Campus_Placement.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Campus Placement.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOo83YNgkhlyoMxrKk3/uOU",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sanky2501/Data-Preprocessing-and-Linear-Regression/blob/master/Campus_Placement.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ycZ7xjHYCUt7",
        "colab_type": "text"
      },
      "source": [
        "#**Preparing The Data**\n",
        "\n",
        "Remember the quality of your inputs decide the quality of your output."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dWs0jNWnEv8D",
        "colab_type": "text"
      },
      "source": [
        "## ~1. Importing Libraries and Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B0zFmhm2CiVZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "54436ffb-148f-4d92-ef59-34c8fd84ff4c"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import missingno\n",
        "import seaborn as sns\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vA8nsE31GSpv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "#Ignore Warnings"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IrNMdc18Ga0u",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "3a1b7d73-9ad7-4ff6-8deb-6bdf8164f8ea"
      },
      "source": [
        "# Read dataset \n",
        "# dataframe=pd.read_csv(\"placement_data.csv\")\n",
        "# Temporarily  kept URL can be changed\n",
        "url = \"https://raw.githubusercontent.com/sanky2501/Data-Preprocessing-and-Linear-Regression/master/Placement_data.csv\"\n",
        "dataframe = pd.read_csv(url)\n",
        "dataframe.head()"
      ],
      "execution_count": 3,
      "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>gender</th>\n",
              "      <th>ssc_p</th>\n",
              "      <th>ssc_b</th>\n",
              "      <th>hsc_p</th>\n",
              "      <th>hsc_b</th>\n",
              "      <th>hsc_s</th>\n",
              "      <th>degree_p</th>\n",
              "      <th>degree_t</th>\n",
              "      <th>workex</th>\n",
              "      <th>etest_p</th>\n",
              "      <th>specialisation</th>\n",
              "      <th>mba_p</th>\n",
              "      <th>status</th>\n",
              "      <th>salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>M</td>\n",
              "      <td>67.00</td>\n",
              "      <td>Others</td>\n",
              "      <td>91.00</td>\n",
              "      <td>Others</td>\n",
              "      <td>Commerce</td>\n",
              "      <td>58.00</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>No</td>\n",
              "      <td>55.0</td>\n",
              "      <td>Mkt&amp;HR</td>\n",
              "      <td>58.80</td>\n",
              "      <td>Placed</td>\n",
              "      <td>270000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>M</td>\n",
              "      <td>79.33</td>\n",
              "      <td>Central</td>\n",
              "      <td>78.33</td>\n",
              "      <td>Others</td>\n",
              "      <td>Science</td>\n",
              "      <td>77.48</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>Yes</td>\n",
              "      <td>86.5</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>66.28</td>\n",
              "      <td>Placed</td>\n",
              "      <td>200000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>M</td>\n",
              "      <td>65.00</td>\n",
              "      <td>Central</td>\n",
              "      <td>68.00</td>\n",
              "      <td>Central</td>\n",
              "      <td>Arts</td>\n",
              "      <td>64.00</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>No</td>\n",
              "      <td>75.0</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>57.80</td>\n",
              "      <td>Placed</td>\n",
              "      <td>250000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>M</td>\n",
              "      <td>56.00</td>\n",
              "      <td>Central</td>\n",
              "      <td>52.00</td>\n",
              "      <td>Central</td>\n",
              "      <td>Science</td>\n",
              "      <td>52.00</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>No</td>\n",
              "      <td>66.0</td>\n",
              "      <td>Mkt&amp;HR</td>\n",
              "      <td>59.43</td>\n",
              "      <td>Not Placed</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>M</td>\n",
              "      <td>85.80</td>\n",
              "      <td>Central</td>\n",
              "      <td>73.60</td>\n",
              "      <td>Central</td>\n",
              "      <td>Commerce</td>\n",
              "      <td>73.30</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>No</td>\n",
              "      <td>96.8</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>55.50</td>\n",
              "      <td>Placed</td>\n",
              "      <td>425000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  gender  ssc_p    ssc_b  hsc_p  ... specialisation  mba_p      status    salary\n",
              "0      M  67.00   Others  91.00  ...         Mkt&HR  58.80      Placed  270000.0\n",
              "1      M  79.33  Central  78.33  ...        Mkt&Fin  66.28      Placed  200000.0\n",
              "2      M  65.00  Central  68.00  ...        Mkt&Fin  57.80      Placed  250000.0\n",
              "3      M  56.00  Central  52.00  ...         Mkt&HR  59.43  Not Placed       NaN\n",
              "4      M  85.80  Central  73.60  ...        Mkt&Fin  55.50      Placed  425000.0\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qW-t-trCHEtB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ad91cf6d-31fd-4d50-d8fb-a25603704092"
      },
      "source": [
        "# Size of data\n",
        "dataframe.shape\n"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(215, 14)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hPjlWcdcHheb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "927e467e-1a61-41eb-bca6-45d2fa494fd8"
      },
      "source": [
        "#Features\n",
        "dataframe.columns"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['gender', 'ssc_p', 'ssc_b', 'hsc_p', 'hsc_b', 'hsc_s', 'degree_p',\n",
              "       'degree_t', 'workex', 'etest_p', 'specialisation', 'mba_p', 'status',\n",
              "       'salary'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xdTrJltQHkI6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "8040edf3-8756-4d74-d65c-2723c1aea660"
      },
      "source": [
        "categorical_cols=[]\n",
        "numerical_cols=[]\n",
        "for i in dataframe.columns:\n",
        "  if dataframe[i].dtype== object:\n",
        "    categorical_cols.append(i)\n",
        "  else:\n",
        "    numerical_cols.append(i)\n",
        "\n",
        "#Datatypes of features\n",
        "dataframe.dtypes"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "gender             object\n",
              "ssc_p             float64\n",
              "ssc_b              object\n",
              "hsc_p             float64\n",
              "hsc_b              object\n",
              "hsc_s              object\n",
              "degree_p          float64\n",
              "degree_t           object\n",
              "workex             object\n",
              "etest_p           float64\n",
              "specialisation     object\n",
              "mba_p             float64\n",
              "status             object\n",
              "salary            float64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9tuImGcjHrRf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "5ebd8536-e4fd-400e-d1ca-2a0ff392a243"
      },
      "source": [
        "# Number of all missing datapoints\n",
        "dataframe.isna().sum()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "gender             0\n",
              "ssc_p              0\n",
              "ssc_b              0\n",
              "hsc_p              0\n",
              "hsc_b              0\n",
              "hsc_s              0\n",
              "degree_p           0\n",
              "degree_t           0\n",
              "workex             0\n",
              "etest_p            0\n",
              "specialisation     0\n",
              "mba_p              0\n",
              "status             0\n",
              "salary            67\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FeAkW3sjINTl",
        "colab_type": "text"
      },
      "source": [
        "We can see that the above data has few missing data points"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2YEN-Vc6IUmL",
        "colab_type": "text"
      },
      "source": [
        "# ~2. Handling the Missing Data\n",
        "Missing data in the training data set can reduce the power / fit of a model or can lead to a biased model because we have not analysed the behavior and relationship with other variables correctly. It can lead to wrong prediction or classification.\n",
        "\n",
        "Methods to treat missing values:\n",
        "\n",
        "1) Deletion\n",
        "\n",
        "2) Mean/ Mode/ Median Imputation\n",
        "\n",
        "3) Prediction Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pRzK2mmnH9Wv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# As we can see that Any feature doesn't has more than 50% missing data\n",
        "# Therefore we will not drop any column or row\n",
        "# We not do Mean/ Median/ Mode Imputation for missing values in salary feature.\n",
        "# Beacuse if the student is placed then only he will get the salary. "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nYoIGsuMcGA3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# In place of missing values in salary column we put value as 0.\n",
        "dataframe.fillna(0, inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dn5Ae0WHd_62",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "8f7bd5e5-6daf-43bf-b447-5c36b43dd93c"
      },
      "source": [
        "# Number of all missing datapoints\n",
        "dataframe.isna().sum()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "gender            0\n",
              "ssc_p             0\n",
              "ssc_b             0\n",
              "hsc_p             0\n",
              "hsc_b             0\n",
              "hsc_s             0\n",
              "degree_p          0\n",
              "degree_t          0\n",
              "workex            0\n",
              "etest_p           0\n",
              "specialisation    0\n",
              "mba_p             0\n",
              "status            0\n",
              "salary            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tQxAHles2Wrq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataframe.drop([\"gender\"],axis=1,inplace=True)\n",
        "dataframe.drop([\"ssc_b\"],axis=1,inplace=True)\n",
        "dataframe.drop([\"hsc_b\"],axis=1,inplace=True)\n",
        "dataframe.drop([\"hsc_s\"],axis=1,inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a3SCSMwS2lT4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "397abca9-9d64-4215-d8fd-99b41506dedb"
      },
      "source": [
        "dataframe.columns"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ssc_p', 'hsc_p', 'degree_p', 'degree_t', 'workex', 'etest_p',\n",
              "       'specialisation', 'mba_p', 'status', 'salary'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PZgNZDPretAZ",
        "colab_type": "text"
      },
      "source": [
        "## Label Encoding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "62I4svLYev2G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Output\n",
        "Y =pd.DataFrame(dataframe[dataframe.columns[-1]])\n",
        "# Input\n",
        "X =pd.DataFrame(dataframe.drop(dataframe.columns[-1],axis=1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8gy958jKfHAA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        },
        "outputId": "0df7387d-f61a-4c34-de12-24ca608be2e5"
      },
      "source": [
        "X.head()"
      ],
      "execution_count": 18,
      "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>ssc_p</th>\n",
              "      <th>hsc_p</th>\n",
              "      <th>degree_p</th>\n",
              "      <th>degree_t</th>\n",
              "      <th>workex</th>\n",
              "      <th>etest_p</th>\n",
              "      <th>specialisation</th>\n",
              "      <th>mba_p</th>\n",
              "      <th>status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>67.00</td>\n",
              "      <td>91.00</td>\n",
              "      <td>58.00</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>No</td>\n",
              "      <td>55.0</td>\n",
              "      <td>Mkt&amp;HR</td>\n",
              "      <td>58.80</td>\n",
              "      <td>Placed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>79.33</td>\n",
              "      <td>78.33</td>\n",
              "      <td>77.48</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>Yes</td>\n",
              "      <td>86.5</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>66.28</td>\n",
              "      <td>Placed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>65.00</td>\n",
              "      <td>68.00</td>\n",
              "      <td>64.00</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>No</td>\n",
              "      <td>75.0</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>57.80</td>\n",
              "      <td>Placed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56.00</td>\n",
              "      <td>52.00</td>\n",
              "      <td>52.00</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>No</td>\n",
              "      <td>66.0</td>\n",
              "      <td>Mkt&amp;HR</td>\n",
              "      <td>59.43</td>\n",
              "      <td>Not Placed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>85.80</td>\n",
              "      <td>73.60</td>\n",
              "      <td>73.30</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>No</td>\n",
              "      <td>96.8</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>55.50</td>\n",
              "      <td>Placed</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   ssc_p  hsc_p  degree_p   degree_t  ... etest_p  specialisation  mba_p      status\n",
              "0  67.00  91.00     58.00   Sci&Tech  ...    55.0          Mkt&HR  58.80      Placed\n",
              "1  79.33  78.33     77.48   Sci&Tech  ...    86.5         Mkt&Fin  66.28      Placed\n",
              "2  65.00  68.00     64.00  Comm&Mgmt  ...    75.0         Mkt&Fin  57.80      Placed\n",
              "3  56.00  52.00     52.00   Sci&Tech  ...    66.0          Mkt&HR  59.43  Not Placed\n",
              "4  85.80  73.60     73.30  Comm&Mgmt  ...    96.8         Mkt&Fin  55.50      Placed\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "geXWhpiRfRY0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "4b2f8acb-e222-4f09-ff6b-6d94a85c9735"
      },
      "source": [
        "Y.head()"
      ],
      "execution_count": 19,
      "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>salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>270000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>200000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>250000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>425000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     salary\n",
              "0  270000.0\n",
              "1  200000.0\n",
              "2  250000.0\n",
              "3       0.0\n",
              "4  425000.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V7Y4z9hAfWq0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        },
        "outputId": "c9fca976-6419-44a8-db2f-39cf6ce2b4a1"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le=LabelEncoder()\n",
        "X_label=X.copy()\n",
        "X_label.head()"
      ],
      "execution_count": 20,
      "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>ssc_p</th>\n",
              "      <th>hsc_p</th>\n",
              "      <th>degree_p</th>\n",
              "      <th>degree_t</th>\n",
              "      <th>workex</th>\n",
              "      <th>etest_p</th>\n",
              "      <th>specialisation</th>\n",
              "      <th>mba_p</th>\n",
              "      <th>status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>67.00</td>\n",
              "      <td>91.00</td>\n",
              "      <td>58.00</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>No</td>\n",
              "      <td>55.0</td>\n",
              "      <td>Mkt&amp;HR</td>\n",
              "      <td>58.80</td>\n",
              "      <td>Placed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>79.33</td>\n",
              "      <td>78.33</td>\n",
              "      <td>77.48</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>Yes</td>\n",
              "      <td>86.5</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>66.28</td>\n",
              "      <td>Placed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>65.00</td>\n",
              "      <td>68.00</td>\n",
              "      <td>64.00</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>No</td>\n",
              "      <td>75.0</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>57.80</td>\n",
              "      <td>Placed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56.00</td>\n",
              "      <td>52.00</td>\n",
              "      <td>52.00</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>No</td>\n",
              "      <td>66.0</td>\n",
              "      <td>Mkt&amp;HR</td>\n",
              "      <td>59.43</td>\n",
              "      <td>Not Placed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>85.80</td>\n",
              "      <td>73.60</td>\n",
              "      <td>73.30</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>No</td>\n",
              "      <td>96.8</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>55.50</td>\n",
              "      <td>Placed</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   ssc_p  hsc_p  degree_p   degree_t  ... etest_p  specialisation  mba_p      status\n",
              "0  67.00  91.00     58.00   Sci&Tech  ...    55.0          Mkt&HR  58.80      Placed\n",
              "1  79.33  78.33     77.48   Sci&Tech  ...    86.5         Mkt&Fin  66.28      Placed\n",
              "2  65.00  68.00     64.00  Comm&Mgmt  ...    75.0         Mkt&Fin  57.80      Placed\n",
              "3  56.00  52.00     52.00   Sci&Tech  ...    66.0          Mkt&HR  59.43  Not Placed\n",
              "4  85.80  73.60     73.30  Comm&Mgmt  ...    96.8         Mkt&Fin  55.50      Placed\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9PDi-1ECfbMv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "3cfa0327-fa87-4c2b-d8d7-6c6e72e343f8"
      },
      "source": [
        "# Categorical Daata\n",
        "cols=[\"degree_t\",\"workex\",\"specialisation\",\"status\"]\n",
        "# Using whole data to form an exhaustive list of levels\n",
        "for col in cols:\n",
        "  data=X[col]\n",
        "  le.fit(data.values)\n",
        "  X_label[col]=le.transform(X_label[col])\n",
        "X_label.head()"
      ],
      "execution_count": 22,
      "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>ssc_p</th>\n",
              "      <th>hsc_p</th>\n",
              "      <th>degree_p</th>\n",
              "      <th>degree_t</th>\n",
              "      <th>workex</th>\n",
              "      <th>etest_p</th>\n",
              "      <th>specialisation</th>\n",
              "      <th>mba_p</th>\n",
              "      <th>status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>67.00</td>\n",
              "      <td>91.00</td>\n",
              "      <td>58.00</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>1</td>\n",
              "      <td>58.80</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>79.33</td>\n",
              "      <td>78.33</td>\n",
              "      <td>77.48</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>86.5</td>\n",
              "      <td>0</td>\n",
              "      <td>66.28</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>65.00</td>\n",
              "      <td>68.00</td>\n",
              "      <td>64.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>0</td>\n",
              "      <td>57.80</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56.00</td>\n",
              "      <td>52.00</td>\n",
              "      <td>52.00</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>1</td>\n",
              "      <td>59.43</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>85.80</td>\n",
              "      <td>73.60</td>\n",
              "      <td>73.30</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>96.8</td>\n",
              "      <td>0</td>\n",
              "      <td>55.50</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   ssc_p  hsc_p  degree_p  degree_t  ...  etest_p  specialisation  mba_p  status\n",
              "0  67.00  91.00     58.00         2  ...     55.0               1  58.80       1\n",
              "1  79.33  78.33     77.48         2  ...     86.5               0  66.28       1\n",
              "2  65.00  68.00     64.00         0  ...     75.0               0  57.80       1\n",
              "3  56.00  52.00     52.00         2  ...     66.0               1  59.43       0\n",
              "4  85.80  73.60     73.30         0  ...     96.8               0  55.50       1\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NnddXJ1WhTdW",
        "colab_type": "text"
      },
      "source": [
        "# One Hot Encoder"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eU1g1ktwgdv-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "9cc2072c-bf74-4929-ee00-a3527949d4db"
      },
      "source": [
        "\n",
        "\n",
        "print(\"degree_t unique values:\")\n",
        "print(dataframe.degree_t.value_counts())\n",
        "\n",
        "print(\"workex unique values:\")\n",
        "print(dataframe.workex.value_counts())\n",
        "\n",
        "print(\"specialisation unique values: \")\n",
        "print(dataframe.specialisation.value_counts())\n",
        "\n",
        "print(\"status unique values:\")\n",
        "print(dataframe.status.value_counts())"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "degree_t unique values:\n",
            "Comm&Mgmt    145\n",
            "Sci&Tech      59\n",
            "Others        11\n",
            "Name: degree_t, dtype: int64\n",
            "workex unique values:\n",
            "No     141\n",
            "Yes     74\n",
            "Name: workex, dtype: int64\n",
            "specialisation unique values: \n",
            "Mkt&Fin    120\n",
            "Mkt&HR      95\n",
            "Name: specialisation, dtype: int64\n",
            "status unique values:\n",
            "Placed        148\n",
            "Not Placed     67\n",
            "Name: status, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nTrJa9aQh7MN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        },
        "outputId": "7eb65cdd-1543-4c03-b46e-ea61ddb67068"
      },
      "source": [
        "# Get one hot encoding of all columns \n",
        "X_one_hot = pd.get_dummies(X,columns=[\"degree_t\",\"workex\",\"specialisation\",\"status\"])\n",
        "X_one_hot.head()"
      ],
      "execution_count": 23,
      "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>ssc_p</th>\n",
              "      <th>hsc_p</th>\n",
              "      <th>degree_p</th>\n",
              "      <th>etest_p</th>\n",
              "      <th>mba_p</th>\n",
              "      <th>degree_t_Comm&amp;Mgmt</th>\n",
              "      <th>degree_t_Others</th>\n",
              "      <th>degree_t_Sci&amp;Tech</th>\n",
              "      <th>workex_No</th>\n",
              "      <th>workex_Yes</th>\n",
              "      <th>specialisation_Mkt&amp;Fin</th>\n",
              "      <th>specialisation_Mkt&amp;HR</th>\n",
              "      <th>status_Not Placed</th>\n",
              "      <th>status_Placed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>67.00</td>\n",
              "      <td>91.00</td>\n",
              "      <td>58.00</td>\n",
              "      <td>55.0</td>\n",
              "      <td>58.80</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>79.33</td>\n",
              "      <td>78.33</td>\n",
              "      <td>77.48</td>\n",
              "      <td>86.5</td>\n",
              "      <td>66.28</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>65.00</td>\n",
              "      <td>68.00</td>\n",
              "      <td>64.00</td>\n",
              "      <td>75.0</td>\n",
              "      <td>57.80</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56.00</td>\n",
              "      <td>52.00</td>\n",
              "      <td>52.00</td>\n",
              "      <td>66.0</td>\n",
              "      <td>59.43</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>85.80</td>\n",
              "      <td>73.60</td>\n",
              "      <td>73.30</td>\n",
              "      <td>96.8</td>\n",
              "      <td>55.50</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   ssc_p  hsc_p  ...  status_Not Placed  status_Placed\n",
              "0  67.00  91.00  ...                  0              1\n",
              "1  79.33  78.33  ...                  0              1\n",
              "2  65.00  68.00  ...                  0              1\n",
              "3  56.00  52.00  ...                  1              0\n",
              "4  85.80  73.60  ...                  0              1\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CeLyHad-jVJ5",
        "colab_type": "text"
      },
      "source": [
        "# Feature Scaling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zY6BTBeXjNzp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "scalable_cols=[\"ssc_p\",\"hsc_p\",\"degree_p\",\"etest_p\",\"mba_p\"]\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "min_max=MinMaxScaler(feature_range=(0, 1)) # The range in which you want to scale the values\n",
        "X_min_max=min_max.fit_transform(X[scalable_cols])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Am32nMaQkb90",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "6fce05f9-1353-422e-d6b7-8137af8f2d53"
      },
      "source": [
        "#Contains Only Float or Integer type values\n",
        "pd.DataFrame(X_min_max,columns=scalable_cols).head()"
      ],
      "execution_count": 26,
      "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>ssc_p</th>\n",
              "      <th>hsc_p</th>\n",
              "      <th>degree_p</th>\n",
              "      <th>etest_p</th>\n",
              "      <th>mba_p</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.538240</td>\n",
              "      <td>0.889621</td>\n",
              "      <td>0.195122</td>\n",
              "      <td>0.104167</td>\n",
              "      <td>0.284483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.792414</td>\n",
              "      <td>0.680890</td>\n",
              "      <td>0.670244</td>\n",
              "      <td>0.760417</td>\n",
              "      <td>0.564843</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.497011</td>\n",
              "      <td>0.510708</td>\n",
              "      <td>0.341463</td>\n",
              "      <td>0.520833</td>\n",
              "      <td>0.247001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.311482</td>\n",
              "      <td>0.247117</td>\n",
              "      <td>0.048780</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.308096</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.925788</td>\n",
              "      <td>0.602965</td>\n",
              "      <td>0.568293</td>\n",
              "      <td>0.975000</td>\n",
              "      <td>0.160795</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      ssc_p     hsc_p  degree_p   etest_p     mba_p\n",
              "0  0.538240  0.889621  0.195122  0.104167  0.284483\n",
              "1  0.792414  0.680890  0.670244  0.760417  0.564843\n",
              "2  0.497011  0.510708  0.341463  0.520833  0.247001\n",
              "3  0.311482  0.247117  0.048780  0.333333  0.308096\n",
              "4  0.925788  0.602965  0.568293  0.975000  0.160795"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "liaC3-9Pkmp5",
        "colab_type": "text"
      },
      "source": [
        "# Some Insights"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sGBaCOBMkfCL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 725
        },
        "outputId": "be62c651-7a39-4ac2-aea4-c0892a3ac57b"
      },
      "source": [
        "# Plotting Histograms to observe frequencies\n",
        "X.hist(figsize=[10,10])"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f14b4fc2908>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f14b4ea4be0>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f14b4e5ee48>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f14b4e1c0f0>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f14b4dce358>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f14b4d815c0>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x720 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "if_dybk0kvWU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 683
        },
        "outputId": "50f3fe0f-fa17-4a51-d70f-001005f4858f"
      },
      "source": [
        "import seaborn as sns\n",
        "sns.pairplot(X)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f14b4bec1d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 900x900 with 30 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DwRZHw3Lk5-M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "33353dbb-9e50-4652-ed18-84644c9949db"
      },
      "source": [
        "X.corr()"
      ],
      "execution_count": 29,
      "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>ssc_p</th>\n",
              "      <th>hsc_p</th>\n",
              "      <th>degree_p</th>\n",
              "      <th>etest_p</th>\n",
              "      <th>mba_p</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>ssc_p</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.511472</td>\n",
              "      <td>0.538404</td>\n",
              "      <td>0.261993</td>\n",
              "      <td>0.388478</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hsc_p</th>\n",
              "      <td>0.511472</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.434206</td>\n",
              "      <td>0.245113</td>\n",
              "      <td>0.354823</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>degree_p</th>\n",
              "      <td>0.538404</td>\n",
              "      <td>0.434206</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.224470</td>\n",
              "      <td>0.402364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>etest_p</th>\n",
              "      <td>0.261993</td>\n",
              "      <td>0.245113</td>\n",
              "      <td>0.224470</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.218055</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mba_p</th>\n",
              "      <td>0.388478</td>\n",
              "      <td>0.354823</td>\n",
              "      <td>0.402364</td>\n",
              "      <td>0.218055</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             ssc_p     hsc_p  degree_p   etest_p     mba_p\n",
              "ssc_p     1.000000  0.511472  0.538404  0.261993  0.388478\n",
              "hsc_p     0.511472  1.000000  0.434206  0.245113  0.354823\n",
              "degree_p  0.538404  0.434206  1.000000  0.224470  0.402364\n",
              "etest_p   0.261993  0.245113  0.224470  1.000000  0.218055\n",
              "mba_p     0.388478  0.354823  0.402364  0.218055  1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XhUJApN4lHsH",
        "colab_type": "text"
      },
      "source": [
        "# Linear Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5OW1aqD5AHVe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn import metrics\n",
        "import seaborn as seabornInstance\n",
        "from sklearn.model_selection import train_test_split\n",
        "from statsmodels.regression.linear_model import OLS"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YRDc9jbsc55d",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "ed385554-43bb-4000-86b8-e0a9480f3ec6"
      },
      "source": [
        "X_label.head()"
      ],
      "execution_count": 32,
      "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>ssc_p</th>\n",
              "      <th>hsc_p</th>\n",
              "      <th>degree_p</th>\n",
              "      <th>degree_t</th>\n",
              "      <th>workex</th>\n",
              "      <th>etest_p</th>\n",
              "      <th>specialisation</th>\n",
              "      <th>mba_p</th>\n",
              "      <th>status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>67.00</td>\n",
              "      <td>91.00</td>\n",
              "      <td>58.00</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>1</td>\n",
              "      <td>58.80</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>79.33</td>\n",
              "      <td>78.33</td>\n",
              "      <td>77.48</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>86.5</td>\n",
              "      <td>0</td>\n",
              "      <td>66.28</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>65.00</td>\n",
              "      <td>68.00</td>\n",
              "      <td>64.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>0</td>\n",
              "      <td>57.80</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56.00</td>\n",
              "      <td>52.00</td>\n",
              "      <td>52.00</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>1</td>\n",
              "      <td>59.43</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>85.80</td>\n",
              "      <td>73.60</td>\n",
              "      <td>73.30</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>96.8</td>\n",
              "      <td>0</td>\n",
              "      <td>55.50</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   ssc_p  hsc_p  degree_p  degree_t  ...  etest_p  specialisation  mba_p  status\n",
              "0  67.00  91.00     58.00         2  ...     55.0               1  58.80       1\n",
              "1  79.33  78.33     77.48         2  ...     86.5               0  66.28       1\n",
              "2  65.00  68.00     64.00         0  ...     75.0               0  57.80       1\n",
              "3  56.00  52.00     52.00         2  ...     66.0               1  59.43       0\n",
              "4  85.80  73.60     73.30         0  ...     96.8               0  55.50       1\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hf2UlDWDdRO0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "091f9f15-b943-473f-dabf-092043f2fd1a"
      },
      "source": [
        "Y.head()"
      ],
      "execution_count": 33,
      "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>salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>270000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>200000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>250000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>425000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     salary\n",
              "0  270000.0\n",
              "1  200000.0\n",
              "2  250000.0\n",
              "3       0.0\n",
              "4  425000.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gFajjXbcdgcU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X_label, Y, test_size = 0.2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JY2VgSjRflRu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "reg = OLS(y_train, X_train).fit()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eAdvSZ6CgO_X",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 756
        },
        "outputId": "485924f1-aadf-40d3-c8a8-569c8008667b"
      },
      "source": [
        "y_pred = pd.DataFrame()   # dataframe for predictions vs actual y-values\n",
        "y_pred['predictions'] = reg.predict(X_test)\n",
        "\n",
        "# Print this directly\n",
        "print(reg.predict(X_test))"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "147    291687.260533\n",
            "196    294743.997025\n",
            "206     15555.087110\n",
            "74     297266.539991\n",
            "43     271317.797784\n",
            "51      -3827.542102\n",
            "193    319364.846663\n",
            "3       17851.293022\n",
            "47     313385.245007\n",
            "163    320716.922764\n",
            "22     273492.653001\n",
            "88     247119.848868\n",
            "103    312431.129701\n",
            "99      15197.081832\n",
            "159    -15684.578497\n",
            "141    -24295.389725\n",
            "50     254903.055751\n",
            "30     238087.216274\n",
            "128    308625.670004\n",
            "4      264314.987476\n",
            "146    268267.104442\n",
            "134    315074.324653\n",
            "90     269511.866321\n",
            "190    -12826.999323\n",
            "71     290327.101347\n",
            "198     12817.465182\n",
            "120    -20436.884152\n",
            "23     334265.696416\n",
            "161    -16839.335214\n",
            "212    292703.500621\n",
            "176    271086.193604\n",
            "181     -6750.713647\n",
            "60     273418.778824\n",
            "139    288965.246459\n",
            "214      6193.236066\n",
            "106     37041.170669\n",
            "49        -30.041892\n",
            "39     313714.666592\n",
            "104    269949.414077\n",
            "21     267298.679640\n",
            "149     18823.277892\n",
            "24     320206.033563\n",
            "98     276495.639398\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "56SQXIQLhDkD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8ee3ccd9-964c-4c91-d8d1-2f4c0d8f38ae"
      },
      "source": [
        "df = pd.DataFrame({'Actual': y_test[\"salary\"], 'Predicted': y_pred[\"predictions\"]})\n",
        "df"
      ],
      "execution_count": 41,
      "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>Actual</th>\n",
              "      <th>Predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>300000.0</td>\n",
              "      <td>291687.260533</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>250000.0</td>\n",
              "      <td>294743.997025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>206</th>\n",
              "      <td>0.0</td>\n",
              "      <td>15555.087110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>336000.0</td>\n",
              "      <td>297266.539991</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>300000.0</td>\n",
              "      <td>271317.797784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>0.0</td>\n",
              "      <td>-3827.542102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193</th>\n",
              "      <td>300000.0</td>\n",
              "      <td>319364.846663</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>17851.293022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>204000.0</td>\n",
              "      <td>313385.245007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163</th>\n",
              "      <td>500000.0</td>\n",
              "      <td>320716.922764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>360000.0</td>\n",
              "      <td>273492.653001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>210000.0</td>\n",
              "      <td>247119.848868</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>240000.0</td>\n",
              "      <td>312431.129701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>0.0</td>\n",
              "      <td>15197.081832</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159</th>\n",
              "      <td>0.0</td>\n",
              "      <td>-15684.578497</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>0.0</td>\n",
              "      <td>-24295.389725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>200000.0</td>\n",
              "      <td>254903.055751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>250000.0</td>\n",
              "      <td>238087.216274</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>400000.0</td>\n",
              "      <td>308625.670004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>425000.0</td>\n",
              "      <td>264314.987476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>233000.0</td>\n",
              "      <td>268267.104442</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>250000.0</td>\n",
              "      <td>315074.324653</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>300000.0</td>\n",
              "      <td>269511.866321</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>190</th>\n",
              "      <td>0.0</td>\n",
              "      <td>-12826.999323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>240000.0</td>\n",
              "      <td>290327.101347</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>0.0</td>\n",
              "      <td>12817.465182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>0.0</td>\n",
              "      <td>-20436.884152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>300000.0</td>\n",
              "      <td>334265.696416</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>161</th>\n",
              "      <td>0.0</td>\n",
              "      <td>-16839.335214</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>212</th>\n",
              "      <td>295000.0</td>\n",
              "      <td>292703.500621</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>220000.0</td>\n",
              "      <td>271086.193604</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>181</th>\n",
              "      <td>0.0</td>\n",
              "      <td>-6750.713647</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>260000.0</td>\n",
              "      <td>273418.778824</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>220000.0</td>\n",
              "      <td>288965.246459</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>214</th>\n",
              "      <td>0.0</td>\n",
              "      <td>6193.236066</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
              "      <td>0.0</td>\n",
              "      <td>37041.170669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>0.0</td>\n",
              "      <td>-30.041892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>411000.0</td>\n",
              "      <td>313714.666592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>360000.0</td>\n",
              "      <td>269949.414077</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>393000.0</td>\n",
              "      <td>267298.679640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>0.0</td>\n",
              "      <td>18823.277892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>360000.0</td>\n",
              "      <td>320206.033563</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>220000.0</td>\n",
              "      <td>276495.639398</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Actual      Predicted\n",
              "147  300000.0  291687.260533\n",
              "196  250000.0  294743.997025\n",
              "206       0.0   15555.087110\n",
              "74   336000.0  297266.539991\n",
              "43   300000.0  271317.797784\n",
              "51        0.0   -3827.542102\n",
              "193  300000.0  319364.846663\n",
              "3         0.0   17851.293022\n",
              "47   204000.0  313385.245007\n",
              "163  500000.0  320716.922764\n",
              "22   360000.0  273492.653001\n",
              "88   210000.0  247119.848868\n",
              "103  240000.0  312431.129701\n",
              "99        0.0   15197.081832\n",
              "159       0.0  -15684.578497\n",
              "141       0.0  -24295.389725\n",
              "50   200000.0  254903.055751\n",
              "30   250000.0  238087.216274\n",
              "128  400000.0  308625.670004\n",
              "4    425000.0  264314.987476\n",
              "146  233000.0  268267.104442\n",
              "134  250000.0  315074.324653\n",
              "90   300000.0  269511.866321\n",
              "190       0.0  -12826.999323\n",
              "71   240000.0  290327.101347\n",
              "198       0.0   12817.465182\n",
              "120       0.0  -20436.884152\n",
              "23   300000.0  334265.696416\n",
              "161       0.0  -16839.335214\n",
              "212  295000.0  292703.500621\n",
              "176  220000.0  271086.193604\n",
              "181       0.0   -6750.713647\n",
              "60   260000.0  273418.778824\n",
              "139  220000.0  288965.246459\n",
              "214       0.0    6193.236066\n",
              "106       0.0   37041.170669\n",
              "49        0.0     -30.041892\n",
              "39   411000.0  313714.666592\n",
              "104  360000.0  269949.414077\n",
              "21   393000.0  267298.679640\n",
              "149       0.0   18823.277892\n",
              "24   360000.0  320206.033563\n",
              "98   220000.0  276495.639398"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iZ9S8_k1iCB3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "outputId": "73031af8-e3cb-4e77-f0bd-5b9e76a3f4e3"
      },
      "source": [
        "df.plot(kind='bar',figsize=(10,8))\n",
        "plt.grid(which='major', linestyle='-', linewidth='0.5', color='green')\n",
        "plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n",
        "plt.show()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5U2eK89qiwA7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "13257706-3cdb-48e4-8c8d-fc7bf6ed576e"
      },
      "source": [
        "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))  \n",
        "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))  \n",
        "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 44840.118507914485\n",
            "Mean Squared Error: 3735076494.520704\n",
            "Root Mean Squared Error: 61115.27218724223\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F9MCv4cwjlbr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}