C(BonusPaid)[T.65142.0]             0          0        nan        nan           0           0\n",
            "C(BonusPaid)[T.70000.0]             0          0        nan        nan           0           0\n",
            "C(BonusPaid)[T.75000.0]     4.319e+05   5.16e-09   8.38e+13      0.000    4.32e+05    4.32e+05\n",
            "C(BonusPaid)[T.100000.0]    6.069e+05   5.16e-09   1.18e+14      0.000    6.07e+05    6.07e+05\n",
            "C(BonusPaid)[T.125000.0]    6.545e+05   5.16e-09   1.27e+14      0.000    6.54e+05    6.54e+05\n",
            "C(BonusPaid)[T.129000.0]   -1.681e+05   5.16e-09  -3.26e+13      0.000   -1.68e+05   -1.68e+05\n",
            "C(BonusPaid)[T.130000.0]            0          0        nan        nan           0           0\n",
            "C(BonusPaid)[T.145000.0]            0          0        nan        nan           0           0\n",
            "C(BonusPaid)[T.165471.0]            0          0        nan        nan           0           0\n",
            "C(BonusPaid)[T.180000.0]            0          0        nan        nan           0           0\n",
            "C(BonusPaid)[T.185000.0]            0          0        nan        nan           0           0\n",
            "C(BonusPaid)[T.236000.0]            0          0        nan        nan           0           0\n",
            "C(BonusPaid)[T.275000.0]    3.069e+05   5.16e-09   5.95e+13      0.000    3.07e+05    3.07e+05\n",
            "C(BonusPaid)[T.279167.0]            0          0        nan        nan           0           0\n",
            "C(BonusPaid)[T.290000.0]            0          0        nan        nan           0           0\n",
            "C(BonusPaid)[T.297000.0]            0          0        nan        nan           0           0\n",
            "C(BonusPaid)[T.300000.0]            0          0        nan        nan           0           0\n",
            "C(BonusPaid)[T.350000.0]            0          0        nan        nan           0           0\n",
            "C(BonusPaid)[T.375000.0]            0          0        nan        nan           0           0\n",
            "C(BonusPaid)[T.500000.0]            0          0        nan        nan           0           0\n",
            "C(BonusPaid)[T.600000.0]            0          0        nan        nan           0           0\n",
            "C(BonusPaid)[T.869917.0]            0          0        nan        nan           0           0\n",
            "C(BonusPaid)[T.1240000.0]  -5.561e+04   5.16e-09  -1.08e+13      0.000   -5.56e+04   -5.56e+04\n",
            "C(BonusPaid)[T.1350000.0]           0          0        nan        nan           0           0\n",
            "C(Capacity)[T.30000.0]     -1.931e+05   5.16e-09  -3.75e+13      0.000   -1.93e+05   -1.93e+05\n",
            "C(Capacity)[T.30200.0]     -3.281e+05   5.16e-09  -6.36e+13      0.000   -3.28e+05   -3.28e+05\n",
            "C(Capacity)[T.30343.0]     -3.382e+05   5.16e-09  -6.56e+13      0.000   -3.38e+05   -3.38e+05\n",
            "C(Capacity)[T.30427.0]     -4.608e+05    6.8e-09  -6.78e+13      0.000   -4.61e+05   -4.61e+05\n",
            "C(Capacity)[T.30456.0]      -3.94e+05    6.8e-09   -5.8e+13      0.000   -3.94e+05   -3.94e+05\n",
            "C(Capacity)[T.30998.0]     -2.847e+05   5.16e-09  -5.52e+13      0.000   -2.85e+05   -2.85e+05\n",
            "C(Capacity)[T.35117.0]      4.319e+05   5.16e-09   8.38e+13      0.000    4.32e+05    4.32e+05\n",
            "C(Capacity)[T.36900.0]     -3.075e+05    6.8e-09  -4.52e+13      0.000   -3.07e+05   -3.07e+05\n",
            "C(Capacity)[T.40727.0]     -5.561e+04   5.16e-09  -1.08e+13      0.000   -5.56e+04   -5.56e+04\n",
            "C(Capacity)[T.42704.0]     -1.681e+05   5.16e-09  -3.26e+13      0.000   -1.68e+05   -1.68e+05\n",
            "C(Capacity)[T.44500.0]      3.712e+05   5.97e-09   6.22e+13      0.000    3.71e+05    3.71e+05\n",
            "C(Capacity)[T.45674.0]      4.253e+04    6.8e-09   6.26e+12      0.000    4.25e+04    4.25e+04\n",
            "C(Capacity)[T.45807.0]     -2.181e+05   5.16e-09  -4.23e+13      0.000   -2.18e+05   -2.18e+05\n",
            "C(Capacity)[T.49256.0]      9.237e+05   9.97e-09   9.27e+13      0.000    9.24e+05    9.24e+05\n",
            "C(Capacity)[T.50000.0]     -2.233e+05    6.8e-09  -3.28e+13      0.000   -2.23e+05   -2.23e+05\n",
            "C(Capacity)[T.60218.0]      3.397e+05    1.5e-08   2.26e+13      0.000     3.4e+05     3.4e+05\n",
            "C(Capacity)[T.61337.0]      4.138e+05   9.97e-09   4.15e+13      0.000    4.14e+05    4.14e+05\n",
            "C(Capacity)[T.62562.0]      3.069e+05   5.16e-09   5.95e+13      0.000    3.07e+05    3.07e+05\n",
            "C(Capacity)[T.75005.0]      6.545e+05   5.16e-09   1.27e+14      0.000    6.54e+05    6.54e+05\n",
            "C(Capacity)[T.79560.0]      8.494e+05   7.77e-09   1.09e+14      0.000    8.49e+05    8.49e+05\n",
            "C(Capacity)[T.80250.0]      6.069e+05   5.16e-09   1.18e+14      0.000    6.07e+05    6.07e+05\n",
            "C(Capacity)[T.86112.0]      3.397e+05    1.5e-08   2.26e+13      0.000     3.4e+05     3.4e+05\n",
            "C(Buyout)[T.175000.0]      -4.608e+05    6.8e-09  -6.78e+13      0.000   -4.61e+05   -4.61e+05\n",
            "C(Buyout)[T.192308.0]               0          0        nan        nan           0           0\n",
            "C(Buyout)[T.300000.0]               0          0        nan        nan           0           0\n",
            "C(Buyout)[T.516667.0]               0          0        nan        nan           0           0\n",
            "C(Buyout)[T.522300.0]               0          0        nan        nan           0           0\n",
            "C(Buyout)[T.556389.0]               0          0        nan        nan           0           0\n",
            "C(Buyout)[T.688500.0]               0          0        nan        nan           0           0\n",
            "C(Buyout)[T.918333.0]               0          0        nan        nan           0           0\n",
            "C(Buyout)[T.950000.0]      -3.281e+05   5.16e-09  -6.36e+13      0.000   -3.28e+05   -3.28e+05\n",
            "C(Buyout)[T.980300.0]               0          0        nan        nan           0           0\n",
            "C(Buyout)[T.1000000.0]     -1.681e+05   5.16e-09  -3.26e+13      0.000   -1.68e+05   -1.68e+05\n",
            "C(Buyout)[T.1020833.0]              0          0        nan        nan           0           0\n",
            "C(Buyout)[T.1150000.0]              0          0        nan        nan           0           0\n",
            "C(Buyout)[T.1233333.0]     -2.233e+05    6.8e-09  -3.28e+13      0.000   -2.23e+05   -2.23e+05\n",
            "C(Buyout)[T.1303294.0]              0          0        nan        nan           0           0\n",
            "C(Buyout)[T.1476060.0]      -3.94e+05    6.8e-09   -5.8e+13      0.000   -3.94e+05   -3.94e+05\n",
            "C(Buyout)[T.1532790.0]     -3.382e+05   5.16e-09  -6.56e+13      0.000   -3.38e+05   -3.38e+05\n",
            "C(Buyout)[T.1822918.0]     -2.847e+05   5.16e-09  -5.52e+13      0.000   -2.85e+05   -2.85e+05\n",
            "C(Buyout)[T.1912500.0]              0          0        nan        nan           0           0\n",
            "C(Buyout)[T.1951708.0]              0          0        nan        nan           0           0\n",
            "C(Buyout)[T.2112500.0]              0          0        nan        nan           0           0\n",
            "C(Buyout)[T.2122917.0]     -2.181e+05   5.16e-09  -4.23e+13      0.000   -2.18e+05   -2.18e+05\n",
            "C(Buyout)[T.2160417.0]              0          0        nan        nan           0           0\n",
            "C(Buyout)[T.2508333.0]     -5.362e+05   9.97e-09  -5.38e+13      0.000   -5.36e+05   -5.36e+05\n",
            "C(Buyout)[T.2671875.0]     -3.075e+05    6.8e-09  -4.52e+13      0.000   -3.07e+05   -3.07e+05\n",
            "C(Buyout)[T.2850000.0]     -1.931e+05   5.16e-09  -3.75e+13      0.000   -1.93e+05   -1.93e+05\n",
            "C(Buyout)[T.2911667.0]              0          0        nan        nan           0           0\n",
            "C(Buyout)[T.2991667.0]              0          0        nan        nan           0           0\n",
            "C(Buyout)[T.3000000.0]              0          0        nan        nan           0           0\n",
            "C(Buyout)[T.3185626.0]              0          0        nan        nan           0           0\n",
            "C(Buyout)[T.3562500.0]              0          0        nan        nan           0           0\n",
            "C(Buyout)[T.3587500.0]              0          0        nan        nan           0           0\n",
            "C(Buyout)[T.3847500.0]              0          0        nan        nan           0           0\n",
            "C(Buyout)[T.4037517.0]      4.253e+04    6.8e-09   6.26e+12      0.000    4.25e+04    4.25e+04\n",
            "C(Buyout)[T.4231250.0]              0          0        nan        nan           0           0\n",
            "C(Buyout)[T.4425000.0]              0          0        nan        nan           0           0\n",
            "C(Buyout)[T.4900000.0]      4.319e+05   5.16e-09   8.38e+13      0.000    4.32e+05    4.32e+05\n",
            "C(Buyout)[T.5208333.0]              0          0        nan        nan           0           0\n",
            "C(Buyout)[T.5291667.0]              0          0        nan        nan           0           0\n",
            "C(Buyout)[T.5440000.0]     -5.561e+04   5.16e-09  -1.08e+13      0.000   -5.56e+04   -5.56e+04\n",
            "C(Buyout)[T.6000000.0]              0          0        nan        nan           0           0\n",
            "C(Buyout)[T.6125000.0]      3.069e+05   5.16e-09   5.95e+13      0.000    3.07e+05    3.07e+05\n",
            "C(Buyout)[T.6633774.0]              0          0        nan        nan           0           0\n",
            "C(Buyout)[T.7000000.0]      6.545e+05   5.16e-09   1.27e+14      0.000    6.54e+05    6.54e+05\n",
            "C(Buyout)[T.7100000.0]              0          0        nan        nan           0           0\n",
            "C(Buyout)[T.7208500.0]              0          0        nan        nan           0           0\n",
            "C(Buyout)[T.7431667.0]              0          0        nan        nan           0           0\n",
            "C(Buyout)[T.7784038.0]              0          0        nan        nan           0           0\n",
            "C(Buyout)[T.8016667.0]              0          0        nan        nan           0           0\n",
            "C(Buyout)[T.9000000.0]              0          0        nan        nan           0           0\n",
            "C(Buyout)[T.9250000.0]              0          0        nan        nan           0           0\n",
            "C(Buyout)[T.10314583.0]             0          0        nan        nan           0           0\n",
            "C(Buyout)[T.10350000.0]             0          0        nan        nan           0           0\n",
            "C(Buyout)[T.12500000.0]             0          0        nan        nan           0           0\n",
            "C(Buyout)[T.12636991.0]             0          0        nan        nan           0           0\n",
            "C(Buyout)[T.14056250.0]             0          0        nan        nan           0           0\n",
            "C(Buyout)[T.15416667.0]             0          0        nan        nan           0           0\n",
            "C(Buyout)[T.15625000.0]             0          0        nan        nan           0           0\n",
            "C(Buyout)[T.16250000.0]     6.795e+05   5.22e-09    1.3e+14      0.000    6.79e+05    6.79e+05\n",
            "C(Buyout)[T.18193750.0]             0          0        nan        nan           0           0\n",
            "C(Buyout)[T.18375000.0]             0          0        nan        nan           0           0\n",
            "C(Buyout)[T.18650000.0]     6.069e+05   5.16e-09   1.18e+14      0.000    6.07e+05    6.07e+05\n",
            "C(Buyout)[T.20833333.0]             0          0        nan        nan           0           0\n",
            "C(Buyout)[T.21516667.0]             0          0        nan        nan           0           0\n",
            "C(Buyout)[T.21958333.0]     8.494e+05   7.77e-09   1.09e+14      0.000    8.49e+05    8.49e+05\n",
            "C(Buyout)[T.22396250.0]             0          0        nan        nan           0           0\n",
            "C(Buyout)[T.26354166.0]             0          0        nan        nan           0           0\n",
            "C(Buyout)[T.27917500.0]             0          0        nan        nan           0           0\n",
            "C(Buyout)[T.32143750.0]             0          0        nan        nan           0           0\n",
            "C(Buyout)[T.33600000.0]             0          0        nan        nan           0           0\n",
            "C(Buyout)[T.35000000.0]             0          0        nan        nan           0           0\n",
            "C(Buyout)[T.38058402.0]             0          0        nan        nan           0           0\n",
            "==============================================================================\n",
            "Omnibus:                        0.918   Durbin-Watson:                   0.346\n",
            "Prob(Omnibus):                  0.632   Jarque-Bera (JB):                0.140\n",
            "Skew:                          -0.080   Prob(JB):                        0.932\n",
            "Kurtosis:                       3.330   Cond. No.                     7.67e+17\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The input rank is higher than the number of observations.\n",
            "[3] The smallest eigenvalue is 4.98e-35. This might indicate that there are\n",
            "strong multicollinearity problems or that the design matrix is singular.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:1100: RuntimeWarning: invalid value encountered in true_divide\n",
            "  return self.params / self.bse\n",
            "/usr/local/lib/python3.6/dist-packages/scipy/stats/_distn_infrastructure.py:877: RuntimeWarning: invalid value encountered in greater\n",
            "  return (self.a < x) & (x < self.b)\n",
            "/usr/local/lib/python3.6/dist-packages/scipy/stats/_distn_infrastructure.py:877: RuntimeWarning: invalid value encountered in less\n",
            "  return (self.a < x) & (x < self.b)\n",
            "/usr/local/lib/python3.6/dist-packages/scipy/stats/_distn_infrastructure.py:1831: RuntimeWarning: invalid value encountered in less_equal\n",
            "  cond2 = cond0 & (x <= self.a)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "jFlgoH7LIhgY",
        "colab_type": "code",
        "outputId": "4836d176-70a2-4991-80da-7c8399df501c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        }
      },
      "cell_type": "code",
      "source": [
        "# review results by variables\n",
        "# specify a simple model\n",
        "my_model = str('TotalPay ~ SchoolPay + BonusPaid + Capacity + Buyout')\n",
        "\n",
        "# fit the model to the training set\n",
        "train_model_fit = smf.ols(my_model, data = data2_train).fit()\n",
        "# summary of model fit to the training set\n",
        "print(train_model_fit.summary())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:               TotalPay   R-squared:                       1.000\n",
            "Model:                            OLS   Adj. R-squared:                  1.000\n",
            "Method:                 Least Squares   F-statistic:                 1.312e+30\n",
            "Date:                Sat, 27 Apr 2019   Prob (F-statistic):          7.12e-294\n",
            "Time:                        22:56:15   Log-Likelihood:                 454.58\n",
            "No. Observations:                  25   AIC:                            -899.2\n",
            "Df Residuals:                      20   BIC:                            -893.1\n",
            "Df Model:                           4                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "Intercept  -2.561e-09   2.66e-09     -0.961      0.348   -8.12e-09       3e-09\n",
            "SchoolPay      1.0000   8.78e-16   1.14e+15      0.000       1.000       1.000\n",
            "BonusPaid   -6.28e-16   2.85e-15     -0.220      0.828   -6.58e-15    5.33e-15\n",
            "Capacity   -4.263e-14   8.23e-14     -0.518      0.610   -2.14e-13    1.29e-13\n",
            "Buyout     -2.064e-16   1.77e-16     -1.166      0.257   -5.76e-16    1.63e-16\n",
            "==============================================================================\n",
            "Omnibus:                        3.067   Durbin-Watson:                   0.235\n",
            "Prob(Omnibus):                  0.216   Jarque-Bera (JB):                2.607\n",
            "Skew:                          -0.761   Prob(JB):                        0.272\n",
            "Kurtosis:                       2.569   Cond. No.                     3.19e+07\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 3.19e+07. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "nHpGOhjMlDUE",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Poor model result**"
      ]
    },
    {
      "metadata": {
        "id": "JZIP79VJaOPE",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Model 2 - Mixed Linear Model Regression**"
      ]
    },
    {
      "metadata": {
        "id": "Az0cy-8bVEjb",
        "colab_type": "code",
        "outputId": "27806ba5-a3f0-42cd-b615-d916d2dc31c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "cell_type": "code",
      "source": [
        "import statsmodels.formula.api as smf\n",
        "# include conference_code as a classifier\n",
        "model2 = smf.mixedlm(\"TotalPay ~ SchoolPay + BonusPaid + Buyout\", data2_train, groups=data2_train[\"Conference_code\"])\n",
        "\n",
        "model2f = model2.fit()\n",
        "\n",
        "print(model2f.summary())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              Mixed Linear Model Regression Results\n",
            "==================================================================\n",
            "Model:               MixedLM  Dependent Variable:  TotalPay       \n",
            "No. Observations:    94       Method:              REML           \n",
            "No. Groups:          11       Scale:               1111645160.8785\n",
            "Min. group size:     5        Likelihood:          -1117.1019     \n",
            "Max. group size:     11       Converged:           Yes            \n",
            "Mean group size:     8.5                                          \n",
            "------------------------------------------------------------------\n",
            "             Coef.     Std.Err.    z    P>|z|   [0.025     0.975] \n",
            "------------------------------------------------------------------\n",
            "Intercept     1477.317 6391.347   0.231 0.817 -11049.492 14004.126\n",
            "SchoolPay        0.995    0.004 267.082 0.000      0.988     1.002\n",
            "BonusPaid       -0.002    0.018  -0.126 0.900     -0.038     0.033\n",
            "Buyout           0.002    0.001   2.783 0.005      0.001     0.004\n",
            "Group Var 33154188.219 2070.548                                   \n",
            "==================================================================\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "pyADiArujwU7",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "    Model: y = 0.995 * schoolpay - 0.002 * BonusPaid + 0.002 * Buyout + 1477.317"
      ]
    },
    {
      "metadata": {
        "id": "pwqFrIZa22FO",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Bonus"
      ]
    },
    {
      "metadata": {
        "id": "byA7EBmn2567",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# copay data2 to create data5\n",
        "data5 = data2.copy()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "NnZS-3vV3HDr",
        "colab_type": "code",
        "outputId": "8169cc53-d0b6-4ba3-fbcc-f5198de763d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        }
      },
      "cell_type": "code",
      "source": [
        "# Observe column data types.\n",
        "data5.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 133 entries, 0 to 133\n",
            "Data columns (total 22 columns):\n",
            "School             133 non-null object\n",
            "Conference         133 non-null object\n",
            "Coach              133 non-null object\n",
            "SchoolPay          133 non-null float64\n",
            "TotalPay           133 non-null float64\n",
            "Bonus              133 non-null float64\n",
            "BonusPaid          133 non-null float64\n",
            "AssistantPay       133 non-null float64\n",
            "Buyout             133 non-null float64\n",
            "Cohort Year        30 non-null float64\n",
            "Sport              30 non-null object\n",
            "State              30 non-null object\n",
            "GSR                30 non-null float64\n",
            "FGR                28 non-null float64\n",
            "ConfAbbr           30 non-null object\n",
            "Stadium            33 non-null object\n",
            "Capacity           33 non-null float64\n",
            "City               33 non-null object\n",
            "State/Province     33 non-null object\n",
            "Home teams         33 non-null object\n",
            "Conference_code    133 non-null int64\n",
            "runiform           133 non-null float64\n",
            "dtypes: float64(11), int64(1), object(10)\n",
            "memory usage: 23.9+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "PkgUSk1b3KRX",
        "colab_type": "code",
        "outputId": "55074d9a-ab31-493e-c12f-f43e0f71035c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "# Display the number of records and features.\n",
        "print('Number of rows: {}'.format(len(data5)))\n",
        "print('Number of columns: {}'.format(len(data5.columns)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of rows: 133\n",
            "Number of columns: 22\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "aZrVIIp73Px0",
        "colab_type": "code",
        "outputId": "b0f7a1ba-7554-4f37-e687-2a829a84a722",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        }
      },
      "cell_type": "code",
      "source": [
        "# Check for missing values.\n",
        "data5.isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "School               0\n",
              "Conference           0\n",
              "Coach                0\n",
              "SchoolPay            0\n",
              "TotalPay             0\n",
              "Bonus                0\n",
              "BonusPaid            0\n",
              "AssistantPay         0\n",
              "Buyout               0\n",
              "Cohort Year        103\n",
              "Sport              103\n",
              "State              103\n",
              "GSR                103\n",
              "FGR                105\n",
              "ConfAbbr           103\n",
              "Stadium            100\n",
              "Capacity           100\n",
              "City               100\n",
              "State/Province     100\n",
              "Home teams         100\n",
              "Conference_code      0\n",
              "runiform             0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "metadata": {
        "id": "8f0ylR2U3Yj0",
        "colab_type": "code",
        "outputId": "f6a33ef7-f890-421e-e798-7e6033d46fb2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        }
      },
      "cell_type": "code",
      "source": [
        "# Correlation of numerical values.\n",
        "data5.corr()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SchoolPay</th>\n",
              "      <th>TotalPay</th>\n",
              "      <th>Bonus</th>\n",
              "      <th>BonusPaid</th>\n",
              "      <th>AssistantPay</th>\n",
              "      <th>Buyout</th>\n",
              "      <th>Cohort Year</th>\n",
              "      <th>GSR</th>\n",
              "      <th>FGR</th>\n",
              "      <th>Capacity</th>\n",
              "      <th>Conference_code</th>\n",
              "      <th>runiform</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>SchoolPay</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.999700</td>\n",
              "      <td>0.382592</td>\n",
              "      <td>0.400795</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.798156</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.032633</td>\n",
              "      <td>0.056977</td>\n",
              "      <td>0.880772</td>\n",
              "      <td>-0.152402</td>\n",
              "      <td>-0.064484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TotalPay</th>\n",
              "      <td>0.999700</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.386164</td>\n",
              "      <td>0.401657</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.801610</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.032701</td>\n",
              "      <td>0.057060</td>\n",
              "      <td>0.880772</td>\n",
              "      <td>-0.151791</td>\n",
              "      <td>-0.065087</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bonus</th>\n",
              "      <td>0.382592</td>\n",
              "      <td>0.386164</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.418601</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.492043</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.496310</td>\n",
              "      <td>-0.322746</td>\n",
              "      <td>0.461000</td>\n",
              "      <td>0.114376</td>\n",
              "      <td>-0.055470</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BonusPaid</th>\n",
              "      <td>0.400795</td>\n",
              "      <td>0.401657</td>\n",
              "      <td>0.418601</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.515406</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.328064</td>\n",
              "      <td>-0.115724</td>\n",
              "      <td>0.029090</td>\n",
              "      <td>0.053152</td>\n",
              "      <td>0.124884</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AssistantPay</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Buyout</th>\n",
              "      <td>0.798156</td>\n",
              "      <td>0.801610</td>\n",
              "      <td>0.492043</td>\n",
              "      <td>0.515406</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.273728</td>\n",
              "      <td>-0.232216</td>\n",
              "      <td>0.803403</td>\n",
              "      <td>-0.050582</td>\n",
              "      <td>0.055959</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cohort Year</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GSR</th>\n",
              "      <td>0.032633</td>\n",
              "      <td>0.032701</td>\n",
              "      <td>-0.496310</td>\n",
              "      <td>-0.328064</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.273728</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.889118</td>\n",
              "      <td>-0.168255</td>\n",
              "      <td>-0.276151</td>\n",
              "      <td>0.065074</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FGR</th>\n",
              "      <td>0.056977</td>\n",
              "      <td>0.057060</td>\n",
              "      <td>-0.322746</td>\n",
              "      <td>-0.115724</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.232216</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.889118</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.131861</td>\n",
              "      <td>-0.212654</td>\n",
              "      <td>-0.012607</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Capacity</th>\n",
              "      <td>0.880772</td>\n",
              "      <td>0.880772</td>\n",
              "      <td>0.461000</td>\n",
              "      <td>0.029090</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.803403</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.168255</td>\n",
              "      <td>-0.131861</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.426948</td>\n",
              "      <td>-0.078657</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Conference_code</th>\n",
              "      <td>-0.152402</td>\n",
              "      <td>-0.151791</td>\n",
              "      <td>0.114376</td>\n",
              "      <td>0.053152</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.050582</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.276151</td>\n",
              "      <td>-0.212654</td>\n",
              "      <td>-0.426948</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.012481</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>runiform</th>\n",
              "      <td>-0.064484</td>\n",
              "      <td>-0.065087</td>\n",
              "      <td>-0.055470</td>\n",
              "      <td>0.124884</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.055959</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.065074</td>\n",
              "      <td>-0.012607</td>\n",
              "      <td>-0.078657</td>\n",
              "      <td>0.012481</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 SchoolPay  TotalPay     Bonus  BonusPaid  AssistantPay  \\\n",
              "SchoolPay         1.000000  0.999700  0.382592   0.400795           NaN   \n",
              "TotalPay          0.999700  1.000000  0.386164   0.401657           NaN   \n",
              "Bonus             0.382592  0.386164  1.000000   0.418601           NaN   \n",
              "BonusPaid         0.400795  0.401657  0.418601   1.000000           NaN   \n",
              "AssistantPay           NaN       NaN       NaN        NaN           NaN   \n",
              "Buyout            0.798156  0.801610  0.492043   0.515406           NaN   \n",
              "Cohort Year            NaN       NaN       NaN        NaN           NaN   \n",
              "GSR               0.032633  0.032701 -0.496310  -0.328064           NaN   \n",
              "FGR               0.056977  0.057060 -0.322746  -0.115724           NaN   \n",
              "Capacity          0.880772  0.880772  0.461000   0.029090           NaN   \n",
              "Conference_code  -0.152402 -0.151791  0.114376   0.053152           NaN   \n",
              "runiform         -0.064484 -0.065087 -0.055470   0.124884           NaN   \n",
              "\n",
              "                   Buyout  Cohort Year       GSR       FGR  Capacity  \\\n",
              "SchoolPay        0.798156          NaN  0.032633  0.056977  0.880772   \n",
              "TotalPay         0.801610          NaN  0.032701  0.057060  0.880772   \n",
              "Bonus            0.492043          NaN -0.496310 -0.322746  0.461000   \n",
              "BonusPaid        0.515406          NaN -0.328064 -0.115724  0.029090   \n",
              "AssistantPay          NaN          NaN       NaN       NaN       NaN   \n",
              "Buyout           1.000000          NaN -0.273728 -0.232216  0.803403   \n",
              "Cohort Year           NaN          NaN       NaN       NaN       NaN   \n",
              "GSR             -0.273728          NaN  1.000000  0.889118 -0.168255   \n",
              "FGR             -0.232216          NaN  0.889118  1.000000 -0.131861   \n",
              "Capacity         0.803403          NaN -0.168255 -0.131861  1.000000   \n",
              "Conference_code -0.050582          NaN -0.276151 -0.212654 -0.426948   \n",
              "runiform         0.055959          NaN  0.065074 -0.012607 -0.078657   \n",
              "\n",
              "                 Conference_code  runiform  \n",
              "SchoolPay              -0.152402 -0.064484  \n",
              "TotalPay               -0.151791 -0.065087  \n",
              "Bonus                   0.114376 -0.055470  \n",
              "BonusPaid               0.053152  0.124884  \n",
              "AssistantPay                 NaN       NaN  \n",
              "Buyout                 -0.050582  0.055959  \n",
              "Cohort Year                  NaN       NaN  \n",
              "GSR                    -0.276151  0.065074  \n",
              "FGR                    -0.212654 -0.012607  \n",
              "Capacity               -0.426948 -0.078657  \n",
              "Conference_code         1.000000  0.012481  \n",
              "runiform                0.012481  1.000000  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "metadata": {
        "id": "nYZOcVbp3c0n",
        "colab_type": "code",
        "outputId": "381feaec-7b1b-4237-b120-b5b14a2d925a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 943
        }
      },
      "cell_type": "code",
      "source": [
        "# Plot a correlation heatmap.\n",
        "plt.subplots(figsize=(18,15))\n",
        "hm1 = sns.heatmap(data5.corr(), annot=True, cmap='YlGnBu')\n",
        "hm1.set_xticklabels(hm1.get_xticklabels(), rotation=90)\n",
        "hm1.xaxis.set_ticks_position('top')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wCyXvJauAywNlEskkiRWsBgZNkVkmmlkvdx8fOkgG+MDM7gSeJmn5Z3efGC6SSNk08kAk\nZmZ3AN2JhtkOAIa7+z/CpgovsTyhuxeZWQdgF+A1d9+4jadWOmqLiJmdTjTPvzPwX6Am8Bd3vzdk\nrlDi1SfKupuYNTUgzCwHGOLuj4fOIpKpzKwtMN/df4y3qwFN3H120GABmNkMops1c4gumLNyJB+A\nmY0sY7e7+4C0hxHZBnUeSClmNoioOBzAKHcfETJPOpnZZ0C3eMWJGsB77t4zdK7QzOwTYG+gHvAB\nMJ6oBsBvggYLQG0R0eoCpZnZ0UmbVYEjge/d/fxAkYJILL0WOodIpjKzCcBe7r4h3i4APnD3XmGT\npZ+ZtS5rv7vPSXcWEdl+OaEDSOYwsxuIhtZ9Hv8bambZdOd9Q6KQkbuvQb8fCebua4kKG93t7scS\nFYnLRmqLyPAy9j2X9hQZwt2HJ/17nGhp12y8iH7bzP5oZi3NrH7iX+hQIhkkL9FxABA/LgiYJ5i4\nk6AlMCB+nKg1lXXMrI6Z/cvMJsT/bjGzOqFziZRFNQ8k2SFEd96LAMzsYWAS2TNndRczS8wvM2Dn\neDsxlC5bK4abme1JVFH/tHhftq7QkdVtodUFtttOZGENCGBw/N9zkvZlVd0ckW1YbGaD3P0lKC66\nmnUrDACY2VVEnaw7Aw8B+URFV/uGzBXIg8BnRB3PACcStclRW32GSCDqPJDN1QUSqytkW6+nqv6W\n7QLgMuB5d59mZjsCZc3PywbZ3hY7ExX8qgsclrR/NXBGkEQZwMxWU1J01okKSP45aKgA3L1t6Awi\nGe73wONxcTwDviVa1SkbHUlUZ2oigLt/H692lY3auXvy9Le/mdnkYGlEUlDNAylmZkOAG4guhoyo\n9sGl7v500GAiklG0uoCUJV6y8w8k1c0B7s22gqIi22JmNQHc/YfQWUJJLE9oZhPdffe41tTYLC2Y\nOBb4k7uPibf7Aje7+55hk4lsSZ0HUoqZNQMShXvGufuCkHnSycyWU0bFdEqmLWTl3N24CnBZleSz\nrgqw2iJiZv8ErgPWAa8DXYAL3f2xoMECyuZiswlmNoxo6PHD8a4TgUJ3Pz1cKpHMYmaHEk3/Kp7q\n5e7XhEsUhpn9kWiK16+B64FTgSfc/Y6gwQIws67AI5SM+F0OnOzuU8KlEimbpi3I5npR8gHYgZcD\nZkm3hqEDZKg/Jj2uChwNbAqUJTS1ReQAd7/EzI4EZhPNy3yfaL5q1omLzfYCEssUDjWzvdw9W+rF\nJPRy965J2++a2afB0ohkGDO7B6gO9AeGAccA44KGSjMzq+Lu6939ZjP7NbCKaErcX939rcDx0srM\nhrr7bUBNd+9qZrUB3H1V4GgiW6WRB1KsjA/AQ4DxWfgBGIC4SnjynYHvA8bJKInhhqFzZIJsbAsz\nm+buu8V3mp9z99fN7NPNLhyzhplNoXSx2VxgUrYNv40LzB7r7l/F2zsS/Xxka7FZkVLMbIq7d0n6\nb03gNXffO3S2dEmapvCou58YOk9IZjbZ3bsl2iR0HpHtoZEHkizbV1sAiocU3gq0AJYCOwBfAruE\nzBXKZkut5QA9yL5imoDaIsnLZjaDaNrCH8ysEfBj4EyhZXOx2YQ/ASPN7Gui6V6tgVPCRhLJKOvi\n/641s+ZEnzGaBcwTQoGZnQDstdmqPQC4+/8CZAplupnNBJrHndAJiemyWdUBLRWDOg9kc/oADH8n\nWiroTXfvHg+rO24bz6nMPqGkkvwm4BtKlinMNmoLwN0vjeserHT3QjNbAxweOldA1wOT4poYxcVm\nw0ZKP3d/x8x2IhqCDPCFu68PmSlTmFkOMMTdH9/myVKZjTCzusBNRKsMOHB/2Ehp93ui5Y43X7UH\novbIms4Ddx9iZk2BN4BBofOIbA9NW5BiWm0hYmYT3L1nPFe3m7t7Ng/JFkkwswHu/m5Zd4sg6+4Y\nlZLNxWaTmdleQBuSbk64+yPBAqVZPGf5HKIRay8BbwHnAhcDn7p71nWyxdN46rn7kni7APgdUZHV\njiGzhWRmVYCq7r4ydJYQzOw0d38gdA4R+Wk08kCKufuTZjaKkg/Af87SD8Ar43mIY4BHzGwRJUMN\ns1K2XxAky/K2+BXwLlveLYIsu2OUzMyGAw8AIxLTvrKRmT0KtAMmA4XxbieqIp4tHiWqlD4WOJ1o\n2p8BR7h71q3bbmbHA/cCa+Lh2X8HHgTGE919zipmVhU4G+hH9Lsxxsz+4+5ZN+3L3R/I8vdTzOwZ\ndz/OzKZSeiUnTVuQjKWRB1KKme1ANE81+Q/5++ESpZ+Z1QLWEs1pP4lo+sYjibsm2WZrFwTufn64\nVGGoLaQsZrY/0dz+PsCzwEPu/kXYVOlnZtOBXT2LP1iY2VR37xw/zgXmA62y8eIQwMw+I+o4mWVm\nuxN1qhzj7tm0klMxM3sGWE3JyjQnAHXd/dhwqcLQ+2k0Ys3d55tZ67KOu/ucdGcS2RaNPJBiZnYj\nMBiYBiTunjnREmzZ5LJ4hYlCoruJmNk/yLLCkUl6kuUXBEnUFkTLSwEPEX0Ivh/YnWiK05tBgwXi\n7m8Db5tZHaJVat42s2+J2uYxd98YNGD6fAY0JbpgzlbF/6/jeiDzsrXjILbB3WcBuPtEM5uZrR0H\nsU7uvmvS9kgz+zxYmrCy/v3U3efH/1UngVQY6jyQZEcAO6vAFQexZUfBoWXsyxa6ICihtoic6u63\nmdmBQAPgRKLh2lnZeQBgZg2A3xK1xSSiJW/7AScD+4ZL9sszs5eJOpprAZ+b2Tig+H3E3bOpEFhX\nM0us0W5AtXg7MQy5drhoQTQ2s4uStusmb7v7vwJkCmmimfVx948AzGwPYELgTKHo/TQW1xG6EWhM\n9LciW/9eSAWgzgNJ9jWQT9KHvmxiZmcRVQHuEK9XnlCLqMp+tmqILggS1BYRi/97CNGUnmlmZqme\nUJmZ2fNEKww8ChyWuJsEPG1m2XBhcHPoAJnC3XNDZ8gw9xO9h25tO9v0AD40s7nxdivgi8Sc9yyb\n46730xL/JHrvmB46iMi2qOaBYGZ3EN012gHoCrxD6T/kWTH/zMzqEd1FvZ7Sy6ytdvdFYVKFZ2a/\nKmu/u7+X7iyhqS0iZvYQ0d+LtkR/M3KBUe7eI2iwQMysv7uPDJ0jE5hZE0qvOpFVfzvNrDqwMTFV\nxcx2Jupkm+3uzwcNJ8FtbW57QjYNX9f7aQkz+8Dd+4bOIbI91HkgmNnJqY67+8PpypIpzGw3YO94\nc7S7TwuZJ7RsvyBIprYoXrO+G/C1u68ws/pAC3efEjhaEGZ2Uln7s6lqOICZHUe0fv0ootEpewN/\ncvfnQuZKJzN7HzjN3WeaWXtgHNEUll2B8e5+acovUMnE76Xt3P2lePtWoiLEAHe6+8StPrkSM7PG\nQNXEtrvPTXF6paX304iZ3UY0heMFSt+8y8oVjCSzqfNAiplZDeBHdy+Mt3OBKu6+Nmyy9DKzc4jW\n6X4h3nU4cJe73x0uVTi6ICihtoiYWV9gsruvMbPfEhVMvC2b7poli0dvJVQF9gMmuvsxgSIFYWaf\nAr9OXACYWSPgbXfvGjZZ+my22sK1QH13P8fMCoBPEseyRVwP43p3/zDe/hz4C1AdONrdjwiZL93M\nbBBwC9AcWES0utV0d98taLAA9H5aIh7Ntzl391PTHkZkG9R5IMXM7CNgf3f/Id6uCbzp7nuFTZZe\nZjYF2Guzdvgwy+YiFtMFQQm1RST+HekKdAH+CwwDjnP3MoehZhszqws85e4Hhc6STskXzvF2DvBp\nNl0wm9mUxHuFmX0A3OTuL8Tbn2bh34oJ7t4zafsjd+8TPx7j7v3CpUu/+D1kANH7Rncz6w/81t1P\nCxwt7fR+KlIxqWCiJKuauGAGcPcf4vmb2caADUnbGykpEJeNcjYbSrgUyAkVJjC1RWSTu7uZHU40\n9PgBM8u6D78prCGqB5FtXjezN4An4+3BwGsB84QwxcxuBr4D2hOvQBJ3KGWjUsUREx0HscZpzpIJ\nNrr7UjPLMbMcdx9pZv8OHSoQvZ/G4pEHW9zN1cgDyUTqPJBka8xs98QcRDPrCawLnCltzCzP3TcR\nVUz/2MyGx4eOBLKu7kOSsi4IXg2YJyS1RWS1mV1GtCzh3vEd5qx9P0laqhCiD7+7As+ESxSGu/8p\nXnIscTf5viwsEngGMBRoAxyQNO2vI9m5KsX3ZraHu3+cvNPM+gDfB8oU0op4NOP7wONmtoioszEb\n6f20xIikx1WJPndm4++HVACatiDFzKwX8BQlf7CaAYPdPSuWKTSzie6+e/y4NyUfgEe7+/hwycLb\n7IJgdBZeEBRTW4CZNQVOICpwNcbM9gEecvd2gaMFsVnV8E3AHHefFypPusWFAZu4+web7e8HzHf3\nr8IkS794NE4Ld78r3v4YaBQf/rO7PxssXADxe+nTRNObEsURewAnE32+GBcoWlolfkeAyUQ3ZXKA\n3xDVPHglWz5nQem/F5u9n64AHs+mvxdbE3fIj8m2acNSMajzQBKdBt+6+wIzywfOAo4CPgf+6u7L\nggZMEzOb5O7dQ+fIZGbWEFjq+sOR9W1hZt2JOhCOBb4B/ufud6R+VuWXjT8XZjYCuMzdp262vzPw\nD3c/LEyy9IvrHBzv7t/G25OJCmjWIOpg2y9kvhDilQXOBRJFAacRFSFeGC5Veul3pITaYtviJV5f\ncff2obOIbC5rh5lKKfcC+8eP9wQuB84jWortPiBbKoY3MrOLtnbQ3f+VzjChxcNKbwCWAdcSTedo\nCOSY2Unu/nrIfOmktoiYWQdgSPxvCdEdRXP3/kGDBaKfi2JNNr8QAHD3qWbWJv1xgipIdBzExrj7\nUmBpvKJRVjGzVvEyhH8NnSUw/Y6UUFskMTMDCoEfknYvAP4cJpFIauo8EIDcpNEFg4nmqQ4Hhsd3\nTbJFLlCT7C6OmOxOoo6kOsC7wMHu/pGZ7UI0RzFbLoxAbZEwAxgNDHT3WQBmdmHYSEHp5yKSqhhg\ntbSlyAz1kjfc/dykzUZknxeIlnLFzIa7+9GB84Si35ESaoskcfHhz929U+gsIttDnQcCkJtULHA/\n4MykY9n0MzLf3a8JHSKD5Ll7olL4Ne7+EYC7z4g6yrOK2iJyFHA8MNLMXieqkZJVDbAZ/VxEJpjZ\nGe5+f/JOMzsdyJq53LGPt9IWZwFZMb9/M8m/CDsGSxGefkdKqC229ImZ9cr2+lpSMWTThaFs3ZPA\ne2a2hKiQz2goLmqzMmSwNMuqT/vboSjp8earbmTNfO6Y2gKI16t/IR5+fThwAdDYzP4DPJ+4kM4i\n+rmIXAA8b2a/oeTDf0+ggKhqeDa5kOh35ARKFwisAhwRLFU4vpXH2Ua/IyXUFlvaA/iNmc0hWn3D\niAYldAkbS2RLKpgoQPHc3WbAm+6+Jt7XAaiZWLqxsjOz+tlSHHJ7mFkhJW9i1YDEkmMGVHX3/FDZ\n0k1tsXVmVo+oaOLgbCsGp5+L0sysP5AYejvN3d8NmSckMxtAUoHAbG2LbfyOuLvXDpUtBP2OlFBb\nlDCz1mXtd/c56c4isi3qPBARERERERGRlHJCBxARERERERGRzKbOAxEREREREZEMY2YPmtkiM/ts\nK8fNzG43s1lmNsXMdk86drKZzYz/nVweedR5IGUyszO3fVZ2UFuUUFuUUFuUUFuUUFuUUFuUUFuU\nUFuUUFuUUFtE1A5Shv8CB6U4fjCwU/zvTOA/ENVyA64iKsjZG7gqrlP1s6jzQLZGf7xKqC1KqC1K\nqC1KqC1KqC1KqC1KqC1KqC1KqC1KqC0iagcpxd3fB1IVdD8ceMQjHwF1zawZcCDwlrsvc/flwFuk\n7oTYLuo8EBEREREREal4dgC+TdqeF+/b2v6fJe/nfgEpX9VaDcmI5S/y6vXMmCyhZUJbtLnqDyG/\nfbGmJ15MxwfeD9oWmzaF/O4lGv3mYna6N2xbAMw8a5/QEbj33nsh8BruHfrcHfLbF2vcdggd+twd\n/Ofiy4/ODh0hI34ubpn6VshvX+yYvw7llqlvBW2LdZss5LcvNuiKC7hu0ttB2+I/J44O+e2L1Wl2\nKDt0uipoW4x4r1fIb1/silvOYdLSEUHbYt6a3JDfHoCzrz+Pl+e+Fvw95LBWB2fGH4yfKfTn9+3x\n47dPnUXpESf3uft9ofJsizoPpEx5NduHjpAx1BYl6v5qUOgIGaPO3mqLhDPP1CjLhDqN+4WOkDH0\nc1GizzGHho6QMXodrbZIqFFjSpyUAAAgAElEQVS/R+gIGePok3/2aOpK4aATDg4dQdIs7ij4OZ0F\n3wEtk7ZbxPu+A/bdbP+on/F9AE1bEBEREREREamIXgJOildd6AOsdPf5wBvAAWZWLy6UeEC872fR\nyAMRERERERGRDGNmTxKNIGhoZvOIVlDIB3D3e4BXgUOAWcBa4JT42DIzuxYYH3+pa9w9VeHF7aLO\nAxEREREREalUzCr+IHt3H7KN4w6cs5VjDwIPlmeeit+iIiIiIiIiIvKLUueBiIiIiIiIiKSkaQsi\nIiIiIiJSqZjuk5c7taiIiIiIiIiIpKTOAxERERERERFJSdMWREREREREpFKpDKstZBq1qIiIiIiI\niIikpM4DEREREREREUlJ0xZERERERESkUtG0hfKnFhURERERERGRlNR5ICIiIiIiIiIpadqCiIiI\niIiIVCpmFjpCpaORByIiIiIiIiKSkjoPRERERERERCQldR6IiIiIiIiISEqqeSAiIiIiIiKVjO6T\nlze1qIiIiIiIiIikpM4DEREREREREUlJ0xZERERERESkUjHTffLyphYVERERERERkZTUeSAiIiIi\nIiIiKWnagoiIiIiIiFQqmrZQ/tSiIiIiIiIiIpKSOg9EREREREREJCVNW5BS7rnpLA7erzuLl66i\n568vCR0nqGxqi3471OPyPu3IyTGe+2IBw6Z8W+r44F2acULH5hS6s3ZjIVd9MJOvVqwlz4xr9+7A\nrg1qkptjvDhzIfdv9tyKbu+W9bhyr3bkmvHMjAXcN7ns13dg24bcecCuHDl8Ip8t+SHNKSWd9u7T\nkisu7EduTg7PvvQ59z06qdTxZk1qcuNf96N2zQJycnO45a6xvDd2bqC0Ut6+nfQ5Hz70HF5UxC77\n7UW3Iw8odbxw40ZG3vEoS76eS5WaNdj/olOp1bgBi2bOZvS9TwLgDj2OO4S2e3Rl04aNvPzXf1O4\ncRNeWEjbPbvTc/ChIV7aT/bd5GlMeDhqi/YD+tLp8C3b4oO7HmHZN3MpqFmDfYaeRs3GDfhh0VJe\nuvhaajdvDEDDndrS5/QhbFz3I29c/a/i569dtoK2/XrT6+Rj0vq6fq59+7bnmksPJifXeHL4RO56\nYEyp482b1uG2fxxJ7VpVyck1rr/1bd4dPZO8vBxu/tvhdOrYjLy8HJ576VPuHDY60KsoH+7Ow7e+\nwKSx06lStYA/XHk8bXduscV5T93zKu+/PoE1q9fx8DvXF+8f9co4Hr9rBPUb1QHgwKP7MmBQn7Tl\n/7ncnRfv/h/Tx02noEo+g/90Ai12arnFefO+/JanbnqCjRs20rF3Rw4/+yjMDIAxL7zPBy+NIScn\nh4577MrAMwYVP2/5ouXcdNr1HHDSQex77IC0va6KwnSfvNxVuM4DM7sCOAEoBIqAs9z94zLO+x3Q\n093PLYfvOTv+WkvMrBCYStR204GT3X3tz/0emeLRZ9/jnoffYNitZ4eOEly2tEWOwV/2as9pr09l\n4Zr1PDOoOyPnLuWrFSU/1iO+WsTTM+YD0L9Vff68x46c+cZnHNi2IQW5xuHPf0LV3BxGHN2TV75e\nxPc/rA/1cspVjsHVfdvzu1emsmDNeoYf1Z13Zy9l1orSv/I18nM5ufMOTF64KlBSSZecHOOqP+7D\nKee/zIJFPzD8oWN4Z/Rsvpq9vPics0/pwWvvzOLJ/02jXZt63H/roQw48rGAqaW8FBUWMWbYMxz6\n13OpUb8uz196E617dqZey2bF58x4ZyxValTj+DuvZtaYCXz82Ivsf9Gp1G/VnCNvvISc3FzWLl/J\ncxdfT+uencjNz2PgVeeTX60KRZsKefHKf9Gy+6406dA24CvdtqKiIsY9+Az7X3Ee1RvU5bXL/0mL\nHp2p26KkLWaNHEtBzeoccdvf+ObDCUx84gX2ueA0AGo2acjAGy8v9TXzq1Utte+Vy26gVe+u6XlB\n5SQnx/j7lYcy5IxHmL9gFa8+fSZvjvyCmV8vLj5n6Fn78PIb03jk6fHstGMjHv3Pb+hz4L8ZeMBu\nFBTksv9Rd1O1aj6jXjyHF16dyrzvVwR8RT/P5LEzmD9vCf9+5jJmTZvLsJuG8/dhQ7c4r0e/3Tjw\nmH5cMPj6LY7tuV83Tr34qHTELXczxk1n8XeLufS/VzB3+hyG3/4sQ++4aIvzht/+LMdeOJhWHVsz\n7Ip7mTF+Oh1778qsyTOZ9uFnXHzPJeQV5LF6+epSz3vpnhfYpVfHdL0ckYrVHWNmewIDgd3dvQuw\nP5Du25zr3L2bu3cCNgC/T/P3/0V9MG4Gy1borilkT1t0aVSLuavWMW/1j2wscl79ejEDWjUodc6a\njYXFj6vl5eIePfZ4O9egal4OG4uKWLOhkMqiS+NazFm1jm/jtnll1mL2a9Ngi/Mu6NWa+yZ/y/rC\nogApJZ267NqYOfNW8u33q9i4qYhX3prF/vuUvshzh5o1CgCoVbOARYsrTf9y1ls8azZ1mjakdpOG\n5Obn0a7v7sweP6XUOXPGT6HDvnsAsOOe3flu6he4O3lVCsjJzQVg04aNxXcVzYz8alUAKCospKiw\nEMPS+Kr+f5bOmk2tpo2o1aQhuXl5tN6rB99OKN0W306YQrt9orZovUd3FkyL2mJ7rPp+IT+uXE3j\nXdqXe/ZfUvfOOzB77jLmzlvOxk2FvPjaZxw4YJfSJznUrBH9P69dqwoLF0cXhO5O9WoF5ObmUK1K\nHhs3FvJDBe+MnzD6M/Y5qAdmxk6dWrP2h3UsX7JlR/tOnVpTr2HtAAl/WdPGTqXn/r0wM1rv2oYf\nf1jHqqUrS52zaulKflz7I613bYOZ0XP/Xkz7cCoAH778Af2P34+8guh+b616tYqf99kHU6jftD5N\n2jRN3wuSrFfRRh40A5a4+3oAd18CYGa9gNuAGsB6YL/4/OZm9jrQDnje3S+Jzx8CXA4Y8Iq7/znV\n/hRGA13i574AtASqAre5+31mdirQxd0viM85A9jV3S/82S0hUk4aV6/CgjUlH04Wrl1Pl0a1tjjv\nhI7NOLlTC/JzcjjltU8BePObJezXugHvD+lD1bxcbvj4K1Zu2JS27L+0ptWrMD/pg9uCNevp2rh0\n2+zasCbNalRh1NxlnN51y6GYUrk0aVSDBYtKOhUXLPqBrrs1KXXOHcPG8+Bth3HisZ2pVjWP3533\ncrpjyi9kzbKV1GhYr3i7RoN6LJo5e6vn5OTmUlC9GutXr6Fq7Zos+nI27939GKuXLKP/eScXdyYU\nFRbx/J9vZOWCxex24D407tAmXS/p/23tshXUaJDUFvXrsmTW7C3Oqd6gpC3yq0VtAfDD4qWMuPR6\n8qtVpdtxh9GkY+lOgtljP6HNnj2KO1kqiqaNa/P9gpKLw/kLV9K9c+n3hlvuHskT953EqSf0plq1\nAo4/42EAXnnrcw4csAuTRv6RalXzufqfr7Ni1bq05i9vyxavpEGTusXb9RvVYdnilT+po2DcqCnM\nmPw1TVs24qShg2jYpN62n5QhVi5ZSd3GJXnrNKzLyiUrqd2gTulzGpa0UZ1G0TkAS+Yt4pupX/Pa\nQ6+Qn5/PwLMOp9XOrVi/bj0jn36HM288m1HPvpu+F1TBaLWF8lfRWvRNoKWZfWlmd5vZr8ysAHga\nGOruXYlGIyT+0nYDBgOdgcFm1tLMmgM3AgPi473M7Iit7d9aEDPLAw4mmsIAcKq79wB6AuebWQPg\nGeAwM8uPzzkFeLB8mkIkvZ6YPp8Dnx3PLeO/5vfdWgPQuVEtCovgV09+zK+fGccpnVrQolbVwEnT\nx4DL99yR68d+HTqKZJCBB+zE86/OYJ9Bj3DGRa9w09X7UcGuf+QX0rhDG47995UcecMlTH7+TTZt\n2AhATm4OR998Gb+59zoWzZrDsrnfB076y6pWrzZH33ktA2+4jJ4nHs2YOx5iw9rSF8mzP/yENn17\nBkr4yzrikM48++Jkeu7/L046+zFuvz6a396t8w4UFjq7D7iZPgf9m7NO3otWLSrOhfIvoUe/3bhj\n+JX889E/0qV3B/5z7VOhI6VVYVERa1ev5fzbL2TgmYN49Lr/4u68+cjr7H30vlSJRy2JpEuFGnng\n7j+YWQ9gb6A/UafB34H57j4+PmcVkOipfsfdV8bbnwOtgQbAKHdfHO9/HNiHaAR2Wftf2CxGNTOb\nHD8eDTwQPz7fzI6MH7cEdnL3j8zsXWCgmU0H8t196mZfDzM7EzgTIK9eT/JqVqwhelKxLVq7nqY1\nSt58mlSvwsI1G7Z6/qtfL+aqvjsBMLBdY8Z8t4xN7iz7cSMTF62iU8OazFv94y+eOx0WrF1Ps5ol\nbdO0Rum2qVGQy071avDYoGhObqNqBdxz0G78/vVpKppYSS1cvIamjWsWbzdtXJOFi9eUOueYwzpy\n2gUjAJj82UKqFORSr241li2v2HcQBWrUr8OaJSX1LdYsXU6N+nXKPKdmg3oUFRayYe06qtSqUeqc\nei2akl+1Csvnfk+j9q2L91epUZ3mnTrw7aTPqd+q+S/7Yn6m6vXrsmZpUlssW0G1+nW3OGft0uXU\niNti47qoLcyM3PzovkqDHVtRq0kjVs9fRIN2UVssmzOPosIiGuzYKn0vqJwsWLSK5k1LfiaaNanD\ngkWl56kff9Tu/Pb3jwLwyafzqFKQR/161TnykC6M+mAmmzYVsXTZGsZPnkvX3Zozd95yKpI3ho/h\n3ZeicmTtdmnJ0oUlNRuWLV5ZXPxwe9SqU/K7M+CwPXj8rhHlF/QX8sGLo/n41bEAtNy5FSsWlfz/\nW7lkBXUaln79dRrWYcWSkjZaubjknLoN69K5XxfMjFa7tCbHjDUr1zB3xhymjJ7MK/e/xLof1mE5\nOeTl59PviL3T8Aolm1W0kQe4e6G7j3L3q4BzgVQVVJInihVSPp0liZoH3dz9PHffYGb7Eo142DMe\n/TCJaPoCwDDgd0SjDh4q6wu6+33u3tPde6rjQNJt6uLVtK5djR1qViU/xzhkx0aMnLu01Dmta5eM\nJvhVy/rMWRldBM1f8yN7NIs+LFbLy6Fro1p8vaLyXCBNXbSaNnWq0aJW1DaHtm/EO3NK2uaHDYXs\n8chY+j8xjv5PjGPyolXqOKjkpk5fRJuWdWjRrBb5eTkc+uv2vDP6m1LnzF+4mj177QBAuzb1KCjI\nU8dBJdGofWtWzl/MqoVLKNy4ia8+mEjrXl1KndO6Z2e+HBVdOH09dhI7dOqAmbFq4RKKCqOaMKsX\nL2PFdwuo1bgB61auZv2aqC7GpvUb+O7TGdTdofRUmEzUoF1rVi9YxOpFSyjctIk5H35Cyx6dS53T\nskdnvno/aos5H0+i6W5RW/y4ajVFRVGNmNULl7BqwSJqNmlY/LzZH0ygbd8e6Xsx5WjyZ9/TtlV9\nWu5Ql/y8XA4/uBNvjpxR6pzv5q+k3x47AtB+x4ZUqZLH0mVr+G7+Svr2jvZXq5bP7l1aMOubJWl/\nDT/XgUf348aHL+bGhy+m5z6deP/1T3B3Zn42h+o1qv6kKQvJ9REmjJnGDm0a/xKRy1Xfw/fmonsv\n4aJ7L2G3vp2Z8PZ43J05n8+mao1qpaYsANRuUIeq1asy5/PZuDsT3h7PbntGv0u77dWZWZNnArB4\n3iI2bSqkRp0anHPr+Vzx2FVc8dhV7H3Ur9hvyP7qOCiDWU7G/6toKtTIAzPbGShy95nxrm5EKx4c\nZGa93H28mdWiZNpCWcYBt5tZQ2A5MAS4I8X+7VEHWO7ua81sF6B4DRl3/9jMWgK7E9dHyGQP33Ee\ne+/ZkYb1ajHr4zu59l/P8fDTo0LHCiJb2qLQ4bqxsxh2UCdyzPjflwuYtWIt5+3ems+WrGbk3GWc\nsOsO7NW8LhuLnFXrN3HZ+18A8MTn3/P3fXbm5aOiD3nPz1zIl8vXpPp2FUqhw9/GzOLBQzqRa9Ey\nlrOWr2Voz9ZMXbyad+csCx1R0qyw0Lnm5tE8cNth5OYYz42YwaxvlnP+Gb34bMZi3h09m+tv+5Dr\nLt+XU47vijtceq3mo1YWObm59D39OF677i6KipydB/ShfstmTHhqBA3btaJNry7svN9ejLz9EZ46\n92qq1KzBfheeAsCCGV/z6fNvkpOXC2b0O2MwVWvXZOns7xh156N4URHuzo577U7rnp23kSS8nNxc\nep9yHO/8465oqcb+e1K3ZXMmPzOCBju2omXPLrTvvxdj7nqYF4ZeRUHNGux9/qkALJw+i0+fHUFO\nbi5mOexx+hCq1Cy5wzzno4kM+HPFXOmosLCIK//xKk/ceyI5uTk8/fwkvvxqMX88pz+fTvuet0Z9\nwTU3vcFNfxvEGSftibtz4ZXRINf/PjmOW687gndfOAczePqFyUz/cmHgV/TzdN+rI5PHTmfosddT\npWo+v7/i+OJjfz75Fm58+GIAHr/rZT54cxIbftzI2YdfQ//D9uDY0w/k9WdH88mYaeTk5lCzdnX+\nkPT8iqBj712Z8fF0bjj5OvKrFDD4j0OKj/3rrH9y0b3RUuBHnXcMT938BJvWb2TnXh3ZpXe0gkLv\ng/bgmVue5KYzbiAvL4/j/3RChasDIpWLbW/V20wQT1m4A6gLbAJmEQ33bxvvr0bUcbA/cAxJSzWa\n2QjgZncf9VMLJm62VOMP7l4yZjU6XoVoekMb4Is439XuPio+finQzd23+RevWqshFed/iKRNm6v+\nEDpCxthUeeoxlouZZ+0TOkJG6NDn7tARMsqXH1XMC6/ydsvUt0JHyBjrNumCI+E/J44OHSFjjHiv\nV+gIGWPemtzQETLGYa0OrhR/MBp0OD/jr6uWfnl7hWrrCjXywN0/AfYq49ASku72x/4b/0s8d2DS\n4yeBJ8v4+lvb3ybpcc0yjq8nKp64Nf2AW1McFxEREREREclYFarzoKIxs7pE0yE+dfd3QucRERER\nERHJBhWxpkCmU+fBL8jdVwAdQucQERERERER+TnUHSMiIiIiIiIiKWnkgYiIiIiIiFQqRoWqRVgh\naOSBiIiIiIiIiKSkzgMRERERERERSUnTFkRERERERKRS0WoL5U8tKiIiIiIiIiIpqfNARERERERE\nRFLStAURERERERGpVDRtofypRUVEREREREQkJXUeiIiIiIiIiEhKmrYgIiIiIiIilYqmLZQ/taiI\niIiIiIiIpKTOAxERERERERFJSZ0HIiIiIiIiIpKSah6IiIiIiIhIJaP75OVNLSoiIiIiIiIiKanz\nQERERERERERS0rQFERERERERqVS0VGP5U4uKiIiIiIiISErqPBARERERERGRlDRtQURERERERCoV\nTVsof2pREREREREREUlJnQciIiIiIiIikpKmLYiIiIiIiEilYrpPXu7UoiIiIiIiIiKSkjoPRERE\nRERERCQlTVsQEZGKryh0ABEREckkWm2h/KlFRURERERERCQldR6IiIiIiIiISEqatiAiIiIiIiKV\nipmFjlDpaOSBiIiIiIiIiKSkzgMRERERERERSUmdByIiIiIiIiKSkmoeiIiIiIiISKWipRrLn1pU\nRERERERERFJS54GIiIiIiIiIpKRpCyIiIiIiIlKpmO6Tlzu1qIiIiIiIiIikpM4DEREREREREUlJ\n0xZERERERESkUtFqC+VPLSoiIiIiIiIiKanzQERERERERERS0rQFERERERERqVQ0baH8qUVFRERE\nREREJCV1HoiIiIiIiIhISpq2ICIiIiIiIpWK6T55uVOLioiIiIiIiEhK6jwQERERERERkZTUeSAi\nIiIiIiIiKanmgYiIiIiIiFQuWqqx3KlFRURERERERCQldR6IiIiIiIiISEqatiAiIiIiIiKVimna\nQrlTi4qIiIiIiIhISuo8EBEREREREZGUNG1BREREREREKhUzCx2h0lHngZRyz01ncfB+3Vm8dBU9\nf31J6DhBZVNb9NuhHpf3aUdOjvHcFwsYNuXbUscH79KMEzo2p9CdtRsLueqDmXy1Yi35OcbVfXei\nU8NaFLnzj4++YvyClYFexS9j75b1uHKvduSa8cyMBdw3+dsyzzuwbUPuPGBXjhw+kc+W/JDmlJJO\ne/dpyRUX9yM3J4dnX/yc+x6ZVOp4syY1ufGq/ahdq4CcnBxuuWss7304N1BaKW/fTvqcDx96Di8q\nYpf99qLbkQeUOl64cSMj73iUJV/PpUrNGux/0anUatyARTNnM/reJwFwhx7HHULbPbqyacNGXv7r\nvyncuAkvLKTtnt3pOfjQEC/tJ/tu8jQmPBy1RfsBfel0+JZt8cFdj7Dsm7kU1KzBPkNPo2bjBvyw\naCkvXXwttZs3BqDhTm3pc/oQNq77kTeu/lfx89cuW0Hbfr3pdfIxaX1dP9e+fdtzzaUHk5NrPDl8\nInc9MKbU8eZN63DbP46kdq2q5OQa19/6Nu+OnkleXg43/+1wOnVsRl5eDs+99Cl3Dhsd6FWUD3fn\n4VtfYNLY6VSpWsAfrjyetju32OK8p+55lfdfn8Ca1et4+J3ri/ePemUcj981gvqN6gBw4NF9GTCo\nT9ry/1zuzot3/4/p46ZTUCWfwX86gRY7tdzivHlffstTNz3Bxg0b6di7I4effVTxhe+YF97ng5fG\nkJOTQ8c9dmXgGYNYtmAp/zztBhq3aARAq45tOOaC49L62iQ7VZrOAzNrALwTbzYFCoHF8XZvd9+w\n2fn1gePc/Z5tfN08YIm71zWz9sBU4AugABgJnOvuXn6vJKxHn32Pex5+g2G3nh06SnDZ0hY5Bn/Z\nqz2nvT6VhWvW88yg7oycu5SvVqwtPmfEV4t4esZ8APq3qs+f99iRM9/4jGN3bgrA4c9/Qv2q+dx3\nYCeOfXESleUXIsfg6r7t+d0rU1mwZj3Dj+rOu7OXMiupbQBq5OdycucdmLxwVaCkki45OcZVl+zD\nKee+zIJFPzD84WN4Z/RsvvpmefE5Z5/ag9femcWTw6fRrm097r/1UAYc8VjA1FJeigqLGDPsGQ79\n67nUqF+X5y+9idY9O1OvZbPic2a8M5YqNapx/J1XM2vMBD5+7EX2v+hU6rdqzpE3XkJObi5rl6/k\nuYuvp3XPTuTm5zHwqvPJr1aFok2FvHjlv2jZfVeadGgb8JVuW1FREeMefIb9rziP6g3q8trl/6RF\nj87UbVHSFrNGjqWgZnWOuO1vfPPhBCY+8QL7XHAaADWbNGTgjZeX+pr51aqW2vfKZTfQqnfX9Lyg\ncpKTY/z9ykMZcsYjzF+wilefPpM3R37BzK8XF58z9Kx9ePmNaTzy9Hh22rERj/7nN/Q58N8MPGA3\nCgpy2f+ou6laNZ9RL57DC69OZd73KwK+op9n8tgZzJ+3hH8/cxmzps1l2E3D+fuwoVuc16Pfbhx4\nTD8uGHz9Fsf23K8bp158VDrilrsZ46az+LvFXPrfK5g7fQ7Db3+WoXdctMV5w29/lmMvHEyrjq0Z\ndsW9zBg/nY69d2XW5JlM+/AzLr7nEvIK8li9fHXxcxo0b8BF91bum1uSeSpNzQN3X+ru3dy9G3AP\ncGtie/OOg1h94Pf/j2/1Rfw9ugLdgMP+/6kzzwfjZrBshe6aQva0RZdGtZi7ah3zVv/IxiLn1a8X\nM6BVg1LnrNlYWPy4Wl4uie6ydnVr8PH86EPNsh83smrDJjo1rJW27L+0Lo1rMWfVOr6N2+aVWYvZ\nr02DLc67oFdr7pv8LesLiwKklHTqsltj5sxbybffr2LjpiJeeXMW++9T+iLPHWrWKACgVs0CFi1Z\nW9aXkgpo8azZ1GnakNpNGpKbn0e7vrsze/yUUufMGT+FDvvuAfB/7N13fFRV/v/x15lJb6QTakKT\nGjoiXaygKPa2a1tdda1rL2vXVVZZ9/e1rNjFggWsoKyKglRpgoTeDD2ddEiZnN8fE1JMGAtJJhne\nz8cjD3LvPffO5x5mMjOfez7n0nnYAPakbMJai19gAA6nE4Dy0rKqq4rGGPyDAwGocLmocLkwNP+h\nttlbUwlPiCO8dSxOPz8Shw9i14rafbFrxRq6jHb3ReLQAaStc/fFb5G/N52DeQXE9+ja4LE3pgHJ\n7UjdmcPO3fspK3fx2ey1nHpCj9qNLISFuv/PI8IDSc90fyG01hISHIDT6SA40I+yMheFhSVNfQoN\nasWCtYweNwhjDN36JFJceID9WXUT7d36JBIVG+GFCBvXuiUpDD5pCMYYEnslcbDwAPnZtUdo5mfn\ncbD4IIm9kjDGMPikIaxbnALA4pmLGHvRifgFuK/3hkf5zmespmBwNPuflsZnRh54Yoy5C7iscvEl\na+1zwCSguzFmNfA/4AngUyASd7/cZ62ddbhjWmvLjDFLgK7GmIj69jXGPAHstdY+XxnHv4Cd1toX\nGuVERf6A+JBA0oqqP5ykF5fQN67um9MlPdtweZ/2+DscXDn7JwA25hQytmMMX2zLICE0iN4x4SSE\nBZKSVVBn/5YoISSQfTU+uKUVldAvvnbf9IoNo01oIPN25nB1v7pDMcW3tI4LJS29OqmYllFIv96t\na7V57pXlvP7cGVx6fjLBwX5ccePMpg5TGklRTh6hsVFVy6ExUWRsST1sG4fTSUBIMCUFRQRFhJGx\nOZXv//sOBVk5jL3p8qpkQoWrgk/u/hd5aZn0PnU08cckNdUp/WHFObmExtToi+hIsram1mkTElPd\nF/7B7r4AKMzMZtY9T+IfHET/C86gdc/aSYLUJStJGjaoxdUsJ8RHsLdG+d6+9DwGJNd+b/j3f+cy\n7eXL+MslxxIcHMBFf50KwBffrOfUE3qwau4dBAf58/BT/yM3/0CTxt/QcjLziGkdWbUcHdeKnMy8\n35UoWDZvDRtXbyehQxyX3XImsa2jfn2nZiIvK4/I+Op4W8VGkpeVR0RMq9ptYqv7qFWcuw1A1u4M\nfk7Zzuw3vsDf358J106kY/eOAOSk5fDMdU8TFBLEuCtPo3NylyY6Kzma+XzywBgzFPgTMAT3+S4z\nxswD7gG6Vo4iwBjjD27DBIkAACAASURBVJxlrc03xsQDi4DDJg+MMaHACcDdwIHD7Ps68B7wvDHG\nCZwPDGqUExVpZNM27GPahn2c3jmO6/oncu/8TXy8OY0ukSFMnziQvYUHWZ2RT4XvVPH8KgPcN6wz\nd8/d5O1QpBmZcGo3Ppm1kden/UT/5NY8/fCJnH7x+xxFLw05jPhjkjj//93P/t1pzHv+bToM6IVf\ngD8Op4NzJ99LSVExXz/1Cjk79xLdsa23w200wVERnPv8YwSGh5G9fSfzJr/EGZPvJyAkuKpN6uKV\njLjhci9G2XjOOi2Z6Z+t5qWpixnUrz3PPnkOJ5z1X/ont8Plsgw8YTKtIoL5ZOpfWPDDdnbu3v/r\nB/VRg0b2ZsTJA/EP8GPOp0t48bH3eeD5v3k7rCbjqqiguKCYm5+9lV2bdvL2429y31sPEBHdivvf\nfYjQiFB2b97FGw+/xp2v3ENQaJC3QxYf5/PJA2Ak8JG19gCAMeZTYBTw9S/aGWCSMWYkUAF0MMbE\nAr8sNDs0WqEC+MRa+40xJqC+fa21W40xBcaYZCARWGatrfMOYIy5BrgGwC9qMH5hLWuInrRsGcUl\nJFQOnwRoHRJIelF9lT5uX27P5KER3QBwWZi0dHvVtmkT+pGa17KvktSUVlxCm7DqvkkIrd03oQFO\nukWF8s6Z7prcuOAApozrzXX/W6dJE31UemYRCa3DqpYT4sNIzyyq1ea8M3ty1c3u3PPqlHQCA51E\nRQaTs993XhtHq9DoVhRlVb+NF2XvJzS6Vb1twmKiqHC5KC0+QGB4aK02Ue0T8A8KZP/OvcR1Taxa\nHxgaQts+x7Br1fpmnzwIiY6kKLtGX+TkEhwdWadNcfZ+Qiv7ouyAuy+MMTj9/QGI6dyR8NZxFOzL\nIKaLuy9yduymwlVBTOeOTXdCDSQtI5+2CdXPiTatW5GWUXs03kXnDOTP170NwMqfdhMY4Ed0VAhn\nn9aXeYu2UF5eQXZOEctX76Rf77YtLnnw1UcL+e7zpQB06dGB7PTqj9I5mXlVkx/+FuGtql87J5wx\nlHdfOOx1vWZj0WcLWPrlEgA6dO9Ibkb1/19eVi6tYmuff6vYVuRmVfdRXmZ1m8jYSJJH9sUYQ8ce\niTiMoSiviLDIsKpShvbHdCCmTQyZuzPo0L3lvWYakzEtryyguVOPVrsMaAUMrByNkAXUl77bVDmP\nwkBr7WO/Yd/XgCuAK3GPRKjDWvuytXawtXawEgfS1FIyC0iMCKZdWBD+DsNpneOYuzO7VpvEiOqX\nwpgO0eyoTBAEOR0E+7n/jAxvG4nLUmuixZYuJaOApFbBtA93983pXeP4dkd13xSWuhj61hLGTlvG\n2GnLWJ2Rr8SBj0tZn0FSh1a0bxuOv5+D00/pyrcLfq7VZl9aAcOGtAOgS1IUAQF+Shz4iLiuieTt\nyyQ/PQtXWTnbFv1I4pC+tdokDk5m8zz3F6ftS1bRrs8xGGPIT8+iwuWeP6YgM4fcPWmEx8dwIK+A\nkiL3383yklL2/LSRyHa1S2Gao5guiRSkZVCQkYWrvJwdi1fSYVByrTYdBiWzbb67L3YsXUVCb3df\nHMwvoKLCPUdMQXoW+WkZhLWOrdovddEKOo1omQM1V6/dS6eO0XRoF4m/n5OJ4/vw9dyNtdrs2ZfH\nyKGdAejaOZbAQD+yc4rYsy+PEce61wcH+zOwb3u2/pzV5OdwpE49dyT/mno7/5p6O4NH92H+/1Zi\nrWXL2h2EhAb9rpKFmvMjrFi4jnZJ8Y0RcoMaMXEUt710F7e9dBe9RySzYs5yrLXsWJ9KUGhwrZIF\ngIiYVgSFBLFjfSrWWlbMWU7vYe7XUu/hyWxdvQWAzN0ZlJe7CG0VSmFuIRWV8yxl78sia08WMW3q\nzskk0tCOhpEHC4CXjDFPA05gInAhUADULF5uBWRYa8uNMScD7X7HY3ja9yPgocrHnvPHT6NpTH3u\nJkYN60lsVDhblz7PY8/MYOoH87wdllccLX3hsvD4kq28Oq4PDmP4eHMaW3OLuWlgImuzCpi7M4dL\nerVjeNtIyios+SXl3DvfPUw/OtifV09NpgLIKCrh7u83en6wFsZl4ZGFW3n9tD44jfs2llv3F3PL\n4ERSMgv4bkeOt0OUJuZyWR59egGvPXsGTodhxsyNbN2+n5uvGcLaDZl8tyCVJ/9vMY/fdzxXXtIP\na+GeR7/zdtjSQBxOJyOuvoDZj79ARYWl+wnHEd2hDSven0Vsl44kDelL9xOHM/fZt3j/xocJDAvl\nxFuvBCBt43Z++uRrHH5OMIaRf72QoIgwslP3MO/5t7EVFVhr6Tx8IImDk38lEu9zOJ0ce+UFfPvE\nC+5bNY4dRmSHtqz+cBYxnTvSYXBfuo4dzsIXpvLpLQ8REBbKqJv/AkD6hq38NH0WDqcTYxwMvfpi\nAsOqrzDv+OFHTri7Zd7pyOWq4P4nvmTaS5ficDr44JNVbN6WyR03jOWndXv5Zt4mHn36K55+5Ez+\netkwrLXcev+nALz53jL+8/hZfPfpDRgDH3y6mg2b0718RkdmwPCerF6ygVvOf5LAIH+u+8dFVdvu\nvvzf/Gvq7QC8+8JMFn29itKDZVw/8VHGnjGU868+lf9NX8DKhetwOB2ERYTwtxr7twQ9j+3FxqUb\nmHT54/gHBnDhHRdXbXvm2qeq7pZwzk3n8f7kaZSXlNF9SE96HNsTgGPHDeXDf7/H03+dhJ+fHxfd\neQnGGLanbOOrqbNxOh0Yh4NzbzmfkIjQemMQaUjGh+4yWMUY8zBQaK2dXLlc34SJGGM+BHoCXwDP\nADOBEGAZ7tKGE4A0at+qccaheRJqPF58fftaa3dXbn8VSLPW3v9rsQd3vNj3/kPkiCU9dPTU9/2a\n8nJvR9C8bLl2tLdDaBaOOfa/3g6hWdm8rGV+8Wpo/075xtshNBsHylvWxION6cVLF3g7hGZj1vdD\nvB1Cs7G7yOntEJqNMzqO94k/GMcMeaHZf6/avPyGFtXXPjnywFr78C+WnwKeqqfdBb9YNfQwh4ys\nbL8V9+0Zf3mcjMPta9zFNscCZ/1a3CIiIiIiItIAWtjdWloCzXnQiConStwG/M9au/3X2ouIiIiI\niIg0Rz458qC5sNamAJ28HYeIiIiIiIjIkVDyQERERERERHyLxtg3OHWpiIiIiIiIiHik5IGIiIiI\niIiIeKSyBREREREREfEtuttCg9PIAxERERERERHxSMkDEREREREREfFIZQsiIiIiIiLiW1S20OA0\n8kBEREREREREPFLyQEREREREREQ8UtmCiIiIiIiI+BZdJm9w6lIRERERERER8UjJAxERERERERHx\nSGULIiIiIiIi4lOs7rbQ4DTyQEREREREREQ8UvJARERERERERDxS8kBEREREREREPNKcByIiIiIi\nIuJbNOVBg9PIAxERERERERHxSMkDEREREREREfFIZQsiIiIiIiLiWxyqW2hoGnkgIiIiIiIiIh4p\neSAiIiIiIiIiHqlsQURERERERHyLUdlCQ9PIAxERERERERHxSMkDERERERERkWbIGDPOGLPJGLPV\nGHNPPdv/Y4xZXfmz2RiTW2Obq8a2z480FpUtiIiIiIiIiG/xgaoFY4wTeAE4GdgNLDfGfG6tXX+o\njbX21hrtbwIG1DjEAWtt/4aKR8mDZibpob95OwRphlIfedHbITQbbe65ztshSDNUekZXb4cgzdDt\nyYneDqHZyC3d6u0Qmo1Xrhjj7RCajalbvB1B8zEmocTbIYjU51hgq7V2O4Ax5n1gIrD+MO0vBh5q\nrGBUtiAiIiIiIiLSxIwx1xhjVtT4ueYXTdoBu2os765cV9+xEoFOwHc1VgdVHvcHY8xZRxqvRh6I\niIiIiIiIb3E0/7oFa+3LwMsNdLiLgBnWWleNdYnW2j3GmM7Ad8aYFGvttj/6ABp5ICIiIiIiItL8\n7AE61FhuX7muPhcB79VcYa3dU/nvdmAetedD+N2UPBARERERERFpfpYD3YwxnYwxAbgTBHXummCM\n6QFEAUtqrIsyxgRW/h4LjODwcyX8JipbEBEREREREWlmrLXlxpgbga8AJ/C6tXadMeZRYIW19lAi\n4SLgfWutrbF7T+AlY0wF7kEDk2repeGPUPJAREREREREfItp/nMe/BbW2i+BL3+x7sFfLD9cz36L\ngeSGjEVlCyIiIiIiIiLikZIHIiIiIiIiIuKRyhZERERERETEt/hG1UKzopEHIiIiIiIiIuKRkgci\nIiIiIiIi4pHKFkRERERERMS3OFS30NA08kBEREREREREPFLyQEREREREREQ8UtmCiIiIiIiI+BZV\nLTQ4jTwQEREREREREY+UPBARERERERERj1S2ICIiIiIiIj7FGtUtNDSNPBARERERERERj5Q8EBER\nERERERGPVLYgIiIiIiIivsWhsoWGppEHIiIiIiIiIuKRkgciIiIiIiIi4pGSByIiIiIiIiLikeY8\nEBEREREREd+iKQ8anEYeiIiIiIiIiIhHSh6IiIiIiIiIiEcqWxARERERERHfYlS30NA08kBERERE\nREREPNLIg6PQyHZR3HdcFxwOw4xNaby6Zlet7Rf2aMMlPdvispbiMhcPLdrCttxi/IzhsVHH0Csm\nDKfD8NmWdF75xb4tyR/tB3+H4eER3egTG06FtTzxwzaWp+V56SyaxpSnr2X8iQPIzM5n8Ml3eTuc\nRjW6QxQPjuyKw2H4cP0+pqyq/zk+rnMs/x3Xm4nTV5KSWcjI9lHceVwnApyGUpdl0pLtLNmT28TR\nS2MZ0yWGB0/tgdMYPli1mxcXp9bbblyPeKac358zXv2BlH35+DsMT5zei+S2EVgLj3y1kR927G/a\n4KVJWGv55z9f5vvvVxIUFMikSbfQu3fXOu0uvfReMjL2ExQUAMDrrz9KTExkU4fb4Ky1PDPpExYv\n2EBQkD8PPH4xPXp1qNPuluteIiszH5fLRf+BnbnzH+fhdDp49t+fs3DeOvz9nbTrEMsDj11MeESw\nF87kyI1JiuahE7rhNIb3U/bx4rId9bYb3y2OKROTmfD2clLSCwDoERvKk6f0ICzASYWFM99ZQYmr\noinDb1CZa9ax4d0PsRWW9mNG0GXCqbW252zcwoZp0ynYtYd+119FmyEDq7Ytn/wcudt+JqpbFwbf\ndkNTh94grLXMfPFjNi3bgH+QP+fffgntutV9Xezesovpk6dRXlJG92N7csbfzsEYwzdvz2b57B8I\nbRUKwKlXTqDHsb3IScvmmb9OIq59HAAdeyRx9i0XNOm5ydHpqE8eGGNcQAru+ThdwI3W2sXejarx\nOAw8MLwrV/0vhfSiEj48cwBzd2azLbe4qs2sbRl8sHEfAGM7RnP30M5c89VaTu0US4DTMPGTlQQ5\nHcw6dzBfbM9gb2GJt07nDzuSfji/ewIAEz9ZSXSQPy+f2ofzP1uF9cqZNI23p3/PlKlf8ep/rvd2\nKI3KYeCR0d24bOYa0gpL+PS8gcxJzWbr/uJa7UL9nVzRtx2r0vKr1uUcLOOvX64lo7iUY6JDeHNC\nX4a/9UNTn4I0AoeBR8f15M/vriQt/yCfX30c32zOZGtWUa12oQFOrjw2kVW7q5NGFw1sD8C4l5YQ\nExLAm5cM5MxXf/DpvxdHq/nzV5Kaupevv36Jn37axMMPv8j06f+ut+3kybeTnNytiSNsXIsXbGDX\njkxmfHEfa9fs4KnHZ/D6tFvrtPvn5MsJCwvCWss9t73Jt1+v5pTxAzl22DFcf8vp+Pk5ef6ZmUx9\ndQ433naGF87kyDgMPHZSd/40fRVpBSV8/ufBzNmWyZbsuu8jVw7swI97qy8+OI3h/53em1u/XM+G\nzEIig/woq2i5iQNbUcG6t97n2LtuJig6isUPTyJ+QF/C27WpahMUE03y1Zfx8+w5dfbvNP5kXKWl\n7Jq7oCnDblCblm8ga08md7zxD3Zt3MGnz03nhmdvq9Pu02enc+7fL6RDj0TeuP8lNq/YQPchvQAY\nefYYRp9/Qp19YtrEcMuLvn1B54g5VLbQ0FS2AAestf2ttf2Ae4EnvR1QY+obF87O/APsLjhIWYXl\ny+2ZnNAxplabojJX1e/Bfk5s5adcW7nsNBDk56CsooKiUhct0ZH0Q5fIUJbuc385yDlYRn5pOX1i\nw5ssdm9YtGwjObmF3g6j0fWLj2BH3gF25bufF7O2ZnByp5g67W47NomXVu2qdTVofVYhGcWlAGzO\nKSbIz0GA3rR8Qv+2rdixv5hduQcoq7DMXJfGKd3j67S7/fiuTFn8MyXl1c+LbrGhLE7NASC7uJT8\ng2X0bRvRZLFL0/n22x8466wTMMbQv38P8vOLyMjI8XZYTWb+3LWMP3MIxhiS+yVRUHCArMy6o/LC\nwoIAcJVXUF5WjqmsST5ueA/8/JwA9OmXSEZ6yxy51T8hgtT9xezKc7+PzNyYwcld4uq0u31kZ6Ys\n31HrfWR0UjQbMwvZkOl+v809WE5FC8405m5PJbR1HCHxcTj8/GgzdDAZP/5Uq01IXAwRHdtj6nm/\njO3dA7+goKYKt1GsX5LCwJPcr4uOPZM4UHSA/Ozar4v87DxKig/SsWcSxhgGnjSEdYtTvBSxiGdK\nHtQWAewHMG5PG2PWGmNSjDEXVq4/3hgzzxgzwxiz0Rjzrql85zPGpBpjYit/H2yMmVf5+xhjzOrK\nn1XGGK9904wPCSStqHqkQHpxCa1DA+q0u6RnG746fwh3DOnMEz9sBeDrn7M4UO5i/sXH8e2FQ3k9\nZTd5peVNFntDOpJ+2JhTyNiOMTgNtAsLondMOAlhgU0WuzSehNAA9tUYSbOvsITWobX/b3vHhtEm\nLJC5Ow7/pWB851jWZRZS2pI/9UmV1hFB7M0/WLW8L/8grcN/8bxICKdNRBBzt2bVWr8hvYCTjonD\naQztI4NJbhNBm4iW/WFY6peenk1CQmzVckJCDOnp2fW2ve++/2PixJt54YX3sdY3/k5kZuTROqG6\n/CK+dSSZGfWX9N187RTGjXmAkJAgTji5X53tMz9ZyrCRPRst1saUEB7IvoLa7yMJv/h70Sc+jLbh\ngXy3vfbzo1NUMNbCW+f244tLh3DtkI5NEnNjObg/l6DoqKrloOgoDu5vmUmhPyo/K4/IuOo+aBUb\nWW/yoFVsZO02WdVtFs9cwP+77l9M//c0iguqR7DkpOXwf9c/zUt3PMfPKdsa8SxEqh31ZQtAsDFm\nNRAEtAEOjQs6B+gP9ANigeXGmPmV2wYAvYG9wCJgBLDQw2PcAdxgrV1kjAkDDnpo2yxM27CPaRv2\ncXrnOK7rn8i98zeRHBeOqwLGvLeUiEA/3jm9H0v25rK7oNmfzh9WXz98vDmNLpEhTJ84kL2FB1md\nkU+Fj3z4E88M8I8RXbjzu42HbdMtKoS7hnXm8plrmi4w8SoDPHByd+74fG2dbR+u3kvX2DBmXj2U\nPXkHWbkrV38vjnKTJ99B69YxFBYWc/PNT/LZZ3M566y6Q5J92bMvXUdJSRkP3vMOK5ZuYejw7lXb\n3nj5G5xOJ+MmDPJihI3HAPeP7cYdszfU2ebnMAxp34oz3lnBgTIX710wgLXpBSzaqXlSjlbHTRjJ\niZecCga+mTqbL17+lPNvv4SI6Fbc885DhEaEsnvLLt5++DVuffkegkKVnK5FA0AbnJIHlWULAMaY\nYcBbxpg+wEjgPWutC0g3xnwPDAHygWXW2t2V+6wGkvCcPFgEPGOMeRf4+NC+hxhjrgGuAUi49HYi\nx5zZkOdXS0ZxCQk1rqS2Dgkkvaj0sO2/3J7JQyPcdZkTusSzcE8O5daSc7CMHzPy6RMb1iKTB0fS\nDy4Lk5Zur9o2bUI/UvMONF6w0mTSikppU2MUSZuwQNJrjFAJC3ByTHQo703sD0BcSAAvn9aHa75c\nS0pmIQmhAUwZ35s7vt3IzvyW97qQ+qXnH6RtjdECbSKCSK9xZTEs0I9j4sN4/7IhAMSFBfDqhf25\n+oPVpOzL57FvNlW1/eiKY9n+i9pnabneffcLPvzwKwCSk7uRllY98iQtLZvWreuWPR1aFxYWwoQJ\nY1izZnOLTR5Mf28hn320BIBefTqSnlZ9VTkjPZe4+FaH3Tcw0J8xY/swf+7aquTBrE+XsfD7dbzw\n6vVV5QwtTVpBCW3Ca7+PpBXUfh/pHhPK+xcOACAuNIDXzu7LVZ+sYV9BCUt357L/QBkAc7dn06d1\neItNHgRFRXIwpzr2gzn7CYpq+ZOD/polny9g2Wz366L9MR3Jzazug7ysXCJiar8uImJakZeVW7tN\nrLtNeFT1YOUh449j6oOvAOAX4IdfgPtrXPtuHYhuG0PWngzaH9OyR6tI86eyhRqstUtwjzKoW5xW\nW80ZAl1UJ2HKqe7Tqk+a1tpJwNVAMLDIGNPjF4/7srV2sLV2cGMmDgBSMgtIjAimXVgQ/g7DaZ3j\nmLuz9rC5xBofksd0iGZH5RfjfUUHGdrG/Uc/2M9Bv7hwtue2zC/NR9IPQU4HwX7u/+bhbSNxWWpN\ntCgt15qMfJJaBdM+3P28mNA1njk/Vz8vCkpdDH5jMaPfWcrod5ayKj2/KnEQHuDktdOTeeqHn1lZ\nYyJFafl+2ptPUnQI7SOD8XcYzuidwDebM6q2F5SUM/Df8xj53AJGPreAVbvzqhIHQX4Ogv3dddwj\nO0VTXmHrTLQoLdef/nQ6n332LJ999iwnnXQcn376HdZaVq/eSHh4CPHx0bXal5e7yMlxD0cuKytn\n3rzldOuW6I3QG8T5F4/knRl38s6MOxl9Qh9mf74cay0pP6USFhZMbFztL0nFxSVV8yCUl7tYNH89\nSZ3c84csWbiBt9/4jsnPXU1QcN0ywpbip7QCOkWF0KGV+33kjB7xfLOtOqlUUOpiwH8XMvKVJYx8\nZQmr9uVz1SdrSEkv4PvUHHrEhhHk58BpDEM7RLIlu+X+vWjVKZGi9AyKM7OoKC9n39IVxA/o6+2w\nGt2wM0dxy4t3ccuLd9F7eDI/znG/LnZuSCUoJLje5EFgSBA7N6RireXHOcvpNSwZoFaJw7rFKbRO\nck82WZhbSEXlfBnZ+7LI3pNFdELdZKVIQ9PIgxoqv9Q7gWxgAXCtMWYqEA2MBu4Eehz+CKQCg4DZ\nwLk1jtvFWpsCpBhjhlQe4/DjnhuRy8LjS7by6rg+OIzh481pbM0t5qaBiazNKmDuzhwu6dWO4W0j\nKauw5JeUc+9891Wzaev38s/R3Zl5jnso4Sdb0tm8v2W+qR1JP0QH+/PqqclUABlFJdz9vVf+K5vU\n1OduYtSwnsRGhbN16fM89swMpn4wz9thNTiXhYcXbGXqGck4jGH6xjS27C/m70OSSMks4NvU+uuX\nAS5Lbkdiq2BuGpzITYPdXwYun7mG7MorSNJyuazlwf9t5K1LBuI0hg9/2sOWzCJuHdOFlH35zNmc\nedh9Y0MDmPqnQVhrScsv4bbPNAmWrxozZjDff7+Ck0++huDgQJ544paqbRMn3sxnnz1LaWkZV1/9\nEGVlLioqXAwb1p8LLjjFi1E3nBGjerF4/gbOPe2fBAUF8MDjF1Vt+/N5T/POjDs5UFzKHTe9Rllp\nORXWMmhIV86+YDgAk5/4mNLScm665kUA+vRN5J4HW96t51zW8uC3m3nr3P44HYYPU/ayJbuI20Z0\nYk1aAXO2ZR123/yScl5dsZOZfx6MxT3y4JfzIrQkDqeTXpdexPKnn8NWVNB+9HDC27dl88czaZXU\nkdYD+5G7PZUfn32J8qJiMlalsPXjWYx68kEAfvjnZAr3peM6WMJ3f7+X5KsuJS65l5fP6vfpfmwv\nNi7fwNNXPo5/YADn335x1bb/+9tTVXdLOOum85g+eRplpWV0H9yT7kPcc37Mfm0me7ftwRiIah3N\n2Te7XxM/p2zjm7dm4/RzYBwOzrr5fEIiQpv+BJu7FjqCqTkzvjJRzx9V41aN4K6Muc9a+0XlJIhP\nAeNx32jgcWvtB8aY44E7rLUTKvd/HlhhrX3TGDMKeA13acM8YLC19nhjzHPAWKACWAdcYa2t9/6G\nPV+bf3T/h0i9Uh950dshNBtt7rnO2yE0K9uvH+PtEJqFpMe+9nYIzUrqA77xhfTIbfZ2AM1GbulW\nb4fQbPR7VnXhh5w90tsRNB9jElrerccby9lJ433iW3fXc95u9t+rtn58aYvq66N+5IG11nmY9Rb3\nSIM7f7F+Hu7EwKHlG2v8vgA4pp5j3dQw0YqIiIiIiIg0Pc15ICIiIiIiIiIeHfUjD0RERERERMTH\naM6DBqeRByIiIiIiIiLikZIHIiIiIiIiIuKRyhZERERERETEt+gyeYNTl4qIiIiIiIiIR0oeiIiI\niIiIiIhHKlsQERERERER36K7LTQ4jTwQEREREREREY+UPBARERERERERj1S2ICIiIiIiIr5FVQsN\nTiMPRERERERERMQjJQ9ERERERERExCOVLYiIiIiIiIhPsQ7VLTQ0jTwQEREREREREY+UPBARERER\nERERj5Q8EBERERERERGPNOeBiIiIiIiI+BajOQ8amkYeiIiIiIiIiIhHSh6IiIiIiIiIiEcqWxAR\nERERERHfoqqFBqeRByIiIiIiIiLikZIHIiIiIiIiIuKRyhZERERERETEtzhUt9DQNPJARERERERE\nRDxS8kBEREREREREPFLZgoiIiIiIiPgWo7KFhqaRByIiIiIiIiLikUYeNDPl5d6OQJqjNvdc5+0Q\nmo19k6Z4O4Tm5fox3o6geShxeTsCaYZyS7d5O4RmY91+6+0Qmo2/j9Pfi0OKynRl9pBv9wV5O4Rm\n4+wkb0cgzZWSByIiIiIiIuJblBtrcCpbEBERERERERGPlDwQEREREREREY9UtiAiIiIiIiK+xaG6\nhYamkQciIiIiIiIi4pGSByIiIiIiIiLikZIHIiIiIiIiIuKR5jwQERERERER36I5DxqcRh6IiIiI\niIiIiEdKHoiIBpM9LgAAIABJREFUiIiIiIiIRypbEBEREREREZ9iVbXQ4DTyQEREREREREQ8UvJA\nRERERERERDxS2YKIiIiIiIj4Ft1tocFp5IGIiIiIiIiIeKTkgYiIiIiIiIh4pLIFERERERER8S1G\nZQsNTSMPRERERERERMQjJQ9ERERERERExCOVLYiIiIiIiIhv0d0WGpxGHoiIiIiIiIiIR0oeiIiI\niIiIiIhHSh6IiIiIiIiIiEea80BERERERER8iy6TNzh1qYiIiIiIiIh4pOSBiIiIiIiIiHiksgUR\nERERERHxLUa3amxoGnkgIiIiIiIiIh4peSAiIiIiIiIiHqls4Sg3qkMU9w/vgtMYPtyYxsurd9Xb\n7tROsTx/Si/O/uhH1mYVNnGUTeNo7ovRHaJ4cGRXHA7Dh+v3MWVV/ec+rnMs/x3Xm4nTV5KSWcjI\n9lHceVwnApyGUpdl0pLtLNmT28TRN60pT1/L+BMHkJmdz+CT7/J2ONJExnSL5cHTeuJ0GD5YuZsX\n52+vtf28Ae24d1wP0vMPAjD1hx18sHI3vRLCefzM3oQF+uGylhfmbWPW2jRvnII0Amstz0z6mMUL\nNhAU5M8Dj19Cj14d6rS75bopZGXm43JV0H9gZ+78x3k4nQ6mPPclC+amYByGqOhwHnz8EuLiW3nh\nTI6ctZZpz35Cyg8bCAgM4Kp7Lyaxe/s67T565UsW/28FxYXFvPjVpKr1X30wj/mzluJ0OgiPDOPK\ney4kNiG6KU/hiOxctZ5Fr8/AVlTQ88ThDDjnlFrbXWVlfPfs22Ru30lQeCgn3fYXIuJj2PXTBpa+\n8zkV5eU4/PwYdtlZtEvuDsDWRSv5ccZX2IoKEgf34bhLz/LGqf1ue1avY8VUd190PWEEfSbW7YtF\nL7xFzs87CQgLZfQtVxEWH0NhRjaf3/4YEW3jAYjt1onjrr4YgG+ffJ4D+/OpqHAR36Mrx/7lQhyO\nlnUNNGvNOjZN+xBbUUG70SPoNGFcre37N21h07QPKdy1h+S/XUXrIYMAKNixiw1vTaP8wEGMw0Gn\nM8aTMHSwN06h5XGobKGhtZjkgTHGBaQABnABN1prFzfB4yYBG4BNQAAwH7jeWlvhYZ/F1trh9ax/\nE5hlrZ3RKMH+Tg4DD4/oyhVfpJBWVMJH5wzgu9RstuYW12oX6u/k8uR2rE7P91Kkje9o7guHgUdG\nd+OymWtIKyzh0/MGMic1m6376577FX3bsSqt+txzDpbx1y/XklFcyjHRIbw5oS/D3/qhqU+hSb09\n/XumTP2KV/9zvbdDkSbiMPDoGb358xvLSMs/yOfXDeebDRlszaydPJyVso+HZq2vte5AmYvbPlpD\nanYx8eGBzLp+OPO3ZpF/sLwpT0EayeIFG9i1I5MZX/yDtWt28NTj03l92m112v1z8hWEhQVhreWe\n297g269Xc8r4gfz5yhO47qbTAPjg3e95bcpX3PPgBU19Gg0i5YcNpO/O4slp97F9/Q7eemYGD7z0\n9zrt+g/vxYlnj+TePz1Ra33Hbu148JVbCQwKYO6ni5j+4iz+9shlTRX+EalwVbDwlQ+Z8OCNhMZE\n8vHdT5M4JJnoDm2q2mz4dgmBYcFc8sLDbF24gqVvf8bJt/+F4PAwxt97LaHRkeTs3Musx17gslf+\nycGCQn5461POfeougluF891zb7F7zSba9+3uxTP9dRUVFSx7/UNO+sdNhMREMvu+p2g/KJnI9tV9\nsXXuEgLCQjjr/x7h58Ur+HHap4z++1UAhLWOZcK/7qtz3FG3XEVASDDWWub/51V2/PAjnYa3nC/Q\ntqKCjW+/x8A7byEoOoqljzxJ3IC+hLVrW9UmKDqK3ldfzo7Z39Ta1xEYQO+/XkFoQmsO7s9l6cNP\nENOnF/6hIU19GiItqmzhgLW2v7W2H3Av8GQTPvY2a21/oC/QC/CY+q0vcdAc9Y0PZ0f+AXYVHKSs\nwvLF1kxOTIqp0+7vQxJ5efUuSlyHzZe0eEdzX/SLj2BH3gF25bvPfdbWDE7uVPfcbzs2iZdW1T73\n9VmFZBSXArA5p5ggPwcBPp7lXbRsIzm5vjHiRH6b/u0j2ZFdxK79ByhzWWam7OOUnvG/ad+fs4tJ\nzXYn4jIKSsguLCU6NKAxw5UmNH9uCuPPHIIxhuR+SRQUHCArM69Ou7CwIABc5RWUl7mq5vA6tB7g\nwIHSFj2316qFaxl+6mCMMXTpnURx4QFys+om2rv0TiIyNqLO+p4DuxEY5H5tdO6VyP7MljOKLWNr\nKhEJsUQkxOL096PLyIGkLl9Tq03qsjUcc/xQADoPG8CelE1Ya4nt3IHQ6EgAojq0wVVahqusjPz0\nbFq1iSO4VTgA7ZN7sP2H1U17Yn9A9tZUwhPiCG8di9PPj8Thg9i1onZf7Fqxhi6j3X2ROHQAaevc\nfeFJQEgwANZVgau8HEPLerHkbU8lpHU8IfFxOPz8SBg6hMxVtfslOC6W8A7t60zyF5rQmtCE1gAE\nRUUSEBFOaUFBk8UuUlNLSh7UFAHsBzBuTxtj1hpjUowxF1auP94YM88YM8MYs9EY864x7lejMSbV\nGBNb+ftgY8y8yt/HGGNWV/6sMsaE13xQa205sBjoaowJM8Z8a4z5sfJxJx5qZ4wprBHb88aYTcaY\nOcBv+7TZRBJCAtlXWFK1nFZUQutffKjtFRtGm9BA5u3MaerwmtTR3BcJoQG1zn1fYQmtQwNrtekd\nG0absEDm7jj8uY/vHMu6zEJKKzx/ABBpaVpHBLE372DV8r78g7SOCKrTbnzv1sy+cQT/vWgAbVrV\n3d6vXSv8nQ525BTX2SYtU2ZGHq0ToqqW41tHkplRN3kAcPO1LzJuzP2EhARywsn9q9a/+OwXnHHS\nw3z1xUquueG0xg650ezPyic6PrJqOToukv1Z9ffFr1nwxVKSh/ZsqNAaXVFOHmGx1c+DsOgoirLz\nDtvG4XQSEBLMwYKiWm22/7Ca2E4dcPr70yohjtw9GeRnZFPhcvHzsp8oytrf+CdzhIpzcgmNqe6L\n0OhIDuTk1mkTElPdF/7BwZRU9kVhZjaz7nmSrx75D+kbttbab84TzzP92rvxDwqi43EDGvlMGlbJ\n/v0ERlf3S2BUJCX7f///Z972n7HlLkLi4xoyPJ9ljWn2Py1NS0oeBFd+qd8IvAo8Vrn+HKA/0A84\nCXjaGHNobNQA4O+4Rwt0Bkb8ymPcAdxQOcpgFHCg5kZjTAhwIu7yiYPA2dbagcBY4N+HkhM1nA10\nr3z8y4AWMSLhEAPcN6wzTy7Z/qttfd3R3BcG+MeILvxz8bbDtukWFcJdwzrzj+83N11gIs3InI0Z\njJz8PeOfX8TCbVn8+9y+tbbHhQXyzHl9ufPjFH7lApv4qGdf+htfzH2U0rJyVizdUrX+bzefzsw5\nD3Pq6YOY/t4CL0bYPCz5egWpm3Yx7uKx3g6lSeXs3MfStz9j9HUXARAYFsKoay5kzjOv89n9/yE8\nPgbTwmr8f6/gqAjOff4xJky6l8GXnsvC596gtLj6o/hJ993IeS8+SUV5OWlrN3kxUu8oyc1j7ctv\n0uuqy3z+uSDNV4uZ84DKsgUAY8ww4C1jTB9gJPCetdYFpBtjvgeGAPnAMmvt7sp9VgNJwEIPj7EI\neMYY8y7wsbV2d2U+oEvl/hb4zFo72xjjDzxhjBkNVADtgNZAzZmwRteIba8x5rv6HtQYcw1wDUDc\nn26n1agzf2/f/CFpxSW0Cau+wpwQGkh6UWnVcmiAk25RobxzZj8A4oIDmDKuN9f9b53PTBR4yNHc\nF2lFpbXOvU1YIOlF1SMRwgKcHBMdynsT3VfK4kICePm0Plzz5VpSMgtJCA1gyvje3PHtRnbmH6xz\nfJGWLj3/IG1rjCRoExFUNTHiIbkHyqp+f3/FLu45tbouOSzQjzcuG8TkOVtYtbvlDMWW+k1/bwGf\nfbQEgF59OpKeVn31MCM91+OEh4GB/owZ24f5c1MYOrx27fq40wdz6/Uvcc0N4xsn8Ebw7ccLmT/L\nPc9Npx4dyMmofn7nZOYSFfv7Jn9ct2Izs96aw93P3YB/QMv5iBoa3YrCGqMCCnP2ExrTqt42YTFR\nVLhclBYfICg81N0+ez9fPfUyY2++lFYJ1VeUk4YkkzQkGYD1Xy9sEV8YQ6IjKcqu7ouinFyCoyPr\ntCnO3k9oZV+UHThAYHgoxhic/v4AxHTuSHjrOAr2ZRDTJbFqX2eAP+0H92X3ijW07dtyRqcERkVR\nklPdLyX7cwmMivKwR23lBw6w6j/P0/XcM4ns2rkxQhT5TZr/X6F6WGuXALHAr43ZKanxu4vqZEk5\n1ede9YnQWjsJuBoIBhYZY3pUbtpWOd/CAGvtw5Xr/lT5+IMqkxrpNY/1O8/nZWvtYGvt4KZKHACk\nZBSQ1CqY9uFB+DsMp3eN49sd2VXbC0tdDH1rCWOnLWPstGWszsj3iS/L9Tma+2JNRn6tc5/QNZ45\nP1efe0Gpi8FvLGb0O0sZ/c5SVqXnVyUOwgOcvHZ6Mk/98DMr03xnEkmRmn7ak0dSTCjto4LxdxrO\nSG7DNxszarWJq5GAO7lHa7Zluofg+jsNL10ygI9X7WX2Ot1lwRecf/Eo3plxF+/MuIvRJyQz+/Pl\nWGtJ+SmVsLBgYuNqf2ksLi6pmgehvNzFovnrSerkrl/euSOzqt3871JIrFzfUpx4zkgeef0OHnn9\nDgaMSmbxVyuw1rJtXSohoUH1zm1wODs27+atydO5+cmriIgK//UdmpH4ronk7cskPz0LV1k52xb+\nSNLg2qOPkoYks3neUgC2L1lF2z7HYIyhpKiY2f+cwtA/T6RNjy619jmQ565rLyksZt1XC+h50rCm\nOaEjENMlkYK0DAoysnCVl7Nj8Uo6DEqu1abDoGS2zXf3xY6lq0jo7e6Lg/kFVFS451UqSM8iPy2D\nsNaxlB08SPF+92uowuViz49riWjbsl4rEZ0SKU7P4EBmlnvkxNLlxA3o++s7AhXl5fz07BTaDD+u\n6g4M8hs5WsBPC9Ny0ro1VH6pdwLZwALgWmPMVCAa99X+O4Eehz8CqcAgYDZwbo3jdrHWpgApxpgh\nlcc43Ow0rYAMa22ZMWYskFhPm/k1YovHXd4w7beeZ2NzWXhk4VZeP60PTmOYsSmNrfuLuWVwIimZ\nBXznob7d1xzNfeGy8PCCrUw9IxmHMUzfmMaW/cX8fUgSKZkFfJuafdh9L0tuR2KrYG4anMhNg90v\ngctnriG7xlVYXzP1uZsYNawnsVHhbF36PI89M4OpH8zzdljSiFwVlgdnreety4fgdBg+XLmbLRmF\n3HpiN1L25DFnYwZXDkvkpB7xuCosuQfKuOMj90RYp/dpw7FJ0USFBHDewHYA3PHRGtanabIrXzBi\nVC8Wz9/Auac9TlBQAA88fnHVtj+f9xTvzLiLA8Wl3HHTq5SVllNhLYOGdOPsC9xVjC/8v5nsTM3A\nYQwJbaO5+4HzvXUqR6zvcT1Zs2QD91z8BAGB/vzl3uq+eOgvk3nk9TsA+PDFmSyd8yOlB8u4/dxH\nGHX6UM76yzg+fHEmJQdK+O9DUwGIiY/i5klXeeVcfi+H08nIqy/gi8dewFZYup9wHNEd27D8vVnE\nde1I0pC+9DhxON89+xbTbniYwLBQTr71SgDWzp5PXlomK6fPZuX02QBMePBGgluFs+i1GWTv2APA\noPPHEdkCvjA7nE6OvfICvn3iBfetGscOI7JDW1Z/OIuYzh3pMLgvXccOZ+ELU/n0locICAtl1M1/\nASB9w1Z+mj4Lh9OJMQ6GXn0xgWGhHMjNZ97TU3CVl2MrLAm9j+GYk0d5+Ux/H4fTSfc/X8iPk5/F\nVlTQdtRwwtq1ZevHnxPRKZH4Af3I257KT89NoayomKzVKWz7ZBbDn3iI9GUr2b95C6WFRexd6B71\n1OfqywlPrHtbWJHGZn5tdtPmosatGqGyBN1a+0XlPANPAeNxlxU8bq39wBhzPHC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MIYM81auzFXs3skHbDW1jHG3CzpDUm9jTENJd0sqZGkSpJ+McbUs9a6/mk8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\nuPV/MXrvcXJCA0IAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1296x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "_p-Zepi63uLO",
        "colab_type": "code",
        "outputId": "590782b9-9e84-400e-df1f-1221ebfad1f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 583
        }
      },
      "cell_type": "code",
      "source": [
        "# Plot correlation heatmap of selected numerical features.\n",
        "plt.subplots(figsize=(12, 9))\n",
        "hm2 = sns.heatmap(data5[['TotalPay', 'SchoolPay', 'BonusPaid', 'Buyout']].corr(), annot=True, cmap='YlGnBu')\n",
        "hm2.set_xticklabels(hm2.get_xticklabels(), rotation=90)\n",
        "hm2.xaxis.set_ticks_position('top')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UoOY+hxHRC9i4urwGPAF8IyIuX/DYzBydmdtk5ja9V9hgiYWVpDIZMWI4zz8/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\nAIAQhwAAhDgEACDEIQAAIQ4BAIgXca6y7Y6ezn4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "TamqvNxf44Jl",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Feature selection.\n",
        "X = data5[['Conference']]\n",
        "y = data5.iloc[:, 4]\n",
        "\n",
        "# One-hot encoding.\n",
        "X = pd.get_dummies(X, columns=['Conference'], drop_first=True)\n",
        "X_copy = X.copy()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4S4juH9m5eGP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X = X.values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "PW4GRwGu5g-E",
        "colab_type": "code",
        "outputId": "88fe95ae-4eaf-4641-818a-7d82eb55ad18",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "cell_type": "code",
      "source": [
        "with pd.option_context('display.max_columns', None):\n",
        "    display(X_copy.head())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Conference_ACC</th>\n",
              "      <th>Conference_Big 12</th>\n",
              "      <th>Conference_Big Ten</th>\n",
              "      <th>Conference_C-USA</th>\n",
              "      <th>Conference_Ind.</th>\n",
              "      <th>Conference_MAC</th>\n",
              "      <th>Conference_Mt. West</th>\n",
              "      <th>Conference_Pac-12</th>\n",
              "      <th>Conference_SEC</th>\n",
              "      <th>Conference_Sun Belt</th>\n",
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              "   Conference_ACC  Conference_Big 12  Conference_Big Ten  Conference_C-USA  \\\n",
              "0               0                  0                   0                 0   \n",
              "1               0                  0                   0                 0   \n",
              "2               0                  0                   0                 0   \n",
              "3               0                  0                   0                 1   \n",
              "4               0                  0                   0                 0   \n",
              "\n",
              "   Conference_Ind.  Conference_MAC  Conference_Mt. West  Conference_Pac-12  \\\n",
              "0                0               0                    1                  0   \n",
              "1                0               1                    0                  0   \n",
              "2                0               0                    0                  0   \n",
              "3                0               0                    0                  0   \n",
              "4                0               0                    0                  0   \n",
              "\n",
              "   Conference_SEC  Conference_Sun Belt  \n",
              "0               0                    0  \n",
              "1               0                    0  \n",
              "2               1                    0  \n",
              "3               0                    0  \n",
              "4               0                    1  "
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          "metadata": {
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      ]
    },
    {
      "metadata": {
        "id": "_qPH1zaZ5nlC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Split the data set to create training and testing sets, and separate the labels\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state = 123)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "O-YgCSuz6NDE",
        "colab_type": "code",
        "outputId": "bb3adb9d-f384-4cf1-e0f4-7a0c2e106ae8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "# List the number of records in each data set.\n",
        "print('Number of training records: ' + str(X_train.shape[0]))\n",
        "print('Number of testing records : ' + str(X_test.shape[0]))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of training records: 106\n",
            "Number of testing records : 27\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "97TM4RW54FhX",
        "colab_type": "code",
        "outputId": "f0f666aa-dd50-46df-db67-d85a7acab132",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "cell_type": "code",
      "source": [
        "# Feature scaling\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc_X = StandardScaler()\n",
        "X_train = sc_X.fit_transform(X_train)\n",
        "X_test = sc_X.transform(X_test)\n",
        "\n",
        "sc_y = StandardScaler()\n",
        "y_train = sc_y.fit_transform(y_train.values.reshape(-1, 1))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype uint8 was converted to float64 by StandardScaler.\n",
            "  warnings.warn(msg, DataConversionWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype uint8 was converted to float64 by StandardScaler.\n",
            "  warnings.warn(msg, DataConversionWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype uint8 was converted to float64 by StandardScaler.\n",
            "  warnings.warn(msg, DataConversionWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "NjvWxzOT6QWB",
        "colab_type": "code",
        "outputId": "8bc25783-ba0b-419b-d8e5-d39d6316d769",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "y_train.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(106, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "metadata": {
        "id": "PAv0xfkM6WnK",
        "colab_type": "code",
        "outputId": "ab41c3cb-f2b9-404e-ad93-1b14b9d523de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1346
        }
      },
      "cell_type": "code",
      "source": [
        "# Fitting several regression models to the training set & comparing them.\n",
        "\n",
        "# Import modules to create models.\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.svm import SVR\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.metrics import mean_squared_error,  mean_squared_log_error, explained_variance_score\n",
        "from numpy import sqrt\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "# Create regression objects.\n",
        "lr = LinearRegression()\n",
        "svr = SVR(kernel = 'linear')\n",
        "dt = DecisionTreeRegressor(random_state = 0)\n",
        "\n",
        "y_df = pd.DataFrame({'y_test' : y_test.values})\n",
        "\n",
        "# Create models and predictions using each method - compare them.\n",
        "for reg in [lr, svr, dt]:\n",
        "    \n",
        "    # Train the model.\n",
        "    reg.fit(X_train, y_train)\n",
        "    print(reg)\n",
        "    print('CV: ' + str(cross_val_score(reg, X, y, cv=10)))\n",
        "    \n",
        "    # Predict using test set.\n",
        "    y_pred = reg.predict(X_test)\n",
        "    y_pred = sc_y.inverse_transform(y_pred)\n",
        "    print(y_pred)\n",
        "    \n",
        "    y_df[str(reg)[:3]] = pd.DataFrame(y_pred)\n",
        "    \n",
        "    # Evaluate variance and MSLE.\n",
        "    print('Var: ' + str(explained_variance_score(y_test.values, y_pred)))\n",
        "    print('MSLE: ' + str(mean_squared_log_error(y_test.values, y_pred)) + \"\\n\\n\")\n",
        "\n",
        "print(\"Test labels:\")\n",
        "print(y_test.values)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
            "         normalize=False)\n",
            "CV: [0.40673346 0.56148169 0.70004072 0.54849867 0.68083389 0.10046503\n",
            " 0.54502241 0.57414535 0.73202111 0.43636741]\n",
            "[[ 624142.85714285]\n",
            " [3005546.8       ]\n",
            " [4079349.33333333]\n",
            " [3391531.        ]\n",
            " [1178446.16666667]\n",
            " [4420532.36363636]\n",
            " [3480287.11111111]\n",
            " [1178446.16666667]\n",
            " [ 624142.85714285]\n",
            " [3480287.11111111]\n",
            " [3391531.        ]\n",
            " [ 773280.5       ]\n",
            " [1178446.16666667]\n",
            " [ 624142.85714285]\n",
            " [3005546.8       ]\n",
            " [ 773280.5       ]\n",
            " [ 773280.5       ]\n",
            " [3391531.        ]\n",
            " [4420532.36363636]\n",
            " [3391531.        ]\n",
            " [1178446.16666667]\n",
            " [3391531.        ]\n",
            " [ 730468.53846154]\n",
            " [1178446.16666667]\n",
            " [3391531.        ]\n",
            " [ 773280.5       ]\n",
            " [4079349.33333333]]\n",
            "Var: 0.6182913079567618\n",
            "MSLE: 0.2043628927413437\n",
            "\n",
            "\n",
            "SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,\n",
            "  gamma='auto_deprecated', kernel='linear', max_iter=-1, shrinking=True,\n",
            "  tol=0.001, verbose=False)\n",
            "CV: [-2.65040610e-04 -2.05861465e-02 -4.60499100e-02 -9.70717874e-02\n",
            " -9.18499785e-02 -1.23345239e-01 -3.88994953e-01 -6.59784211e-03\n",
            " -7.86695355e-02 -4.69450995e-01]\n",
            "[ 624553.87580863 3063648.02754663 3936281.16825032 2832833.10700355\n",
            " 1331564.52100725 3686281.17329087 3686875.53038956 1331564.52100725\n",
            "  624553.87580863 3686875.53038956 2832833.10700355  713387.54781821\n",
            " 1331564.52100725  624553.87580863 3063648.02754663  713387.54781821\n",
            "  713387.54781821 2832833.10700355 3686281.17329087 2832833.10700355\n",
            " 1331564.52100725 2832833.10700355  810148.02012798 1331564.52100725\n",
            " 2832833.10700355  713387.54781821 3936281.16825032]\n",
            "Var: 0.6407618298206728\n",
            "MSLE: 0.2014857172633331\n",
            "\n",
            "\n",
            "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
            "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
            "           min_impurity_split=None, min_samples_leaf=1,\n",
            "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
            "           presort=False, random_state=0, splitter='best')\n",
            "CV: [0.40673346 0.56148169 0.70004072 0.54849867 0.68083389 0.10046503\n",
            " 0.54502241 0.57414535 0.73202111 0.43636741]\n",
            "[ 624142.85714286 3005546.8        4079349.33333333 3391531.\n",
            " 1178446.16666667 4420532.36363636 3480287.11111111 1178446.16666667\n",
            "  624142.85714286 3480287.11111111 3391531.          773280.5\n",
            " 1178446.16666667  624142.85714286 3005546.8         773280.5\n",
            "  773280.5        3391531.         4420532.36363636 3391531.\n",
            " 1178446.16666667 3391531.          730468.53846154 1178446.16666667\n",
            " 3391531.          773280.5        4079349.33333333]\n",
            "Var: 0.618291307956762\n",
            "MSLE: 0.2043628927413435\n",
            "\n",
            "\n",
            "Test labels:\n",
            "[ 712500. 2625965. 3800000. 4058061. 2163000. 6070000. 4840717. 2000000.\n",
            "  600000. 5000000. 2514859. 1550000. 1100000.  825000. 2000000.  885000.\n",
            " 1650010. 3000000. 2812523. 1831580. 2600000. 5000000. 1425000. 1629000.\n",
            " 4000000. 1800000. 7504000.]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "eQf5XeMi6Ziw",
        "colab_type": "code",
        "outputId": "b35b6e0c-2059-4e51-9117-de0731e9f931",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "cell_type": "code",
      "source": [
        "# Labels of test values.\n",
        "print(y_test.values)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 712500. 2625965. 3800000. 4058061. 2163000. 6070000. 4840717. 2000000.\n",
            "  600000. 5000000. 2514859. 1550000. 1100000.  825000. 2000000.  885000.\n",
            " 1650010. 3000000. 2812523. 1831580. 2600000. 5000000. 1425000. 1629000.\n",
            " 4000000. 1800000. 7504000.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Ev2r1I6G6a84",
        "colab_type": "code",
        "outputId": "f0ed78ef-46ac-449d-b59b-06f45dd45491",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 886
        }
      },
      "cell_type": "code",
      "source": [
        "# Test labels vs predicted values.\n",
        "display(y_df)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>y_test</th>\n",
              "      <th>Lin</th>\n",
              "      <th>SVR</th>\n",
              "      <th>Dec</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>712500.0</td>\n",
              "      <td>6.241429e+05</td>\n",
              "      <td>6.245539e+05</td>\n",
              "      <td>6.241429e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2625965.0</td>\n",
              "      <td>3.005547e+06</td>\n",
              "      <td>3.063648e+06</td>\n",
              "      <td>3.005547e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3800000.0</td>\n",
              "      <td>4.079349e+06</td>\n",
              "      <td>3.936281e+06</td>\n",
              "      <td>4.079349e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4058061.0</td>\n",
              "      <td>3.391531e+06</td>\n",
              "      <td>2.832833e+06</td>\n",
              "      <td>3.391531e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2163000.0</td>\n",
              "      <td>1.178446e+06</td>\n",
              "      <td>1.331565e+06</td>\n",
              "      <td>1.178446e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6070000.0</td>\n",
              "      <td>4.420532e+06</td>\n",
              "      <td>3.686281e+06</td>\n",
              "      <td>4.420532e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>4840717.0</td>\n",
              "      <td>3.480287e+06</td>\n",
              "      <td>3.686876e+06</td>\n",
              "      <td>3.480287e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2000000.0</td>\n",
              "      <td>1.178446e+06</td>\n",
              "      <td>1.331565e+06</td>\n",
              "      <td>1.178446e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>600000.0</td>\n",
              "      <td>6.241429e+05</td>\n",
              "      <td>6.245539e+05</td>\n",
              "      <td>6.241429e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>5000000.0</td>\n",
              "      <td>3.480287e+06</td>\n",
              "      <td>3.686876e+06</td>\n",
              "      <td>3.480287e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2514859.0</td>\n",
              "      <td>3.391531e+06</td>\n",
              "      <td>2.832833e+06</td>\n",
              "      <td>3.391531e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1550000.0</td>\n",
              "      <td>7.732805e+05</td>\n",
              "      <td>7.133875e+05</td>\n",
              "      <td>7.732805e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1100000.0</td>\n",
              "      <td>1.178446e+06</td>\n",
              "      <td>1.331565e+06</td>\n",
              "      <td>1.178446e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>825000.0</td>\n",
              "      <td>6.241429e+05</td>\n",
              "      <td>6.245539e+05</td>\n",
              "      <td>6.241429e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2000000.0</td>\n",
              "      <td>3.005547e+06</td>\n",
              "      <td>3.063648e+06</td>\n",
              "      <td>3.005547e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>885000.0</td>\n",
              "      <td>7.732805e+05</td>\n",
              "      <td>7.133875e+05</td>\n",
              "      <td>7.732805e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1650010.0</td>\n",
              "      <td>7.732805e+05</td>\n",
              "      <td>7.133875e+05</td>\n",
              "      <td>7.732805e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>3000000.0</td>\n",
              "      <td>3.391531e+06</td>\n",
              "      <td>2.832833e+06</td>\n",
              "      <td>3.391531e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2812523.0</td>\n",
              "      <td>4.420532e+06</td>\n",
              "      <td>3.686281e+06</td>\n",
              "      <td>4.420532e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1831580.0</td>\n",
              "      <td>3.391531e+06</td>\n",
              "      <td>2.832833e+06</td>\n",
              "      <td>3.391531e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2600000.0</td>\n",
              "      <td>1.178446e+06</td>\n",
              "      <td>1.331565e+06</td>\n",
              "      <td>1.178446e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>5000000.0</td>\n",
              "      <td>3.391531e+06</td>\n",
              "      <td>2.832833e+06</td>\n",
              "      <td>3.391531e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>1425000.0</td>\n",
              "      <td>7.304685e+05</td>\n",
              "      <td>8.101480e+05</td>\n",
              "      <td>7.304685e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>1629000.0</td>\n",
              "      <td>1.178446e+06</td>\n",
              "      <td>1.331565e+06</td>\n",
              "      <td>1.178446e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>4000000.0</td>\n",
              "      <td>3.391531e+06</td>\n",
              "      <td>2.832833e+06</td>\n",
              "      <td>3.391531e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>1800000.0</td>\n",
              "      <td>7.732805e+05</td>\n",
              "      <td>7.133875e+05</td>\n",
              "      <td>7.732805e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>7504000.0</td>\n",
              "      <td>4.079349e+06</td>\n",
              "      <td>3.936281e+06</td>\n",
              "      <td>4.079349e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       y_test           Lin           SVR           Dec\n",
              "0    712500.0  6.241429e+05  6.245539e+05  6.241429e+05\n",
              "1   2625965.0  3.005547e+06  3.063648e+06  3.005547e+06\n",
              "2   3800000.0  4.079349e+06  3.936281e+06  4.079349e+06\n",
              "3   4058061.0  3.391531e+06  2.832833e+06  3.391531e+06\n",
              "4   2163000.0  1.178446e+06  1.331565e+06  1.178446e+06\n",
              "5   6070000.0  4.420532e+06  3.686281e+06  4.420532e+06\n",
              "6   4840717.0  3.480287e+06  3.686876e+06  3.480287e+06\n",
              "7   2000000.0  1.178446e+06  1.331565e+06  1.178446e+06\n",
              "8    600000.0  6.241429e+05  6.245539e+05  6.241429e+05\n",
              "9   5000000.0  3.480287e+06  3.686876e+06  3.480287e+06\n",
              "10  2514859.0  3.391531e+06  2.832833e+06  3.391531e+06\n",
              "11  1550000.0  7.732805e+05  7.133875e+05  7.732805e+05\n",
              "12  1100000.0  1.178446e+06  1.331565e+06  1.178446e+06\n",
              "13   825000.0  6.241429e+05  6.245539e+05  6.241429e+05\n",
              "14  2000000.0  3.005547e+06  3.063648e+06  3.005547e+06\n",
              "15   885000.0  7.732805e+05  7.133875e+05  7.732805e+05\n",
              "16  1650010.0  7.732805e+05  7.133875e+05  7.732805e+05\n",
              "17  3000000.0  3.391531e+06  2.832833e+06  3.391531e+06\n",
              "18  2812523.0  4.420532e+06  3.686281e+06  4.420532e+06\n",
              "19  1831580.0  3.391531e+06  2.832833e+06  3.391531e+06\n",
              "20  2600000.0  1.178446e+06  1.331565e+06  1.178446e+06\n",
              "21  5000000.0  3.391531e+06  2.832833e+06  3.391531e+06\n",
              "22  1425000.0  7.304685e+05  8.101480e+05  7.304685e+05\n",
              "23  1629000.0  1.178446e+06  1.331565e+06  1.178446e+06\n",
              "24  4000000.0  3.391531e+06  2.832833e+06  3.391531e+06\n",
              "25  1800000.0  7.732805e+05  7.133875e+05  7.732805e+05\n",
              "26  7504000.0  4.079349e+06  3.936281e+06  4.079349e+06"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "6KgGn1TD6iap",
        "colab_type": "code",
        "outputId": "4e2c1a8d-983c-49fd-8bc3-cf3f462f57b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        }
      },
      "cell_type": "code",
      "source": [
        "# Use grid search to find the best parameters.\n",
        "# Scoring methods - explained_variance, neg_mean_squared_error.\n",
        "# Grid search parameters - 'max_features': [1, 2, 3, 4].\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "parameters = [{'C': [0.01, 0.1, 1, 10, 100], 'epsilon': [0, 0.01, 0.1, 0.5, 1, 2, 4]}, {'min_samples_split' : range(10,100,10), 'max_depth': range(1,20,2)}]\n",
        "models = [svr, dt]\n",
        "for i in range(2):\n",
        "    mod = GridSearchCV(estimator = models[i], param_grid = parameters[i], scoring='neg_mean_squared_error', cv=10, n_jobs=-1, return_train_score=True)\n",
        "    print(mod.fit(X_train, y_train))\n",
        "    print('Training MSE: ' + str(abs(mod.best_score_)))\n",
        "    print('Test MSE: ' + str(abs(mod.score(X_test, sc_y.transform(y_test.values.reshape(-1, 1))))) + \"\\n\\n\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "GridSearchCV(cv=10, error_score='raise-deprecating',\n",
            "       estimator=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,\n",
            "  gamma='auto_deprecated', kernel='linear', max_iter=-1, shrinking=True,\n",
            "  tol=0.001, verbose=False),\n",
            "       fit_params=None, iid='warn', n_jobs=-1,\n",
            "       param_grid={'C': [0.01, 0.1, 1, 10, 100], 'epsilon': [0, 0.01, 0.1, 0.5, 1, 2, 4]},\n",
            "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
            "       scoring='neg_mean_squared_error', verbose=0)\n",
            "Training MSE: 0.39524479839961013\n",
            "Test MSE: 0.41125970190691485\n",
            "\n",
            "\n",
            "GridSearchCV(cv=10, error_score='raise-deprecating',\n",
            "       estimator=DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
            "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
            "           min_impurity_split=None, min_samples_leaf=1,\n",
            "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
            "           presort=False, random_state=0, splitter='best'),\n",
            "       fit_params=None, iid='warn', n_jobs=-1,\n",
            "       param_grid={'min_samples_split': range(10, 100, 10), 'max_depth': range(1, 20, 2)},\n",
            "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
            "       scoring='neg_mean_squared_error', verbose=0)\n",
            "Training MSE: 0.3944098249674579\n",
            "Test MSE: 0.41337100439547025\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
            "  DeprecationWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "QL-_Yx1y6lfC",
        "colab_type": "code",
        "outputId": "205c2200-0cd8-49d1-be48-44abc45eadad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 9302
        }
      },
      "cell_type": "code",
      "source": [
        "# Print model cross validation results.\n",
        "for key in mod.cv_results_.keys():\n",
        "    print(str(key) + ': \\n' + str(mod.cv_results_[key]) + '\\n\\n')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "mean_fit_time: \n",
            "[0.00052898 0.00027742 0.00146484 0.00025928 0.00028927 0.00090711\n",
            " 0.00026624 0.00025668 0.0002599  0.00033212 0.00027943 0.00029132\n",
            " 0.0002923  0.00028167 0.00030985 0.00029428 0.00033388 0.00028567\n",
            " 0.00032599 0.00032685 0.00029964 0.00031681 0.00030003 0.00029581\n",
            " 0.00028455 0.00027676 0.00027061 0.0005137  0.00031142 0.00031662\n",
            " 0.00085065 0.00031068 0.00029719 0.00029914 0.00027716 0.00030475\n",
            " 0.00033622 0.00051844 0.0003088  0.0003134  0.00030775 0.00030334\n",
            " 0.00029113 0.00026975 0.00025811 0.00033083 0.00047743 0.00030842\n",
            " 0.00030675 0.0003015  0.00029447 0.0002744  0.00030036 0.00097306\n",
            " 0.0012445  0.00030894 0.00031786 0.00030427 0.00036464 0.00028925\n",
            " 0.00026231 0.00028179 0.00025597 0.00031273 0.00031476 0.00032175\n",
            " 0.00029883 0.00029831 0.00030205 0.00028014 0.00029001 0.0002749\n",
            " 0.00032763 0.00030718 0.0003171  0.00030365 0.00029478 0.0002908\n",
            " 0.00027587 0.00019391 0.0001663  0.00020957 0.00019906 0.00021005\n",
            " 0.00019865 0.00019083 0.00018928 0.00018468 0.00017867 0.00036461]\n",
            "\n",
            "\n",
            "std_fit_time: \n",
            "[6.70320880e-04 3.31673065e-05 3.61486910e-03 2.84922173e-05\n",
            " 3.73453690e-05 1.92142046e-03 1.82486179e-05 2.48196375e-05\n",
            " 1.64686501e-05 9.24445098e-05 1.60138503e-05 2.61660344e-05\n",
            " 2.12831820e-05 1.15184553e-05 6.66724416e-05 1.65319003e-05\n",
            " 1.59089706e-04 6.93260227e-05 4.02256800e-05 2.02221827e-05\n",
            " 1.31769357e-05 1.74521097e-05 1.48108415e-05 1.45701064e-05\n",
            " 1.65178656e-05 2.13500609e-05 2.32740988e-05 5.66001099e-04\n",
            " 1.43058300e-05 1.86607284e-05 1.52491983e-03 1.36463972e-05\n",
            " 1.30679700e-05 3.41328568e-05 2.15874167e-05 5.87463379e-05\n",
            " 4.27607294e-05 6.35361870e-04 1.99913090e-05 1.67087884e-05\n",
            " 3.29926521e-05 1.64764489e-05 1.29628030e-05 3.43549839e-05\n",
            " 2.07638356e-05 1.49859685e-05 4.61897615e-04 4.60064095e-05\n",
            " 1.94933107e-05 1.55857929e-05 1.60943843e-05 1.32841946e-05\n",
            " 3.16558108e-05 2.11689682e-03 2.13552740e-03 1.90489731e-05\n",
            " 1.79919323e-05 2.18159644e-05 1.57829505e-04 2.17588272e-05\n",
            " 2.89111996e-05 2.44709582e-05 1.65601227e-05 2.28173058e-05\n",
            " 3.82035253e-05 2.17565935e-05 1.98950840e-05 1.78141282e-05\n",
            " 3.81038019e-05 2.02383532e-05 4.83078875e-05 2.85907520e-05\n",
            " 1.86210252e-05 1.40825279e-05 1.87540121e-05 1.44097160e-05\n",
            " 2.20426196e-05 1.53060457e-05 3.09383270e-05 4.36368596e-05\n",
            " 1.09216790e-05 1.12537610e-05 1.07740140e-05 2.82493320e-05\n",
            " 2.33888019e-05 1.23845544e-05 1.41100498e-05 1.33370424e-05\n",
            " 2.08400751e-05 3.34564188e-04]\n",
            "\n",
            "\n",
            "mean_score_time: \n",
            "[0.00037556 0.00036356 0.00035255 0.00034096 0.00036342 0.00036328\n",
            " 0.0003583  0.00034695 0.00175052 0.00105791 0.00035155 0.00035458\n",
            " 0.00037208 0.00034893 0.00037403 0.00040605 0.00037425 0.00048985\n",
            " 0.00080373 0.00038791 0.00036321 0.0003608  0.00034668 0.00035841\n",
            " 0.00035498 0.00035605 0.00036209 0.00040877 0.00035496 0.00036359\n",
            " 0.00105813 0.0003571  0.00036066 0.0011003  0.00034568 0.00161762\n",
            " 0.00036216 0.00092297 0.00086205 0.0003468  0.00086858 0.00105288\n",
            " 0.00176525 0.00034854 0.00034533 0.00035253 0.00035713 0.00036414\n",
            " 0.00035644 0.00035191 0.00032985 0.00033681 0.00172694 0.00034764\n",
            " 0.00036228 0.00035188 0.00036709 0.00034814 0.00037823 0.00034788\n",
            " 0.00033855 0.00034633 0.0003449  0.00034847 0.00035269 0.00036058\n",
            " 0.00035064 0.00034909 0.00035739 0.00034764 0.00037034 0.00034301\n",
            " 0.00034494 0.00033741 0.00033689 0.00033915 0.00034909 0.00033603\n",
            " 0.00043993 0.00021632 0.00021496 0.00021427 0.00021555 0.00022645\n",
            " 0.00022769 0.00021741 0.0002171  0.00021629 0.00022106 0.00033109]\n",
            "\n",
            "\n",
            "std_score_time: \n",
            "[3.38611128e-05 3.35864764e-05 2.34735290e-05 2.16914921e-05\n",
            " 3.43690202e-05 1.73000733e-05 2.72552795e-05 2.04219953e-05\n",
            " 2.82178859e-03 2.15621178e-03 2.08447252e-05 1.53189457e-05\n",
            " 3.99793480e-05 1.55284457e-05 3.08244520e-05 1.38360875e-04\n",
            " 3.69174907e-05 2.96879408e-04 1.28564606e-03 2.65033110e-05\n",
            " 3.97602137e-05 2.52185667e-05 1.36942161e-05 1.24087881e-05\n",
            " 2.49692558e-05 1.83002995e-05 2.62390560e-05 1.03789183e-04\n",
            " 1.78466459e-05 2.45494738e-05 2.07304773e-03 2.01018960e-05\n",
            " 2.18267098e-05 2.19678142e-03 2.98085111e-05 3.72054130e-03\n",
            " 2.28394546e-05 1.69334773e-03 1.54577072e-03 2.34982526e-05\n",
            " 1.54730497e-03 2.11593214e-03 2.84079295e-03 3.20490711e-05\n",
            " 1.93909804e-05 2.47499156e-05 4.02261604e-05 2.24966723e-05\n",
            " 1.64581366e-05 1.92880483e-05 1.45315097e-05 2.39757237e-05\n",
            " 2.73476927e-03 1.61039176e-05 1.77442537e-05 3.20093170e-05\n",
            " 2.74307906e-05 2.70286764e-05 4.34975341e-05 1.96675675e-05\n",
            " 2.23610700e-05 1.97859268e-05 2.06540406e-05 1.92630409e-05\n",
            " 2.25393339e-05 3.76979206e-05 2.97792441e-05 1.79969866e-05\n",
            " 1.96645470e-05 1.84516419e-05 5.32840775e-05 1.67138226e-05\n",
            " 2.47056874e-05 1.97490040e-05 1.73780815e-05 1.64741027e-05\n",
            " 1.64760176e-05 2.34517609e-05 3.40769729e-04 1.10200203e-05\n",
            " 1.36342078e-05 7.03884169e-06 1.13825584e-05 2.79295536e-05\n",
            " 2.61785337e-05 1.25234224e-05 1.35736201e-05 1.00892478e-05\n",
            " 1.68141058e-05 5.76698074e-05]\n",
            "\n",
            "\n",
            "param_max_depth: \n",
            "[1 1 1 1 1 1 1 1 1 3 3 3 3 3 3 3 3 3 5 5 5 5 5 5 5 5 5 7 7 7 7 7 7 7 7 7 9\n",
            " 9 9 9 9 9 9 9 9 11 11 11 11 11 11 11 11 11 13 13 13 13 13 13 13 13 13 15\n",
            " 15 15 15 15 15 15 15 15 17 17 17 17 17 17 17 17 17 19 19 19 19 19 19 19\n",
            " 19 19]\n",
            "\n",
            "\n",
            "param_min_samples_split: \n",
            "[10 20 30 40 50 60 70 80 90 10 20 30 40 50 60 70 80 90 10 20 30 40 50 60\n",
            " 70 80 90 10 20 30 40 50 60 70 80 90 10 20 30 40 50 60 70 80 90 10 20 30\n",
            " 40 50 60 70 80 90 10 20 30 40 50 60 70 80 90 10 20 30 40 50 60 70 80 90\n",
            " 10 20 30 40 50 60 70 80 90 10 20 30 40 50 60 70 80 90]\n",
            "\n",
            "\n",
            "params: \n",
            "[{'max_depth': 1, 'min_samples_split': 10}, {'max_depth': 1, 'min_samples_split': 20}, {'max_depth': 1, 'min_samples_split': 30}, {'max_depth': 1, 'min_samples_split': 40}, {'max_depth': 1, 'min_samples_split': 50}, {'max_depth': 1, 'min_samples_split': 60}, {'max_depth': 1, 'min_samples_split': 70}, {'max_depth': 1, 'min_samples_split': 80}, {'max_depth': 1, 'min_samples_split': 90}, {'max_depth': 3, 'min_samples_split': 10}, {'max_depth': 3, 'min_samples_split': 20}, {'max_depth': 3, 'min_samples_split': 30}, {'max_depth': 3, 'min_samples_split': 40}, {'max_depth': 3, 'min_samples_split': 50}, {'max_depth': 3, 'min_samples_split': 60}, {'max_depth': 3, 'min_samples_split': 70}, {'max_depth': 3, 'min_samples_split': 80}, {'max_depth': 3, 'min_samples_split': 90}, {'max_depth': 5, 'min_samples_split': 10}, {'max_depth': 5, 'min_samples_split': 20}, {'max_depth': 5, 'min_samples_split': 30}, {'max_depth': 5, 'min_samples_split': 40}, {'max_depth': 5, 'min_samples_split': 50}, {'max_depth': 5, 'min_samples_split': 60}, {'max_depth': 5, 'min_samples_split': 70}, {'max_depth': 5, 'min_samples_split': 80}, {'max_depth': 5, 'min_samples_split': 90}, {'max_depth': 7, 'min_samples_split': 10}, {'max_depth': 7, 'min_samples_split': 20}, {'max_depth': 7, 'min_samples_split': 30}, {'max_depth': 7, 'min_samples_split': 40}, {'max_depth': 7, 'min_samples_split': 50}, {'max_depth': 7, 'min_samples_split': 60}, {'max_depth': 7, 'min_samples_split': 70}, {'max_depth': 7, 'min_samples_split': 80}, {'max_depth': 7, 'min_samples_split': 90}, {'max_depth': 9, 'min_samples_split': 10}, {'max_depth': 9, 'min_samples_split': 20}, {'max_depth': 9, 'min_samples_split': 30}, {'max_depth': 9, 'min_samples_split': 40}, {'max_depth': 9, 'min_samples_split': 50}, {'max_depth': 9, 'min_samples_split': 60}, {'max_depth': 9, 'min_samples_split': 70}, {'max_depth': 9, 'min_samples_split': 80}, {'max_depth': 9, 'min_samples_split': 90}, {'max_depth': 11, 'min_samples_split': 10}, {'max_depth': 11, 'min_samples_split': 20}, {'max_depth': 11, 'min_samples_split': 30}, {'max_depth': 11, 'min_samples_split': 40}, {'max_depth': 11, 'min_samples_split': 50}, {'max_depth': 11, 'min_samples_split': 60}, {'max_depth': 11, 'min_samples_split': 70}, {'max_depth': 11, 'min_samples_split': 80}, {'max_depth': 11, 'min_samples_split': 90}, {'max_depth': 13, 'min_samples_split': 10}, {'max_depth': 13, 'min_samples_split': 20}, {'max_depth': 13, 'min_samples_split': 30}, {'max_depth': 13, 'min_samples_split': 40}, {'max_depth': 13, 'min_samples_split': 50}, {'max_depth': 13, 'min_samples_split': 60}, {'max_depth': 13, 'min_samples_split': 70}, {'max_depth': 13, 'min_samples_split': 80}, {'max_depth': 13, 'min_samples_split': 90}, {'max_depth': 15, 'min_samples_split': 10}, {'max_depth': 15, 'min_samples_split': 20}, {'max_depth': 15, 'min_samples_split': 30}, {'max_depth': 15, 'min_samples_split': 40}, {'max_depth': 15, 'min_samples_split': 50}, {'max_depth': 15, 'min_samples_split': 60}, {'max_depth': 15, 'min_samples_split': 70}, {'max_depth': 15, 'min_samples_split': 80}, {'max_depth': 15, 'min_samples_split': 90}, {'max_depth': 17, 'min_samples_split': 10}, {'max_depth': 17, 'min_samples_split': 20}, {'max_depth': 17, 'min_samples_split': 30}, {'max_depth': 17, 'min_samples_split': 40}, {'max_depth': 17, 'min_samples_split': 50}, {'max_depth': 17, 'min_samples_split': 60}, {'max_depth': 17, 'min_samples_split': 70}, {'max_depth': 17, 'min_samples_split': 80}, {'max_depth': 17, 'min_samples_split': 90}, {'max_depth': 19, 'min_samples_split': 10}, {'max_depth': 19, 'min_samples_split': 20}, {'max_depth': 19, 'min_samples_split': 30}, {'max_depth': 19, 'min_samples_split': 40}, {'max_depth': 19, 'min_samples_split': 50}, {'max_depth': 19, 'min_samples_split': 60}, {'max_depth': 19, 'min_samples_split': 70}, {'max_depth': 19, 'min_samples_split': 80}, {'max_depth': 19, 'min_samples_split': 90}]\n",
            "\n",
            "\n",
            "split0_test_score: \n",
            "[-0.6288719  -0.6288719  -0.6288719  -0.6288719  -0.6288719  -0.6288719\n",
            " -0.6288719  -0.6288719  -0.6288719  -0.30735799 -0.30735799 -0.30735799\n",
            " -0.30735799 -0.30735799 -0.30735799 -0.30735799 -0.46637914 -0.6288719\n",
            " -0.17880956 -0.17880956 -0.17880956 -0.17880956 -0.17880956 -0.21696956\n",
            " -0.30735799 -0.46637914 -0.6288719  -0.1765405  -0.1765405  -0.1765405\n",
            " -0.17624743 -0.17880956 -0.21696956 -0.30735799 -0.46637914 -0.6288719\n",
            " -0.17862598 -0.17790953 -0.1765405  -0.17624743 -0.17880956 -0.21696956\n",
            " -0.30735799 -0.46637914 -0.6288719  -0.17862598 -0.17790953 -0.1765405\n",
            " -0.17624743 -0.17880956 -0.21696956 -0.30735799 -0.46637914 -0.6288719\n",
            " -0.17862598 -0.17790953 -0.1765405  -0.17624743 -0.17880956 -0.21696956\n",
            " -0.30735799 -0.46637914 -0.6288719  -0.17862598 -0.17790953 -0.1765405\n",
            " -0.17624743 -0.17880956 -0.21696956 -0.30735799 -0.46637914 -0.6288719\n",
            " -0.17862598 -0.17790953 -0.1765405  -0.17624743 -0.17880956 -0.21696956\n",
            " -0.30735799 -0.46637914 -0.6288719  -0.17862598 -0.17790953 -0.1765405\n",
            " -0.17624743 -0.17880956 -0.21696956 -0.30735799 -0.46637914 -0.6288719 ]\n",
            "\n",
            "\n",
            "split1_test_score: \n",
            "[-0.76641069 -0.76641069 -0.76641069 -0.76641069 -0.76641069 -0.76641069\n",
            " -0.76641069 -0.76641069 -0.76641069 -0.56036025 -0.56036025 -0.56036025\n",
            " -0.56036025 -0.56036025 -0.56036025 -0.56036025 -0.57014801 -0.76641069\n",
            " -0.14105042 -0.14105042 -0.14105042 -0.14105042 -0.14105042 -0.62782807\n",
            " -0.56036025 -0.57014801 -0.76641069 -0.13748523 -0.13748523 -0.13748523\n",
            " -0.13746364 -0.14105042 -0.62782807 -0.56036025 -0.57014801 -0.76641069\n",
            " -0.13256878 -0.13596491 -0.13596491 -0.13746364 -0.14105042 -0.62782807\n",
            " -0.56036025 -0.57014801 -0.76641069 -0.12753103 -0.13596491 -0.13596491\n",
            " -0.13746364 -0.14105042 -0.62782807 -0.56036025 -0.57014801 -0.76641069\n",
            " -0.12753103 -0.13596491 -0.13596491 -0.13746364 -0.14105042 -0.62782807\n",
            " -0.56036025 -0.57014801 -0.76641069 -0.12753103 -0.13596491 -0.13596491\n",
            " -0.13746364 -0.14105042 -0.62782807 -0.56036025 -0.57014801 -0.76641069\n",
            " -0.12753103 -0.13596491 -0.13596491 -0.13746364 -0.14105042 -0.62782807\n",
            " -0.56036025 -0.57014801 -0.76641069 -0.12753103 -0.13596491 -0.13596491\n",
            " -0.13746364 -0.14105042 -0.62782807 -0.56036025 -0.57014801 -0.76641069]\n",
            "\n",
            "\n",
            "split2_test_score: \n",
            "[-0.60458343 -0.60458343 -0.60458343 -0.60458343 -0.60458343 -0.60458343\n",
            " -0.60458343 -0.60458343 -0.60458343 -0.68544283 -0.68544283 -0.68544283\n",
            " -0.68544283 -0.68544283 -0.68544283 -0.68544283 -0.6512701  -0.60458343\n",
            " -0.57308711 -0.57308711 -0.57308711 -0.57308711 -0.57308711 -0.55773055\n",
            " -0.68544283 -0.6512701  -0.60458343 -0.56401239 -0.56401239 -0.56401239\n",
            " -0.56752826 -0.57308711 -0.55773055 -0.68544283 -0.6512701  -0.60458343\n",
            " -0.56068075 -0.56068331 -0.56401239 -0.56752826 -0.57308711 -0.55773055\n",
            " -0.68544283 -0.6512701  -0.60458343 -0.5559685  -0.56068331 -0.56401239\n",
            " -0.56752826 -0.57308711 -0.55773055 -0.68544283 -0.6512701  -0.60458343\n",
            " -0.5559685  -0.56068331 -0.56401239 -0.56752826 -0.57308711 -0.55773055\n",
            " -0.68544283 -0.6512701  -0.60458343 -0.5559685  -0.56068331 -0.56401239\n",
            " -0.56752826 -0.57308711 -0.55773055 -0.68544283 -0.6512701  -0.60458343\n",
            " -0.5559685  -0.56068331 -0.56401239 -0.56752826 -0.57308711 -0.55773055\n",
            " -0.68544283 -0.6512701  -0.60458343 -0.5559685  -0.56068331 -0.56401239\n",
            " -0.56752826 -0.57308711 -0.55773055 -0.68544283 -0.6512701  -0.60458343]\n",
            "\n",
            "\n",
            "split3_test_score: \n",
            "[-0.59488974 -0.59488974 -0.59488974 -0.59488974 -0.59488974 -0.59488974\n",
            " -0.59488974 -0.59488974 -0.59488974 -0.41906201 -0.41906201 -0.41906201\n",
            " -0.41906201 -0.41906201 -0.41906201 -0.41906201 -0.44858907 -0.59488974\n",
            " -0.11972157 -0.11972157 -0.11972157 -0.11972157 -0.11972157 -0.27738234\n",
            " -0.41906201 -0.44858907 -0.59488974 -0.11800442 -0.11800442 -0.11800442\n",
            " -0.11965819 -0.11972157 -0.27738234 -0.41906201 -0.44858907 -0.59488974\n",
            " -0.11454419 -0.11692935 -0.11692935 -0.11965819 -0.11972157 -0.27738234\n",
            " -0.41906201 -0.44858907 -0.59488974 -0.11454419 -0.11692935 -0.11692935\n",
            " -0.11965819 -0.11972157 -0.27738234 -0.41906201 -0.44858907 -0.59488974\n",
            " -0.11454419 -0.11692935 -0.11692935 -0.11965819 -0.11972157 -0.27738234\n",
            " -0.41906201 -0.44858907 -0.59488974 -0.11454419 -0.11692935 -0.11692935\n",
            " -0.11965819 -0.11972157 -0.27738234 -0.41906201 -0.44858907 -0.59488974\n",
            " -0.11454419 -0.11692935 -0.11692935 -0.11965819 -0.11972157 -0.27738234\n",
            " -0.41906201 -0.44858907 -0.59488974 -0.11454419 -0.11692935 -0.11692935\n",
            " -0.11965819 -0.11972157 -0.27738234 -0.41906201 -0.44858907 -0.59488974]\n",
            "\n",
            "\n",
            "split4_test_score: \n",
            "[-0.74253021 -0.74253021 -0.74253021 -0.74253021 -0.74253021 -0.74253021\n",
            " -0.74253021 -0.74253021 -0.74253021 -0.46500821 -0.46500821 -0.46500821\n",
            " -0.46500821 -0.46500821 -0.46500821 -0.46500821 -0.49476823 -0.74253021\n",
            " -0.37282237 -0.37282237 -0.37282237 -0.37282237 -0.37282237 -0.43477716\n",
            " -0.46500821 -0.49476823 -0.74253021 -0.40022629 -0.40022629 -0.40022629\n",
            " -0.37786605 -0.37282237 -0.43477716 -0.46500821 -0.49476823 -0.74253021\n",
            " -0.39760782 -0.39760782 -0.39760782 -0.37786605 -0.37282237 -0.43477716\n",
            " -0.46500821 -0.49476823 -0.74253021 -0.39760782 -0.39760782 -0.39760782\n",
            " -0.37786605 -0.37282237 -0.43477716 -0.46500821 -0.49476823 -0.74253021\n",
            " -0.39760782 -0.39760782 -0.39760782 -0.37786605 -0.37282237 -0.43477716\n",
            " -0.46500821 -0.49476823 -0.74253021 -0.39760782 -0.39760782 -0.39760782\n",
            " -0.37786605 -0.37282237 -0.43477716 -0.46500821 -0.49476823 -0.74253021\n",
            " -0.39760782 -0.39760782 -0.39760782 -0.37786605 -0.37282237 -0.43477716\n",
            " -0.46500821 -0.49476823 -0.74253021 -0.39760782 -0.39760782 -0.39760782\n",
            " -0.37786605 -0.37282237 -0.43477716 -0.46500821 -0.49476823 -0.74253021]\n",
            "\n",
            "\n",
            "split5_test_score: \n",
            "[-0.54028024 -0.54028024 -0.54028024 -0.54028024 -0.54028024 -0.54028024\n",
            " -0.54028024 -0.54028024 -0.54028024 -0.29049062 -0.29049062 -0.29049062\n",
            " -0.29049062 -0.29049062 -0.29049062 -0.29049062 -0.39006105 -0.54028024\n",
            " -0.11718066 -0.11718066 -0.11718066 -0.11718066 -0.11718066 -0.23362155\n",
            " -0.29049062 -0.39006105 -0.54028024 -0.11491673 -0.11491673 -0.11491673\n",
            " -0.1152126  -0.11718066 -0.23362155 -0.29049062 -0.39006105 -0.54028024\n",
            " -0.11626881 -0.11844829 -0.11491673 -0.1152126  -0.11718066 -0.23362155\n",
            " -0.29049062 -0.39006105 -0.54028024 -0.11626881 -0.11844829 -0.11491673\n",
            " -0.1152126  -0.11718066 -0.23362155 -0.29049062 -0.39006105 -0.54028024\n",
            " -0.11626881 -0.11844829 -0.11491673 -0.1152126  -0.11718066 -0.23362155\n",
            " -0.29049062 -0.39006105 -0.54028024 -0.11626881 -0.11844829 -0.11491673\n",
            " -0.1152126  -0.11718066 -0.23362155 -0.29049062 -0.39006105 -0.54028024\n",
            " -0.11626881 -0.11844829 -0.11491673 -0.1152126  -0.11718066 -0.23362155\n",
            " -0.29049062 -0.39006105 -0.54028024 -0.11626881 -0.11844829 -0.11491673\n",
            " -0.1152126  -0.11718066 -0.23362155 -0.29049062 -0.39006105 -0.54028024]\n",
            "\n",
            "\n",
            "split6_test_score: \n",
            "[-1.0752881  -1.0752881  -1.0752881  -1.0752881  -1.0752881  -1.0752881\n",
            " -1.0752881  -1.0752881  -1.0752881  -0.47411641 -0.47411641 -0.47411641\n",
            " -0.47411641 -0.47411641 -0.47411641 -0.47411641 -0.42842648 -1.0752881\n",
            " -0.27811983 -0.27811983 -0.27811983 -0.27811983 -0.27811983 -0.38319083\n",
            " -0.47411641 -0.42842648 -1.0752881  -0.27326741 -0.27326741 -0.27326741\n",
            " -0.27574687 -0.27811983 -0.38319083 -0.47411641 -0.42842648 -1.0752881\n",
            " -0.28868089 -0.28868089 -0.28935856 -0.27574687 -0.27811983 -0.38319083\n",
            " -0.47411641 -0.42842648 -1.0752881  -0.28991345 -0.28868089 -0.28935856\n",
            " -0.27574687 -0.27811983 -0.38319083 -0.47411641 -0.42842648 -1.0752881\n",
            " -0.28991345 -0.28868089 -0.28935856 -0.27574687 -0.27811983 -0.38319083\n",
            " -0.47411641 -0.42842648 -1.0752881  -0.28991345 -0.28868089 -0.28935856\n",
            " -0.27574687 -0.27811983 -0.38319083 -0.47411641 -0.42842648 -1.0752881\n",
            " -0.28991345 -0.28868089 -0.28935856 -0.27574687 -0.27811983 -0.38319083\n",
            " -0.47411641 -0.42842648 -1.0752881  -0.28991345 -0.28868089 -0.28935856\n",
            " -0.27574687 -0.27811983 -0.38319083 -0.47411641 -0.42842648 -1.0752881 ]\n",
            "\n",
            "\n",
            "split7_test_score: \n",
            "[-1.67450634 -1.67450634 -1.67450634 -1.67450634 -1.67450634 -1.67450634\n",
            " -1.67450634 -1.67450634 -1.67450634 -1.29805915 -1.29805915 -1.29805915\n",
            " -1.29805915 -1.29805915 -1.29805915 -1.29805915 -1.09242033 -1.67450634\n",
            " -0.76789493 -0.76789493 -0.76789493 -0.76789493 -0.76789493 -0.92740565\n",
            " -1.29805915 -1.09242033 -1.67450634 -0.77540299 -0.77540299 -0.77540299\n",
            " -0.77342668 -0.76789493 -0.92740565 -1.29805915 -1.09242033 -1.67450634\n",
            " -0.77554216 -0.77554216 -0.77554216 -0.77342668 -0.76789493 -0.92740565\n",
            " -1.29805915 -1.09242033 -1.67450634 -0.77554216 -0.77554216 -0.77554216\n",
            " -0.77342668 -0.76789493 -0.92740565 -1.29805915 -1.09242033 -1.67450634\n",
            " -0.77554216 -0.77554216 -0.77554216 -0.77342668 -0.76789493 -0.92740565\n",
            " -1.29805915 -1.09242033 -1.67450634 -0.77554216 -0.77554216 -0.77554216\n",
            " -0.77342668 -0.76789493 -0.92740565 -1.29805915 -1.09242033 -1.67450634\n",
            " -0.77554216 -0.77554216 -0.77554216 -0.77342668 -0.76789493 -0.92740565\n",
            " -1.29805915 -1.09242033 -1.67450634 -0.77554216 -0.77554216 -0.77554216\n",
            " -0.77342668 -0.76789493 -0.92740565 -1.29805915 -1.09242033 -1.67450634]\n",
            "\n",
            "\n",
            "split8_test_score: \n",
            "[-1.39469178 -1.39469178 -1.39469178 -1.39469178 -1.39469178 -1.39469178\n",
            " -1.39469178 -1.39469178 -1.39469178 -1.22542391 -1.22542391 -1.22542391\n",
            " -1.22542391 -1.22542391 -1.22542391 -1.22542391 -1.22899043 -1.39469178\n",
            " -0.70784736 -0.70784736 -0.70784736 -0.70784736 -0.70784736 -0.75495921\n",
            " -1.22542391 -1.22899043 -1.39469178 -0.71050813 -0.71050813 -0.71050813\n",
            " -0.70839589 -0.70784736 -0.75495921 -1.22542391 -1.22899043 -1.39469178\n",
            " -0.70717509 -0.70717509 -0.71324842 -0.70839589 -0.70784736 -0.75495921\n",
            " -1.22542391 -1.22899043 -1.39469178 -0.70717509 -0.70717509 -0.71324842\n",
            " -0.70839589 -0.70784736 -0.75495921 -1.22542391 -1.22899043 -1.39469178\n",
            " -0.70717509 -0.70717509 -0.71324842 -0.70839589 -0.70784736 -0.75495921\n",
            " -1.22542391 -1.22899043 -1.39469178 -0.70717509 -0.70717509 -0.71324842\n",
            " -0.70839589 -0.70784736 -0.75495921 -1.22542391 -1.22899043 -1.39469178\n",
            " -0.70717509 -0.70717509 -0.71324842 -0.70839589 -0.70784736 -0.75495921\n",
            " -1.22542391 -1.22899043 -1.39469178 -0.70717509 -0.70717509 -0.71324842\n",
            " -0.70839589 -0.70784736 -0.75495921 -1.22542391 -1.22899043 -1.39469178]\n",
            "\n",
            "\n",
            "split9_test_score: \n",
            "[-2.36217515 -2.36217515 -2.36217515 -2.36217515 -2.36217515 -2.36217515\n",
            " -2.36217515 -2.36217515 -2.36217515 -1.54847139 -1.54847139 -1.54847139\n",
            " -1.54847139 -1.54847139 -1.54847139 -1.54847139 -1.69008943 -2.36217515\n",
            " -0.78225805 -0.78225805 -0.78225805 -0.78225805 -0.78225805 -1.26755711\n",
            " -1.54847139 -1.69008943 -2.36217515 -0.77967889 -0.77967889 -0.77967889\n",
            " -0.77980092 -0.77980092 -1.26755711 -1.54847139 -1.69008943 -2.36217515\n",
            " -0.78002203 -0.7796827  -0.7796827  -0.77980092 -0.77980092 -1.26755711\n",
            " -1.54847139 -1.69008943 -2.36217515 -0.78002203 -0.7796827  -0.7796827\n",
            " -0.77980092 -0.77980092 -1.26755711 -1.54847139 -1.69008943 -2.36217515\n",
            " -0.78002203 -0.7796827  -0.7796827  -0.77980092 -0.77980092 -1.26755711\n",
            " -1.54847139 -1.69008943 -2.36217515 -0.78002203 -0.7796827  -0.7796827\n",
            " -0.77980092 -0.77980092 -1.26755711 -1.54847139 -1.69008943 -2.36217515\n",
            " -0.78002203 -0.7796827  -0.7796827  -0.77980092 -0.77980092 -1.26755711\n",
            " -1.54847139 -1.69008943 -2.36217515 -0.78002203 -0.7796827  -0.7796827\n",
            " -0.77980092 -0.77980092 -1.26755711 -1.54847139 -1.69008943 -2.36217515]\n",
            "\n",
            "\n",
            "mean_test_score: \n",
            "[-1.01622492 -1.01622492 -1.01622492 -1.01622492 -1.01622492 -1.01622492\n",
            " -1.01622492 -1.01622492 -1.01622492 -0.71194009 -0.71194009 -0.71194009\n",
            " -0.71194009 -0.71194009 -0.71194009 -0.71194009 -0.73238338 -1.01622492\n",
            " -0.39519425 -0.39519425 -0.39519425 -0.39519425 -0.39519425 -0.55813707\n",
            " -0.71194009 -0.73238338 -1.01622492 -0.396336   -0.396336   -0.396336\n",
            " -0.39440982 -0.39496244 -0.55813707 -0.71194009 -0.73238338 -1.01622492\n",
            " -0.39639114 -0.39711117 -0.39758497 -0.39440982 -0.39496244 -0.55813707\n",
            " -0.71194009 -0.73238338 -1.01622492 -0.39549563 -0.39711117 -0.39758497\n",
            " -0.39440982 -0.39496244 -0.55813707 -0.71194009 -0.73238338 -1.01622492\n",
            " -0.39549563 -0.39711117 -0.39758497 -0.39440982 -0.39496244 -0.55813707\n",
            " -0.71194009 -0.73238338 -1.01622492 -0.39549563 -0.39711117 -0.39758497\n",
            " -0.39440982 -0.39496244 -0.55813707 -0.71194009 -0.73238338 -1.01622492\n",
            " -0.39549563 -0.39711117 -0.39758497 -0.39440982 -0.39496244 -0.55813707\n",
            " -0.71194009 -0.73238338 -1.01622492 -0.39549563 -0.39711117 -0.39758497\n",
            " -0.39440982 -0.39496244 -0.55813707 -0.71194009 -0.73238338 -1.01622492]\n",
            "\n",
            "\n",
            "std_test_score: \n",
            "[0.5612134  0.5612134  0.5612134  0.5612134  0.5612134  0.5612134\n",
            " 0.5612134  0.5612134  0.5612134  0.42631124 0.42631124 0.42631124\n",
            " 0.42631124 0.42631124 0.42631124 0.42631124 0.40972411 0.5612134\n",
            " 0.26181345 0.26181345 0.26181345 0.26181345 0.26181345 0.31647305\n",
            " 0.42631124 0.40972411 0.5612134  0.26325504 0.26325504 0.26325504\n",
            " 0.26276425 0.26147151 0.31647305 0.42631124 0.40972411 0.5612134\n",
            " 0.26264821 0.26180455 0.2631892  0.26276425 0.26147151 0.31647305\n",
            " 0.42631124 0.40972411 0.5612134  0.26282785 0.26180455 0.2631892\n",
            " 0.26276425 0.26147151 0.31647305 0.42631124 0.40972411 0.5612134\n",
            " 0.26282785 0.26180455 0.2631892  0.26276425 0.26147151 0.31647305\n",
            " 0.42631124 0.40972411 0.5612134  0.26282785 0.26180455 0.2631892\n",
            " 0.26276425 0.26147151 0.31647305 0.42631124 0.40972411 0.5612134\n",
            " 0.26282785 0.26180455 0.2631892  0.26276425 0.26147151 0.31647305\n",
            " 0.42631124 0.40972411 0.5612134  0.26282785 0.26180455 0.2631892\n",
            " 0.26276425 0.26147151 0.31647305 0.42631124 0.40972411 0.5612134 ]\n",
            "\n",
            "\n",
            "rank_test_score: \n",
            "[73 73 73 73 73 73 73 73 73 49 49 49 49 49 49 49 64 73 15 15 15 15 15 41\n",
            " 49 64 73 25 25 25  1  8 41 49 64 73 28 29 35  1  8 41 49 64 73 20 29 35\n",
            "  1  8 41 49 64 73 20 29 35  1  8 41 49 64 73 20 29 35  1  8 41 49 64 73\n",
            " 20 29 35  1  8 41 49 64 73 20 29 35  1  8 41 49 64 73]\n",
            "\n",
            "\n",
            "split0_train_score: \n",
            "[-0.87251385 -0.87251385 -0.87251385 -0.87251385 -0.87251385 -0.87251385\n",
            " -0.87251385 -0.87251385 -0.87251385 -0.6044908  -0.6044908  -0.6044908\n",
            " -0.6044908  -0.6044908  -0.6044908  -0.6044908  -0.70879229 -0.87251385\n",
            " -0.35344813 -0.35344813 -0.35344813 -0.35344813 -0.35344813 -0.47964162\n",
            " -0.6044908  -0.70879229 -0.87251385 -0.35153311 -0.35153311 -0.35153311\n",
            " -0.35236978 -0.35344813 -0.47964162 -0.6044908  -0.70879229 -0.87251385\n",
            " -0.34990212 -0.35064995 -0.35153311 -0.35236978 -0.35344813 -0.47964162\n",
            " -0.6044908  -0.70879229 -0.87251385 -0.34990212 -0.35064995 -0.35153311\n",
            " -0.35236978 -0.35344813 -0.47964162 -0.6044908  -0.70879229 -0.87251385\n",
            " -0.34990212 -0.35064995 -0.35153311 -0.35236978 -0.35344813 -0.47964162\n",
            " -0.6044908  -0.70879229 -0.87251385 -0.34990212 -0.35064995 -0.35153311\n",
            " -0.35236978 -0.35344813 -0.47964162 -0.6044908  -0.70879229 -0.87251385\n",
            " -0.34990212 -0.35064995 -0.35153311 -0.35236978 -0.35344813 -0.47964162\n",
            " -0.6044908  -0.70879229 -0.87251385 -0.34990212 -0.35064995 -0.35153311\n",
            " -0.35236978 -0.35344813 -0.47964162 -0.6044908  -0.70879229 -0.87251385]\n",
            "\n",
            "\n",
            "split1_train_score: \n",
            "[-0.85169747 -0.85169747 -0.85169747 -0.85169747 -0.85169747 -0.85169747\n",
            " -0.85169747 -0.85169747 -0.85169747 -0.57282829 -0.57282829 -0.57282829\n",
            " -0.57282829 -0.57282829 -0.57282829 -0.57282829 -0.69374087 -0.85169747\n",
            " -0.35825832 -0.35825832 -0.35825832 -0.35825832 -0.35825832 -0.47615406\n",
            " -0.57282829 -0.69374087 -0.85169747 -0.35651815 -0.35651815 -0.35651815\n",
            " -0.35731959 -0.35825832 -0.47615406 -0.57282829 -0.69374087 -0.85169747\n",
            " -0.35572826 -0.35592872 -0.35592872 -0.35731959 -0.35825832 -0.47615406\n",
            " -0.57282829 -0.69374087 -0.85169747 -0.35519801 -0.35592872 -0.35592872\n",
            " -0.35731959 -0.35825832 -0.47615406 -0.57282829 -0.69374087 -0.85169747\n",
            " -0.35519801 -0.35592872 -0.35592872 -0.35731959 -0.35825832 -0.47615406\n",
            " -0.57282829 -0.69374087 -0.85169747 -0.35519801 -0.35592872 -0.35592872\n",
            " -0.35731959 -0.35825832 -0.47615406 -0.57282829 -0.69374087 -0.85169747\n",
            " -0.35519801 -0.35592872 -0.35592872 -0.35731959 -0.35825832 -0.47615406\n",
            " -0.57282829 -0.69374087 -0.85169747 -0.35519801 -0.35592872 -0.35592872\n",
            " -0.35731959 -0.35825832 -0.47615406 -0.57282829 -0.69374087 -0.85169747]\n",
            "\n",
            "\n",
            "split2_train_score: \n",
            "[-0.87008236 -0.87008236 -0.87008236 -0.87008236 -0.87008236 -0.87008236\n",
            " -0.87008236 -0.87008236 -0.87008236 -0.55864302 -0.55864302 -0.55864302\n",
            " -0.55864302 -0.55864302 -0.55864302 -0.55864302 -0.68436253 -0.87008236\n",
            " -0.31011373 -0.31011373 -0.31011373 -0.31011373 -0.31011373 -0.44165524\n",
            " -0.55864302 -0.68436253 -0.87008236 -0.30917381 -0.30917381 -0.30917381\n",
            " -0.30947746 -0.31011373 -0.44165524 -0.55864302 -0.68436253 -0.87008236\n",
            " -0.30804779 -0.30882067 -0.30917381 -0.30947746 -0.31011373 -0.44165524\n",
            " -0.55864302 -0.68436253 -0.87008236 -0.30750167 -0.30882067 -0.30917381\n",
            " -0.30947746 -0.31011373 -0.44165524 -0.55864302 -0.68436253 -0.87008236\n",
            " -0.30750167 -0.30882067 -0.30917381 -0.30947746 -0.31011373 -0.44165524\n",
            " -0.55864302 -0.68436253 -0.87008236 -0.30750167 -0.30882067 -0.30917381\n",
            " -0.30947746 -0.31011373 -0.44165524 -0.55864302 -0.68436253 -0.87008236\n",
            " -0.30750167 -0.30882067 -0.30917381 -0.30947746 -0.31011373 -0.44165524\n",
            " -0.55864302 -0.68436253 -0.87008236 -0.30750167 -0.30882067 -0.30917381\n",
            " -0.30947746 -0.31011373 -0.44165524 -0.55864302 -0.68436253 -0.87008236]\n",
            "\n",
            "\n",
            "split3_train_score: \n",
            "[-0.87159758 -0.87159758 -0.87159758 -0.87159758 -0.87159758 -0.87159758\n",
            " -0.87159758 -0.87159758 -0.87159758 -0.58850029 -0.58850029 -0.58850029\n",
            " -0.58850029 -0.58850029 -0.58850029 -0.58850029 -0.70743289 -0.87159758\n",
            " -0.35947365 -0.35947365 -0.35947365 -0.35947365 -0.35947365 -0.47081425\n",
            " -0.58850029 -0.70743289 -0.87159758 -0.35745582 -0.35745582 -0.35745582\n",
            " -0.35808574 -0.35947365 -0.47081425 -0.58850029 -0.70743289 -0.87159758\n",
            " -0.35632353 -0.35678816 -0.35678816 -0.35808574 -0.35947365 -0.47081425\n",
            " -0.58850029 -0.70743289 -0.87159758 -0.35515913 -0.35678816 -0.35678816\n",
            " -0.35808574 -0.35947365 -0.47081425 -0.58850029 -0.70743289 -0.87159758\n",
            " -0.35515913 -0.35678816 -0.35678816 -0.35808574 -0.35947365 -0.47081425\n",
            " -0.58850029 -0.70743289 -0.87159758 -0.35515913 -0.35678816 -0.35678816\n",
            " -0.35808574 -0.35947365 -0.47081425 -0.58850029 -0.70743289 -0.87159758\n",
            " -0.35515913 -0.35678816 -0.35678816 -0.35808574 -0.35947365 -0.47081425\n",
            " -0.58850029 -0.70743289 -0.87159758 -0.35515913 -0.35678816 -0.35678816\n",
            " -0.35808574 -0.35947365 -0.47081425 -0.58850029 -0.70743289 -0.87159758]\n",
            "\n",
            "\n",
            "split4_train_score: \n",
            "[-0.85417503 -0.85417503 -0.85417503 -0.85417503 -0.85417503 -0.85417503\n",
            " -0.85417503 -0.85417503 -0.85417503 -0.58453705 -0.58453705 -0.58453705\n",
            " -0.58453705 -0.58453705 -0.58453705 -0.58453705 -0.70228161 -0.85417503\n",
            " -0.33209486 -0.33209486 -0.33209486 -0.33209486 -0.33209486 -0.45367539\n",
            " -0.58453705 -0.70228161 -0.85417503 -0.32910201 -0.32910201 -0.32910201\n",
            " -0.33019836 -0.33209486 -0.45367539 -0.58453705 -0.70228161 -0.85417503\n",
            " -0.32764877 -0.32764877 -0.32861093 -0.33019836 -0.33209486 -0.45367539\n",
            " -0.58453705 -0.70228161 -0.85417503 -0.32593407 -0.32764877 -0.32861093\n",
            " -0.33019836 -0.33209486 -0.45367539 -0.58453705 -0.70228161 -0.85417503\n",
            " -0.32593407 -0.32764877 -0.32861093 -0.33019836 -0.33209486 -0.45367539\n",
            " -0.58453705 -0.70228161 -0.85417503 -0.32593407 -0.32764877 -0.32861093\n",
            " -0.33019836 -0.33209486 -0.45367539 -0.58453705 -0.70228161 -0.85417503\n",
            " -0.32593407 -0.32764877 -0.32861093 -0.33019836 -0.33209486 -0.45367539\n",
            " -0.58453705 -0.70228161 -0.85417503 -0.32593407 -0.32764877 -0.32861093\n",
            " -0.33019836 -0.33209486 -0.45367539 -0.58453705 -0.70228161 -0.85417503]\n",
            "\n",
            "\n",
            "split5_train_score: \n",
            "[-0.8830384  -0.8830384  -0.8830384  -0.8830384  -0.8830384  -0.8830384\n",
            " -0.8830384  -0.8830384  -0.8830384  -0.60630468 -0.60630468 -0.60630468\n",
            " -0.60630468 -0.60630468 -0.60630468 -0.60630468 -0.7181949  -0.8830384\n",
            " -0.36139087 -0.36139087 -0.36139087 -0.36139087 -0.36139087 -0.47800173\n",
            " -0.60630468 -0.7181949  -0.8830384  -0.35946608 -0.35946608 -0.35946608\n",
            " -0.36023991 -0.36139087 -0.47800173 -0.60630468 -0.7181949  -0.8830384\n",
            " -0.35784684 -0.35836513 -0.35946608 -0.36023991 -0.36139087 -0.47800173\n",
            " -0.60630468 -0.7181949  -0.8830384  -0.35668244 -0.35836513 -0.35946608\n",
            " -0.36023991 -0.36139087 -0.47800173 -0.60630468 -0.7181949  -0.8830384\n",
            " -0.35668244 -0.35836513 -0.35946608 -0.36023991 -0.36139087 -0.47800173\n",
            " -0.60630468 -0.7181949  -0.8830384  -0.35668244 -0.35836513 -0.35946608\n",
            " -0.36023991 -0.36139087 -0.47800173 -0.60630468 -0.7181949  -0.8830384\n",
            " -0.35668244 -0.35836513 -0.35946608 -0.36023991 -0.36139087 -0.47800173\n",
            " -0.60630468 -0.7181949  -0.8830384  -0.35668244 -0.35836513 -0.35946608\n",
            " -0.36023991 -0.36139087 -0.47800173 -0.60630468 -0.7181949  -0.8830384 ]\n",
            "\n",
            "\n",
            "split6_train_score: \n",
            "[-0.85786852 -0.85786852 -0.85786852 -0.85786852 -0.85786852 -0.85786852\n",
            " -0.85786852 -0.85786852 -0.85786852 -0.58162655 -0.58162655 -0.58162655\n",
            " -0.58162655 -0.58162655 -0.58162655 -0.58162655 -0.70693848 -0.85786852\n",
            " -0.34065513 -0.34065513 -0.34065513 -0.34065513 -0.34065513 -0.45839586\n",
            " -0.58162655 -0.70693848 -0.85786852 -0.33900739 -0.33900739 -0.33900739\n",
            " -0.33954714 -0.34065513 -0.45839586 -0.58162655 -0.70693848 -0.85786852\n",
            " -0.33740726 -0.33740726 -0.33829109 -0.33954714 -0.34065513 -0.45839586\n",
            " -0.58162655 -0.70693848 -0.85786852 -0.33560437 -0.33740726 -0.33829109\n",
            " -0.33954714 -0.34065513 -0.45839586 -0.58162655 -0.70693848 -0.85786852\n",
            " -0.33560437 -0.33740726 -0.33829109 -0.33954714 -0.34065513 -0.45839586\n",
            " -0.58162655 -0.70693848 -0.85786852 -0.33560437 -0.33740726 -0.33829109\n",
            " -0.33954714 -0.34065513 -0.45839586 -0.58162655 -0.70693848 -0.85786852\n",
            " -0.33560437 -0.33740726 -0.33829109 -0.33954714 -0.34065513 -0.45839586\n",
            " -0.58162655 -0.70693848 -0.85786852 -0.33560437 -0.33740726 -0.33829109\n",
            " -0.33954714 -0.34065513 -0.45839586 -0.58162655 -0.70693848 -0.85786852]\n",
            "\n",
            "\n",
            "split7_train_score: \n",
            "[-0.79649612 -0.79649612 -0.79649612 -0.79649612 -0.79649612 -0.79649612\n",
            " -0.79649612 -0.79649612 -0.79649612 -0.52794874 -0.52794874 -0.52794874\n",
            " -0.52794874 -0.52794874 -0.52794874 -0.52794874 -0.64351281 -0.79649612\n",
            " -0.29542433 -0.29542433 -0.29542433 -0.29542433 -0.29542433 -0.40750114\n",
            " -0.52794874 -0.64351281 -0.79649612 -0.29257721 -0.29257721 -0.29257721\n",
            " -0.29355588 -0.29542433 -0.40750114 -0.52794874 -0.64351281 -0.79649612\n",
            " -0.29106066 -0.29106066 -0.29180437 -0.29355588 -0.29542433 -0.40750114\n",
            " -0.52794874 -0.64351281 -0.79649612 -0.28990839 -0.29106066 -0.29180437\n",
            " -0.29355588 -0.29542433 -0.40750114 -0.52794874 -0.64351281 -0.79649612\n",
            " -0.28990839 -0.29106066 -0.29180437 -0.29355588 -0.29542433 -0.40750114\n",
            " -0.52794874 -0.64351281 -0.79649612 -0.28990839 -0.29106066 -0.29180437\n",
            " -0.29355588 -0.29542433 -0.40750114 -0.52794874 -0.64351281 -0.79649612\n",
            " -0.28990839 -0.29106066 -0.29180437 -0.29355588 -0.29542433 -0.40750114\n",
            " -0.52794874 -0.64351281 -0.79649612 -0.28990839 -0.29106066 -0.29180437\n",
            " -0.29355588 -0.29542433 -0.40750114 -0.52794874 -0.64351281 -0.79649612]\n",
            "\n",
            "\n",
            "split8_train_score: \n",
            "[-0.78517104 -0.78517104 -0.78517104 -0.78517104 -0.78517104 -0.78517104\n",
            " -0.78517104 -0.78517104 -0.78517104 -0.53079178 -0.53079178 -0.53079178\n",
            " -0.53079178 -0.53079178 -0.53079178 -0.53079178 -0.62691965 -0.78517104\n",
            " -0.30188774 -0.30188774 -0.30188774 -0.30188774 -0.30188774 -0.42598197\n",
            " -0.53079178 -0.62691965 -0.78517104 -0.2994757  -0.2994757  -0.2994757\n",
            " -0.30047834 -0.30188774 -0.42598197 -0.53079178 -0.62691965 -0.78517104\n",
            " -0.29842213 -0.29842213 -0.29911655 -0.30047834 -0.30188774 -0.42598197\n",
            " -0.53079178 -0.62691965 -0.78517104 -0.29726985 -0.29842213 -0.29911655\n",
            " -0.30047834 -0.30188774 -0.42598197 -0.53079178 -0.62691965 -0.78517104\n",
            " -0.29726985 -0.29842213 -0.29911655 -0.30047834 -0.30188774 -0.42598197\n",
            " -0.53079178 -0.62691965 -0.78517104 -0.29726985 -0.29842213 -0.29911655\n",
            " -0.30047834 -0.30188774 -0.42598197 -0.53079178 -0.62691965 -0.78517104\n",
            " -0.29726985 -0.29842213 -0.29911655 -0.30047834 -0.30188774 -0.42598197\n",
            " -0.53079178 -0.62691965 -0.78517104 -0.29726985 -0.29842213 -0.29911655\n",
            " -0.30047834 -0.30188774 -0.42598197 -0.53079178 -0.62691965 -0.78517104]\n",
            "\n",
            "\n",
            "split9_train_score: \n",
            "[-0.69779572 -0.69779572 -0.69779572 -0.69779572 -0.69779572 -0.69779572\n",
            " -0.69779572 -0.69779572 -0.69779572 -0.47756258 -0.47756258 -0.47756258\n",
            " -0.47756258 -0.47756258 -0.47756258 -0.47756258 -0.58551719 -0.69779572\n",
            " -0.29770332 -0.29770332 -0.29770332 -0.29770332 -0.29770332 -0.37409587\n",
            " -0.47756258 -0.58551719 -0.69779572 -0.29581022 -0.29581022 -0.29581022\n",
            " -0.29660043 -0.29660043 -0.37409587 -0.47756258 -0.58551719 -0.69779572\n",
            " -0.29427595 -0.29505027 -0.29505027 -0.29660043 -0.29660043 -0.37409587\n",
            " -0.47756258 -0.58551719 -0.69779572 -0.29312367 -0.29505027 -0.29505027\n",
            " -0.29660043 -0.29660043 -0.37409587 -0.47756258 -0.58551719 -0.69779572\n",
            " -0.29312367 -0.29505027 -0.29505027 -0.29660043 -0.29660043 -0.37409587\n",
            " -0.47756258 -0.58551719 -0.69779572 -0.29312367 -0.29505027 -0.29505027\n",
            " -0.29660043 -0.29660043 -0.37409587 -0.47756258 -0.58551719 -0.69779572\n",
            " -0.29312367 -0.29505027 -0.29505027 -0.29660043 -0.29660043 -0.37409587\n",
            " -0.47756258 -0.58551719 -0.69779572 -0.29312367 -0.29505027 -0.29505027\n",
            " -0.29660043 -0.29660043 -0.37409587 -0.47756258 -0.58551719 -0.69779572]\n",
            "\n",
            "\n",
            "mean_train_score: \n",
            "[-0.83404361 -0.83404361 -0.83404361 -0.83404361 -0.83404361 -0.83404361\n",
            " -0.83404361 -0.83404361 -0.83404361 -0.56332338 -0.56332338 -0.56332338\n",
            " -0.56332338 -0.56332338 -0.56332338 -0.56332338 -0.67776932 -0.83404361\n",
            " -0.33104501 -0.33104501 -0.33104501 -0.33104501 -0.33104501 -0.44659171\n",
            " -0.56332338 -0.67776932 -0.83404361 -0.32901195 -0.32901195 -0.32901195\n",
            " -0.32978726 -0.33093472 -0.44659171 -0.56332338 -0.67776932 -0.83404361\n",
            " -0.32766633 -0.32801417 -0.32857631 -0.32978726 -0.33093472 -0.44659171\n",
            " -0.56332338 -0.67776932 -0.83404361 -0.32662837 -0.32801417 -0.32857631\n",
            " -0.32978726 -0.33093472 -0.44659171 -0.56332338 -0.67776932 -0.83404361\n",
            " -0.32662837 -0.32801417 -0.32857631 -0.32978726 -0.33093472 -0.44659171\n",
            " -0.56332338 -0.67776932 -0.83404361 -0.32662837 -0.32801417 -0.32857631\n",
            " -0.32978726 -0.33093472 -0.44659171 -0.56332338 -0.67776932 -0.83404361\n",
            " -0.32662837 -0.32801417 -0.32857631 -0.32978726 -0.33093472 -0.44659171\n",
            " -0.56332338 -0.67776932 -0.83404361 -0.32662837 -0.32801417 -0.32857631\n",
            " -0.32978726 -0.33093472 -0.44659171 -0.56332338 -0.67776932 -0.83404361]\n",
            "\n",
            "\n",
            "std_train_score: \n",
            "[0.05498721 0.05498721 0.05498721 0.05498721 0.05498721 0.05498721\n",
            " 0.05498721 0.05498721 0.05498721 0.03838995 0.03838995 0.03838995\n",
            " 0.03838995 0.03838995 0.03838995 0.03838995 0.0418288  0.05498721\n",
            " 0.02594946 0.02594946 0.02594946 0.02594946 0.02594946 0.03310878\n",
            " 0.03838995 0.0418288  0.05498721 0.02606915 0.02606915 0.02606915\n",
            " 0.026033   0.02609288 0.03310878 0.03838995 0.0418288  0.05498721\n",
            " 0.02608427 0.02612537 0.02615366 0.026033   0.02609288 0.03310878\n",
            " 0.03838995 0.0418288  0.05498721 0.0261818  0.02612537 0.02615366\n",
            " 0.026033   0.02609288 0.03310878 0.03838995 0.0418288  0.05498721\n",
            " 0.0261818  0.02612537 0.02615366 0.026033   0.02609288 0.03310878\n",
            " 0.03838995 0.0418288  0.05498721 0.0261818  0.02612537 0.02615366\n",
            " 0.026033   0.02609288 0.03310878 0.03838995 0.0418288  0.05498721\n",
            " 0.0261818  0.02612537 0.02615366 0.026033   0.02609288 0.03310878\n",
            " 0.03838995 0.0418288  0.05498721 0.0261818  0.02612537 0.02615366\n",
            " 0.026033   0.02609288 0.03310878 0.03838995 0.0418288  0.05498721]\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "j_qMVlun6qsp",
        "colab_type": "code",
        "outputId": "e1a8c08a-ca17-4bb7-c027-6a35123c4c8c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "cell_type": "code",
      "source": [
        "# Find the best estimator.\n",
        "print(mod.best_estimator_)\n",
        "print('Training MSE: ' + str(abs(mod.best_score_)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "DecisionTreeRegressor(criterion='mse', max_depth=7, max_features=None,\n",
            "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
            "           min_impurity_split=None, min_samples_leaf=1,\n",
            "           min_samples_split=40, min_weight_fraction_leaf=0.0,\n",
            "           presort=False, random_state=0, splitter='best')\n",
            "Training MSE: 0.3944098249674579\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "cWj8TbCn6tM0",
        "colab_type": "code",
        "outputId": "a1f6f010-4f99-4e89-e1a5-d48dda3dbfca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "cell_type": "code",
      "source": [
        "# Predict on test set.\n",
        "results = mod.predict(X_test)\n",
        "sc_y.inverse_transform(results.reshape(1, -1))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 624142.85714286, 3005546.8       , 4079349.33333333,\n",
              "        3391531.        ,  838851.27272727, 4420532.36363636,\n",
              "        3480287.11111111,  838851.27272727,  624142.85714286,\n",
              "        3480287.11111111, 3391531.        ,  838851.27272727,\n",
              "         838851.27272727,  624142.85714286, 3005546.8       ,\n",
              "         838851.27272727,  838851.27272727, 3391531.        ,\n",
              "        4420532.36363636, 3391531.        ,  838851.27272727,\n",
              "        3391531.        ,  838851.27272727,  838851.27272727,\n",
              "        3391531.        ,  838851.27272727, 4079349.33333333]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "metadata": {
        "id": "utTyuDad6u5Q",
        "colab_type": "code",
        "outputId": "b83b1079-77b8-4e3a-cea5-409def3f51e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# Mean squared error of test set.\n",
        "print('Train MSE: ' + str(abs(mod.score(X_test, sc_y.transform(y_test.values.reshape(-1, 1))))))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train MSE: 0.41337100439547025\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}