ML_Ensemble_Classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Importing the libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import time\n",
    "from sklearn.metrics import confusion_matrix,accuracy_score,classification_report,auc,roc_curve,roc_auc_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Import the Dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data_response.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Driving_License</th>\n",
       "      <th>Region_Code</th>\n",
       "      <th>Previously_Insured</th>\n",
       "      <th>Vehicle_Age</th>\n",
       "      <th>Vehicle_Damage</th>\n",
       "      <th>Annual_Premium</th>\n",
       "      <th>Policy_Sales_Channel</th>\n",
       "      <th>Vintage</th>\n",
       "      <th>Response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10770</td>\n",
       "      <td>10771</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1-2 Year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>48521.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>206</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19506</td>\n",
       "      <td>19507</td>\n",
       "      <td>Male</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1-2 Year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2630.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>223</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6228</td>\n",
       "      <td>6229</td>\n",
       "      <td>Male</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1-2 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>29620.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0     id  Gender  Age  Driving_License  Region_Code  \\\n",
       "0       10770  10771  Female   42                1         11.0   \n",
       "1       19506  19507    Male   48                1         34.0   \n",
       "2        6228   6229    Male   76                1         28.0   \n",
       "\n",
       "   Previously_Insured Vehicle_Age Vehicle_Damage  Annual_Premium  \\\n",
       "0                   0    1-2 Year            Yes         48521.0   \n",
       "1                   0    1-2 Year            Yes          2630.0   \n",
       "2                   1    1-2 Year             No         29620.0   \n",
       "\n",
       "   Policy_Sales_Channel  Vintage  Response  \n",
       "0                 122.0      206         1  \n",
       "1                 156.0      223         1  \n",
       "2                  26.0       69         0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Check the basic statistics of the whole dataset and give theinterpretations\n",
    "* Discuss the need of basic statistics in data science\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>Age</th>\n",
       "      <th>Driving_License</th>\n",
       "      <th>Region_Code</th>\n",
       "      <th>Previously_Insured</th>\n",
       "      <th>Annual_Premium</th>\n",
       "      <th>Policy_Sales_Channel</th>\n",
       "      <th>Vintage</th>\n",
       "      <th>Response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7351.088300</td>\n",
       "      <td>7352.088300</td>\n",
       "      <td>40.018900</td>\n",
       "      <td>0.998000</td>\n",
       "      <td>26.447800</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>31125.978400</td>\n",
       "      <td>106.871600</td>\n",
       "      <td>154.910000</td>\n",
       "      <td>0.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7130.381057</td>\n",
       "      <td>7130.381057</td>\n",
       "      <td>14.850555</td>\n",
       "      <td>0.044679</td>\n",
       "      <td>12.719947</td>\n",
       "      <td>0.470236</td>\n",
       "      <td>16975.427752</td>\n",
       "      <td>55.389534</td>\n",
       "      <td>83.716177</td>\n",
       "      <td>0.476993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2630.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2499.750000</td>\n",
       "      <td>2500.750000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24867.250000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4999.500000</td>\n",
       "      <td>5000.500000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>32234.000000</td>\n",
       "      <td>124.000000</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8036.250000</td>\n",
       "      <td>8037.250000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>40251.500000</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>227.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>28123.000000</td>\n",
       "      <td>28124.000000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>267698.000000</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0            id           Age  Driving_License  \\\n",
       "count  10000.000000  10000.000000  10000.000000     10000.000000   \n",
       "mean    7351.088300   7352.088300     40.018900         0.998000   \n",
       "std     7130.381057   7130.381057     14.850555         0.044679   \n",
       "min        0.000000      1.000000     20.000000         0.000000   \n",
       "25%     2499.750000   2500.750000     26.000000         1.000000   \n",
       "50%     4999.500000   5000.500000     39.000000         1.000000   \n",
       "75%     8036.250000   8037.250000     50.000000         1.000000   \n",
       "max    28123.000000  28124.000000     84.000000         1.000000   \n",
       "\n",
       "        Region_Code  Previously_Insured  Annual_Premium  Policy_Sales_Channel  \\\n",
       "count  10000.000000        10000.000000    10000.000000          10000.000000   \n",
       "mean      26.447800            0.330000    31125.978400            106.871600   \n",
       "std       12.719947            0.470236    16975.427752             55.389534   \n",
       "min        0.000000            0.000000     2630.000000              1.000000   \n",
       "25%       15.000000            0.000000    24867.250000             26.000000   \n",
       "50%       28.000000            0.000000    32234.000000            124.000000   \n",
       "75%       34.000000            1.000000    40251.500000            152.000000   \n",
       "max       52.000000            1.000000   267698.000000            163.000000   \n",
       "\n",
       "            Vintage      Response  \n",
       "count  10000.000000  10000.000000  \n",
       "mean     154.910000      0.350000  \n",
       "std       83.716177      0.476993  \n",
       "min       10.000000      0.000000  \n",
       "25%       83.000000      0.000000  \n",
       "50%      155.000000      0.000000  \n",
       "75%      227.000000      1.000000  \n",
       "max      299.000000      1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Show the dependent variable (Response) distribution and write the observations of it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Response', ylabel='Density'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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agtlXDrDImrl3Apvd/YdBtZMs7e3OizsrWVQyIvDlgbszuTAHQ+Pw0v80t7az91gdx+qaGTook1nj8hmTPzDZZaWMwAIeWAh8DFhvZmuj933d3R8PsM2E2Xiwmqr6Ft6Z5OEZgEFZkeWDtU+r9Bdt7c6y7RUs315JQ3RF1YfWHsQMrpk1mr9993TGDRuU5CqTL7CAd/flQHK7tgHqGH+/YMrwJFcSMaUolxe2V1Db1EpuQBt+i6SC6sYWfrNiH3uP1VMyMo/zJw2nKC+bC6cW8Pj6w/zi5T08v62C26+fm7Dlu1OVrmTtpeU7KigZmUdRXmKWB+7O5MLI8sGv7j6a7FJEAlPT2MLPlu3i0IlGPnreOD7+jmKmjcgjf1AWs8bmc+tVJfzxlncycvAAPvHzlTy9+UiyS04qBXwvNLa0sXLP8YTs3hSrCcMjywcv366Al3Bqam3jrpf2UN3Ywk0Li5k1Nr/L4yYMz+EPf/MOzho9mM/es5qyPccSW2gKUcD3wso9x2hubU/69MjOOpYPXr6jb11LIBILd+fBNQc4fKKRGxZMYMLwnNMenzcgk59/4jxG5w/ks/esprK2f17prYDvheXbK8lMNxZMTOzywN2ZUpjLtiO1lFc3JrsUkbgq23Oc1/af4PKZI5g2Ii+m7xmem82PbpjHiYYWvvTbtbT3w+tEFPC9sHxHJfPGD2VQVmq9mTk5umzBizsrk1yJSPxU1Tfz2IZDTCnM5eJpPXvTdMaowXzrmpm8sL2S36zcF1CFqUsB30NHa5vYeLA6qcsTnMqoIQMYOihT4/ASGu7Og2sPgMMH544hrRfXnFw/fzwXTB7O9x/fwuET/evVrQK+h17cGQnPVBp/75BmxgVTCli2vULLB0sobDpUzbYjtVwxcwRDc7J6dQ4z43sfPIfmtna+89imOFeY2hTwPfTc1nLyB2VyzpghyS6lS5dOL6KiJvIqQ6Qva21v54kNhynKy+b8SWd2vUlxQQ6fvngyj607xOp9x+NUYepTwPdAW7vz3NYKLplWSEaSlgfuziXTCzGDZ7aUJ7sUkTPy6u5jHK1r5qqzR5KedubXTH76okkU5Gbzvcc295tXuKmZUilq7etVHKtr5rIZI5JdyikV5GYze2w+TyvgpQ9raG7jmS3lTC7MiXnWTHdysjP40hVTKdt7nD9t7B8XQCnge+DZLeWkpxkXp/jlz5eVFLFuf1W/nfsrfd/z28ppaG7jqrNHxXUxv78sHceUolz+9YkttLS1x+28qUoB3wNPbynn3AlDGTIoM9mlnNZlJUW4w3NbddGT9D1V9c28tPMoc8blMzrOK0NmpKdx65Ul7K6s4zevhn/apAI+RgerGth8qJpFJUXJLqVbZ40eTFFeNs9qmEb6oOe2VeAOV8wMZih00YwiFkwcxv88s4PG6EqUYaWAj9GzWyNheVkfCHgz47KSIpZtq+gXL0MlPKrqm1m15zjnFg8lf1DvpkV2x8z48hXTqKhp4lev7A2kjVShgI/RM5vLGTds4BubXKe6S0uKqGlqpWxP/5kSJn3f89siw4qX9PCK1Z5aMGk4F0wezk+e30VDc3h78Qr4GDS2tKXM7k2xunBKAVnpaW+88hBJdScaWijbe5x5E4LrvXf2pSumUVkb7l68Aj4GL+88SmNLO5f2geGZDjnZGSyYNKzfr4ctfceybZErsIPuvXc4r3gYF04p4KfLdlLf3JqQNhNNAR+DJzcdJicrPeVWj+zOFTNHsLOijh3lNckuReS0qhtaWLnnGPPGD+31kgS9seTyqVTWNoe2F6+A70ZrWztPbjzCZTNGMCAzPdnl9Mi7zxqJGfxx/eFklyJyWi9sr6DdnUumJ/ZVcmnxMN45tYCfPr8rlL14BXw3Xt0TuVz66rNHJruUHhsxeADnjh/K4xsU8JK6ahpbWLH7GHPGDWVYAnvvHZZcPpWjdc388uXw9eIDC3gz+z8zKzezDUG1kQh/XH+YgZnpCe9ZxMuVZ49k86Fq9h6tS3YpIl16YXsl7e5cOj05V4ifOyHai1+2i7qmcPXig+zB3wVcGeD5A9fW7jyx8TCXlhQyMKtvDc90uOqcUQA8uu5QkisRebtI7/0os8fmMzw3O2l1fOmKaRyra+YXIevFBxbw7r4M6NO73b6y6ygVNU2855zRyS6l18bkD+TcCUN5eO3BZJci8jbLd1TS2uZcmuRXyPPGD+XiaYUsXbaT2hD14pM+Bm9mi82szMzKKipSa+2UB9YcIC87g0Uz+ubwTIcPzBnN1iM1bD6kNeIldRytbeKVXUeZPS6fgrzk9d47LLl8KsfrW/jFy3uSXUrcJD3g3X2pu5e6e2lhYeqs0tjY0sYTGw5z5dkj+9zsmZO9Z9ZoMtIssvWZSIr46bJdtLY5lyRp7P1kc8cP5ZLphSxdtis0vfikB3yq+vPmI9Q2tfLBuWOSXcoZG5aTxTunFvDw2oO09cOd5SX1HKlu5O6X9jBnXD5FeQOSXc4bllw+jar6Fu5+aU+yS4kLBfwp/L5sP6OGDGDBGW4Vlio+fO5YDp1oZPmOymSXIsLtz+ygrd1ZlGKb58wZl89lJUUsXbaLmsaWZJdzxoKcJvkb4GVgupntN7NPBtVWvO0/Xs+y7RV8pHRcXLYKSwVXzBzBsJwsfrsy/GtgS2p7/Vg9967cx1+cNy4p8967s+TyqZxoCEcvPshZNNe5+yh3z3T3se5+Z1BtxdvvVr4OwF+Ujk1yJfGTnZHOh+aO4alNR7TTkyTVfz+9HTPjC5dNSXYpXZo1Np9FJUX87IXdVPfxXryGaE7S2tbO78r2c/G0QsYOHZTscuLqL88bR0ub84dV+5NdivRTOytquW/1fm5cMIFRQ+K7W1M8Lbl8GicaWrjrxT3JLuWMKOBP8qeNRzhc3cj188cnu5S4mzoij/MnDeOXL++lVRuBSBL8y+ObGZiZzmcvnZzsUk7rnLFDuHzGCH72wq4+/YpXAX+SO5fvYsLwQSn35k+83LxwIgeqGnhyk5YRlsR6flsFf95czhcWTaUgiVetxurWq0poaG7j3/+0Ndml9JoCvpPV+46zel8VN11QHJo3V0+2aMYIxg8bxJ3Ldye7FOlHWtra+adHNlI8fBA3LSxOdjkxmVKUy00Li/lt2eus21+V7HJ6RQHfydLnd5E3IIOPlI5LdimBSU8zbl5YzKq9x1mx62iyy5F+4u6X9rCzoo5vvncm2Rl958LBLy6ayvCcbL798Eba++A1JAr4qE0Hq3li42FuXjiRnOyMZJcTqI/OH09Bbja3Pb092aVIP1BZ28Rtf97OxdMK+8Sm9Z3lDcjk766czup9VX3ySnAFfNRtT28jb0AGN184MdmlBG5AZjqfuXgSL+08yso9fXo9OOkD/vmxzTS0tPHN987sM3sad/bheWOZPS6f7z2+har65mSX0yMKeOC116v408Yj3LxwIkMGZia7nIS4YcEECnKz+dc/bsG97730lL7h6c1HeGDNAT576RSmFOUmu5xeSUsz/vkDZ3O8vpl/enRTssvpkX4f8O7Otx/ZSEFuNp+6aFKyy0mYgVnpfOVd0yjbe5zH1muteIm/43XNfP2B9UwfkcfnL03Ni5pidfaYIXzuksncv/oAf+5DM9D6fcA/tPYga/ZV8dUrp5Mb8rH3k32kdBwzRg3mXx7fQkNzW7LLkRBxd7563zqO1TXzH38xm6yMvh81n79sKjNGDear963j8InGZJcTk77/Wz8DR2ub+M6jm5g1dgjXzgvPsgSxSk8z/uGamRyoauA/nuy7c30l9dz10h6e2nSEv7uyhLPHDEl2OXGRlZHG7dfPpbGljVvuXdMnLhbs1wH/rYc3Ut3Ywg+unUVaSOe9d+f8ScO5YcF47nxxN6v2Hk92ORICL+88yncf28zlM4q4eWG4Ji1MLszlux84mxW7j/HdxzYnu5xu9duA/33Z6zy27hC3LJpKycjByS4nqb529QxGDxnILfeu6XOzBCS17Kyo5bP3rGJiQQ7/+ZdzQtlx+tC8sXzywonc9dIefvVKau/h2i8DfsOBE3zjwQ1cMHk4n7k4tdfESITc7Axuv34uR6ob+fLvXtOmINIrh0408PE7XyU9zbjj46XkDQjvjLSvXVXCZSVFfPOhDTy6LnX3O+53Af/6sXo+efdKCnKy+J/r5pKR3u9+BV2aO34o37rmLJ7ZUs63H96oqZPSI68fq+ejS1+huqGFu26aT3FBTrJLClRGehr/e/08zpswjCX3ruWR11Iz5PtVuh060cCNd66gsaWdn980n+F9YMGjRPrY+RP49EWT+OUre/nBn7Yq5CUmGw+e4CM/eZmq+hZ+8cn5oXlTtTsDs9K58xOlzBs/lC/eu4a7Xtydcs+ZfhPw247U8KEfvcTR2mZ+ftN5TB+Zl+ySUtKtV5Vw3fzx/Pi5nXz9gQ19YqaAJM+Daw7w4R+/hBncu/h85o4fmuySEipvQCZ33zyfRSVFfPuRTdx63/qUmnLcLyZ+3796P3//4AZyszP43affwczR/ftN1dMxM773wbMZOiiTHz23kx3lNfz3dXNTenMGSbzK2ib+6ZFNPPzaQUonDOXHN55LYV7/fEU8MCudpR8r5YdPbeP2Z3ewcu8x/u3aWZw7YViySwt3D37f0Xr++u6VfPl3r3H26CE89PmFCvcYmBlfvbKE2z46h00Hq7nih8v4+Yu7aVFvvt+rb25l6bKdXPrvz/H4+kN8+Ypp3Lv4/H4b7h3S0oyvvHs6v/7rBTQ2t/HhH7/MknvXsKeyLql1hbIHv+HACe5+aQ8PrDlAVkYaX7+6hJsXTtQbqj30/jljmDMun79/cAP/+Mgm7ly+m8UXTeIDc8cwOMQzJOSt3J2NB6u5f/UBHlizn+P1LVw8rZBvvndmn11fJigXTCngqS9fzI+e28HPXtjNw68d5IqZI/jQvLFcMr0w4UslW5BvCpjZlcBtQDpwh7t//3THl5aWellZWY/baWhu49U9x1i+vYLnt1Ww7Ugt2RlpXDd/PH9zyWRGDB7Qux8gQX69Yl/cznX9gvhvNejuPLe1gtue3s7a16vIzkjjgsnDuWhaIRdNK2RSQU6fXCVQTu1YXTMr9xxj5e5jLNseeU5lphuLSkbwqYsmce6E+Iy1p/rf/pkor27kzhd3c9+q/VTWNjNkYCaLZhQxv3gYpcXDmFwYn+eNma1y99IuHwsq4M0sHdgGXAHsB1YC17n7KZdj603AN7a0Me87T1Hf3EZWehqlxUN591kj+cCcMQwZ1Dd6mX3pj3z9/hPct3o/z2+rYHf05WdOVjpTRuQxtSiX0fkDKcrLpjAvm8EDMsnJTmdQVgaDstIZmJlOerqRbkZ6mpH2xmf0D0QXOp6bHU9R73zfG8eA89bj3vo9kRut7U5jcxv1zW00tEQ+GpvbqGlqpbK2icqaZg5XN7K7spZdFXWU10T2Ic3KSGPuuHzeO3s018waRf6grLj+jH3pb7+3WtvaWb6jkgfWHOCF7ZUcq4tcTJibnUFxwSAmFuQyuTCHWxZN7dXz4HQBH+QQzXxgh7vvihZxL/B+IK7rbQ7ITOcr75rOpMIcFkwczsCsvrNbTF90ztghnDM2Mg1u39F6XtxZydbDNWw7UsOybRVU1DbRmz5DmvFG6J9J1htn9g/Fmf4701Xg+hv/iTz2lvuJhHbnwE6WYTlZTCzI4aJphUwuzKW0eCizxg7pUzswpaKM9DQumV7EJdOLcHd2VtRRtucYmw9Vs6uyjrWvH2fd/iqWXD4t/m3H/YxvGgO83unr/cCCkw8ys8XA4uiXtWYWz1WvCoDKOJ4vKHGr84Z4nOTU+t3vM2ApVedeYE3XD6VUnadyQx+pk1PUaV/t9fkmnOqBIAO+q77Q2/on7r4UWBpIAWZlp3rpkkpUZ3ypzvhSnfGVyDqDnFayH+i8e/VYIDWv5xURCaEgA34lMNXMJppZFvBR4OEA2xMRkU4CG6Jx91Yz+zzwJyLTJP/P3TcG1d4pBDL0EwDVGV+qM75UZ3wlrM5A58GLiEjy6NJOEZGQUsCLiIRUqALezIaZ2VNmtj36+W3XU5vZODN71sw2m9lGM7slgfVdaWZbzWyHmd3axeNmZv8dfXydmc1LVG09rPOGaH3rzOwlM5udinV2Ou48M2szs2sTWV+n9rut08wuMbO10b/J51OtRjMbYmaPmNlr0RpvSnSN0Tr+z8zKzWzDKR5PledQd3Um5jnk7qH5AH4A3Bq9fSvwr10cMwqYF72dR2Q5hZkJqC0d2AlMArKA105uF7ga+CORawjOB1Yk4XcYS50XAEOjt69K1To7HfcM8DhwbSrWCeQTucJ7fPTrohSs8esdzyegEDgGZCXh93kRMA/YcIrHk/4cirHOhDyHQtWDJ7IUwt3R23cDHzj5AHc/5O6ro7drgM1ErroN2htLN7h7M9CxdENn7wd+4RGvAPlmNioBtfWoTnd/yd2PR798hcg1DokWy+8T4AvAfUB5IovrJJY6rwfud/d9AO6e6FpjqdGBPIsslpJLJOBbE1smuPuyaNunkgrPoW7rTNRzKGwBP8LdD0EkyIGi0x1sZsXAXGBF8KV1uXTDyf+wxHJM0HpawyeJ9JgSrds6zWwM8EHgJwms62Sx/D6nAUPN7DkzW2VmH09YdRGx1Hg7MIPIxYrrgVvcPRU3CEiF51BPBfYc6nPrwZvZn4GRXTz0jR6eJ5dIz26Ju1fHo7bumuzivpPnqMa0vEPAYq7BzC4l8sd5YaAVdS2WOv8L+Dt3b0viapWx1JkBnAssAgYCL5vZK+6+LejiomKp8d3AWuAyYDLwlJm9kKDnTk+kwnMoZkE/h/pcwLv75ad6zMyOmNkodz8UfVnW5UtdM8skEu73uPv9AZV6sliWbkiF5R1iqsHMZgF3AFe5+9EE1dZZLHWWAvdGw70AuNrMWt39wYRUGBHr//dKd68D6sxsGTCbyPtDiRBLjTcB3/fIoPEOM9sNlACvJqbEmKXCcygmiXgOhW2I5mHgr6K3/wp46OQDomOIdwKb3f2HCawtlqUbHgY+Hp0JcD5womPIKZXqNLPxwP3AxxLYyzxZt3W6+0R3L3b3YuAPwGcTHO4x1Unk7/SdZpZhZoOIrLq6OcVq3EfkFQZmNgKYDuxKYI2xSoXnULcS9hxKxjvMQX0Aw4Gnge3Rz8Oi948GHo/evpDIS7Z1RF5yrgWuTlB9VxPple0EvhG97zPAZ6K3Dfjf6OPrgdIk/R67q/MO4Hin319ZKtZ50rF3kYRZNLHWCfwtkZk0G4gMG6ZUjdHn0JPRv8sNwI1J+l3+BjgEtBDprX8yRZ9D3dWZkOeQlioQEQmpsA3RiIhIlAJeRCSkFPAiIiGlgBcRCSkFvIhISPW5C51ETsXM2ohMjcsAdhOZY1yV1KJEkkg9eAmTBnef4+5nE1no6XPJLkgkmRTwElYvE11kyswmm9kT0YW8XjCzkuj9HzGzDdE1zpdF7/uEmT0UPX6rmf1DxwnN7MvR4zeY2ZLofcUW2VvgZ9F10p80s4HRx75oZpuia37fG70vJ7pW+EozW2NmXa2AKRIXGqKR0DGzdCKX1d8ZvWspkSsIt5vZAuBHRBbN+hbwbnc/YGb5nU4xHzgbqAdWmtljRK5+vonIMgIGrLDIxhzHganAde7+KTP7HfBh4FdE9iSY6O5Nnc7/DeAZd785et+rZvZnj6xDIxJX6sFLmAw0s7XAUWAYkRUPc4lsrvD76GM/JbLpC8CLwF1m9ikim150eMrdj7p7A5H1Qi6Mfjzg7nXuXhu9/53R43e7+9ro7VVAcfT2OuAeM7uRN9dOfxdwa7SW54ABwPh4/PAiJ1MPXsKkwd3nmNkQ4FEiY/B3AVXuPufkg939M9Ee/XuAtWbWcczJ63c4XS9D26Gp0+02Ikv+Ej3vRcD7gG+a2VnR83zY3bf24OcS6RX14CV03P0E8EXgK0ADsNvMPgJv7Nk5O3p7sruvcPdvAZW8uczsFRbZ33cgkV3BXgSWAR8ws0FmlkNkI5EXTlWDmaUB49z9WeCrRLblywX+BHwhuqopZjY3rj+8SCcKeAkld19DZG/RjwI3AJ80s9eAjby5Hd2/mdl6i2yMvCx6PMBy4JdEVvm7z93LPLLN411E1j9fAdwRbeNU0oFfmdl6YA3wn9Epm98BMoF10Xa/E5+fWOTttJqkSCdm9gkiS8x+Ptm1iJwp9eBFREJKPXgRkZBSD15EJKQU8CIiIaWAFxEJKQW8iEhIKeBFRELq/wOc92SJTHZTNgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df.Response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. show that how the independent variables are distributed with dependent variable\n",
    "Note: Use groupby or hue in plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Gender', ylabel='Age'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x = 'Gender', y= 'Age' , hue = 'Response',data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Do the required Data Preprocessing\n",
    "Note:\n",
    "1. Discuss about what is encoding why do we need encoding and Discuss about Lable\n",
    "encoding and Onehot encoding\n",
    "2. Convert all categorical columns into numerical by using\n",
    "Lable encoder or\n",
    "one hot encoder or\n",
    "get dummies or\n",
    "use any method of mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(columns = (['Unnamed: 0','id']),axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dummy= pd.get_dummies(df,drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 12)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dummy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "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>Age</th>\n",
       "      <th>Driving_License</th>\n",
       "      <th>Region_Code</th>\n",
       "      <th>Previously_Insured</th>\n",
       "      <th>Annual_Premium</th>\n",
       "      <th>Policy_Sales_Channel</th>\n",
       "      <th>Vintage</th>\n",
       "      <th>Response</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>Vehicle_Age_&lt; 1 Year</th>\n",
       "      <th>Vehicle_Age_&gt; 2 Years</th>\n",
       "      <th>Vehicle_Damage_Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>48521.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>206</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2630.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>223</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>76</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>29620.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>29781.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>140</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>4</th>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0</td>\n",
       "      <td>23826.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>261</td>\n",
       "      <td>1</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": [
       "   Age  Driving_License  Region_Code  Previously_Insured  Annual_Premium  \\\n",
       "0   42                1         11.0                   0         48521.0   \n",
       "1   48                1         34.0                   0          2630.0   \n",
       "2   76                1         28.0                   1         29620.0   \n",
       "3   48                1         15.0                   0         29781.0   \n",
       "4   41                1         46.0                   0         23826.0   \n",
       "\n",
       "   Policy_Sales_Channel  Vintage  Response  Gender_Male  Vehicle_Age_< 1 Year  \\\n",
       "0                 122.0      206         1            0                     0   \n",
       "1                 156.0      223         1            1                     0   \n",
       "2                  26.0       69         0            1                     0   \n",
       "3                  26.0      140         1            1                     0   \n",
       "4                 124.0      261         1            1                     0   \n",
       "\n",
       "   Vehicle_Age_> 2 Years  Vehicle_Damage_Yes  \n",
       "0                      0                   1  \n",
       "1                      0                   1  \n",
       "2                      0                   0  \n",
       "3                      0                   1  \n",
       "4                      0                   1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dummy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Devide the whole data into X and y\n",
    "Note:\n",
    "Use that iloc or loc methods\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dummy['Vehicle_Age_SM_1_Year'] = df_dummy['Vehicle_Age_< 1 Year']\n",
    "df_dummy['Vehicle_Age_GR_2_Years'] = df_dummy['Vehicle_Age_> 2 Years']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dummy.drop(columns = (['Vehicle_Age_< 1 Year','Vehicle_Age_> 2 Years']),axis = 1 , inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Driving_License', 'Region_Code', 'Previously_Insured',\n",
       "       'Annual_Premium', 'Policy_Sales_Channel', 'Vintage', 'Response',\n",
       "       'Gender_Male', 'Vehicle_Damage_Yes', 'Vehicle_Age_SM_1_Year',\n",
       "       'Vehicle_Age_GR_2_Years'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dummy.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting data into independent and dependent\n",
    "X = df_dummy[['Age', 'Driving_License', 'Region_Code', 'Previously_Insured','Annual_Premium', 'Policy_Sales_Channel', 'Vintage','Gender_Male','Vehicle_Damage_Yes','Vehicle_Age_SM_1_Year','Vehicle_Age_GR_2_Years']]# independent variable\n",
    "\n",
    "Y = df_dummy['Response'] # dependent variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "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>Age</th>\n",
       "      <th>Driving_License</th>\n",
       "      <th>Region_Code</th>\n",
       "      <th>Previously_Insured</th>\n",
       "      <th>Annual_Premium</th>\n",
       "      <th>Policy_Sales_Channel</th>\n",
       "      <th>Vintage</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>Vehicle_Damage_Yes</th>\n",
       "      <th>Vehicle_Age_SM_1_Year</th>\n",
       "      <th>Vehicle_Age_GR_2_Years</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>48521.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>206</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2630.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>223</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>29620.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>29781.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>140</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0</td>\n",
       "      <td>23826.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>261</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Driving_License  Region_Code  Previously_Insured  Annual_Premium  \\\n",
       "0   42                1         11.0                   0         48521.0   \n",
       "1   48                1         34.0                   0          2630.0   \n",
       "2   76                1         28.0                   1         29620.0   \n",
       "3   48                1         15.0                   0         29781.0   \n",
       "4   41                1         46.0                   0         23826.0   \n",
       "\n",
       "   Policy_Sales_Channel  Vintage  Gender_Male  Vehicle_Damage_Yes  \\\n",
       "0                 122.0      206            0                   1   \n",
       "1                 156.0      223            1                   1   \n",
       "2                  26.0       69            1                   0   \n",
       "3                  26.0      140            1                   1   \n",
       "4                 124.0      261            1                   1   \n",
       "\n",
       "   Vehicle_Age_SM_1_Year  Vehicle_Age_GR_2_Years  \n",
       "0                      0                       0  \n",
       "1                      0                       0  \n",
       "2                      0                       0  \n",
       "3                      0                       0  \n",
       "4                      0                       0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    0\n",
       "3    1\n",
       "4    1\n",
       "Name: Response, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. split the data into train and test\n",
    "Note:\n",
    "\n",
    "* Use train test split method\n",
    "\n",
    "* Discuss that why do we need train and test split in model fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split the data into two parts training and testing\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,Y_train,Y_test = train_test_split(X,Y, test_size = 0.3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((7000, 11), (3000, 11), (7000,), (3000,))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape,X_test.shape,Y_train.shape,Y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9. Apply The Random Forest Model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(criterion='entropy', random_state=0)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "classifier = RandomForestClassifier(n_estimators=100,criterion='entropy',random_state=0)\n",
    "classifier.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10. Find The Classification Metrics and Give the Observations to\n",
    "Metric for Random Forest algorithms\n",
    "\n",
    "Discuss the every metric and why these metrics are usefull for model.\n",
    "\n",
    "* Confusion Matrix\n",
    "\n",
    "    * Precision\n",
    "\n",
    "    * Recall\n",
    "\n",
    "    * Specificity\n",
    "\n",
    "    * Sensitivity\n",
    "\n",
    "    * Accuracy\n",
    "\n",
    "    * F1-score\n",
    "    \n",
    "    * True Positive Rate\n",
    "\n",
    "    * False Positive Rate\n",
    "\n",
    "* Classification Report\n",
    "\n",
    "* ROC_AUC Curve\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1528  411]\n",
      " [ 319  742]]\n",
      "Accuracy score: 0.7566666666666667\n",
      "Precision score: 0.6435385949696444\n",
      "Recall score: 0.6993402450518379\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "cm = metrics.confusion_matrix(Y_test, y_pred) \n",
    "accuracy = metrics.accuracy_score(Y_test, y_pred) \n",
    "precision = metrics.precision_score(Y_test, y_pred) \n",
    "recall = metrics.recall_score(Y_test, y_pred)\n",
    "\n",
    "print(cm)\n",
    "print(\"Accuracy score:\",accuracy)\n",
    "print(\"Precision score:\",precision)\n",
    "print(\"Recall score:\",recall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.79      0.81      1939\n",
      "           1       0.64      0.70      0.67      1061\n",
      "\n",
      "    accuracy                           0.76      3000\n",
      "   macro avg       0.74      0.74      0.74      3000\n",
      "weighted avg       0.76      0.76      0.76      3000\n",
      "\n",
      "accuracy_score :  0.7566666666666667\n"
     ]
    }
   ],
   "source": [
    "# classification_report\n",
    "print(classification_report(Y_test, y_pred))\n",
    "print('accuracy_score : ',accuracy_score(Y_test,y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate predictions\n",
    "\n",
    "predictions_train = classifier.predict(X_train)\n",
    "predictions_test = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find False Positive Rate & True Positive Rate\n",
    "\n",
    "FPR, TPR, a = roc_curve(Y_train, predictions_train)\n",
    "FPR1, TPR1, a1 = roc_curve(Y_test,predictions_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 1.] [0. 1. 1.] [2 1 0]\n",
      "[0.         0.21196493 1.        ] [0.         0.69934025 1.        ] [2 1 0]\n"
     ]
    }
   ],
   "source": [
    "print(FPR, TPR, a)\n",
    "print(FPR1, TPR1, a1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train AUC: 1.000\n",
      "Test AUC: 0.744\n"
     ]
    }
   ],
   "source": [
    "# calculate AUC\n",
    "\n",
    "auc_train = auc(FPR, TPR)\n",
    "print('Train AUC: %0.3f' % auc_train)\n",
    "\n",
    "auc_test = auc(FPR1, TPR1)\n",
    "print('Test AUC: %0.3f' % auc_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Dj06XJD8f2gFq1EslqvZs9my47bbkDLQHH4RvfAOOOirtVNpXFjVJkqSKYN2S7ZcuLpwAa+YXjtdtDk06w3F9CkpZe6h+UDpZtVcWLEg2CHntNejeHR5/PNkoRGWbv4WSJEnlzdrF2y9dXDgB1i4sHK/XEpqeAA1/XljKDqiTSlTtuyVLYMAA+Pe/ky33f/7ztBOpOFnUJEmSyqoYYe2ibZYuTkiK2brFBRcEqH8kNOuW3Ev2ZSmr5p7sZVlODlSpApddBl27wtSpUL162qlU3CxqkiRJZUGMsHr+9ksXF02A9UuT8VAJ6h8FLboXHhx9aFuoemBaiVXM1q2D+++Hv/8dPv44mUlzk5Dyy6ImSZKUaWKEVZ9tv3Rx0QTYsDwZD1nQ4Bhoedr2pSy7RmqRVbLWroVjj4Xzz0/ORXMGrfyzqEmSJKUpxmT7+22XLi6akJxdBlCpMjRoBUf1LCxlh7SBbN+pl3c5OfD//h+8/36yQcj48VDLVasVhkVNkiSptOTnw8o5Xz2nbNPqZLxSFTi4FbTqVXBwdMeklFWpllpkpWPSJPjud5N70G65JXnOklaxWNQkSZJKQn4+rPhfwdLFjwtmyibB5oJSlpUNB7eGNt9OClmjjsnjylXTTK0UxZjcd5afD2ecAUOHwtFHp51KabGoSZIk7a/8PFg+a/uli4smwZa1yXhW1eQesna9C0tZg1ZQOTu9zMoo774LfftCw4bQvz8ceKAlraKzqEmSJO2NvFxYPnP7pYuLJkHO+mS8cjU4tB10uLBwS/wGx0BWlRRDK1O9/TYccUTy6wcegBNPTDePModFTZIkaVfycmHZJ9ufU7Z4MuRuTMarVE9K2bE/LNjoowPUPxqyfIul3fvwQ7j55mQ3x0cfhW7d0k6kTOOfIpIkSQB5ObBk+vbnlH0xBXI3JeNVakDD9tD50sLdF+sfCZWyUoussmf2bGjRAl59Fa6+Gs46y7PQtHMWNUmSVPHkboEl07Y/p+yLqZC3ORnPPjCZHevSp7CU1TvCUqZ9NmsW3HorTJiQLHe89da0EynTWdQkSVL5lrs5KWHbbon/xTTIz0nGq9ZOZspOuKxwS/yDWkClSqlFVvmxZUvy88UXw89/Dn//O1T2HbiKwJeJJEkqP3I2frWULZkO+bnJeLU6yUzZSVcWlLIOULe5pUzF7osv4Pe/h48/hrfegvfec4mj9o5FTZIklU1bNiT3kG27Jf6S6RDzkvED6iZlrOtVhVvi1zncd8sqcdOnQ8+ecNVVMGCALzntG4uaJEnKfFvWJ7stbrsl/tIZEPOT8er1kyJ21BmFpaz2Yb5DVqlZuxbuvRdycuB3v4PJk5Oz0KR9ZVGTJEmZZfPar5ayZZ8WlrIaBydF7JhzCs8pq9XYUqbUvPoqXHklfP/7cO21yUvRkqb9ZVGTJEnp2bSmYNniNlviL58FxGS85qFJEWv9rcJzyg5saClT6rZsgccfh3btoH17eP99OPTQtFOpPLGoSZKk0rFxVWEh+3JL/BX/Kxyv1TgpYu2+s00p852vMs/zz8NNN8Epp8DZZ0OTJmknUnlkUZMkScVvw4rtZ8kWTYSVcwrHax+WFLEO3y8sZTUPTimstGf5+TB0aLJJSLVq8MorcOSRaadSeWZRkyRJ+2f98oJZsgmF95Wt+qxwvE7TZIOPTj8q3BK/Rv00kkp7LcbkHrR+/eDww6FrV+jVK+1UqggsapIkqejWLd2hlE2E1Z8XjtdtBo06QZefFJay6gelElXaX//7H7RoASNGwCOPwPHHp51IFYlFTZIk7dzaL7YvZIsmwJoFheMHHQGHHQ/H/6yglLVPzi6TyrgPPoCbb06WO772GjzwQNqJVBFZ1CRJquhihLWLt1+6uGgirF1UcEGAei3h8K6Fs2QN20O12qlFlkrCli2wdClcfTXcdhuceaYbjCo9FjVJkiqSGJNZsS93XfyynK1fUnBBgPpHQfOTCw+OPrQdVPVQKJVfn3wCt94KNWrAY48lM2pS2ixqkiSVVzEm949tu3Rx4QTYsCwZD5WgwTHQ8huFpeyQtlC1ZmqRpdL28svwm98kSx0vuijtNFIhi5okSeVBjLBq3vZLFxdOgI0rkvGQBQe3gqPO2L6UZVdPLbKUlkWL4M47oXNn+P73YcoUqFIl7VTS9ixqkiSVNTEmZ5Jtu3Rx0UTYtCoZr1Q5KWXHnF1wRllHOKQNVDkgrcRSxnj4Ybj7bvjVr5KSVq1a2omknbOoSZKUyfLzYcXsHbbEnwSbVyfjlarAIa2h9XmFpezg1lDFd5/Sl9asgT//GX78Y/jmN+Hii6GmK3yV4SxqkiRlivx8WD5r+6WLiyfB5jXJeFbVZGas7fnbl7LK2ellljLcwIFw333wwx9CrVrQtGnaiaSisahJkpSG/DxYNnP7pYuLJ8GWdcl45WrJPWTtv1uwHX7HZDljljfSSHuyeTO89BL07g2HHgoffggHH5x2KmnvWNQkSSppebmw7NPtzylbPBlyNiTjlQ9ItsDveFHhOWUNjraUSXspNxeeegp+/3s49VTo1cudHFV2WdQkSSpOeTmwdMb255QtngK5G5PxKjWSUtbp4sLdF+sdCVn+lSztq/x8mDsXGjRIzkAbMQKOOCLtVNL+8W8FSZL2Ve4WWDp9+3PKFk+BvM3JeHbNZHasy4+3KWUtoVJWepmlciTG5By0W26Brl3hoYfgkUfSTiUVD4uaJElFkbsZlkzbfkv8JdMgb0syXrVWUsqO/1lhKTvoCKhUKbXIUnm2ZQu8916y3f7f/paciSaVJxY1SZJ2lLMJlkzdoZRNh/ycZLxa7aSUnXB54e6LdZtbyqRSMGYM3HQT9OwJ114LPXqknUgqGRY1SVLFlrMxWa649ZyyiclyxvzcZPyAukkpO+kX25SyZhBCapGlimrgQHj2WejfH047Le00UsmyqEmSKo4tG5LdFrfdEn/pDIh5yXj1ekkRO/K0wlJWp6mlTErRjBnJPWhXXQWXXQZ9+/q/pCqGIhW1EMKZwH1AFvC3GOOAHcZrA/8AmhZ8zntijP+vmLNKklR0m9d9tZQt+wRifjJeo0FSxI45q/CcstpNfAcoZZDf/AZeeaVws5As9+FRBbLHohZCyAIeAk4D5gMfhhCGxRinbXPZL4BpMcZeIYQGwCchhKdjjFtKJLUkSdvatCY5LHrbLfGXzQRiMl7zkKSItT638JyyWo0sZVIGWrgQ7rsPbrsNLr0UBgyAKh4pqAqoKDNqxwOzYoyzAUIIzwLnAdsWtQgcGEIIQE1gBZBbzFklSYJNq7cpZAVb4i+fVTh+YKOkiLXtXbj74oGHppNVUpFt2gQ33wwvvJAsb8zKgnbt0k4lpacoRa0x8Pk2j+cDJ+xwzYPAMGAhcCDwvRi/XFsiSdI+2rhy+1myRRNhxezC8VpNkiLW/sKknDXqCDUPTierpH2yejW8/Taccw60agVTpkCNGmmnktJXlKK2s3UhcYfHZwATgFOBI4DXQgjvxBjXbPeJQrgMuAygadOmex1WklSObVixzf1kBT+vmlc4XrspNOoAHX9QuNFHjfppJJVUDNavhwcegL/8BX72M+jVC/r0STuVlDmKUtTmA4dt87gJyczZtn4MDIgxRmBWCGEOcAwwdtuLYoyDgEEAXbp02bHsSZIqivXLtpklm5Bsib/6s8LxOocnZazzpcnPh3aAGvXSSCqpmG3eDF98kfy8bBl89BE0aJB2KinzFKWofQgcGUJoDiwALgQu2uGaz4BvAO+EEA4BjgZmI0nSuiXbL11cOAHWzC8cP6gFNOkCx/UpmCnrkJxdJqlcycmBv/892RzkqqvgV7+Ce+5JO5WUufZY1GKMuSGEXwIjSbbnfzzGODWEcHnB+CPAHcATIYTJJEslr48xLivB3JKkTLR28fZLFxdNhLXbLMKo1xKanlhYyA5tDwfUSSWqpNKRnw95efDYY/D++/Daa9C8edqppMxXpHPUYoyvAq/u8Nwj2/x6IXB68UaTJGWsGGHNwsJdF78sZ+u+KLggQP0joVm3wvvJDm0H1WqllVhSKYsRhg6FW2+FO++Eyy9PfkgqmiIVNUlSBRYjrJ6//dLFRRNg/dJkPFSC+kdDix7bl7KqNVOLLCl9P/sZLF4MTzwBxx6bdhqp7LGoSZIKxQirPtt+6eKiCbBheTIesqDBMdDytG1KWVvIdi9tSfDuu/C73yX3ot17L9T032ukfWZRk6SKKkZYOXf7pYuLJiZnlwFUqgwNWsHRPZNC1rAjHNIGsqunlVhShsrLg299C5YuTZY5NmqUdiKp7LOoSVJFkJ8PK+d8tZRtWp2MV6oCB7eCVr2SQtaoIxzcBqpUSy2ypMw3dSo8+yzcfjvccQd06ABhZyfwStprFjVJKm/y82HF/7YvZIsmwuY1yXhWdjIz1ubb25Sy1lC5anqZJZUpS5bAb34D48Ylm4UAdOyYaiSp3LGoSVJZlp8Hy2ftUMomwZa1yXhW1eQesnbfSbbDb9QxWc5YOTvF0JLKqvnzYeZMOO44+OY34f/9P6jsu0mpRPi/liSVFXm5sHzm9ueULZ4MOeuT8coHJKWsw4WF55Q1OAayqqSXWVK5sGQJ/OEP8OKLyWYhPXrAJZeknUoq3yxqkpSJ8nJh6YztzylbPBlyNybjVaonW+Af+8PC3RfrHwVZ/rEuqfisXAm5uTBpUrJByNSpUN39hKRS4d/okpS2vBxYMn37c8q+mAK5m5Lx7JpwaHvofOk2pexIqJSVWmRJ5du6dXDffTBoEDz4IPTqlSx1lFR6LGqSVJpyt8CSadvvvvjFVMjbkoxnH5gsWTzup8nPDTtCvSMsZZJKxebNkJ2dLG+sVAnGj4d69dJOJVVMFjVJKim5m5MStl0pmwb5Ocl41drQsD2c8PPCc8oOapG8O5KkUpSTA48/DnffDa++Cn/8Y9qJJFnUJKk45Gz8ailbMh3yc5PxanWSGbKTrizcEr9ucw8ckpS6GOEb34Ajj4Q334TDD087kSSwqEnS3tuyIbmHbNst8ZdMh5iXjB9wUFLEun6zsJTVOdxSJiljxAj/+hc88kgygzZ8ONSokXYqSduyqEnS7mxe99VStnQGxPxkvHr9pIgddWbhOWW1D7OUScpYy5bBmWdC48bwpz9BlSrJD0mZxaImSV/avDY5LHrbLfGXfQrEZLzGwUkRO+acwnPKajW2lEkqE956K9kc5Jpr4LHHoEOHtBNJ2h2LmqSKadPqglI2oXBL/OWz2FrKDmyYFLE23y7cEr9Ww9TiStK++uQTuOoqWL8e7rwz+bclS5qU+Sxqksq/jasKZ8m+LGUr/lc4XqtxUsTaf7dwS/wDD0klqiQVl8mTk90c69ZNZtF69nQBgFSWWNQklS8bVmy/dHHRBFg5t3C89mFJGev4fWh4bPLrmg3SySpJJWDmTLj1Vpg4Ee6/Hzp1gubN004laW9Z1CSVXeuXw6KPC2fJFk2AVZ8VjtdpmsyOdbq48JyyGp7cKql8+uwzaNAA3nsPzjgDnnoKsrLSTiVpX1nUJJUN65ZuP0u2aCKs/rxwvG5zaNwZuvykoJR1gOoHpZNVkkrR4sXw+9/Dyy/Dv/8Nl16adiJJxcGiJinzrP3iq6VszYLC8YOOgMOOh+MvK7inrAMcUCedrJKUks2boWpVuPpqOPFEmDoVDjgg7VSSiotFTVJ6YoS1i7ZfurhwAqxbXHBBgHot4fCuhQdHH9oOqtVOK7EkpW7tWvjzn5OljRMmwD//mXYiSSXBoiapdMSYzIotnLD9Zh/rlyTjoRLUPwpanLJ9Kat6YGqRJSnTrF+fbA7yne/ABx9AjRppJ5JUUixqkopfjMn9Y9suXVw4ATYsS8ZDJWhwDLT8xvalLNt3HJK0oy1bkgOq330Xnn46mUWzoEnln0VN0v6JEVbN237p4qKJsHFFMh6y4OBWcNSZBQdHd4BD2kJ29fQyS1IZMXky9O4N3bvDgAHJc5Y0qWKwqEkquhhhxewdzimbCJtWJeOVKiel7JizC0rZsXBIa6ji3e2SVFT5+TBkSHJY9be/Da++Ci1bpp1KUmmzqEnaufz8glI2YZtSNgk2r07GK1WBQ9pA6/MKSlnH5HHlqqlFlqSy7p134Fe/gmbN4PbboXp1S5pUUVnUJCWlbPms7WfJFk+CzWuS8ayqSQlr17tgO/yOcHBrqJydYmhJKj/efBOOOCLZbv+vf4Xjjks7kaS0WdSkiiY/D5bN3P6cssWTYcu6ZLxyteQesvbfLTw4+uBWkFUlvcySVE69/z7cdFOyYcgjj8Dxx6edSFKmsKhJ5VleLiz7ZPtzyhZPhpwNyXjlA5LdFjteVLj7Yv2jIcs/GiSpJH3yCRx1FIwaBddeC2ecASGknUpSJvHdmFRe5OXA0hnbn1O2eArkbkzGq9SAhu2h08XblLKjoFJWepklqYL55BO45RaYNi1Z7njDDWknkpSpLGpSWZS7BZZO3/6cssVTIG9zMp59YFLKuvwkWbrYqCPUa2kpk6SUbNoEWVlw2WXJj2eeSR5L0q5Y1KRMl7sZlkzb/pyyJdMgb0syXrVWUsaO/xk0Ojb59UFHQKVKKYaWJAEsWgT9+yeHVL/7Lrz1VtqJJJUVFjUp03wxFT57f5tSNh3yc5KxarWTZYsnXF64JX7d5pYyScpAM2fC6afDNdfAn/7kPWiS9o5FTcokM16FZ7+f/PqAukkRO+kX25SyZv5NL0kZbM0aGDgwWer4hz/A1KnJWWiStLcsalKmyNkEI26ABq3gouegTlNLmSSVIcOHwxVXwI9+lGwSEoIlTdK+s6hJmWLMA7BqHlw8FOoennYaSVIRbN4Mjz4KbdrAscfC2LFw8MFpp5JUHnhji5QJVi+AdwbCMedAi+5pp5EkFcE//wlt28KUKcmZaIceakmTVHycUZMyweu3Qn4enHFn2kkkSbuRnw9DhsA550Dt2jByJLRokXYqSeWRM2pS2uaNgcnPw9euTjYLkSRlnBhh2LBkeeM//5lsGnLGGZY0SSXHGTUpTfl5MPw6qNUYuvVNO40kaQcxwiefwNFHwzvvwP/7f9CpU9qpJFUEFjUpTR8/BYsnQe/HILtG2mkkSdt47z246SaoUgVGjIA//jHtRJIqEoualJaNK+GN26FpV2jbO+00kqQCmzbBqlVw/fVw663wzW96Woqk0mdRk9Iy6i7YsAJ6DvAdgCRlgOnT4ZZboEYNeOIJePfdtBNJqsjcTERKw5IZMHYQdL4UGnZIO40kVXjDh8P55yc/Hn887TSS5IyaVPpihBHXQ9WacGq/tNNIUoW1YAH07w8dO8KPfwyTJ0Nl3xlJyhDOqEmlbcYrMHsUdL8RatRLO40kVUgPPwzdukHr1nDppZCdbUmTlFn8I0kqTTmbYOSN0KAVHNcn7TSSVKGsWgV/+lNSzM48Ey65JLkfTZIykTNqUmka8wCsmpdsIJJVJe00klRh3HMPdOgAlSpB/frJQdWWNEmZzBk1qbSsXgDvDIRjzoEW3dNOI0nl3qZN8MILcNFF0KwZjBuXlDRJKgucUZNKy+u3Qn4enHFn2kkkqVzLyYFHH4W2bWH0aNi8GS64wJImqWxxRk0qDfPGwOTn4eRroW6ztNNIUrmUlwezZsFhhyU7OL7+ejKTJkllkTNqUknLz4Ph10GtxtCtb9ppJKnciRH+/W849li4/36oXj352ZImqSxzRk0qaR8/BYsnQe/HINs71yWpuMSY3Ic2blxySPWTTyZnoklSeeCMmlSSNq6EN26Hpl2hbe+000hSufHOO3DKKcnMWbdu8NJLljRJ5YszalJJGnUXbFiRbMcfQtppJKlcuPdeeO45uPNOOPXUtNNIUslwRk0qKUtmwNhB0PlSaNgh7TSSVKZNnQrnnw9vvgk//3mym6MlTVJ5ZlGTSkKMMOJ6qFoTTu2XdhpJKtN+/Wv47nfhwguhe3c44AAXKUgq/yxqUkmY8QrMHgXdb4Qa9dJOI0llzuefQ9++sG4dXHYZTJyYlLVKvnORVEH4x51U3HI2wcgboUErOK5P2mkkqUzZsgWuuSbZKOTIIyE7G445Bip7V72kCsaiJhW3MQ/AqnnJBiJZVdJOI0llwsqV8MILUKUKdOkCU6bAlVcmRU2SKiKLmlScVi+AdwbCMedAi+5pp5GkjLduHfTvn2ytP2NGcu/ZD3+YHFotSRWZCwmk4vT6rZCfB2fcmXYSScpoGzfCokXJPWfr1sH48VDPW3olaStn1KTiMm8MTH4evnY11G2WdhpJykhbtsAjj0DbtjBsGDRrBgMGWNIkaUfOqEnFIT8Phl8HtRpDt75pp5GkjJOXl/z4xz/go4+S89AOPzztVJKUuZxRk4rDx0/B4klw2u2QXSPtNJKUMfLzYcgQ6NABhg+Hn/wE/vY3S5ok7YkzatL+2rgS3rgdmnaFtr3TTiNJGeXyy2HJEhg8GNq1SzuNJJUdzqhJ+2vUXbBhRbIdfwhpp5Gk1I0aBd27J4dW33cfvPiiJU2S9pYzatL+WDIDxg6CzpdCww5pp5GkVOXnQ69esHo13HknHHZY2okkqexyRk3aVzHCiOuhak04tV/aaSQpNZMmwbXXJosK/vhHeOcdOOWUtFNJUtlWpKIWQjgzhPBJCGFWCOGGXVzTPYQwIYQwNYTwVvHGlDLQjFdg9ijofiPUcF9pSRXP0qXw/e/DRRfBiScmz7Vu7SpwSSoOe1z6GELIAh4CTgPmAx+GEIbFGKdtc00d4GHgzBjjZyGEg0sor5QZcjbByBuhQSs4rk/aaSSpVM2bBzNmwMknJ0sd//EPyMpKO5UklS9FmVE7HpgVY5wdY9wCPAuct8M1FwH/ijF+BhBjXFK8MaUMM+YBWDUv2UAkq0raaSSpVCxeDFddBaeemsymHXBAMptmSZOk4leUzUQaA59v83g+cMIO1xwFVAkhjAIOBO6LMT5ZLAmlTLN6AbwzEI45B1p0TzuNJJW45cthyxaYORNatIApU5KSJkkqOUWZUdvZSvO4w+PKQGfgbOAMoF8I4aivfKIQLgshfBRC+Gjp0qV7HVbKCK/fCvl5cMadaSeRpBK1Zg387nfQqRN8+GGy1LFvX0uaJJWGosyozQe23WC3CbBwJ9csizGuB9aHEN4GOgCfbntRjHEQMAigS5cuO5Y9KfPNGwOTn4eTr4W6zdJOI0klYuNGqFYN7r472XJ/wgSoWzftVJJUsRRlRu1D4MgQQvMQQjZwITBsh2uGAl8PIVQOIVQnWRo5vXijSinLz4Ph10GtxtCtb9ppJKnYbdkCDz0EbdrAtGnQvz/8/veWNElKwx5n1GKMuSGEXwIjgSzg8Rjj1BDC5QXjj8QYp4cQRgCTgHzgbzHGKSUZXCp1Hz8FiydB78cgu0baaSSp2J1+Ohx9NLz1lodVS1LairL0kRjjq8CrOzz3yA6P/wj8sfiiSRlk40p443Zo2hXa9k47jSQVi/x8eP55ePhheO01GD7c+88kKVMUqahJFd6ou2DDimQ7fk9ylVQOrFgB3/hGsovjww9DdnbaiSRJ27KoSXuyZAaMHQSdL4WGHdJOI0n75c034YMP4IYb4OmnoXXrtBNJknbGoibtToww4nqoWhNO7Zd2GknaZ598AldeCTk5cOedyeIAS5okZS6LmrQ7M16B2aPgzLugRr2000jSXpswATZvhkaN4Lrrkg1DXMEtSZmvKNvzSxVTziYYeSM0aAXH9Uk7jSTtlRkz4LvfhYsvhvXrk10czzjDkiZJZYUzatKujHkAVs2Di4dCVpW000hSkcydC/Xrw/jx8K1vwfe+B1lZaaeSJO0ti5q0M6sXwDsD4ZhzoEX3tNNI0h4tXJjcezZyZLLl/kUXpZ1IkrQ/XPoo7czrt0J+HpxxZ9pJJGm3NmxIfr7+ejjqKJgyBY49Nt1MkqT954yatKN5Y2Dy83DytVC3WdppJGmnVq+GgQPhH/+Ajz+Gp55KO5EkqTg5oyZtKz8Phl8HtRpDt75pp5Gkndq4EY4/PjlBZNw4qFUr7USSpOLmjJq0rY+fgsWToPdjkF0j7TSStNXmzTBoELz1FgwZkmy7f8ABaaeSJJUUZ9SkL21cCW/cDk27QtveaaeRpK2mToU2bWDaNLjvvuQ5S5oklW/OqElfGnUXbFgBPQd40JCk1OXnw3PPJcscL7oI/vMfaNEi7VSSpNLijJoEsGQGjB0EnS+Fhh3STiOpgnv77WTnxn/9C046CapVs6RJUkXjjJoUI4y4HqrWhFP7pZ1GUgUVI7z+OjRvDgceCE884Tb7klSRWdSkGa/A7FHQ826oUS/tNJIqoPfeg5tuSlZdP/igBU2S5NJHVXQ5m2DkjdCgFXTpk3YaSRXMlCnJTNqHH8LNN8ObbyabhkiS5IyaKrYxD8CqeXDxUMjyfwdJpWPaNLjlFpg1C0aOhGuuSTuRJCnTOKOmimv1AnhnILTqBS26p51GUgWwYQPk5SXF7IILYPx4OOSQtFNJkjKRUwiquF6/FfLz4PT+aSeRVM7Nnw/9+yeHVI8Zk2y1L0nS7jijpopp3hiY/Dx87Wqo2yztNJLKsdmz4etfh7Zt4a23PKZRklQ0zqip4snPg+HXQa3G0K1v2mkklUOrVsE99yRLHQcOhOnTk7PQJEkqKmfUVPGMfxIWT4LTbofsGmmnkVTOjBgBHTpA5cpw663Jc5Y0SdLeckZNFcvGlfDmHdC0K7TtnXYaSeXEpk3wyCPQqhV07gzjxkH9+mmnkiSVZc6oqWIZdVdS1nre5Y0ikorFs88mZ5/NmgXt20ODBpY0SdL+c0ZNFceS6TB2EHS6BBq2TzuNpDIsLw+eew569Uq213/jDWjWLO1UkqTyxBk1VQwxwogboGpNOLVf2mkklVExwr/+BR07wrBhsH499OhhSZMkFT9n1FQxzHgFZo+CnndDjXppp5FUxsQIU6YkW+x//DH84x/JhiGSJJUUi5rKv5xNMPJGaNAKuvRJO42kMubtt+Gmm6B6dXjlFbjjjrQTSZIqAouayr8xD8CqeXDxUMjyJS+paNavT85Bu/VWuP32ZImjJEmlxXetKt9WL4B3BkKrXtCie9ppJJUBU6ZAv35QsyY89RT8979pJ5IkVURuJqLy7bVbID8PTu+fdhJJZcDIkfC978FFF8Hf/552GklSReaMmsqveWNgyhA4+Vqo2yztNJIy1OefJ0sbO3SAn/8cJk6Eyv7tKElKmTNqKp/y82D4dVCrMXTrm3YaSRnqL3+BU06BTp3gssugShVLmiQpM/jXkcqn8U/C4knQ+zHIrpF2GkkZZMUK+OMf4eKL4eyz4dJL4YAD0k4lSdL2nFFT+bNxJbx5BzTtCm17p51GUga5+2449thkq/0mTaBpU0uaJCkzOaOm8mfUXUlZ63kXhJB2Gkkp27gRnn0WLrkEjjkmObD6oIPSTiVJ0u45o6byZcl0GDsIOl0CDdunnUZSinJyknvQ2rSBCRNg82Y491xLmiSpbHBGTeVHjDDiBqhaE07tl3YaSSnJy4Pp0+HII+F//4NRo5IljpIklSXOqKn8mPEKzB4FPW6CGvXSTiOplOXnw/PPQ/v28MgjULUq3HOPJU2SVDY5o6byIWcTjLwRGrSCLn3STiOpFMUIGzbAlCnwzDPJ/Wjt2qWdSpKk/eOMmsqHMQ/AqnnQcwBk+e8PUkXx3/9Ct25w//1wwgnw739b0iRJ5YPvaFX2rV4A7wyEVr2gRfe000gqJfffnyx1/MMf4OST004jSVLxckZNZd9rt0B+HpzeP+0kkkrYpEnJzo3/+Q/8/Ofw9tuWNElS+WRRU9k2bwxMGQJfuxrqNks7jaQS9Otfww9+AJdeCqedlmwW4lGJkqTyyqKmsis/D4ZfB7UaQ7e+aaeRVALmzYNf/AJWr4Yrr0zOQzv/fAuaJKn8s6ip7Br/JCyeBKfdDtk10k4jqRjl5MAvfwmnngodOkD16tCyJWRlpZ1MkqTSYVFT2bRxJbx5BzTtCm17p51GUjFZvjzZYr9KleTes6lT4bLLkseSJFUkFjWVTaPuSspaz7tcAyWVA2vXwm23QadOyXLHGOG734Vq1dJOJklSOtyeX2XPkukwdhB0ugQatk87jaT9sGEDzJ8PNWpAbm5yD1rdummnkiQpfc6oqWyJEUbcAFVrwqn90k4jaR9t3gwPPght2sCrr0LjxtC/vyVNkqQvOaOmsmXGKzB7FPS8G2rUSzuNpL2Um5v8eP55mDwZ3nkHmjRJO5UkSZnHGTWVHTmbYOSN0KAVdOmTdhpJeyE/H559Ftq3T2bQfvQj+OtfLWmSJO2KM2oqO8Y8AKvmwcVDIcuXrlSWXHklLFsGQ4ZA69Zpp5EkKfM5o6ayYfUCeGcgtOoFLbqnnUZSEbzxBnztazB3Ltx3nyVNkqS94bSEyobXboH8PDi9f9pJJO1BjHD22bB+Pdx1FzRrlnYiSZLKHmfUlPnmjYEpQ+BrV0PdZmmnkbQLH38MV1+dFLX77oNRo6Bbt7RTSZJUNlnUlNny82D4dVCrMXTrm3YaSTuxbFlyOPUll8CppyZn0B95pGfRS5K0P1z6qMw2/klYPAl6PwbZNdJOI2kbc+bAlClwxhlJUTv/fKjkP/9JklQs/CtVmWvjSnjzDmjaFdr2TjuNpAKLFiW7OJ52GqxZA9nZcMEFljRJkoqTM2rKXKPuSspaz7tcQyVlgKVLYfNmmD8fjjkG/vxnqFo17VSSJJVP/vunMtOS6TB2EHS6BBq2TzuNVKGtXg233AJdusC4cXDiicmmIZY0SZJKjjNqyjwxwogboGpNOLVf2mmkCmv9ejjggGQHR4CJE6FOnVQjSZJUYVjUlHlmvAKzR0HPu6FGvbTTSBXOpk3w17/CvffC0KHJbJokSSpdFjVllpxNMPJGaNAKuvRJO41UIZ19Nhx9NLz3HjRqlHYaSZIqJouaMsuYB2DVPLh4KGT58pRKQ34+PPssPPggvPkmvPqq959JkpQ23wkrc6xeAO8MhFa9oEX3tNNIFcLq1XDyyckM2uOPQ7VqaSeSJElgUVMmee0WyM+D0/unnUQq12KE116D0aPhtttgyBA48si0U0mSpG1Z1JQZ5o2BKUPg5GuhbrO000jl1iefwGWXQVYW9C/4NxFLmiRJmceipvTl58Hw66BWY+jWN+00Urk0bhxs3AhHHJHs4viNb6SdSJIk7U6RDrwOIZwZQvgkhDArhHDDbq47LoSQF0K4oPgiqtwb/yQsngSn3Q7ZNdJOI5Ur06dD797Qpw/k5EDDhpY0SZLKgj3OqIUQsoCHgNOA+cCHIYRhMcZpO7nuLmBkSQRVObVxJbx5BzTtCm17p51GKjf+9z+oXz8pat/9LnznO1CpSP80J0mSMkFR/to+HpgVY5wdY9wCPAuct5PrrgJeAJYUYz6Vd6PuSspaz7sghLTTSGXe/Pnw85/DmWcmZe388+F737OkSZJU1hTlr+7GwOfbPJ5f8NxWIYTGwLeBR4ovmsq9JdNh7CDodAk0bJ92GqlMW7cu+fm226B9e5gyBTp1SjWSJEnaD0XZTGRn0xxxh8f3AtfHGPPCbmZFQgiXAZcBNG3atIgRVS7FCCNugKo14dR+aaeRyqyVK+Gee2DwYBg/Hv72t7QTSZKk4lCUGbX5wGHbPG4CLNzhmi7AsyGEucAFwMMhhG/t+IlijINijF1ijF0aNGiwb4lVPsx4BWaPgh43QY16aaeRyqTNm6FrV8jOhgkToE6dtBNJkqTiUpQZtQ+BI0MIzYEFwIXARdteEGNs/uWvQwhPAC/HGF8svpgqV3I2wcgboUEr6NIn7TRSmbJpE/zlL/Dmm/DSSzBxYlLUJElS+bLHGbUYYy7wS5LdHKcD/4wxTg0hXB5CuLykA6ocGvMArJoHPQdAlkf5SUU1fTq0aQOzZ8OgQclzljRJksqnIr1LjjG+Cry6w3M73Tgkxnjp/sdSubV6AbwzEFr1ghbd004jZby8PHjmmWSzkJ/+NJlJO/zwtFNJkqSS5obNKl2v3QL5eXB6/7STSBnv7behY0d49dXkkOoqVSxpkiRVFK47U+mZNwamDIGTr4W6zdJOI2WkGGHECGjeHA46CJ5+OtluX5IkVSzOqKl05OfB8OugVmPo1jftNFJGeucd+PrXYeDAZMlj27aWNEmSKipn1FQ6xj8JiydB78cgu0baaaSM8vHH0KFDckh1//7QvXvaiSRJUtosaip5G1fCm3dA067QtnfaaaSMMXky9OsHn38OL78MV1yRdiJJkpQpXPqokjfqrqSs9bwLQkg7jZS6tWshPx9++1v44Q/hww+hYcO0U0mSpEzijJpK1pLpMHYQdLoEGnqzjSq2zz6D22+HSZPg/feTWTRJkqSdcUZNJSdGGH49VK0Jp/ZLO42UqnnzoEcP6NIF3n0XKvmnryRJ2g1n1FRyZrwMc96CnndDjXppp5FK3YoVcPfdyVLHhx6C6dMhOzvtVJIkqSzw33RVMnI2wciboEEr6NIn7TRSqRs5Eo49FmrWhAEDkucsaZIkqaicUVPJGPMArJoHFw+FLF9mqhg2bkxmzo46Crp1S7bdP+igtFNJkqSyyBk1Fb/VC+CdgdCqF7TonnYaqVQMHgxt2sD8+XDCCUlBs6RJkqR95VSHit9rt0B+HpzeP+0kUonKzYWnn4Zzz4XDDoO33kp+liRJ2l/OqKl4zRsDU4bA166Gus3STiOViBjhn/+EDh3gtddg8+ZkqaMlTZIkFRdn1FR88vNg+LVQqzF065t2GqnYxQjjx0PnzvDpp/Dcc9C2bdqpJElSeWRRU/EZ/yQsngy9H4PsGmmnkYrVm2/CzTdD7dowdGjya0mSpJJiUVPx2LgS3rwDmnaFtr3TTiMVm7VrYcsWuOuuZJv9k09OO5EkSaoILGoqHqMGJGWt510QQtpppP02YQL06we1aiUbhowcmXYiSZJUkbiZiPbfkukw9lHodAk0bJ92Gmm/xAivvw4/+hH85Cfwj3+knUiSJFVEzqhp/8QIw6+HqjXh1H5pp5H22dy58LvfQbt28KtfJTNqWVlpp5IkSRWVM2raPzNehjlvQY+boEa9tNNI++SRR+Ab34CuXeGqq5KCZkmTJElpckZN+y5nE4y8CRq0gi590k4j7ZVly+Duu+EHP0gOrL70UqhWLe1UkiRJCWfUtO/GPACr5kHPAZBl51fZMWBAchZa3bpwxBHQqJElTZIkZRbfXWvfrF4A7wyEVr2gRfe000h7tH59sjHIT38KHTvCxIlQp07aqSRJknbOGTXtm9dugfw8OL1/2kmk3crJgQcfhLZt4dNPYfNmOPNMS5okScpszqhp780bA1OGwMnXQd1maaeRdio3FyZPTgraokXw7rvQuHHaqSRJkorGGTXtnfw8GH4t1GoM3a5JO430Ffn5MHhwss3+3/4GVarAnXda0iRJUtnijJr2zvgnYfFk6P0YZNdIO420VYywbh3MnAkvvJD8aN067VSSJEn7xhk1Fd3GlfDmHdC0K7TtnXYaCUgK2uuvw0knwf33Q6dOMGSIJU2SJJVtzqip6EYNSMpaz7sghLTTSMQIDz8Mzz8Pf/oTfO1raSeSJEkqHs6oqWiWTIexj0KnS6Bh+7TTqIIbPx7OOgtefRUuuwz++19LmiRJKl+cUdOexQjDr4eqNeHUfmmnUQX361/Dm2/C7bcnZc3JXUmSVB45o6Y9m/EyzHkLetwENeqlnUYV0OzZyczZ8uXwq18lM2rnnmtJkyRJ5ZdFTbuXswlG3gQNWkGXPmmnUQWTmwuXXw6nnw4nnAC1a8Phh0Ml/+SSJEnlnG93tHtjHoBV86DnAMhypaxKx9Kl8MQTULkynHkmTJ0KffokjyVJkioCi5p2bfUCeGcgtOoFLbqnnUYVwJo10K8fHHdcUtZihG99C6pWTTuZJElS6fLfp7Vrr90C+Xlwev+0k6icW78e5s2D+vWTWbOJE5NljpIkSRWVM2rauXljYMoQ+NqvoG6ztNOonNq0Ce69F9q0gf/8Bw4+GG691ZImSZLkjJq+Kj8Phl8LtRpDt2vSTqNyKCcn+fHSSzBzJoweDY0apZ1KkiQpczijpq8a/yQsngyn3Q7ZNdJOo3IkLw+efhratUsOq/7e9+ChhyxpkiRJO3JGTdvbuBLevAOadoW2vdNOo3IixuTMs1/9Ktkk5MUX4Zhj0k4lSZKUuZxR0/ZGDUjKWs+7PE1Y+y1GGDEiOQPtf/+DP/8ZnnvOkiZJkrQnzqip0JLpMPZR6HQJNGyfdhqVcTHCWWcl96Lddx8ccUTaiSRJksoOZ9SUiBGGXw9Va8Kp/dJOozLso4/g5z+H/Hx4+GF4/XU46aS0U0mSJJUtFjUlZrwMc96CHjdBjXppp1EZtHw5nH8+/Oxn0KsXVKoEzZunnUqSJKlscumjIGcTjLwJGrSCLn3STqMyZtas5IDq886Diy+Gc89NSpokSZL2nW+nBGMegFXzoOcAyLK7q2gWLIDLLoOePWHLFqhcGb71LUuaJElScfBdeUW3egG8MxBa9YIW3dNOozLgiy9g40ZYsQI6doQHH4Ts7LRTSZIklS/+23dF99otkJ8Hp/dPO4ky3MqVcOONyVb7EyZAp05w5ZWWNEmSpJLgjFpFNm8MTBkCJ18HdZulnUYZau1aqF4d/vpXqFYNJk2CWrXSTiVJklS+WdQqqvw8GH4t1GoM3a5JO40y0MaN8Je/wP33w4svwg03pJ1IkiSp4rCoVVTjn4TFk6H3Y5BdI+00yiAxQgjJxiBHHw3vvw+HHpp2KkmSpIrFolYRbVwJb94BTbtC295pp1GGyMuDp59OZtDefhtefhmqVEk7lSRJUsVkUauIRg1IylrPu5KpE1V4a9dC167Qrh0880xyT5okSZLSY1GraJZMh7GPQqdLoGH7tNMoRTHC8OHJ7NmAAfDSS9CsWdqpJEmSBBa1iiVGGH49VK0Jp/ZLO41S9Mkn8JOfQI0a0L/gZAZLmiRJUuawqFUkM16GOW9Bz7uhRr200ygFY8fCunXJEsff/x5OOSXtRJIkSdoZD7yuKHI2wciboEEr6NIn7TQqZVOmwHnnwRVXQKVK0KCBJU2SJCmTOaNWUYx5AFbNg4uHQpa/7RXFp59C/fowdy5cfDGcf777x0iSJJUFvmOvCFYvgHcGQqte0KJ72mlUCubNg9tvh3ffTXZxPOectBNJkiRpb7j0sSJ47RbIz4PT+6edRCVs9erk57vvhuOPT5Y8du6cbiZJkiTtPWfUyrt5Y2DKEDj5OqjbLO00KiHLlyfl7J//hHHj4KGH0k4kSZKk/eGMWnmWnwfDr4VajaHbNWmnUQmIEbZsSTYGqVULJk+Ggw5KO5UkSZL2lzNq5dn4J2HxZOj9GGTXSDuNitGGDcms2WuvwciR8PHHUKVK2qkkSZJUXJxRK682roQ374CmXaFt77TTqBh98gm0bQsLF8I//pHs4mhJkyRJKl+cUSuvRg1IylrPu9yPvRzIzYWnnko2C/nlL+Htt6FJk7RTSZIkqaQ4o1YeLZkOYx+FTpdAw/Zpp9F+evttaN8e3nwTevWCypUtaZIkSeWdM2rlTYww/HqoWhNO7Zd2Gu2jGOHll6FZMzj44GQ3x7Zt004lSZKk0uKMWnkz42WY8xb0uAlq1Es7jfbBqFHQtSs8/DBkZcExx1jSJEmSKhpn1MqTnE0w8iZo0Aq69Ek7jfbShx/CscfC7NnJmWhf/3raiSRJkpSWIs2ohRDODCF8EkKYFUK4YSfjPwghTCr4MTqE0KH4o2qPxjwAq+ZBzwGQZQcvKyZMgHPOgSuvhC++gJ/8xJImSZJU0e2xqIUQsoCHgJ5Aa+D7IYTWO1w2BzglxtgeuAMYVNxBtQerF8A7A6FVL2jRPe00KoJVqyA/H+64A/r0gbFjoXHjtFNJkiQpExRl2uV4YFaMcTZACOFZ4Dxg2pcXxBhHb3P9+4B70pW2126B/Dw4vX/aSbQHc+bA734HU6fCBx/ACy+knUiSJEmZpihLHxsDn2/zeH7Bc7vSBxi+P6G0l+aNhilD4Gu/grrN0k6jXYgR5s+H00+Hbt1g9Gio5HY+kiRJ2omizKjt7LTkuNMLQ+hBUtS67WL8MuAygKZNmxYxonYrPw+GXwe1GkO3a9JOo51YtgwGDIA1a2DQIJg+PTkLTZIkSdqVovx7/nzgsG0eNwEW7nhRCKE98DfgvBjj8p19ohjjoBhjlxhjlwYNGuxLXu1o/JOweDKcdjtk10g7jXbwn/9A585Qvz78+c/Jc5Y0SZIk7UlR3jJ+CBwZQmgOLAAuBC7a9oIQQlPgX8CPYoyfFntK7dzGlfDG7dC0K7TtnXYaFVi/Hh58EI44Ak47DSZOhDp10k4lSZKksmSPM2oxxlzgl8BIYDrwzxjj1BDC5SGEywsuuwWoBzwcQpgQQvioxBKr0KgBsGkV9LwLws5WqKq0DR6cHE69dCmccgrUrm1JkyRJ0t4r0iKsGOOrwKs7PPfINr/+KfDT4o2m3VoyHcY+Cp0ugYbt005ToeXmwt//DueeCy1awLvvus2+JEmS9o97zpVFMcLw66FqTTi1X9ppKrTBg6FdO3jnnaSwnXCCJU2SJEn7z20NyqIZL8Oct6Dn3VCjXtppKpwY4f334aSTYMEC+Ne/oFWrtFNJkiSpPHFGrazJ2Qgjb4QGraBLn7TTVCgxJrs4nnAC3HknbN4M//d/ljRJkiQVP2fUyprRD8Kqz+DioZDlb19pWbky2a/l/vvh3nuha9e0E0mSJKk8851+WbJ6Abw7EFr1ghbd005TIYwbBzffnOzcOHgwvPxy2okkSZJUEbj0sSx57RbIz4PT+6edpNyLEUaNgj594Ior4Jln0k4kSZKkisQZtbJi3miYMgROvg7qNks7Tbn1v//B734HbdrAtdfC+PFQyX/OkCRJUinzLWhZkJ8Hw6+DWo2h2zVppym3HnkEzjwTuneH3/wmKWiWNEmSJKXBGbWyYPyTsHgy9H4MsmuknaZcWbIEBgyAiy6Cb38bfvxjqFo17VSSJEmq6JwvyHQbV8Ibt0PTrtC2d9ppypU//AGOPx4aNoTWreGQQyxpkiRJygwWtUw3agBsWgU970r2h9d+WbcOHngAcnOT89AmTkzuRatePe1kkiRJUiGLWiZbMh3GPgqdLoGG7dNOU6bl5CTnn7VrB59/nhxWfeqpULt22skkSZKkr/IetUwVIwy/HqrWhFP7pZ2mzMrJgY8/hs6dYfVqGD06WeooSZIkZTJn1DLVjJdhzlvQ4yaoUS/tNGVOXh784x/Qti089RRkZcGtt1rSJEmSVDY4o5aJcjbCyBuhQSvo0iftNGVKjMnM2eefw8svw7BhcPTRaaeSJEmS9o4zaplo9IOw6jPoOQCy7NJFESOMGAHHHZdsFtKuHTz7rCVNkiRJZZMtINOsXgDvDoRWvaBF97TTlAkxJodVv/ACPPggnHhi2okkSZKk/eOMWqZ57RaI+XD6nWknyXgffghnnAFDh8LPfgavv25JkyRJUvngjFommTcapgyBk6+DuoennSaj/frXMGoU3HEHnHWWR8xJkiSpfHFGLVPk58Hw66BWY+h2TdppMtKsWfDjH8OSJfCb38BHH8HZZ1vSJEmSVP5Y1DLF+Cdh8WQ4/Q7IrpF2moySlweXXZbMnHXvDvXqQePGUMlXryRJksop3+pmgo0r4Y3b4fCvQZvz006TMb74Av761+QMtG9/G6ZMgUsuSR5LkiRJ5ZlFLROMGgCbVsGZA1zHR3IO2m9/CyecAOvWJbs69uwJ2dlpJ5MkSZJKh5uJpG3JdBj7KHS+FBq2TztNqtauhdmzoUkTqFkTJk+GAw9MO5UkSZJU+pxRS1OMMPx6qFoTetycdprUbNwIf/pTckj1f/+b3IN2002WNEmSJFVczqilacbLMOct6Hk31KiXdppSt2VL8uO11+Czz+D99+HQQ9NOJUmSJKXPGbW05GyEkTdCg1bQpU/aaUpVXh78/e/Qti28+mqyUch991nSJEmSpC85o5aW0Q/Cqs/g4mGQVTF+G2JM9kr5zW9g6VJ45RU48si0U0mSJEmZp2I0hEyzegG8OxBa9YIWp6SdpsTFmMyc3XILPPNMcj+aW+xLkiRJu2ZRS8Nrt0DMh9PvTDtJiYsxOag6Px8eeQSOPjrtRJIkSVLm8x610jZvNEwZAl2vhrqHp52mxHzwAVx6KeTmwqOPwsiRcNxxaaeSJEmSygZn1EpTfh4Mvw5qNYZu16SdpkSsWJEUtIULoX9/qFw5ORdNkiRJUtFZ1ErT+Cdh8WS44HHIrpF2mmL1yScwbhx873tw+eXQs2eycYgkSZKkvefSx9KycSW8cTsc/jVoc37aaYrN/Pnwk5/AuecmG4RkZSX3pFnSJEmSpH3njFppGTUANq2CMweUixazaBGsXw8bN8KJJ8Jf/wpVqqSdSpIkSSofnFErDUumw9hHofOl0LB92mn2y/LlcN11cNJJMGUKtGsHl11mSZMkSZKKkzNqJS1GGH49VK0JPW5OO80+W7MGqleHJ5+EOnWSklazZtqpJEmSpPLJolbSZrwMc96CnndDjXppp9lrGzbAgw/Cww/DCy9A375pJ5IkSZLKP4taScrZCCNvhAatoEuftNPslRiTW+m+853kkOqxY+Hgg9NOJUmSJFUMFrWSNPpBWPUZXDwMssrGf+rc3GR54333wejRMGxYspOjJEmSpNJTNtpDWbR6Abw7EFr1ghanpJ2mSNavh+OPh86d4V//ghrl66g3SZIkqcywqJWU126BmA+n35l2kt2KEV56Cd58E+69F/7zH2jcOO1UkiRJUsVmUSsJ80bDlCFw8nVQ9/C00+zSp5/CxRfDQQfBHXckz1nSJEmSpPRZ1Ipbfh4Mvw5qNYZu16SdZqfGjIHVq+GEE+Cee6Bbt7QTSZIkSdqWB14Xt/FPwuLJcPodkJ1ZN3lNngxnnw3XXJOciVa3riVNkiRJykTOqBWnjSvhjdvh8K9Bm/PTTrPV9OlQvz4sWgSXXQbnnptsvS9JkiQpM1nUitOoAbBpFZw5ICOa0Jw5cNttyRloTz0Fp5+ediJJkiRJReHSx+KyZDqMfRQ6XwoN26caZfny5OcHHoCTT06WPHbpkmokSZIkSXvBGbXiECMMvx6q1oQeN6cWY+lSGDAgOQNt7FgYODC1KJIkSZL2gzNqxWHGyzDnLehxE9SoV+pfPkbIyYFvfhMOPhimTIEGDUo9hiRJkqRi4oza/srZCCNvhAatoEufUv3S69fD/fcnh1S/+SaMHw9ZWaUaQZIkSVIJcEZtf41+EFZ9Bj3vgqzS670zZ0KbNrBiBTz/fLJ3iSVNkiRJKh+cUdsfqxfAuwOhVS9ocUqJf7mcHHjiiaScXXttcnB1w4Yl/mUlSZIklTJn1PbHa7dAzIfT7yzxL/X229CuHYweDd/9LlSqZEmTJEmSyitn1PbVvNEwZQicfB3UPbxEvkSMMHQoNG0KjRvDiy/CMceUyJeSJEmSlEGcUdsX+Xkw/Dqo1Ri6XVMiX+KNN+CEE+Bvf4MDDoAjjrCkSZIkSRWFM2r7YvyTsHgyXPA4ZNco1k89ejQcdxwsXgz33QcnnVSsn16SJEkZLCcnh/nz57Np06a0o6gYVatWjSZNmlClSpUif4xFbW9tXAlv3A6Hfw3anF9sn3bcOLjpJli9OtnF8Qc/KLZPLUmSpDJi/vz5HHjggTRr1owQQtpxVAxijCxfvpz58+fTvHnzIn+cSx/31qgBsGkVnDkg2RN/Py1fntyL9qc/wS9/mcyoNWmy/zElSZJU9mzatIl69epZ0sqREAL16tXb61lSZ9T2xhfTYOyj0PlSaNh+vz7VrFnwu9/BtGkwdiw880zxRJQkSVLZZkkrf/bl99QZtaKKEUbcAFVrQo+b9+vTLFoEZ58Np54KH3zgQdWSJEnKDMuXL6djx4507NiRQw89lMaNG299vGXLlt1+7EcffcTVV1+911/z448/JoTAyJEjtz43d+5c2rZtu911t912G/fcc8/Wx/fccw/HHHMMbdu2pUOHDjz55JO7/TozZszgpJNOomrVqtt9nh3NmTOHE044gSOPPJLvfe97W7/vGCNXX301LVu2pH379owfP37rx4wYMYKjjz6ali1bMmDAgL36/nfFolZUM16GOW9Bj5ugRr29/vAlS6BvX/jJT5Lzz6ZNgx//GCo7pylJkqQMUa9ePSZMmMCECRO4/PLL6du379bH2dnZ5Obm7vJju3Tpwv3337/XX3Pw4MF069aNwYMHF/ljHnnkEV577TXGjh3LlClTePvtt4kx7vZjDjroIO6//37+7//+b7fXXX/99fTt25eZM2dSt25dHnvsMQCGDx/OzJkzmTlzJoMGDeKKK64AIC8vj1/84hcMHz6cadOmMXjwYKZNm1bk72VXLGpFkbMRRt4IDVpBlz57/eFvvAHHH5+chfbQQ8lzzqJJkiSpLLj00kv59a9/TY8ePbj++usZO3YsXbt25dhjj6Vr16588sknAIwaNYpzzjkHSGa/fvKTn9C9e3datGixywIXY2TIkCE88cQT/Oc//ynyfVy///3vefjhh6lVqxYAtWvX5pJLLtntxxx88MEcd9xxu915McbIm2++yQUXXADAJZdcwosvvgjA0KFDufjiiwkhcOKJJ7Jq1SoWLVrE2LFjadmyJS1atCA7O5sLL7yQoUOHFun72B3nc4pi9IOw6jO4eBhkFe0/2dq1cP/90KwZnHceTJoEBa8jSZIkaY9+99JUpi1cU6yfs3WjWtzaq81ef9ynn37K66+/TlZWFmvWrOHtt9+mcuXKvP7669x444288MILX/mYGTNm8N///pe1a9dy9NFHc8UVV3ylJL333ns0b96cI444gu7du/Pqq69y/vm731l97dq1rF27liOOOGKn47fccgtdunTh3HPP3evvc/ny5dSpU4fKBcvemjRpwoIFCwBYsGABhx122NZrvxzb2fMffPDBXn/tHVnU9mT1fHh3ILTqBS1OKdKHPPMM3HgjfO97cOaZULNmCWeUJEmSStB3vvMdsgqWhK1evZpLLrmEmTNnEkIgJydnpx9z9tlnU7VqVapWrcrBBx/MF198QZMdtjcfPHgwF154IQAXXnghTz31FOeff/4uN98IIRBj3O3mHLfffvu+fIsAO10++eXX2tXY7j5mf1jU9uS1WyDmw+l37vaynBx4/HE491w45hgYMya5F02SJEnaF/sy81VSatSosfXX/fr1o0ePHvz73/9m7ty5dO/efacfU7Vq1a2/zsrK+sr9bXl5ebzwwgsMGzaMO++8c+t5Y2vXrqVevXqsXLlyu+tXrFhB8+bNqVWrFjVq1GD27Nm0aNGi+L5JoH79+qxatYrc3FwqV67M/PnzadSoEZDMlH3++edbr/1ybMuWLTt9fn95j9ruzBsNU16ArldD3cN3ednTT0Pbtsk2+yFAp06WNEmSJJVPq1evpnHjxgA88cQT+/x5Xn/9dTp06MDnn3/O3LlzmTdvHr179+bFF1+kZs2aNGzYkDfeeANIStqIESPo1q0bAL/97W/5xS9+wZo1ydLQNWvWMGjQoP37xkhmwnr06MGQIUMA+Pvf/855550HwLnnnsuTTz5JjJH333+f2rVr07BhQ4477jhmzpzJnDlz2LJlC88+++w+LbvckUVtV/LzYPh1UKsxdLvmq8P58NZbya9XrICXXoLHHoNDDy3dmJIkSVJpuu666/jtb3/L1772NfLy8vb58wwePJhvf/vb2z3Xu3dvnik4YPjJJ5+kf//+dOzYkVNPPZVbb711631pV1xxBT169OC4446jbdu2nHLKKVSvXh1I7lEbNmzYV77e4sWLadKkCQMHDqR///40adJka9E766yzWLhwIQB33XUXAwcOpGXLlixfvpw+ffpsvaZFixa0bNmSn/3sZzz88MMAVK5cmQcffJAzzjiDVq1a8d3vfpc2bfZ/NjTsaRvLktKlS5f40UcfpfK1d+V7fx0DwHM/Pwk+ehxe7gsXPA5te2+9JkYYMQL69Ut2cXzuOahWLa3EkiRJKk+mT59Oq1at0o6hErCz39sQwrgYY5edXe89ajuzcSW8cQcc/jVoU7jrzLJlkJ0Njz6abLN/wgkpZpQkSZJUbhVp6WMI4cwQwichhFkhhBt2Mh5CCPcXjE8KIXQq/qilaNQA2LQKzhwAITB2LJx+Ovzyl8kW+//6lyVNkiRJUsnZY1ELIWQBDwE9gdbA90MIrXe4rCdwZMGPy4C/FHPOUnNYzlwY+yh0vpR4aHvefReuuAKuuQb24rB0SZIkSdpnRVn6eDwwK8Y4GyCE8CxwHjBtm2vOA56MyQ1v74cQ6oQQGsYYFxV74pIUI5eseYSZa9ty65MDaDMxOQ/tww+hktuuSJIkSSolRakfjYHPt3k8v+C5vb0m4x23eTRjxnTk7H8O48xzqnLDDcl2+5Y0SZIkSaWpKDNqOztWe8etIotyDSGEy0iWRtK0adMifOnSdVStXA7v/ClT/lmT7APSTiNJkiSpoirKXNF84LBtHjcBFu7DNcQYB8UYu8QYuzRo0GBvs5a4c398Ax1ufZXsA9wMU5IkSRXP8uXL6dixIx07duTQQw+lcePGWx9v2bJljx8/atQoRo8evdtrzjvvPE466aTtnrv00ku3HjL9pZo1a2799aeffspZZ51Fy5Ytt55V9sUXX+z264wbN4527drRsmVLrr76anZ2LNnTTz+99fvr2LEjlSpVYsKECdtdc+6559K2bduvfOyQIUMIIVBSR44Vpah9CBwZQmgeQsgGLgR2PEFuGHBxwe6PJwKry9z9aV8KO5sclCRJksq/evXqMWHCBCZMmMDll19O3759tz7Ozs7e48fvqaitWrWK8ePHs2rVKubMmVOkTJs2beLss8/miiuuYNasWUyfPp0rrriCpUuX7vbjrrjiCgYNGsTMmTOZOXMmI0aM+Mo1P/jBD7Z+f0899RTNmjWjY8eOW8f/9a9/bVcYv7R27Vruv/9+TijBreD3WNRijLnAL4GRwHTgnzHGqSGEy0MIlxdc9iowG5gFPApcWUJ5JUmSJJWicePGccopp9C5c2fOOOMMFi1K5mPuv/9+WrduTfv27bnwwguZO3cujzzyCH/+85/p2LEj77zzzlc+1wsvvECvXr248MILefbZZ4v09Z955hlOOukkevXqtfW5Hj167HSW60uLFi1izZo1nHTSSYQQuPjii3nxxRd3+3UGDx7M97///a2P161bx8CBA7n55pu/cm2/fv247rrrqFatWpG+h31RpDV+McZXScrYts89ss2vI/CL4o0mSZIkVWDDb4DFk4v3cx7aDnoOKPLlMUauuuoqhg4dSoMGDXjuuee46aabePzxxxkwYABz5syhatWqrFq1ijp16nD55ZdTs2ZN/u///m+nn2/w4MHceuutHHLIIVxwwQX89re/3WOGKVOm0Llz552OLVy4kJ/+9Ke8+up2VYUFCxbQpEmTrY+bNGnCggULdvt1nnvuOYYOHbr1cb9+/fjNb35D9erVt7vu448/5vPPP+ecc87hnnvu2WP+feXNWJIkSZJ2avPmzUyZMoXTTjsNgLy8PBo2bAhA+/bt+cEPfsC3vvUtvvWtb+3xc33xxRfMmjWLbt26EUKgcuXKTJkyhbZt2xJ2cvvRzp7bUaNGjb5S0oCd3o+2u8/3wQcfUL169a2zdBMmTGDWrFn8+c9/Zu7cuVuvy8/Pp2/fvjzxxBN7zLa/LGqSJElSJtqLma+SEmOkTZs2jBkz5itjr7zyCm+//TbDhg3jjjvuYOrUqbv9XM899xwrV66kefPmAKxZs4Znn32W/v37U69ePVauXLn12hUrVlC/fn0A2rRpw1tvvbVXuZs0acL8+fO3Pp4/fz6NGjXa5fXPPvvsdssex4wZw7hx42jWrBm5ubksWbKE7t27M3ToUKZMmUL37t0BWLx4Meeeey7Dhg2jS5cue5VxTzwhTJIkSdJOVa1alaVLl24tajk5OUydOpX8/Hw+//xzevTowd13382qVatYt24dBx54IGvXrt3p5xo8eDAjRoxg7ty5zJ07l3Hjxm29T6179+4899xzW3eWfOKJJ+jRowcAF110EaNHj+aVV17Z+rlGjBjB5Mm7XhbasGFDDjzwQN5//31ijDz55JOcd955O702Pz+f559/ngsvvHDrc1dccQULFy5k7ty5vPvuuxx11FGMGjWK2rVrs2zZsq3fw4knnlgiJQ0sapIkSZJ2oVKlSgwZMoTrr7+eDh060LFjR0aPHk1eXh4//OEPadeuHcceeyx9+/alTp069OrVi3//+99f2Uxk7ty5fPbZZ5x44olbn2vevDm1atXigw8+4JxzzuHrX/86nTt3pmPHjrz33nvcddddABxwwAG8/PLLPPDAAxx55JG0bt2aJ554goMPPpiFCxdy1lln7TT7X/7yF37605/SsmVLjjjiCHr27AnAsGHDuOWWW7Ze9/bbb9OkSRNatGhREv8J91nY2frN0tClS5dYUmcOSJIkSWXR9OnTadWqVdoxVAJ29nsbQhgXY9zpdJwzapIkSZKUYSxqkiRJkpRhLGqSJEmSlGEsapIkSVIGSWsPCZWcffk9tahJkiRJGaJatWosX77cslaOxBhZvnw51apV26uP88BrSZIkKUN8eVDz0qVL046iYlStWjWaNGmyVx9jUZMkSZIyRJUqVWjevHnaMZQBXPooSZIkSRnGoiZJkiRJGcaiJkmSJEkZJqS1o0wIYSkwL5Uvvnv1gWVph1C55etLJc3XmEqSry+VJF9fKkmZ+vo6PMbYYGcDqRW1TBVC+CjG2CXtHCqffH2ppPkaU0ny9aWS5OtLJaksvr5c+ihJkiRJGcaiJkmSJEkZxqL2VYPSDqByzdeXSpqvMZUkX18qSb6+VJLK3OvLe9QkSZIkKcM4oyZJkiRJGabCFrUQwpkhhE9CCLNCCDfsZDyEEO4vGJ8UQuiURk6VTUV4ff2g4HU1KYQwOoTQIY2cKpv29Pra5rrjQgh5IYQLSjOfyraivL5CCN1DCBNCCFNDCG+VdkaVbUX4O7J2COGlEMLEgtfYj9PIqbInhPB4CGFJCGHKLsbL1Pv7ClnUQghZwENAT6A18P0QQusdLusJHFnw4zLgL6UaUmVWEV9fc4BTYoztgTsog+umlY4ivr6+vO4uYGTpJlRZVpTXVwihDvAwcG6MsQ3wndLOqbKriH+G/QKYFmPsAHQH/hRCyC7VoCqrngDO3M14mXp/XyGLGnA8MCvGODvGuAV4Fjhvh2vOA56MifeBOiGEhqUdVGXSHl9fMcbRMcaVBQ/fB5qUckaVXUX58wvgKuAFYElphlOZV5TX10XAv2KMnwHEGH2NaW8U5TUWgQNDCAGoCawAcks3psqiGOPbJK+XXSlT7+8ralFrDHy+zeP5Bc/t7TXSzuzta6cPMLxEE6k82ePrK4TQGPg28Egp5lL5UJQ/v44C6oYQRoUQxoUQLi61dCoPivIaexBoBSwEJgO/ijHml048lXNl6v195bQDpCTs5Lkdt78syjXSzhT5tRNC6EFS1LqVaCKVJ0V5fd0LXB9jzEv+QVoqsqK8vioDnYFvAAcAY0II78cYPy3pcCoXivIaOwOYAJwKHAG8FkJ4J8a4poSzqfwrU+/vK2pRmw8cts3jJiT/arO310g7U6TXTgihPfA3oGeMcXkpZVPZV5TXVxfg2YKSVh84K4SQG2N8sVQSqiwr6t+Py2KM64H1IYS3gQ6ARU1FUZTX2I+BATE5Q2pWCGEOcAwwtnQiqhwrU+/vK+rSxw+BI0MIzQtuTr0QGLbDNcOAiwt2hzkRWB1jXFTaQVUm7fH1FUJoCvwL+JH/Cq29tMfXV4yxeYyxWYyxGTAEuNKSpiIqyt+PQ4GvhxAqhxCqAycA00s5p8quorzGPiOZsSWEcAhwNDC7VFOqvCpT7+8r5IxajDE3hPBLkt3QsoDHY4xTQwiXF4w/ArwKnAXMAjaQ/OuOtEdFfH3dAtQDHi6Y9ciNMXZJK7PKjiK+vqR9UpTXV4xxeghhBDAJyAf+FmPc6VbY0o6K+GfYHcATIYTJJEvVro8xLksttMqMEMJgkp1C64cQ5gO3AlWgbL6/D8mssiRJkiQpU1TUpY+SJEmSlLEsapIkSZKUYSxqkiRJkpRhLGqSJEmSlGEsapIkSZKUYSxqkiRJkpRhLGqSJEmSlGEsapIkSZKUYf4//nfXx4AqQV8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the ROC curve\n",
    "\n",
    "plt.figure(figsize=(15,8))\n",
    "plt.plot(FPR, TPR, label = 'Train AUC: %0.3f'%auc_train)\n",
    "plt.plot(FPR1, TPR1, label = 'Test AUC: %0.3f'%auc_test)\n",
    "\n",
    "plt.plot([1,0],[1,0], ls = '--', color = 'blue', lw = 0.90)\n",
    "\n",
    "plt.title('ROC Curve')\n",
    "plt.legend()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 12. Apply ada boosting and gradient boosting models\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# AdaBoostClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "adaclass = AdaBoostClassifier(n_estimators=50,learning_rate=1)\n",
    "model_adaclass = adaclass.fit(X_train, Y_train)\n",
    "y_pred_adaclass = model_adaclass.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GradientBoostingClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "gb_model = GradientBoostingClassifier(n_estimators=20, learning_rate=0.5, max_features=2, max_depth=2, random_state=0)\n",
    "gb_model.fit(X_train, Y_train)\n",
    "y_pred_gb_model = gb_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 13. Find The Classification Metrics and Give the Observations to Metric for ada and gradient boosting algorithms\n",
    "* Confusion Matrix\n",
    "\n",
    "    * Precision\n",
    "\n",
    "    * Recall\n",
    "\n",
    "    * Specificity\n",
    "\n",
    "    * Sensitivity\n",
    "\n",
    "    * Accuracy\n",
    "\n",
    "    * F1-score\n",
    "\n",
    "    * True Positive Rate\n",
    "\n",
    "    * False Positive Rate\n",
    "\n",
    "* Classification Report\n",
    "\n",
    "* ROC_AUC Curve\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AdaBoostClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1488  451]\n",
      " [ 233  828]]\n",
      "Accuracy score: 0.772\n",
      "Precision score: 0.6473807662236122\n",
      "Recall score: 0.7803958529688972\n"
     ]
    }
   ],
   "source": [
    "cm_ab = metrics.confusion_matrix(Y_test, y_pred_adaclass) \n",
    "accuracy_ab = metrics.accuracy_score(Y_test, y_pred_adaclass) \n",
    "precision_ab = metrics.precision_score(Y_test, y_pred_adaclass) \n",
    "recall_ab = metrics.recall_score(Y_test, y_pred_adaclass)\n",
    "\n",
    "print(cm_ab)\n",
    "print(\"Accuracy score:\",accuracy_ab)\n",
    "print(\"Precision score:\",precision_ab)\n",
    "print(\"Recall score:\",recall_ab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.77      0.81      1939\n",
      "           1       0.65      0.78      0.71      1061\n",
      "\n",
      "    accuracy                           0.77      3000\n",
      "   macro avg       0.76      0.77      0.76      3000\n",
      "weighted avg       0.79      0.77      0.78      3000\n",
      "\n",
      "accuracy_score :  0.772\n"
     ]
    }
   ],
   "source": [
    "# classification_report\n",
    "print(classification_report(Y_test, y_pred_adaclass))\n",
    "print('accuracy_score : ',accuracy_score(Y_test,y_pred_adaclass))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate predictions\n",
    "\n",
    "predictions_train_ab = model_adaclass.predict(X_train)\n",
    "predictions_test_ab = model_adaclass.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find False Positive Rate & True Positive Rate\n",
    "\n",
    "FPR_1, TPR_1, b = roc_curve(Y_train, predictions_train_ab)\n",
    "FPR_2, TPR_2, b1 = roc_curve(Y_test,predictions_test_ab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.         0.23898268 1.        ] [0.         0.79827798 1.        ] [2 1 0]\n",
      "[0.         0.23259412 1.        ] [0.         0.78039585 1.        ] [2 1 0]\n"
     ]
    }
   ],
   "source": [
    "print(FPR_1, TPR_1, b)\n",
    "print(FPR_2, TPR_2, b1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train AUC: 0.780\n",
      "Test AUC: 0.774\n"
     ]
    }
   ],
   "source": [
    "# calculate AUC\n",
    "\n",
    "auc_train_1 = auc(FPR_1, TPR_1)\n",
    "print('Train AUC: %0.3f' % auc_train_1)\n",
    "\n",
    "auc_test_1 = auc(FPR_2, TPR_2)\n",
    "print('Test AUC: %0.3f' % auc_test_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the ROC curve\n",
    "\n",
    "plt.figure(figsize=(15,8))\n",
    "plt.plot(FPR_1, TPR_1, label = 'Train AUC: %0.3f'%auc_train_1)\n",
    "plt.plot(FPR_2, TPR_2, label = 'Test AUC: %0.3f'%auc_test_1)\n",
    "\n",
    "plt.plot([1,0],[1,0], ls = '--', color = 'blue', lw = 0.90)\n",
    "\n",
    "plt.title('ROC Curve')\n",
    "plt.legend()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1484  455]\n",
      " [ 225  836]]\n",
      "Accuracy score: 0.7733333333333333\n",
      "Precision score: 0.6475600309837335\n",
      "Recall score: 0.7879359095193214\n"
     ]
    }
   ],
   "source": [
    "cm_gb = metrics.confusion_matrix(Y_test, y_pred_gb_model) \n",
    "accuracy_gb = metrics.accuracy_score(Y_test, y_pred_gb_model) \n",
    "precision_gb = metrics.precision_score(Y_test, y_pred_gb_model) \n",
    "recall_gb = metrics.recall_score(Y_test, y_pred_gb_model)\n",
    "\n",
    "print(cm_gb)\n",
    "print(\"Accuracy score:\",accuracy_gb)\n",
    "print(\"Precision score:\",precision_gb)\n",
    "print(\"Recall score:\",recall_gb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.77      0.81      1939\n",
      "           1       0.65      0.79      0.71      1061\n",
      "\n",
      "    accuracy                           0.77      3000\n",
      "   macro avg       0.76      0.78      0.76      3000\n",
      "weighted avg       0.79      0.77      0.78      3000\n",
      "\n",
      "accuracy_score :  0.7733333333333333\n"
     ]
    }
   ],
   "source": [
    "# classification_report\n",
    "print(classification_report(Y_test, y_pred_gb_model))\n",
    "print('accuracy_score : ',accuracy_score(Y_test,y_pred_gb_model))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate predictions\n",
    "\n",
    "predictions_train_gb = gb_model.predict(X_train)\n",
    "predictions_test_gb = gb_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find False Positive Rate & True Positive Rate\n",
    "\n",
    "FPR_011, TPR_011, c = roc_curve(Y_train, predictions_train_gb)\n",
    "FPR_022, TPR_022, c1 = roc_curve(Y_test,predictions_test_gb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.         0.24402543 1.        ] [0.         0.80729807 1.        ] [2 1 0]\n",
      "[0.         0.23465704 1.        ] [0.         0.78793591 1.        ] [2 1 0]\n"
     ]
    }
   ],
   "source": [
    "print(FPR_011, TPR_011, c)\n",
    "print(FPR_022, TPR_022, c1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train AUC: 0.782\n",
      "Test AUC: 0.777\n"
     ]
    }
   ],
   "source": [
    "# calculate AUC\n",
    "\n",
    "auc_train_3 = auc(FPR_011, TPR_011)\n",
    "print('Train AUC: %0.3f' % auc_train_3)\n",
    "\n",
    "auc_test_3 = auc(FPR_022, TPR_022)\n",
    "print('Test AUC: %0.3f' % auc_test_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the ROC curve\n",
    "\n",
    "plt.figure(figsize=(15,8))\n",
    "plt.plot(FPR_011, TPR_011, label = 'Train AUC: %0.3f'%auc_train_3)\n",
    "plt.plot(FPR_022, TPR_022, label = 'Test AUC: %0.3f'%auc_test_3)\n",
    "\n",
    "plt.plot([1,0],[1,0], ls = '--', color = 'blue', lw = 0.90)\n",
    "\n",
    "plt.title('ROC Curve')\n",
    "plt.legend()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 15. Try to find out according to metrics which algorithm is appropriate for the given data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for the Given data i tested 3 model, one is Random Foresy and another is ADA Boosting & Gradient Boosting , and after checking all those 3 model precision,recall,f1-score,support,accuracy,macro avg,weighted avg,accuracy_score,Train AUC,Test AUC, and then i found that The Gradient Boosting Model is better Curve and accuracy ponint."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 17. Discuss what is the difference between model parameters and hyper parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestClassifier(),\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_features': [1, 2, 3],\n",
       "                         'n_estimators': [20, 50, 100]})"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "pram_grid = {'n_estimators' : [20,50,100],\n",
    "            'criterion': ['gini', 'entropy'],\n",
    "            'max_features':[1,2,3]}\n",
    "model_hyp = RandomForestClassifier()\n",
    "grid_results = GridSearchCV(model_hyp, pram_grid, cv = 5)\n",
    "grid_results.fit(X_train, Y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_hyp = grid_results.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30.927688121795654\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "\n",
    "grid_results.fit(X_train.values, Y_train.values)\n",
    "\n",
    "end = time.time()\n",
    "\n",
    "print(end-start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 18. Apply the Hyper parameter tuning for Random forest algorithm and now check the metrics\n",
    "\n",
    "* Confusion Matrix\n",
    "\n",
    "    * Precision\n",
    "\n",
    "    * Recall\n",
    "\n",
    "    * Specificity\n",
    "\n",
    "    * Sensitivity\n",
    "\n",
    "    * Accuracy\n",
    "\n",
    "    * F1-score\n",
    "\n",
    "    * True Positive Rate\n",
    "\n",
    "    * False Positive Rate\n",
    "\n",
    "* Classification Report\n",
    "\n",
    "* ROC_AUC Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best_score_ :  0.7515714285714286\n",
      "best_params_ :  {'criterion': 'entropy', 'max_features': 3, 'n_estimators': 100}\n",
      "best_estimator_ :  RandomForestClassifier(criterion='entropy', max_features=3)\n"
     ]
    }
   ],
   "source": [
    "print('best_score_ : ',grid_results.best_score_)\n",
    "print('best_params_ : ', grid_results.best_params_)\n",
    "print('best_estimator_ : ', grid_results.best_estimator_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1528  411]\n",
      " [ 328  733]]\n",
      "Accuracy score: 0.7536666666666667\n",
      "Precision score: 0.6407342657342657\n",
      "Recall score: 0.6908576814326107\n"
     ]
    }
   ],
   "source": [
    "cm_hyp = metrics.confusion_matrix(Y_test, y_pred_hyp) \n",
    "accuracy_hyp = metrics.accuracy_score(Y_test, y_pred_hyp) \n",
    "precision_hyp = metrics.precision_score(Y_test, y_pred_hyp) \n",
    "recall_hyp = metrics.recall_score(Y_test, y_pred_hyp)\n",
    "\n",
    "print(cm_hyp)\n",
    "print(\"Accuracy score:\",accuracy_hyp)\n",
    "print(\"Precision score:\",precision_hyp)\n",
    "print(\"Recall score:\",recall_hyp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.79      0.81      1939\n",
      "           1       0.64      0.69      0.66      1061\n",
      "\n",
      "    accuracy                           0.75      3000\n",
      "   macro avg       0.73      0.74      0.74      3000\n",
      "weighted avg       0.76      0.75      0.76      3000\n",
      "\n",
      "accuracy_score :  0.7536666666666667\n"
     ]
    }
   ],
   "source": [
    "# classification_report\n",
    "print(classification_report(Y_test, y_pred_hyp))\n",
    "print('accuracy_score : ',accuracy_score(Y_test,y_pred_hyp))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate predictions\n",
    "\n",
    "predictions_train_hyp = grid_results.predict(X_train)\n",
    "predictions_test_hyp = grid_results.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find False Positive Rate & True Positive Rate\n",
    "\n",
    "FPR_001, TPR_001, d = roc_curve(Y_train, predictions_train_hyp)\n",
    "FPR_002, TPR_002, d1 = roc_curve(Y_test,predictions_test_hyp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 1.] [0. 1. 1.] [2 1 0]\n",
      "[0.         0.20577617 1.        ] [0.         0.67577757 1.        ] [2 1 0]\n"
     ]
    }
   ],
   "source": [
    "print(FPR_001, TPR_001, d)\n",
    "print(FPR_002, TPR_002, d1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train AUC: 1.000\n",
      "Test AUC: 0.735\n"
     ]
    }
   ],
   "source": [
    "# calculate AUC\n",
    "\n",
    "auc_train_4 = auc(FPR_001, TPR_001)\n",
    "print('Train AUC: %0.3f' % auc_train_4)\n",
    "\n",
    "auc_test_4 = auc(FPR_002, TPR_002)\n",
    "print('Test AUC: %0.3f' % auc_test_4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the ROC curve\n",
    "\n",
    "plt.figure(figsize=(15,8))\n",
    "plt.plot(FPR_001, TPR_001, label = 'Train AUC: %0.3f'%auc_train_4)\n",
    "plt.plot(FPR_002, TPR_002, label = 'Test AUC: %0.3f'%auc_test_4)\n",
    "\n",
    "plt.plot([1,0],[1,0], ls = '--', color = 'blue', lw = 0.90)\n",
    "\n",
    "plt.title('ROC Curve')\n",
    "plt.legend()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 19. Now discuss that what you find difference from model parameter and hyper parameters in Random forest algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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