Module 2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "63sBpZJAXoJG"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.feature_selection import RFE\n",
    "import seaborn as sns\n",
    "from sklearn import tree\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, classification_report, confusion_matrix\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.over_sampling import ADASYN \n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from imblearn.under_sampling import NearMiss\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from imblearn.under_sampling import CondensedNearestNeighbour\n",
    "from sklearn.ensemble import ExtraTreesClassifier, GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('final.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_U5rfY7UXoJN"
   },
   "outputs": [
    {
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       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>23</td>\n",
       "      <td>39</td>\n",
       "      <td>31</td>\n",
       "      <td>71</td>\n",
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       "      <td>2016</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <td>6</td>\n",
       "      <td>1310</td>\n",
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       "      <td>113</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1016</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>23</td>\n",
       "      <td>39</td>\n",
       "      <td>31</td>\n",
       "      <td>71</td>\n",
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       "      <td>2016</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>1330</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1603</td>\n",
       "      <td>...</td>\n",
       "      <td>113</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1016</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>23</td>\n",
       "      <td>39</td>\n",
       "      <td>31</td>\n",
       "      <td>71</td>\n",
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       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>700</td>\n",
       "      <td>290.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1536</td>\n",
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       "      <td>113</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1016</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>23</td>\n",
       "      <td>39</td>\n",
       "      <td>31</td>\n",
       "      <td>71</td>\n",
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       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>800</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>113</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1016</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>23</td>\n",
       "      <td>39</td>\n",
       "      <td>31</td>\n",
       "      <td>71</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>1240</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1600</td>\n",
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       "      <td>113</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1016</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>23</td>\n",
       "      <td>39</td>\n",
       "      <td>31</td>\n",
       "      <td>71</td>\n",
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       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1158</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1629</td>\n",
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       "      <td>113</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1016</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>23</td>\n",
       "      <td>39</td>\n",
       "      <td>31</td>\n",
       "      <td>71</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1424</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1611</td>\n",
       "      <td>...</td>\n",
       "      <td>113</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1016</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>23</td>\n",
       "      <td>39</td>\n",
       "      <td>31</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>714</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1536</td>\n",
       "      <td>...</td>\n",
       "      <td>113</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1016</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>23</td>\n",
       "      <td>39</td>\n",
       "      <td>31</td>\n",
       "      <td>71</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>1325</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1604</td>\n",
       "      <td>...</td>\n",
       "      <td>113</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1016</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>23</td>\n",
       "      <td>39</td>\n",
       "      <td>31</td>\n",
       "      <td>71</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>735</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>23</td>\n",
       "      <td>39</td>\n",
       "      <td>31</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>745</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1602</td>\n",
       "      <td>...</td>\n",
       "      <td>113</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1009</td>\n",
       "      <td>46</td>\n",
       "      <td>31</td>\n",
       "      <td>28</td>\n",
       "      <td>40</td>\n",
       "      <td>32</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>1310</td>\n",
       "      <td>91.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1600</td>\n",
       "      <td>...</td>\n",
       "      <td>113</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1009</td>\n",
       "      <td>46</td>\n",
       "      <td>31</td>\n",
       "      <td>28</td>\n",
       "      <td>40</td>\n",
       "      <td>32</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>1330</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1603</td>\n",
       "      <td>...</td>\n",
       "      <td>113</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1009</td>\n",
       "      <td>46</td>\n",
       "      <td>31</td>\n",
       "      <td>28</td>\n",
       "      <td>40</td>\n",
       "      <td>32</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>700</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1536</td>\n",
       "      <td>...</td>\n",
       "      <td>113</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1009</td>\n",
       "      <td>46</td>\n",
       "      <td>31</td>\n",
       "      <td>28</td>\n",
       "      <td>40</td>\n",
       "      <td>32</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>800</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1624</td>\n",
       "      <td>...</td>\n",
       "      <td>113</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1009</td>\n",
       "      <td>46</td>\n",
       "      <td>31</td>\n",
       "      <td>28</td>\n",
       "      <td>40</td>\n",
       "      <td>32</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>1240</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1600</td>\n",
       "      <td>...</td>\n",
       "      <td>113</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1009</td>\n",
       "      <td>46</td>\n",
       "      <td>31</td>\n",
       "      <td>28</td>\n",
       "      <td>40</td>\n",
       "      <td>32</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1150</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1620</td>\n",
       "      <td>...</td>\n",
       "      <td>113</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1009</td>\n",
       "      <td>46</td>\n",
       "      <td>31</td>\n",
       "      <td>28</td>\n",
       "      <td>40</td>\n",
       "      <td>32</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1424</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1611</td>\n",
       "      <td>...</td>\n",
       "      <td>113</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1009</td>\n",
       "      <td>46</td>\n",
       "      <td>31</td>\n",
       "      <td>28</td>\n",
       "      <td>40</td>\n",
       "      <td>32</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>735</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1555</td>\n",
       "      <td>...</td>\n",
       "      <td>113</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1009</td>\n",
       "      <td>46</td>\n",
       "      <td>31</td>\n",
       "      <td>28</td>\n",
       "      <td>40</td>\n",
       "      <td>32</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1802144</th>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>2136</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29</td>\n",
       "      <td>...</td>\n",
       "      <td>116</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1020</td>\n",
       "      <td>3</td>\n",
       "      <td>66</td>\n",
       "      <td>10</td>\n",
       "      <td>69</td>\n",
       "      <td>69</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1802145</th>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>104</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>638</td>\n",
       "      <td>...</td>\n",
       "      <td>116</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1021</td>\n",
       "      <td>80</td>\n",
       "      <td>54</td>\n",
       "      <td>4</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1802146</th>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>636</td>\n",
       "      <td>...</td>\n",
       "      <td>116</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1021</td>\n",
       "      <td>80</td>\n",
       "      <td>54</td>\n",
       "      <td>4</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1802147</th>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>111</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>628</td>\n",
       "      <td>...</td>\n",
       "      <td>116</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1020</td>\n",
       "      <td>70</td>\n",
       "      <td>55</td>\n",
       "      <td>3</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1802148</th>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>2115</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>537</td>\n",
       "      <td>...</td>\n",
       "      <td>119</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1026</td>\n",
       "      <td>32</td>\n",
       "      <td>33</td>\n",
       "      <td>7</td>\n",
       "      <td>36</td>\n",
       "      <td>32</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1802149</th>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>2333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>611</td>\n",
       "      <td>...</td>\n",
       "      <td>119</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1026</td>\n",
       "      <td>32</td>\n",
       "      <td>33</td>\n",
       "      <td>7</td>\n",
       "      <td>36</td>\n",
       "      <td>32</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1802150</th>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>2146</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>113</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1022</td>\n",
       "      <td>17</td>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "      <td>42</td>\n",
       "      <td>38</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1802151</th>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>1141</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1959</td>\n",
       "      <td>...</td>\n",
       "      <td>116</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1024</td>\n",
       "      <td>77</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1802152</th>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>1735</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>157</td>\n",
       "      <td>...</td>\n",
       "      <td>248</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>1022</td>\n",
       "      <td>100</td>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "      <td>33</td>\n",
       "      <td>29</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1802153</th>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2015</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200</td>\n",
       "      <td>...</td>\n",
       "      <td>248</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>1022</td>\n",
       "      <td>100</td>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "      <td>33</td>\n",
       "      <td>29</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1802154</th>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>920</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1214</td>\n",
       "      <td>...</td>\n",
       "      <td>122</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1022</td>\n",
       "      <td>100</td>\n",
       "      <td>56</td>\n",
       "      <td>9</td>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1802155</th>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>855</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1154</td>\n",
       "      <td>...</td>\n",
       "      <td>122</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1022</td>\n",
       "      <td>100</td>\n",
       "      <td>56</td>\n",
       "      <td>9</td>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1802156</th>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>104</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>640</td>\n",
       "      <td>...</td>\n",
       "      <td>143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1021</td>\n",
       "      <td>100</td>\n",
       "      <td>55</td>\n",
       "      <td>7</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1802157</th>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>515</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>648</td>\n",
       "      <td>...</td>\n",
       "      <td>143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1021</td>\n",
       "      <td>100</td>\n",
       "      <td>55</td>\n",
       "      <td>7</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1802158</th>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>636</td>\n",
       "      <td>...</td>\n",
       "      <td>143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1021</td>\n",
       "      <td>100</td>\n",
       "      <td>55</td>\n",
       "      <td>7</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1802159</th>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>111</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>628</td>\n",
       "      <td>...</td>\n",
       "      <td>143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1020</td>\n",
       "      <td>100</td>\n",
       "      <td>55</td>\n",
       "      <td>6</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1802160</th>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>530</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>715</td>\n",
       "      <td>...</td>\n",
       "      <td>113</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1017</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>23</td>\n",
       "      <td>48</td>\n",
       "      <td>43</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1802161</th>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>515</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>728</td>\n",
       "      <td>...</td>\n",
       "      <td>113</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1017</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>23</td>\n",
       "      <td>48</td>\n",
       "      <td>43</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1802162</th>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>605</td>\n",
       "      <td>-13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>717</td>\n",
       "      <td>...</td>\n",
       "      <td>113</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1017</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>23</td>\n",
       "      <td>48</td>\n",
       "      <td>43</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1802163</th>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>600</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>730</td>\n",
       "      <td>...</td>\n",
       "      <td>113</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1017</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>23</td>\n",
       "      <td>48</td>\n",
       "      <td>43</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1802164</th>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>1735</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>157</td>\n",
       "      <td>...</td>\n",
       "      <td>116</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1024</td>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "      <td>13</td>\n",
       "      <td>31</td>\n",
       "      <td>26</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1802165</th>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>2325</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>504</td>\n",
       "      <td>...</td>\n",
       "      <td>122</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1019</td>\n",
       "      <td>97</td>\n",
       "      <td>31</td>\n",
       "      <td>13</td>\n",
       "      <td>32</td>\n",
       "      <td>25</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1802166</th>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>2321</td>\n",
       "      <td>67.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>510</td>\n",
       "      <td>...</td>\n",
       "      <td>122</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1019</td>\n",
       "      <td>97</td>\n",
       "      <td>31</td>\n",
       "      <td>13</td>\n",
       "      <td>32</td>\n",
       "      <td>25</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1802167</th>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>2320</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>513</td>\n",
       "      <td>...</td>\n",
       "      <td>122</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1019</td>\n",
       "      <td>97</td>\n",
       "      <td>31</td>\n",
       "      <td>13</td>\n",
       "      <td>32</td>\n",
       "      <td>25</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1802168</th>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>40</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>456</td>\n",
       "      <td>...</td>\n",
       "      <td>122</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1019</td>\n",
       "      <td>97</td>\n",
       "      <td>31</td>\n",
       "      <td>13</td>\n",
       "      <td>32</td>\n",
       "      <td>25</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1802169</th>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>2319</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>520</td>\n",
       "      <td>...</td>\n",
       "      <td>122</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1019</td>\n",
       "      <td>97</td>\n",
       "      <td>31</td>\n",
       "      <td>13</td>\n",
       "      <td>32</td>\n",
       "      <td>25</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1802170</th>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>905</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1159</td>\n",
       "      <td>...</td>\n",
       "      <td>122</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1020</td>\n",
       "      <td>81</td>\n",
       "      <td>49</td>\n",
       "      <td>8</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1802171</th>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>855</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1154</td>\n",
       "      <td>...</td>\n",
       "      <td>122</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1020</td>\n",
       "      <td>81</td>\n",
       "      <td>49</td>\n",
       "      <td>8</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1802172</th>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>104</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>640</td>\n",
       "      <td>...</td>\n",
       "      <td>122</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9</td>\n",
       "      <td>1019</td>\n",
       "      <td>100</td>\n",
       "      <td>46</td>\n",
       "      <td>8</td>\n",
       "      <td>49</td>\n",
       "      <td>47</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1802173</th>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>636</td>\n",
       "      <td>...</td>\n",
       "      <td>122</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9</td>\n",
       "      <td>1019</td>\n",
       "      <td>100</td>\n",
       "      <td>46</td>\n",
       "      <td>8</td>\n",
       "      <td>49</td>\n",
       "      <td>47</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1802174 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         YEAR  QUARTER  MONTH  DAY_OF_MONTH  ORIGIN  DEST  CRS_DEP_TIME  \\\n",
       "0        2016        1      1             1      13     6           745   \n",
       "1        2016        1      1             1      10     6          1310   \n",
       "2        2016        1      1             1       9     6          1330   \n",
       "3        2016        1      1             1       8     6           800   \n",
       "4        2016        1      1             1      11     6          1240   \n",
       "...       ...      ...    ...           ...     ...   ...           ...   \n",
       "1802169  2017        4     12            17       8    11          2319   \n",
       "1802170  2017        4     12            17      11     5           905   \n",
       "1802171  2017        4     12            17       4     5           855   \n",
       "1802172  2017        4     12            17      14     5           104   \n",
       "1802173  2017        4     12            17      13     5            17   \n",
       "\n",
       "         DEP_DELAY  DEP_DEL15  CRS_ARR_TIME  ...  weatherCode  precipMM  \\\n",
       "0             -4.0        0.0          1602  ...          113       0.0   \n",
       "1             -4.0        0.0          1600  ...          113       0.0   \n",
       "2             38.0        1.0          1603  ...          113       0.0   \n",
       "3             -2.0        0.0          1624  ...          113       0.0   \n",
       "4             -4.0        0.0          1600  ...          113       0.0   \n",
       "...            ...        ...           ...  ...          ...       ...   \n",
       "1802169       -7.0        0.0           520  ...          122       0.0   \n",
       "1802170        9.0        0.0          1159  ...          122       0.0   \n",
       "1802171       -4.0        0.0          1154  ...          122       0.0   \n",
       "1802172        6.0        0.0           640  ...          122       0.0   \n",
       "1802173      102.0        1.0           636  ...          122       0.0   \n",
       "\n",
       "         visibility  pressure  cloudcover  DewPointF  WindGustKmph  tempF  \\\n",
       "0                10      1015           0         32            26     41   \n",
       "1                10      1015           0         32            26     41   \n",
       "2                10      1015           0         32            26     41   \n",
       "3                10      1015           0         32            26     41   \n",
       "4                10      1015           0         32            26     41   \n",
       "...             ...       ...         ...        ...           ...    ...   \n",
       "1802169           3      1019          97         31            13     32   \n",
       "1802170          10      1020          81         49             8     55   \n",
       "1802171          10      1020          81         49             8     55   \n",
       "1802172           9      1019         100         46             8     49   \n",
       "1802173           9      1019         100         46             8     49   \n",
       "\n",
       "         WindChillF  humidity  \n",
       "0                32        74  \n",
       "1                32        74  \n",
       "2                32        74  \n",
       "3                32        74  \n",
       "4                32        74  \n",
       "...             ...       ...  \n",
       "1802169          25        98  \n",
       "1802170          55        80  \n",
       "1802171          55        80  \n",
       "1802172          47        91  \n",
       "1802173          47        91  \n",
       "\n",
       "[1802174 rows x 23 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['ARR_DELAY','Unnamed: 0'],1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1802174 entries, 0 to 1802173\n",
      "Data columns (total 23 columns):\n",
      "YEAR             int64\n",
      "QUARTER          int64\n",
      "MONTH            int64\n",
      "DAY_OF_MONTH     int64\n",
      "ORIGIN           int64\n",
      "DEST             int64\n",
      "CRS_DEP_TIME     int64\n",
      "DEP_DELAY        float64\n",
      "DEP_DEL15        float64\n",
      "CRS_ARR_TIME     int64\n",
      "ARR_DEL15        float64\n",
      "windspeedKmph    int64\n",
      "winddirDegree    int64\n",
      "weatherCode      int64\n",
      "precipMM         float64\n",
      "visibility       int64\n",
      "pressure         int64\n",
      "cloudcover       int64\n",
      "DewPointF        int64\n",
      "WindGustKmph     int64\n",
      "tempF            int64\n",
      "WindChillF       int64\n",
      "humidity         int64\n",
      "dtypes: float64(4), int64(19)\n",
      "memory usage: 316.2 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.astype(int)\n",
    "#df = df.drop(['DEP_DELAY','DEP_DEL15'],1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZDwvofsZXoJQ"
   },
   "source": [
    "## Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0PnJZKNUXoJR"
   },
   "outputs": [],
   "source": [
    "X = df.drop(\"ARR_DEL15\", axis = 1)\n",
    "Y = df[\"ARR_DEL15\"]\n",
    "x_train, x_test, y_train, y_test = train_test_split( X, Y, test_size=0.20, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BKX7QDjvXoJU"
   },
   "outputs": [],
   "source": [
    "clf = tree.DecisionTreeClassifier()\n",
    "clf = clf.fit(x_train,y_train)\n",
    "y_pred = clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0plhk8faXoJW"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test score      : 0.8704568910985584\n",
      "precision score : 0.6819620253164557\n",
      "recall score    : 0.710578099239483\n",
      "f1 score        : 0.6959760385466857\n"
     ]
    }
   ],
   "source": [
    "print(\"test score      : \"+str(clf.score(x_test,y_test))) \n",
    "print(\"precision score : \"+str(precision_score(y_test,y_pred))) \n",
    "print(\"recall score    : \"+str(recall_score(y_test,y_pred))) \n",
    "print(\"f1 score        : \"+str(f1_score(y_test,y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "D7gsuSSxXoJZ"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.92      0.91      0.92     71306\n",
      "         1.0       0.68      0.71      0.70     18803\n",
      "\n",
      "    accuracy                           0.87     90109\n",
      "   macro avg       0.80      0.81      0.81     90109\n",
      "weighted avg       0.87      0.87      0.87     90109\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lw-OpvwhXoJc"
   },
   "source": [
    "## Sampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3y3R1C_6XoJc"
   },
   "source": [
    "### SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NVxWjb8rXoJd"
   },
   "outputs": [],
   "source": [
    "sm = SMOTE('auto', random_state=12, ratio=1.0)\n",
    "X = np.array(df.drop(\"ARR_DEL15\", axis = 1))\n",
    "Y = np.array(df[\"ARR_DEL15\"])\n",
    "x_train, x_test, y_train, y_test = train_test_split( X, Y, test_size=0.20, random_state=42)\n",
    "x_tr, y_tr = sm.fit_sample(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VJ-MJYdCXoJg"
   },
   "outputs": [],
   "source": [
    "clf = tree.DecisionTreeClassifier()\n",
    "clf = clf.fit(x_tr, y_tr)\n",
    "y_pred = clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0rm33eh0XoJi"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Score: 0.8664728273535385\n",
      "precision score : 0.6724568284855585\n",
      "recall score : 0.7020688188055098\n",
      "f1 score : 0.6869438517978873\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.92      0.91      0.92     71306\n",
      "         1.0       0.67      0.70      0.69     18803\n",
      "\n",
      "    accuracy                           0.87     90109\n",
      "   macro avg       0.80      0.81      0.80     90109\n",
      "weighted avg       0.87      0.87      0.87     90109\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Test Score: \"+ str(clf.score(x_test,y_test))) \n",
    "print(\"precision score : \"+str(precision_score(y_test,y_pred))) \n",
    "print(\"recall score : \"+str(recall_score(y_test,y_pred))) \n",
    "print(\"f1 score : \"+str(f1_score(y_test,y_pred)))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "e3Pi2wrdXoJl"
   },
   "source": [
    "### ADAYSN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mpjYp5aZXoJm"
   },
   "outputs": [],
   "source": [
    "sm = ADASYN()\n",
    "X = np.array(df.drop(\"ARR_DEL15\", axis = 1))\n",
    "Y = np.array(df[\"ARR_DEL15\"])\n",
    "x_train, x_test, y_train, y_test = train_test_split( X, Y, test_size=0.20, random_state=42)\n",
    "x_train, y_train = sm.fit_sample(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "26GC7WIfXoJp"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Score: 0.7285779682883183\n"
     ]
    }
   ],
   "source": [
    "clf = tree.DecisionTreeClassifier()\n",
    "clf = clf.fit(x_train,y_train)\n",
    "y_pred = clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MxKlNrX2XoJs"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision score : 0.3631161383600423\n",
      "recall score : 0.39692152409790565\n",
      "f1 score : 0.37926702367960213\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.84      0.82      0.83    285138\n",
      "         1.0       0.36      0.40      0.38     75297\n",
      "\n",
      "    accuracy                           0.73    360435\n",
      "   macro avg       0.60      0.61      0.60    360435\n",
      "weighted avg       0.74      0.73      0.73    360435\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Test Score: \"+ str(clf.score(x_test,y_test))) \n",
    "print(\"precision score : \"+str(precision_score(y_test,y_pred))) \n",
    "print(\"recall score : \"+str(recall_score(y_test,y_pred))) \n",
    "print(\"f1 score : \"+str(f1_score(y_test,y_pred)))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MqLrkXvbXoJv"
   },
   "source": [
    "### Random OverSampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "d84-U7IDXoJw"
   },
   "outputs": [],
   "source": [
    "ros = RandomOverSampler(random_state=42)\n",
    "X = np.array(df.drop(\"ARR_DEL15\", axis = 1))\n",
    "Y = np.array(df[\"ARR_DEL15\"])\n",
    "x_train, x_test, y_train, y_test = train_test_split( X, Y, test_size=0.20, random_state=42)\n",
    "x_train, y_train = ros.fit_resample(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kFb2XkeCXoJy"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Score: 0.8708865676196818\n"
     ]
    }
   ],
   "source": [
    "clf = tree.DecisionTreeClassifier()\n",
    "clf = clf.fit(x_train,y_train)\n",
    "y_pred = clf.predict(x_test)\n",
    "\n",
    "print(\"Test Score: \"+ str(clf.score(x_test,y_test))) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Bzi4okloXoJ1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision score : 0.6889134176949054\n",
      "recall score : 0.6963341745251693\n",
      "f1 score : 0.6926039196517626\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.92      0.92      0.92    285145\n",
      "         1.0       0.69      0.70      0.69     75290\n",
      "\n",
      "    accuracy                           0.87    360435\n",
      "   macro avg       0.80      0.81      0.81    360435\n",
      "weighted avg       0.87      0.87      0.87    360435\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"precision score : \"+str(precision_score(y_test,y_pred))) \n",
    "print(\"recall score : \"+str(recall_score(y_test,y_pred))) \n",
    "print(\"f1 score : \"+str(f1_score(y_test,y_pred)))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KCZAyPfoXoJ5"
   },
   "source": [
    "### Near Miss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HeaK6u8DXoJ6"
   },
   "outputs": [],
   "source": [
    "nm = NearMiss()\n",
    "X = np.array(df.drop(\"ARR_DEL15\", axis = 1))\n",
    "Y = np.array(df[\"ARR_DEL15\"])\n",
    "x_train, x_test, y_train, y_test = train_test_split( X, Y, test_size=0.20, random_state=42)\n",
    "x_train, y_train = nm.fit_resample(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1oLhLE66XoJ9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Score: 0.4714775202186247\n"
     ]
    }
   ],
   "source": [
    "clf = tree.DecisionTreeClassifier()\n",
    "clf = clf.fit(x_train,y_train)\n",
    "y_pred = clf.predict(x_test)\n",
    "\n",
    "print(\"Test Score: \"+ str(clf.score(x_test,y_test))) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rRKaCo55XoKA"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision score : 0.23406111924208076\n",
      "recall score : 0.6732804759817789\n",
      "f1 score : 0.3473637329130837\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.83      0.42      0.56    285138\n",
      "         1.0       0.23      0.67      0.35     75297\n",
      "\n",
      "    accuracy                           0.47    360435\n",
      "   macro avg       0.53      0.55      0.45    360435\n",
      "weighted avg       0.70      0.47      0.51    360435\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"precision score : \"+str(precision_score(y_test,y_pred))) \n",
    "print(\"recall score : \"+str(recall_score(y_test,y_pred))) \n",
    "print(\"f1 score : \"+str(f1_score(y_test,y_pred)))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "U-IAwahZXoKE"
   },
   "source": [
    "### Random UnderSampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "p9YaXZJ3XoKF"
   },
   "outputs": [],
   "source": [
    "rus = RandomUnderSampler()\n",
    "X = np.array(df.drop(\"ARR_DEL15\", axis = 1))\n",
    "Y = np.array(df[\"ARR_DEL15\"])\n",
    "x_train, x_test, y_train, y_test = train_test_split( X, Y, test_size=0.20, random_state=42)\n",
    "x_train, y_train = rus.fit_resample(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rMa9giJ6XoKJ"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Score: 0.7917155659134102\n"
     ]
    }
   ],
   "source": [
    "clf = tree.DecisionTreeClassifier()\n",
    "clf = clf.fit(x_train,y_train)\n",
    "y_pred = clf.predict(x_test)\n",
    "\n",
    "print(\"Test Score: \"+ str(clf.score(x_test,y_test))) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YQqQbCPaXoKL"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision score : 0.5008977997699646\n",
      "recall score : 0.8040111568601408\n",
      "f1 score : 0.6172498355774672\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.94      0.79      0.86    285145\n",
      "         1.0       0.50      0.80      0.62     75290\n",
      "\n",
      "    accuracy                           0.79    360435\n",
      "   macro avg       0.72      0.80      0.74    360435\n",
      "weighted avg       0.85      0.79      0.81    360435\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"precision score : \"+str(precision_score(y_test,y_pred))) \n",
    "print(\"recall score : \"+str(recall_score(y_test,y_pred))) \n",
    "print(\"f1 score : \"+str(f1_score(y_test,y_pred)))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extra Trees Classifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array(df.drop(\"ARR_DEL15\", axis = 1))\n",
    "Y = np.array(df[\"ARR_DEL15\"])\n",
    "x_train, x_test, y_train, y_test = train_test_split( X, Y, test_size=0.20, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rishi/.local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Score: 0.9084578356707866\n"
     ]
    }
   ],
   "source": [
    "clf = ExtraTreesClassifier()\n",
    "clf.fit(x_train, y_train) \n",
    "y_pred = clf.predict(x_test)\n",
    "\n",
    "print(\"Test Score: \"+ str(clf.score(x_test,y_test))) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision score : 0.8277513444818126\n",
      "recall score : 0.709377075308806\n",
      "f1 score : 0.7640062082925049\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.93      0.96      0.94    285145\n",
      "         1.0       0.83      0.71      0.76     75290\n",
      "\n",
      "    accuracy                           0.91    360435\n",
      "   macro avg       0.88      0.84      0.85    360435\n",
      "weighted avg       0.91      0.91      0.91    360435\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"precision score : \"+str(precision_score(y_test,y_pred))) \n",
    "print(\"recall score : \"+str(recall_score(y_test,y_pred))) \n",
    "print(\"f1 score : \"+str(f1_score(y_test,y_pred)))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "OJ4S7EsNXoKV"
   },
   "source": [
    "### SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm = SMOTE()\n",
    "X = np.array(df.drop(\"ARR_DEL15\", axis = 1))\n",
    "Y = np.array(df[\"ARR_DEL15\"])\n",
    "x_train, x_test, y_train, y_test = train_test_split( X, Y, test_size=0.20, random_state=42)\n",
    "x_train, y_train = sm.fit_resample(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PxJ3YbFYXoKW"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rishi/.local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Score: 0.905336607155243\n"
     ]
    }
   ],
   "source": [
    "clf = ExtraTreesClassifier()\n",
    "clf.fit(x_train, y_train) \n",
    "y_pred = clf.predict(x_test)\n",
    "print(\"Test Score: \"+ str(clf.score(x_test,y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aqOYY6CbXoKY",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision score : 0.7993485152546317\n",
      "recall score : 0.7300836764510559\n",
      "f1 score : 0.7631476647970233\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.93      0.95      0.94    285145\n",
      "         1.0       0.80      0.73      0.76     75290\n",
      "\n",
      "    accuracy                           0.91    360435\n",
      "   macro avg       0.86      0.84      0.85    360435\n",
      "weighted avg       0.90      0.91      0.90    360435\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"precision score : \"+str(precision_score(y_test,y_pred))) \n",
    "print(\"recall score : \"+str(recall_score(y_test,y_pred))) \n",
    "print(\"f1 score : \"+str(f1_score(y_test,y_pred)))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ADASYN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm = ADASYN()\n",
    "X = np.array(df.drop(\"ARR_DEL15\", axis = 1))\n",
    "Y = np.array(df[\"ARR_DEL15\"])\n",
    "x_train, x_test, y_train, y_test = train_test_split( X, Y, test_size=0.20, random_state=42)\n",
    "x_train, y_train = sm.fit_sample(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rishi/.local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Score: 0.9044404677681135\n",
      "precision score : 0.7932093891321513\n",
      "recall score : 0.7338424757603932\n",
      "f1 score : 0.7623719341819312\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.93      0.95      0.94    285145\n",
      "         1.0       0.79      0.73      0.76     75290\n",
      "\n",
      "    accuracy                           0.90    360435\n",
      "   macro avg       0.86      0.84      0.85    360435\n",
      "weighted avg       0.90      0.90      0.90    360435\n",
      "\n"
     ]
    }
   ],
   "source": [
    "clf = ExtraTreesClassifier()\n",
    "clf.fit(x_train, y_train) \n",
    "y_pred = clf.predict(x_test)\n",
    "print(\"Test Score: \"+ str(clf.score(x_test,y_test)))\n",
    "print(\"precision score : \"+str(precision_score(y_test,y_pred))) \n",
    "print(\"recall score : \"+str(recall_score(y_test,y_pred))) \n",
    "print(\"f1 score : \"+str(f1_score(y_test,y_pred)))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random OverSampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "ros = RandomOverSampler(random_state=42)\n",
    "X = np.array(df.drop(\"ARR_DEL15\", axis = 1))\n",
    "Y = np.array(df[\"ARR_DEL15\"])\n",
    "x_train, x_test, y_train, y_test = train_test_split( X, Y, test_size=0.20, random_state=42)\n",
    "x_train, y_train = ros.fit_resample(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rishi/.local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Score: 0.9070290066170045\n",
      "precision score : 0.8273371149205553\n",
      "recall score : 0.7012750697303759\n",
      "f1 score : 0.7591080311700261\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.92      0.96      0.94    285145\n",
      "         1.0       0.83      0.70      0.76     75290\n",
      "\n",
      "    accuracy                           0.91    360435\n",
      "   macro avg       0.88      0.83      0.85    360435\n",
      "weighted avg       0.90      0.91      0.90    360435\n",
      "\n"
     ]
    }
   ],
   "source": [
    "clf = ExtraTreesClassifier()\n",
    "clf.fit(x_train, y_train) \n",
    "y_pred = clf.predict(x_test)\n",
    "print(\"Test Score: \"+ str(clf.score(x_test,y_test)))\n",
    "print(\"precision score : \"+str(precision_score(y_test,y_pred))) \n",
    "print(\"recall score : \"+str(recall_score(y_test,y_pred))) \n",
    "print(\"f1 score : \"+str(f1_score(y_test,y_pred)))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Near Miss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "nm = NearMiss()\n",
    "X = np.array(df.drop(\"ARR_DEL15\", axis = 1))\n",
    "Y = np.array(df[\"ARR_DEL15\"])\n",
    "x_train, x_test, y_train, y_test = train_test_split( X, Y, test_size=0.20, random_state=42)\n",
    "x_train, y_train = nm.fit_resample(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rishi/.local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Score: 0.7164398573945372\n",
      "precision score : 0.41263122358486276\n",
      "recall score : 0.844175853366981\n",
      "f1 score : 0.5543146942495454\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.94      0.68      0.79    285145\n",
      "         1.0       0.41      0.84      0.55     75290\n",
      "\n",
      "    accuracy                           0.72    360435\n",
      "   macro avg       0.68      0.76      0.67    360435\n",
      "weighted avg       0.83      0.72      0.74    360435\n",
      "\n"
     ]
    }
   ],
   "source": [
    "clf = ExtraTreesClassifier()\n",
    "clf.fit(x_train, y_train) \n",
    "y_pred = clf.predict(x_test)\n",
    "print(\"Test Score: \"+ str(clf.score(x_test,y_test)))\n",
    "print(\"precision score : \"+str(precision_score(y_test,y_pred))) \n",
    "print(\"recall score : \"+str(recall_score(y_test,y_pred))) \n",
    "print(\"f1 score : \"+str(f1_score(y_test,y_pred)))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random UnderSampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "rus = RandomUnderSampler()\n",
    "X = np.array(df.drop(\"ARR_DEL15\", axis = 1))\n",
    "Y = np.array(df[\"ARR_DEL15\"])\n",
    "x_train, x_test, y_train, y_test = train_test_split( X, Y, test_size=0.20, random_state=42)\n",
    "x_train, y_train = rus.fit_resample(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rishi/.local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Score: 0.8768266122879299\n",
      "precision score : 0.6694010045401706\n",
      "recall score : 0.8107318368973303\n",
      "f1 score : 0.733318916840866\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.95      0.89      0.92    285145\n",
      "         1.0       0.67      0.81      0.73     75290\n",
      "\n",
      "    accuracy                           0.88    360435\n",
      "   macro avg       0.81      0.85      0.83    360435\n",
      "weighted avg       0.89      0.88      0.88    360435\n",
      "\n"
     ]
    }
   ],
   "source": [
    "clf = ExtraTreesClassifier()\n",
    "clf.fit(x_train, y_train) \n",
    "y_pred = clf.predict(x_test)\n",
    "print(\"Test Score: \"+ str(clf.score(x_test,y_test)))\n",
    "print(\"precision score : \"+str(precision_score(y_test,y_pred))) \n",
    "print(\"recall score : \"+str(recall_score(y_test,y_pred))) \n",
    "print(\"f1 score : \"+str(f1_score(y_test,y_pred)))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array(df.drop(\"ARR_DEL15\", axis = 1))\n",
    "Y = np.array(df[\"ARR_DEL15\"])\n",
    "x_train, x_test, y_train, y_test = train_test_split( X, Y, test_size=0.20, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Score: 0.9169031864275112\n"
     ]
    }
   ],
   "source": [
    "clf = GradientBoostingClassifier()\n",
    "clf.fit(x_train, y_train) \n",
    "y_pred = clf.predict(x_test)\n",
    "\n",
    "print(\"Test Score: \"+ str(clf.score(x_test,y_test))) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision score : 0.8949734297412667\n",
      "recall score : 0.6822552795856024\n",
      "f1 score : 0.7742698873271282\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.92      0.98      0.95    285145\n",
      "         1.0       0.89      0.68      0.77     75290\n",
      "\n",
      "    accuracy                           0.92    360435\n",
      "   macro avg       0.91      0.83      0.86    360435\n",
      "weighted avg       0.92      0.92      0.91    360435\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"precision score : \"+str(precision_score(y_test,y_pred))) \n",
    "print(\"recall score : \"+str(recall_score(y_test,y_pred))) \n",
    "print(\"f1 score : \"+str(f1_score(y_test,y_pred)))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "b3BR1WwFXoKa"
   },
   "source": [
    "## Sampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm = SMOTE()\n",
    "x_train, y_train = sm.fit_resample(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cAH3tIfOXoKb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Score: 0.9124502337453355\n"
     ]
    }
   ],
   "source": [
    "clf = GradientBoostingClassifier()\n",
    "clf.fit(x_train, y_train) \n",
    "y_pred = clf.predict(x_test)\n",
    "\n",
    "print(\"Test Score: \"+ str(clf.score(x_test,y_test))) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "566B8sDLXoKd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision score : 0.8303471613741427\n",
      "recall score : 0.7300305485456235\n",
      "f1 score : 0.7769641796952306\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.93      0.96      0.95    285145\n",
      "         1.0       0.83      0.73      0.78     75290\n",
      "\n",
      "    accuracy                           0.91    360435\n",
      "   macro avg       0.88      0.85      0.86    360435\n",
      "weighted avg       0.91      0.91      0.91    360435\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"precision score : \"+str(precision_score(y_test,y_pred))) \n",
    "print(\"recall score : \"+str(recall_score(y_test,y_pred))) \n",
    "print(\"f1 score : \"+str(f1_score(y_test,y_pred)))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pipelining Output to Module 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = df[df['YEAR']==2016]\n",
    "x_train = train.drop(\"ARR_DEL15\", axis = 1)\n",
    "y_train = train[\"ARR_DEL15\"]\n",
    "\n",
    "test = df[df['YEAR']==2017]\n",
    "x_test = test.drop(\"ARR_DEL15\", axis = 1)\n",
    "y_test = test[\"ARR_DEL15\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Score: 0.9112215326494539\n",
      "precision score : 0.8356835323215976\n",
      "recall score : 0.7242496255405184\n",
      "f1 score : 0.7759864354492975\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.93      0.96      0.94    700984\n",
      "         1.0       0.84      0.72      0.78    188939\n",
      "\n",
      "    accuracy                           0.91    889923\n",
      "   macro avg       0.88      0.84      0.86    889923\n",
      "weighted avg       0.91      0.91      0.91    889923\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sm = SMOTE()\n",
    "x_train, y_train = sm.fit_resample(x_train, y_train)\n",
    "\n",
    "clf = GradientBoostingClassifier()\n",
    "clf.fit(x_train, y_train)\n",
    "y_pred = clf.predict(x_test)\n",
    "\n",
    "print(\"Test Score: \"+ str(clf.score(x_test,y_test))) \n",
    "print(\"precision score : \"+str(precision_score(y_test,y_pred))) \n",
    "print(\"recall score : \"+str(recall_score(y_test,y_pred))) \n",
    "print(\"f1 score : \"+str(f1_score(y_test,y_pred)))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rishi/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "tdf = df[df['YEAR']==2017]\n",
    "tdf['ARR_DEL15'] = y_pred\n",
    "tdf.to_csv('pipeline.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ADASYN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm = ADASYN()\n",
    "X = np.array(df.drop(\"ARR_DEL15\", axis = 1))\n",
    "Y = np.array(df[\"ARR_DEL15\"])\n",
    "x_train, x_test, y_train, y_test = train_test_split( X, Y, test_size=0.20, random_state=42)\n",
    "x_train, y_train = sm.fit_sample(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Score: 0.9082358816430147\n",
      "precision score : 0.800441249733115\n",
      "recall score : 0.7469119404967459\n",
      "f1 score : 0.7727506956611356\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.93      0.95      0.94    285145\n",
      "         1.0       0.80      0.75      0.77     75290\n",
      "\n",
      "    accuracy                           0.91    360435\n",
      "   macro avg       0.87      0.85      0.86    360435\n",
      "weighted avg       0.91      0.91      0.91    360435\n",
      "\n"
     ]
    }
   ],
   "source": [
    "clf = GradientBoostingClassifier()\n",
    "clf.fit(x_train, y_train) \n",
    "y_pred = clf.predict(x_test)\n",
    "\n",
    "print(\"Test Score: \"+ str(clf.score(x_test,y_test)))\n",
    "print(\"precision score : \"+str(precision_score(y_test,y_pred))) \n",
    "print(\"recall score : \"+str(recall_score(y_test,y_pred))) \n",
    "print(\"f1 score : \"+str(f1_score(y_test,y_pred)))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Over Sampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "ros = RandomOverSampler(random_state=42)\n",
    "X = np.array(df.drop(\"ARR_DEL15\", axis = 1))\n",
    "Y = np.array(df[\"ARR_DEL15\"])\n",
    "x_train, x_test, y_train, y_test = train_test_split( X, Y, test_size=0.20, random_state=42)\n",
    "x_train, y_train = ros.fit_resample(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Score: 0.895015744863845\n",
      "precision score : 0.7304048234280792\n",
      "recall score : 0.7884181166157525\n",
      "f1 score : 0.7583035258048032\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.94      0.92      0.93    285145\n",
      "         1.0       0.73      0.79      0.76     75290\n",
      "\n",
      "    accuracy                           0.90    360435\n",
      "   macro avg       0.84      0.86      0.85    360435\n",
      "weighted avg       0.90      0.90      0.90    360435\n",
      "\n"
     ]
    }
   ],
   "source": [
    "clf = GradientBoostingClassifier()\n",
    "clf.fit(x_train, y_train) \n",
    "y_pred = clf.predict(x_test)\n",
    "\n",
    "print(\"Test Score: \"+ str(clf.score(x_test,y_test)))\n",
    "print(\"precision score : \"+str(precision_score(y_test,y_pred))) \n",
    "print(\"recall score : \"+str(recall_score(y_test,y_pred))) \n",
    "print(\"f1 score : \"+str(f1_score(y_test,y_pred)))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Near Miss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "nm = NearMiss()\n",
    "X = np.array(df.drop(\"ARR_DEL15\", axis = 1))\n",
    "Y = np.array(df[\"ARR_DEL15\"])\n",
    "x_train, x_test, y_train, y_test = train_test_split( X, Y, test_size=0.20, random_state=42)\n",
    "x_train, y_train = nm.fit_resample(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Score: 0.7490476784995908\n",
      "precision score : 0.4458438107212253\n",
      "recall score : 0.8289414264842608\n",
      "f1 score : 0.5798284976355714\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.94      0.73      0.82    285145\n",
      "         1.0       0.45      0.83      0.58     75290\n",
      "\n",
      "    accuracy                           0.75    360435\n",
      "   macro avg       0.69      0.78      0.70    360435\n",
      "weighted avg       0.84      0.75      0.77    360435\n",
      "\n"
     ]
    }
   ],
   "source": [
    "clf = GradientBoostingClassifier()\n",
    "clf.fit(x_train, y_train) \n",
    "y_pred = clf.predict(x_test)\n",
    "\n",
    "print(\"Test Score: \"+ str(clf.score(x_test,y_test)))\n",
    "print(\"precision score : \"+str(precision_score(y_test,y_pred))) \n",
    "print(\"recall score : \"+str(recall_score(y_test,y_pred))) \n",
    "print(\"f1 score : \"+str(f1_score(y_test,y_pred)))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Under Sampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "rus = RandomUnderSampler()\n",
    "X = np.array(df.drop(\"ARR_DEL15\", axis = 1))\n",
    "Y = np.array(df[\"ARR_DEL15\"])\n",
    "x_train, x_test, y_train, y_test = train_test_split( X, Y, test_size=0.20, random_state=42)\n",
    "x_train, y_train = rus.fit_resample(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Score: 0.8947799187093374\n",
      "precision score : 0.7295175616408064\n",
      "recall score : 0.7887103200956302\n",
      "f1 score : 0.7579600354843032\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.94      0.92      0.93    285145\n",
      "         1.0       0.73      0.79      0.76     75290\n",
      "\n",
      "    accuracy                           0.89    360435\n",
      "   macro avg       0.84      0.86      0.85    360435\n",
      "weighted avg       0.90      0.89      0.90    360435\n",
      "\n"
     ]
    }
   ],
   "source": [
    "clf = GradientBoostingClassifier()\n",
    "clf.fit(x_train, y_train) \n",
    "y_pred = clf.predict(x_test)\n",
    "\n",
    "print(\"Test Score: \"+ str(clf.score(x_test,y_test)))\n",
    "print(\"precision score : \"+str(precision_score(y_test,y_pred))) \n",
    "print(\"recall score : \"+str(recall_score(y_test,y_pred))) \n",
    "print(\"f1 score : \"+str(f1_score(y_test,y_pred)))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  }
 ],
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