Random Forest Classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing the libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.formula.api as sm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Email No.</th>\n",
       "      <th>the</th>\n",
       "      <th>to</th>\n",
       "      <th>ect</th>\n",
       "      <th>and</th>\n",
       "      <th>for</th>\n",
       "      <th>of</th>\n",
       "      <th>a</th>\n",
       "      <th>you</th>\n",
       "      <th>hou</th>\n",
       "      <th>...</th>\n",
       "      <th>connevey</th>\n",
       "      <th>jay</th>\n",
       "      <th>valued</th>\n",
       "      <th>lay</th>\n",
       "      <th>infrastructure</th>\n",
       "      <th>military</th>\n",
       "      <th>allowing</th>\n",
       "      <th>ff</th>\n",
       "      <th>dry</th>\n",
       "      <th>Prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Email 1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Email 2</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Email 3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Email 4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Email 5</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 3002 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Email No.  the  to  ect  and  for  of    a  you  hou  ...  connevey  jay  \\\n",
       "0   Email 1    0   0    1    0    0   0    2    0    0  ...         0    0   \n",
       "1   Email 2    8  13   24    6    6   2  102    1   27  ...         0    0   \n",
       "2   Email 3    0   0    1    0    0   0    8    0    0  ...         0    0   \n",
       "3   Email 4    0   5   22    0    5   1   51    2   10  ...         0    0   \n",
       "4   Email 5    7   6   17    1    5   2   57    0    9  ...         0    0   \n",
       "\n",
       "   valued  lay  infrastructure  military  allowing  ff  dry  Prediction  \n",
       "0       0    0               0         0         0   0    0           0  \n",
       "1       0    0               0         0         0   1    0           0  \n",
       "2       0    0               0         0         0   0    0           0  \n",
       "3       0    0               0         0         0   0    0           0  \n",
       "4       0    0               0         0         0   1    0           0  \n",
       "\n",
       "[5 rows x 3002 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset=pd.read_csv('emails.csv') \n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>the</th>\n",
       "      <th>to</th>\n",
       "      <th>ect</th>\n",
       "      <th>and</th>\n",
       "      <th>for</th>\n",
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       "      <th>hou</th>\n",
       "      <th>in</th>\n",
       "      <th>...</th>\n",
       "      <th>connevey</th>\n",
       "      <th>jay</th>\n",
       "      <th>valued</th>\n",
       "      <th>lay</th>\n",
       "      <th>infrastructure</th>\n",
       "      <th>military</th>\n",
       "      <th>allowing</th>\n",
       "      <th>ff</th>\n",
       "      <th>dry</th>\n",
       "      <th>Prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5172.000000</td>\n",
       "      <td>5172.000000</td>\n",
       "      <td>5172.000000</td>\n",
       "      <td>5172.000000</td>\n",
       "      <td>5172.000000</td>\n",
       "      <td>5172.000000</td>\n",
       "      <td>5172.000000</td>\n",
       "      <td>5172.000000</td>\n",
       "      <td>5172.000000</td>\n",
       "      <td>5172.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5172.000000</td>\n",
       "      <td>5172.000000</td>\n",
       "      <td>5172.000000</td>\n",
       "      <td>5172.000000</td>\n",
       "      <td>5172.000000</td>\n",
       "      <td>5172.000000</td>\n",
       "      <td>5172.000000</td>\n",
       "      <td>5172.000000</td>\n",
       "      <td>5172.000000</td>\n",
       "      <td>5172.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.640565</td>\n",
       "      <td>6.188128</td>\n",
       "      <td>5.143852</td>\n",
       "      <td>3.075599</td>\n",
       "      <td>3.124710</td>\n",
       "      <td>2.627030</td>\n",
       "      <td>55.517401</td>\n",
       "      <td>2.466551</td>\n",
       "      <td>2.024362</td>\n",
       "      <td>10.600155</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005027</td>\n",
       "      <td>0.012568</td>\n",
       "      <td>0.010634</td>\n",
       "      <td>0.098028</td>\n",
       "      <td>0.004254</td>\n",
       "      <td>0.006574</td>\n",
       "      <td>0.004060</td>\n",
       "      <td>0.914733</td>\n",
       "      <td>0.006961</td>\n",
       "      <td>0.290023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.745009</td>\n",
       "      <td>9.534576</td>\n",
       "      <td>14.101142</td>\n",
       "      <td>6.045970</td>\n",
       "      <td>4.680522</td>\n",
       "      <td>6.229845</td>\n",
       "      <td>87.574172</td>\n",
       "      <td>4.314444</td>\n",
       "      <td>6.967878</td>\n",
       "      <td>19.281892</td>\n",
       "      <td>...</td>\n",
       "      <td>0.105788</td>\n",
       "      <td>0.199682</td>\n",
       "      <td>0.116693</td>\n",
       "      <td>0.569532</td>\n",
       "      <td>0.096252</td>\n",
       "      <td>0.138908</td>\n",
       "      <td>0.072145</td>\n",
       "      <td>2.780203</td>\n",
       "      <td>0.098086</td>\n",
       "      <td>0.453817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>62.250000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>210.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>344.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>1898.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>223.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 3001 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               the           to          ect          and          for  \\\n",
       "count  5172.000000  5172.000000  5172.000000  5172.000000  5172.000000   \n",
       "mean      6.640565     6.188128     5.143852     3.075599     3.124710   \n",
       "std      11.745009     9.534576    14.101142     6.045970     4.680522   \n",
       "min       0.000000     0.000000     1.000000     0.000000     0.000000   \n",
       "25%       0.000000     1.000000     1.000000     0.000000     1.000000   \n",
       "50%       3.000000     3.000000     1.000000     1.000000     2.000000   \n",
       "75%       8.000000     7.000000     4.000000     3.000000     4.000000   \n",
       "max     210.000000   132.000000   344.000000    89.000000    47.000000   \n",
       "\n",
       "                of            a          you          hou           in  ...  \\\n",
       "count  5172.000000  5172.000000  5172.000000  5172.000000  5172.000000  ...   \n",
       "mean      2.627030    55.517401     2.466551     2.024362    10.600155  ...   \n",
       "std       6.229845    87.574172     4.314444     6.967878    19.281892  ...   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "25%       0.000000    12.000000     0.000000     0.000000     1.000000  ...   \n",
       "50%       1.000000    28.000000     1.000000     0.000000     5.000000  ...   \n",
       "75%       2.000000    62.250000     3.000000     1.000000    12.000000  ...   \n",
       "max      77.000000  1898.000000    70.000000   167.000000   223.000000  ...   \n",
       "\n",
       "          connevey          jay       valued          lay  infrastructure  \\\n",
       "count  5172.000000  5172.000000  5172.000000  5172.000000     5172.000000   \n",
       "mean      0.005027     0.012568     0.010634     0.098028        0.004254   \n",
       "std       0.105788     0.199682     0.116693     0.569532        0.096252   \n",
       "min       0.000000     0.000000     0.000000     0.000000        0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000        0.000000   \n",
       "50%       0.000000     0.000000     0.000000     0.000000        0.000000   \n",
       "75%       0.000000     0.000000     0.000000     0.000000        0.000000   \n",
       "max       4.000000     7.000000     2.000000    12.000000        3.000000   \n",
       "\n",
       "          military     allowing           ff          dry   Prediction  \n",
       "count  5172.000000  5172.000000  5172.000000  5172.000000  5172.000000  \n",
       "mean      0.006574     0.004060     0.914733     0.006961     0.290023  \n",
       "std       0.138908     0.072145     2.780203     0.098086     0.453817  \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "75%       0.000000     0.000000     1.000000     0.000000     1.000000  \n",
       "max       4.000000     3.000000   114.000000     4.000000     1.000000  \n",
       "\n",
       "[8 rows x 3001 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "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>Email No.</th>\n",
       "      <th>the</th>\n",
       "      <th>to</th>\n",
       "      <th>ect</th>\n",
       "      <th>and</th>\n",
       "      <th>for</th>\n",
       "      <th>of</th>\n",
       "      <th>a</th>\n",
       "      <th>you</th>\n",
       "      <th>hou</th>\n",
       "      <th>...</th>\n",
       "      <th>connevey</th>\n",
       "      <th>jay</th>\n",
       "      <th>valued</th>\n",
       "      <th>lay</th>\n",
       "      <th>infrastructure</th>\n",
       "      <th>military</th>\n",
       "      <th>allowing</th>\n",
       "      <th>ff</th>\n",
       "      <th>dry</th>\n",
       "      <th>Prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 3002 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Email No., the, to, ect, and, for, of, a, you, hou, in, on, is, this, enron, i, be, that, will, have, with, your, at, we, s, are, it, by, com, as, from, gas, or, not, me, deal, if, meter, hpl, please, re, e, any, our, corp, can, d, all, has, was, know, need, an, forwarded, new, t, may, up, j, mmbtu, should, do, am, get, out, see, no, there, price, daren, but, been, company, l, these, let, so, would, m, into, xls, farmer, attached, us, information, they, message, day, time, my, one, what, only, http, th, volume, mail, contract, which, month, ...]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 3002 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# verify that there are no null columns in the dataset\n",
    "dataset[dataset.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical = [var for var in dataset.columns if dataset[var].dtype=='O']\n",
    "numerical = [var for var in dataset.columns if dataset[var].dtype!='O']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 1 categorical variables : \n",
      " ['Email No.']\n"
     ]
    }
   ],
   "source": [
    "print('There are {} categorical variables : \\n'.format(len(categorical)), categorical)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Email No.  contains  5172  labels\n"
     ]
    }
   ],
   "source": [
    "# check for cardinality in categorical variables\n",
    "for var in categorical:\n",
    "    print(var, ' contains ', len(dataset[var].unique()), ' labels')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          the      to     ect     and     for      of       a     you     hou  \\\n",
      "count  5172.0  5172.0  5172.0  5172.0  5172.0  5172.0  5172.0  5172.0  5172.0   \n",
      "mean      7.0     6.0     5.0     3.0     3.0     3.0    56.0     2.0     2.0   \n",
      "std      12.0    10.0    14.0     6.0     5.0     6.0    88.0     4.0     7.0   \n",
      "min       0.0     0.0     1.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
      "25%       0.0     1.0     1.0     0.0     1.0     0.0    12.0     0.0     0.0   \n",
      "50%       3.0     3.0     1.0     1.0     2.0     1.0    28.0     1.0     0.0   \n",
      "75%       8.0     7.0     4.0     3.0     4.0     2.0    62.0     3.0     1.0   \n",
      "max     210.0   132.0   344.0    89.0    47.0    77.0  1898.0    70.0   167.0   \n",
      "\n",
      "           in  ...  connevey     jay  valued     lay  infrastructure  \\\n",
      "count  5172.0  ...    5172.0  5172.0  5172.0  5172.0          5172.0   \n",
      "mean     11.0  ...       0.0     0.0     0.0     0.0             0.0   \n",
      "std      19.0  ...       0.0     0.0     0.0     1.0             0.0   \n",
      "min       0.0  ...       0.0     0.0     0.0     0.0             0.0   \n",
      "25%       1.0  ...       0.0     0.0     0.0     0.0             0.0   \n",
      "50%       5.0  ...       0.0     0.0     0.0     0.0             0.0   \n",
      "75%      12.0  ...       0.0     0.0     0.0     0.0             0.0   \n",
      "max     223.0  ...       4.0     7.0     2.0    12.0             3.0   \n",
      "\n",
      "       military  allowing      ff     dry  Prediction  \n",
      "count    5172.0    5172.0  5172.0  5172.0      5172.0  \n",
      "mean        0.0       0.0     1.0     0.0         0.0  \n",
      "std         0.0       0.0     3.0     0.0         0.0  \n",
      "min         0.0       0.0     0.0     0.0         0.0  \n",
      "25%         0.0       0.0     0.0     0.0         0.0  \n",
      "50%         0.0       0.0     0.0     0.0         0.0  \n",
      "75%         0.0       0.0     1.0     0.0         1.0  \n",
      "max         4.0       3.0   114.0     4.0         1.0  \n",
      "\n",
      "[8 rows x 3001 columns] 2\n"
     ]
    }
   ],
   "source": [
    "# view summary statistics in numerical variables to check for outliers\n",
    "print(round(dataset[numerical].describe()),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
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       "    .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>Email No.</th>\n",
       "      <th>the</th>\n",
       "      <th>to</th>\n",
       "      <th>ect</th>\n",
       "      <th>and</th>\n",
       "      <th>for</th>\n",
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       "      <th>you</th>\n",
       "      <th>hou</th>\n",
       "      <th>...</th>\n",
       "      <th>connevey</th>\n",
       "      <th>jay</th>\n",
       "      <th>valued</th>\n",
       "      <th>lay</th>\n",
       "      <th>infrastructure</th>\n",
       "      <th>military</th>\n",
       "      <th>allowing</th>\n",
       "      <th>ff</th>\n",
       "      <th>dry</th>\n",
       "      <th>Prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1111</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2222</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3333</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4444</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 3002 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Email No.  the  to  ect  and  for  of    a  you  hou  ...  connevey  jay  \\\n",
       "0          0    0   0    1    0    0   0    2    0    0  ...         0    0   \n",
       "1       1111    8  13   24    6    6   2  102    1   27  ...         0    0   \n",
       "2       2222    0   0    1    0    0   0    8    0    0  ...         0    0   \n",
       "3       3333    0   5   22    0    5   1   51    2   10  ...         0    0   \n",
       "4       4444    7   6   17    1    5   2   57    0    9  ...         0    0   \n",
       "\n",
       "   valued  lay  infrastructure  military  allowing  ff  dry  Prediction  \n",
       "0       0    0               0         0         0   0    0           0  \n",
       "1       0    0               0         0         0   1    0           0  \n",
       "2       0    0               0         0         0   0    0           0  \n",
       "3       0    0               0         0         0   0    0           0  \n",
       "4       0    0               0         0         0   1    0           0  \n",
       "\n",
       "[5 rows x 3002 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use LabelEncoder to replace purchased (dependent variable) with 0 and 1 \n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "dataset['Email No.']= LabelEncoder().fit_transform(dataset['Email No.'])\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = dataset['Prediction']\n",
    "x = dataset.drop(['Prediction'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Email No.  the  to  ect  and  for  of    a  you  hou  ...  enhancements  \\\n",
      "0          0    0   0    1    0    0   0    2    0    0  ...             0   \n",
      "1       1111    8  13   24    6    6   2  102    1   27  ...             0   \n",
      "2       2222    0   0    1    0    0   0    8    0    0  ...             0   \n",
      "3       3333    0   5   22    0    5   1   51    2   10  ...             0   \n",
      "4       4444    7   6   17    1    5   2   57    0    9  ...             0   \n",
      "\n",
      "   connevey  jay  valued  lay  infrastructure  military  allowing  ff  dry  \n",
      "0         0    0       0    0               0         0         0   0    0  \n",
      "1         0    0       0    0               0         0         0   1    0  \n",
      "2         0    0       0    0               0         0         0   0    0  \n",
      "3         0    0       0    0               0         0         0   0    0  \n",
      "4         0    0       0    0               0         0         0   1    0  \n",
      "\n",
      "[5 rows x 3001 columns]\n"
     ]
    }
   ],
   "source": [
    "print(x.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0\n",
      "1    0\n",
      "2    0\n",
      "3    0\n",
      "4    0\n",
      "Name: Prediction, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(y.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting the dataset into training and test set  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "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,random_state = 0) # func returns train and test data. It takes dataset and then split size test_size =0.3 means 30% data is for test and rest for training and random_state \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Email No.  the  to  ect  and  for  of    a  you  hou  ...  enhancements  \\\n",
      "3459       2735    5   4    1    2    2   3   27    7    0  ...             0   \n",
      "1385        430    3   6    4    6    0   2   57    2    0  ...             0   \n",
      "1380        425    1   0    1    0    0   1    8    0    0  ...             0   \n",
      "4462       3849   37  16    5   22    5  19  228    6    2  ...             0   \n",
      "3840       3158    2   1    1    0    2   0   10    0    0  ...             0   \n",
      "\n",
      "      connevey  jay  valued  lay  infrastructure  military  allowing  ff  dry  \n",
      "3459         0    0       0    0               0         0         0   0    0  \n",
      "1385         0    0       0    0               0         0         0   1    0  \n",
      "1380         0    0       0    0               0         0         0   0    0  \n",
      "4462         0    0       0    2               0         0         0   1    0  \n",
      "3840         0    0       0    0               0         0         0   0    0  \n",
      "\n",
      "[5 rows x 3001 columns]\n"
     ]
    }
   ],
   "source": [
    "print(x_train.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Email No.  the  to  ect  and  for  of    a  you  hou  ...  enhancements  \\\n",
      "3324       2585   14   7    2    2    2   4   37    0    1  ...             0   \n",
      "15          667    6   2    1    0    2   0   36    3    1  ...             0   \n",
      "4950       4391   40   6    4    6    5  11  130    3    0  ...             0   \n",
      "3964       3295   42  19    1    8    7   7  214    1    3  ...             0   \n",
      "2315       1464    8  14    8    3    3   0   87    0    4  ...             0   \n",
      "\n",
      "      connevey  jay  valued  lay  infrastructure  military  allowing  ff  dry  \n",
      "3324         0    0       0    0               0         0         0   0    0  \n",
      "15           0    0       0    0               0         0         0   0    0  \n",
      "4950         0    0       0    0               0         0         0   1    0  \n",
      "3964         0    0       0    1               0         4         0   3    0  \n",
      "2315         0    0       0    0               0         0         0   5    0  \n",
      "\n",
      "[5 rows x 3001 columns]\n"
     ]
    }
   ],
   "source": [
    "print(x_test.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3459    1\n",
      "1385    0\n",
      "1380    0\n",
      "4462    1\n",
      "3840    0\n",
      "1452    1\n",
      "2849    0\n",
      "3145    0\n",
      "1271    0\n",
      "1021    0\n",
      "Name: Prediction, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(y_train[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3324    0\n",
      "15      0\n",
      "4950    0\n",
      "3964    1\n",
      "2315    0\n",
      "861     1\n",
      "2350    0\n",
      "1767    0\n",
      "965     0\n",
      "2630    1\n",
      "Name: Prediction, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(y_test[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "x_train=scaler.fit_transform(x_train) # apply on whole x data \n",
    "x_test=scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build Model "
   ]
  },
  {
   "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) # only 10 trees will b ok as dataset is only 400+ records. Default is 100.    \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": [
    "# Evaluate Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1088   23]\n",
      " [  21  420]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, average_precision_score\n",
    "cm = confusion_matrix(y_test,y_pred)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.98      0.98      1111\n",
      "           1       0.95      0.95      0.95       441\n",
      "\n",
      "    accuracy                           0.97      1552\n",
      "   macro avg       0.96      0.97      0.97      1552\n",
      "weighted avg       0.97      0.97      0.97      1552\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cr = classification_report(y_test,y_pred)\n",
    "print(cr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9716494845360825"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9164654650811013\n"
     ]
    }
   ],
   "source": [
    "average_precision= average_precision_score(y_test,y_pred)\n",
    "print(average_precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '2-class Precision-Recall curve: AP=0.92')"
      ]
     },
     "execution_count": 25,
     "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": [
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import plot_precision_recall_curve\n",
    "disp = plot_precision_recall_curve(classifier, x_test, y_test)\n",
    "disp.ax_.set_title('2-class Precision-Recall curve: '\n",
    "                   'AP={0:0.2f}'.format(average_precision))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}