sales.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This notebook is to predict the sales win/loss by analysing and preprocessing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sales_data = pd.read_csv(\"salesdata.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Opportunity Number</th>\n",
       "      <th>Supplies Subgroup</th>\n",
       "      <th>Supplies Group</th>\n",
       "      <th>Region</th>\n",
       "      <th>Route To Market</th>\n",
       "      <th>Elapsed Days In Sales Stage</th>\n",
       "      <th>Opportunity Result</th>\n",
       "      <th>Sales Stage Change Count</th>\n",
       "      <th>Total Days Identified Through Closing</th>\n",
       "      <th>Total Days Identified Through Qualified</th>\n",
       "      <th>Opportunity Amount USD</th>\n",
       "      <th>Client Size By Revenue</th>\n",
       "      <th>Client Size By Employee Count</th>\n",
       "      <th>Revenue From Client Past Two Years</th>\n",
       "      <th>Competitor Type</th>\n",
       "      <th>Ratio Days Identified To Total Days</th>\n",
       "      <th>Ratio Days Validated To Total Days</th>\n",
       "      <th>Ratio Days Qualified To Total Days</th>\n",
       "      <th>Deal Size Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1641984</td>\n",
       "      <td>Exterior Accessories</td>\n",
       "      <td>Car Accessories</td>\n",
       "      <td>Northwest</td>\n",
       "      <td>Fields Sales</td>\n",
       "      <td>76</td>\n",
       "      <td>Won</td>\n",
       "      <td>13</td>\n",
       "      <td>104</td>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0.69636</td>\n",
       "      <td>0.113985</td>\n",
       "      <td>0.154215</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1658010</td>\n",
       "      <td>Exterior Accessories</td>\n",
       "      <td>Car Accessories</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>Reseller</td>\n",
       "      <td>63</td>\n",
       "      <td>Loss</td>\n",
       "      <td>2</td>\n",
       "      <td>163</td>\n",
       "      <td>163</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1674737</td>\n",
       "      <td>Motorcycle Parts</td>\n",
       "      <td>Performance &amp; Non-auto</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>Reseller</td>\n",
       "      <td>24</td>\n",
       "      <td>Won</td>\n",
       "      <td>7</td>\n",
       "      <td>82</td>\n",
       "      <td>82</td>\n",
       "      <td>7750</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1675224</td>\n",
       "      <td>Shelters &amp; RV</td>\n",
       "      <td>Performance &amp; Non-auto</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>Reseller</td>\n",
       "      <td>16</td>\n",
       "      <td>Loss</td>\n",
       "      <td>5</td>\n",
       "      <td>124</td>\n",
       "      <td>124</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Known</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1689785</td>\n",
       "      <td>Exterior Accessories</td>\n",
       "      <td>Car Accessories</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>Reseller</td>\n",
       "      <td>69</td>\n",
       "      <td>Loss</td>\n",
       "      <td>11</td>\n",
       "      <td>91</td>\n",
       "      <td>13</td>\n",
       "      <td>69756</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.141125</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Opportunity Number     Supplies Subgroup          Supplies Group  \\\n",
       "0             1641984  Exterior Accessories         Car Accessories   \n",
       "1             1658010  Exterior Accessories         Car Accessories   \n",
       "2             1674737      Motorcycle Parts  Performance & Non-auto   \n",
       "3             1675224         Shelters & RV  Performance & Non-auto   \n",
       "4             1689785  Exterior Accessories         Car Accessories   \n",
       "\n",
       "      Region Route To Market  Elapsed Days In Sales Stage Opportunity Result  \\\n",
       "0  Northwest    Fields Sales                           76                Won   \n",
       "1    Pacific        Reseller                           63               Loss   \n",
       "2    Pacific        Reseller                           24                Won   \n",
       "3    Midwest        Reseller                           16               Loss   \n",
       "4    Pacific        Reseller                           69               Loss   \n",
       "\n",
       "   Sales Stage Change Count  Total Days Identified Through Closing  \\\n",
       "0                        13                                    104   \n",
       "1                         2                                    163   \n",
       "2                         7                                     82   \n",
       "3                         5                                    124   \n",
       "4                        11                                     91   \n",
       "\n",
       "   Total Days Identified Through Qualified  Opportunity Amount USD  \\\n",
       "0                                      101                       0   \n",
       "1                                      163                       0   \n",
       "2                                       82                    7750   \n",
       "3                                      124                       0   \n",
       "4                                       13                   69756   \n",
       "\n",
       "   Client Size By Revenue  Client Size By Employee Count  \\\n",
       "0                       5                              5   \n",
       "1                       3                              5   \n",
       "2                       1                              1   \n",
       "3                       1                              1   \n",
       "4                       1                              1   \n",
       "\n",
       "   Revenue From Client Past Two Years Competitor Type  \\\n",
       "0                                   0         Unknown   \n",
       "1                                   0         Unknown   \n",
       "2                                   0         Unknown   \n",
       "3                                   0           Known   \n",
       "4                                   0         Unknown   \n",
       "\n",
       "   Ratio Days Identified To Total Days  Ratio Days Validated To Total Days  \\\n",
       "0                              0.69636                            0.113985   \n",
       "1                              0.00000                            1.000000   \n",
       "2                              1.00000                            0.000000   \n",
       "3                              1.00000                            0.000000   \n",
       "4                              0.00000                            0.141125   \n",
       "\n",
       "   Ratio Days Qualified To Total Days  Deal Size Category  \n",
       "0                            0.154215                   1  \n",
       "1                            0.000000                   1  \n",
       "2                            0.000000                   1  \n",
       "3                            0.000000                   1  \n",
       "4                            0.000000                   4  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Opportunity Number                           int64\n",
       "Supplies Subgroup                           object\n",
       "Supplies Group                              object\n",
       "Region                                      object\n",
       "Route To Market                             object\n",
       "Elapsed Days In Sales Stage                  int64\n",
       "Opportunity Result                          object\n",
       "Sales Stage Change Count                     int64\n",
       "Total Days Identified Through Closing        int64\n",
       "Total Days Identified Through Qualified      int64\n",
       "Opportunity Amount USD                       int64\n",
       "Client Size By Revenue                       int64\n",
       "Client Size By Employee Count                int64\n",
       "Revenue From Client Past Two Years           int64\n",
       "Competitor Type                             object\n",
       "Ratio Days Identified To Total Days        float64\n",
       "Ratio Days Validated To Total Days         float64\n",
       "Ratio Days Qualified To Total Days         float64\n",
       "Deal Size Category                           int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(style=\"whitegrid\",color_codes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Opportunity Number</th>\n",
       "      <th>Elapsed Days In Sales Stage</th>\n",
       "      <th>Sales Stage Change Count</th>\n",
       "      <th>Total Days Identified Through Closing</th>\n",
       "      <th>Total Days Identified Through Qualified</th>\n",
       "      <th>Opportunity Amount USD</th>\n",
       "      <th>Client Size By Revenue</th>\n",
       "      <th>Client Size By Employee Count</th>\n",
       "      <th>Revenue From Client Past Two Years</th>\n",
       "      <th>Ratio Days Identified To Total Days</th>\n",
       "      <th>Ratio Days Validated To Total Days</th>\n",
       "      <th>Ratio Days Qualified To Total Days</th>\n",
       "      <th>Deal Size Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7.802500e+04</td>\n",
       "      <td>78025.000000</td>\n",
       "      <td>78025.000000</td>\n",
       "      <td>78025.000000</td>\n",
       "      <td>78025.000000</td>\n",
       "      <td>78025.000000</td>\n",
       "      <td>78025.000000</td>\n",
       "      <td>78025.000000</td>\n",
       "      <td>78025.000000</td>\n",
       "      <td>78025.000000</td>\n",
       "      <td>78025.000000</td>\n",
       "      <td>78025.000000</td>\n",
       "      <td>78025.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.653429e+06</td>\n",
       "      <td>43.595348</td>\n",
       "      <td>2.955732</td>\n",
       "      <td>16.728356</td>\n",
       "      <td>16.314348</td>\n",
       "      <td>91637.260750</td>\n",
       "      <td>1.620481</td>\n",
       "      <td>1.604050</td>\n",
       "      <td>0.303287</td>\n",
       "      <td>0.203063</td>\n",
       "      <td>0.488314</td>\n",
       "      <td>0.185048</td>\n",
       "      <td>3.436745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.054848e+06</td>\n",
       "      <td>26.585600</td>\n",
       "      <td>1.497242</td>\n",
       "      <td>16.729593</td>\n",
       "      <td>16.562597</td>\n",
       "      <td>133161.029157</td>\n",
       "      <td>1.235796</td>\n",
       "      <td>1.210744</td>\n",
       "      <td>0.930290</td>\n",
       "      <td>0.364985</td>\n",
       "      <td>0.448077</td>\n",
       "      <td>0.340283</td>\n",
       "      <td>1.656890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.641984e+06</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>1.000000</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>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.900423e+06</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>15000.000000</td>\n",
       "      <td>1.000000</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>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.545569e+06</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>49000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.448000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.228329e+06</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>105099.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.197225</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.188552</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.009427e+07</td>\n",
       "      <td>210.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>208.000000</td>\n",
       "      <td>208.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Opportunity Number  Elapsed Days In Sales Stage  \\\n",
       "count        7.802500e+04                 78025.000000   \n",
       "mean         7.653429e+06                    43.595348   \n",
       "std          1.054848e+06                    26.585600   \n",
       "min          1.641984e+06                     0.000000   \n",
       "25%          6.900423e+06                    19.000000   \n",
       "50%          7.545569e+06                    43.000000   \n",
       "75%          8.228329e+06                    65.000000   \n",
       "max          1.009427e+07                   210.000000   \n",
       "\n",
       "       Sales Stage Change Count  Total Days Identified Through Closing  \\\n",
       "count              78025.000000                           78025.000000   \n",
       "mean                   2.955732                              16.728356   \n",
       "std                    1.497242                              16.729593   \n",
       "min                    1.000000                               0.000000   \n",
       "25%                    2.000000                               4.000000   \n",
       "50%                    3.000000                              12.000000   \n",
       "75%                    3.000000                              24.000000   \n",
       "max                   23.000000                             208.000000   \n",
       "\n",
       "       Total Days Identified Through Qualified  Opportunity Amount USD  \\\n",
       "count                             78025.000000            78025.000000   \n",
       "mean                                 16.314348            91637.260750   \n",
       "std                                  16.562597           133161.029157   \n",
       "min                                   0.000000                0.000000   \n",
       "25%                                   4.000000            15000.000000   \n",
       "50%                                  12.000000            49000.000000   \n",
       "75%                                  24.000000           105099.000000   \n",
       "max                                 208.000000          1000000.000000   \n",
       "\n",
       "       Client Size By Revenue  Client Size By Employee Count  \\\n",
       "count            78025.000000                   78025.000000   \n",
       "mean                 1.620481                       1.604050   \n",
       "std                  1.235796                       1.210744   \n",
       "min                  1.000000                       1.000000   \n",
       "25%                  1.000000                       1.000000   \n",
       "50%                  1.000000                       1.000000   \n",
       "75%                  1.000000                       1.000000   \n",
       "max                  5.000000                       5.000000   \n",
       "\n",
       "       Revenue From Client Past Two Years  \\\n",
       "count                        78025.000000   \n",
       "mean                             0.303287   \n",
       "std                              0.930290   \n",
       "min                              0.000000   \n",
       "25%                              0.000000   \n",
       "50%                              0.000000   \n",
       "75%                              0.000000   \n",
       "max                              4.000000   \n",
       "\n",
       "       Ratio Days Identified To Total Days  \\\n",
       "count                         78025.000000   \n",
       "mean                              0.203063   \n",
       "std                               0.364985   \n",
       "min                               0.000000   \n",
       "25%                               0.000000   \n",
       "50%                               0.000000   \n",
       "75%                               0.197225   \n",
       "max                               1.000000   \n",
       "\n",
       "       Ratio Days Validated To Total Days  Ratio Days Qualified To Total Days  \\\n",
       "count                        78025.000000                        78025.000000   \n",
       "mean                             0.488314                            0.185048   \n",
       "std                              0.448077                            0.340283   \n",
       "min                              0.000000                            0.000000   \n",
       "25%                              0.000000                            0.000000   \n",
       "50%                              0.448000                            0.000000   \n",
       "75%                              1.000000                            0.188552   \n",
       "max                              1.000000                            1.000000   \n",
       "\n",
       "       Deal Size Category  \n",
       "count        78025.000000  \n",
       "mean             3.436745  \n",
       "std              1.656890  \n",
       "min              1.000000  \n",
       "25%              2.000000  \n",
       "50%              3.000000  \n",
       "75%              5.000000  \n",
       "max              7.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Opportunity Number                         78025\n",
       "Supplies Subgroup                          78025\n",
       "Supplies Group                             78025\n",
       "Region                                     78025\n",
       "Route To Market                            78025\n",
       "Elapsed Days In Sales Stage                78025\n",
       "Opportunity Result                         78025\n",
       "Sales Stage Change Count                   78025\n",
       "Total Days Identified Through Closing      78025\n",
       "Total Days Identified Through Qualified    78025\n",
       "Opportunity Amount USD                     78025\n",
       "Client Size By Revenue                     78025\n",
       "Client Size By Employee Count              78025\n",
       "Revenue From Client Past Two Years         78025\n",
       "Competitor Type                            78025\n",
       "Ratio Days Identified To Total Days        78025\n",
       "Ratio Days Validated To Total Days         78025\n",
       "Ratio Days Qualified To Total Days         78025\n",
       "Deal Size Category                         78025\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_data.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Opportunity Number</th>\n",
       "      <th>Supplies Subgroup</th>\n",
       "      <th>Supplies Group</th>\n",
       "      <th>Region</th>\n",
       "      <th>Route To Market</th>\n",
       "      <th>Elapsed Days In Sales Stage</th>\n",
       "      <th>Opportunity Result</th>\n",
       "      <th>Sales Stage Change Count</th>\n",
       "      <th>Total Days Identified Through Closing</th>\n",
       "      <th>Total Days Identified Through Qualified</th>\n",
       "      <th>Opportunity Amount USD</th>\n",
       "      <th>Client Size By Revenue</th>\n",
       "      <th>Client Size By Employee Count</th>\n",
       "      <th>Revenue From Client Past Two Years</th>\n",
       "      <th>Competitor Type</th>\n",
       "      <th>Ratio Days Identified To Total Days</th>\n",
       "      <th>Ratio Days Validated To Total Days</th>\n",
       "      <th>Ratio Days Qualified To Total Days</th>\n",
       "      <th>Deal Size Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>78024</th>\n",
       "      <td>10094255</td>\n",
       "      <td>Interior Accessories</td>\n",
       "      <td>Car Accessories</td>\n",
       "      <td>Mid-Atlantic</td>\n",
       "      <td>Reseller</td>\n",
       "      <td>0</td>\n",
       "      <td>Loss</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>90000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Opportunity Number     Supplies Subgroup   Supplies Group  \\\n",
       "78024            10094255  Interior Accessories  Car Accessories   \n",
       "\n",
       "             Region Route To Market  Elapsed Days In Sales Stage  \\\n",
       "78024  Mid-Atlantic        Reseller                            0   \n",
       "\n",
       "      Opportunity Result  Sales Stage Change Count  \\\n",
       "78024               Loss                         1   \n",
       "\n",
       "       Total Days Identified Through Closing  \\\n",
       "78024                                      0   \n",
       "\n",
       "       Total Days Identified Through Qualified  Opportunity Amount USD  \\\n",
       "78024                                        0                   90000   \n",
       "\n",
       "       Client Size By Revenue  Client Size By Employee Count  \\\n",
       "78024                       1                              1   \n",
       "\n",
       "       Revenue From Client Past Two Years Competitor Type  \\\n",
       "78024                                   0         Unknown   \n",
       "\n",
       "       Ratio Days Identified To Total Days  \\\n",
       "78024                                  0.0   \n",
       "\n",
       "       Ratio Days Validated To Total Days  Ratio Days Qualified To Total Days  \\\n",
       "78024                                 0.0                                 0.0   \n",
       "\n",
       "       Deal Size Category  \n",
       "78024                   4  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_data.tail(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26e77a30240>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(11.7,8.27)})\n",
    "# plt.figure(figsize=(11.7,8.27))\n",
    "sns.countplot('Route To Market',data=sales_data,hue='Opportunity Result')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26e776e1a20>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1202.4x955.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(16.7,13.27)})\n",
    "\n",
    "sns.violinplot(x='Opportunity Result',y='Client Size By Revenue',data=sales_data,inner=None,hue='Opportunity Result')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### With this,we have analysed the data enough to preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we will preprocess the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We have several columns in the dataframe whose values are all strings. Our processing models are operate on integers.\n",
    "\n",
    "### So, we need to encode these string values into integer values using the scikit-learn's LabelEncoder() methods\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's first check out the unique values of all string type columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Supplies subgroup : ['Exterior Accessories' 'Motorcycle Parts' 'Shelters & RV'\n",
      " 'Garage & Car Care' 'Batteries & Accessories' 'Performance Parts'\n",
      " 'Towing & Hitches' 'Replacement Parts' 'Tires & Wheels'\n",
      " 'Interior Accessories' 'Car Electronics']\n",
      "Region : ['Northwest' 'Pacific' 'Midwest' 'Southwest' 'Mid-Atlantic' 'Northeast'\n",
      " 'Southeast']\n",
      "Route to Market : ['Fields Sales' 'Reseller' 'Other' 'Telesales' 'Telecoverage']\n",
      "Opprtunity Result: ['Won' 'Loss']\n",
      "Competitor Type :  ['Unknown' 'Known' 'None']\n",
      "Supplies Group : ['Car Accessories' 'Performance & Non-auto' 'Tires & Wheels'\n",
      " 'Car Electronics']\n"
     ]
    }
   ],
   "source": [
    "print(\"Supplies subgroup :\",sales_data['Supplies Subgroup'].unique())\n",
    "print(\"Region :\",sales_data['Region'].unique())\n",
    "print(\"Route to Market :\",sales_data['Route To Market'].unique())\n",
    "print(\"Opprtunity Result:\",sales_data['Opportunity Result'].unique())\n",
    "print(\"Competitor Type : \",sales_data['Competitor Type'].unique())\n",
    "print(\"Supplies Group :\",sales_data['Supplies Group'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#All these strings will be encoded in the below cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "le = preprocessing.LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "sales_data['Supplies Group'] = le.fit_transform(sales_data['Supplies Group'])\n",
    "sales_data['Region'] = le.fit_transform(sales_data['Region'])\n",
    "sales_data['Route To Market'] = le.fit_transform(sales_data['Route To Market'])\n",
    "sales_data['Opportunity Result'] = le.fit_transform(sales_data['Opportunity Result'])\n",
    "sales_data['Competitor Type'] = le.fit_transform(sales_data['Competitor Type'])\n",
    "sales_data['Supplies Subgroup'] = le.fit_transform(sales_data['Supplies Subgroup'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Opportunity Number</th>\n",
       "      <th>Supplies Subgroup</th>\n",
       "      <th>Supplies Group</th>\n",
       "      <th>Region</th>\n",
       "      <th>Route To Market</th>\n",
       "      <th>Elapsed Days In Sales Stage</th>\n",
       "      <th>Opportunity Result</th>\n",
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       "      <th>Total Days Identified Through Closing</th>\n",
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       "      <th>Opportunity Amount USD</th>\n",
       "      <th>Client Size By Revenue</th>\n",
       "      <th>Client Size By Employee Count</th>\n",
       "      <th>Revenue From Client Past Two Years</th>\n",
       "      <th>Competitor Type</th>\n",
       "      <th>Ratio Days Identified To Total Days</th>\n",
       "      <th>Ratio Days Validated To Total Days</th>\n",
       "      <th>Ratio Days Qualified To Total Days</th>\n",
       "      <th>Deal Size Category</th>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1675224</td>\n",
       "      <td>8</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>124</td>\n",
       "      <td>124</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.141125</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Opportunity Number  Supplies Subgroup  Supplies Group  Region  \\\n",
       "0             1641984                  2               0       3   \n",
       "1             1658010                  2               0       4   \n",
       "2             1674737                  5               2       4   \n",
       "3             1675224                  8               2       1   \n",
       "4             1689785                  2               0       4   \n",
       "\n",
       "   Route To Market  Elapsed Days In Sales Stage  Opportunity Result  \\\n",
       "0                0                           76                   1   \n",
       "1                2                           63                   0   \n",
       "2                2                           24                   1   \n",
       "3                2                           16                   0   \n",
       "4                2                           69                   0   \n",
       "\n",
       "   Sales Stage Change Count  Total Days Identified Through Closing  \\\n",
       "0                        13                                    104   \n",
       "1                         2                                    163   \n",
       "2                         7                                     82   \n",
       "3                         5                                    124   \n",
       "4                        11                                     91   \n",
       "\n",
       "   Total Days Identified Through Qualified  Opportunity Amount USD  \\\n",
       "0                                      101                       0   \n",
       "1                                      163                       0   \n",
       "2                                       82                    7750   \n",
       "3                                      124                       0   \n",
       "4                                       13                   69756   \n",
       "\n",
       "   Client Size By Revenue  Client Size By Employee Count  \\\n",
       "0                       5                              5   \n",
       "1                       3                              5   \n",
       "2                       1                              1   \n",
       "3                       1                              1   \n",
       "4                       1                              1   \n",
       "\n",
       "   Revenue From Client Past Two Years  Competitor Type  \\\n",
       "0                                   0                2   \n",
       "1                                   0                2   \n",
       "2                                   0                2   \n",
       "3                                   0                0   \n",
       "4                                   0                2   \n",
       "\n",
       "   Ratio Days Identified To Total Days  Ratio Days Validated To Total Days  \\\n",
       "0                              0.69636                            0.113985   \n",
       "1                              0.00000                            1.000000   \n",
       "2                              1.00000                            0.000000   \n",
       "3                              1.00000                            0.000000   \n",
       "4                              0.00000                            0.141125   \n",
       "\n",
       "   Ratio Days Qualified To Total Days  Deal Size Category  \n",
       "0                            0.154215                   1  \n",
       "1                            0.000000                   1  \n",
       "2                            0.000000                   1  \n",
       "3                            0.000000                   1  \n",
       "4                            0.000000                   4  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Now all our data is in the integer format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## And now, we will be splitting the data into training and testing sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = [col for col in sales_data.columns if col not in ['Opportunity Number','Opportunity Result']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = sales_data[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "   Supplies Subgroup  Supplies Group  Region  Route To Market  \\\n",
       "0                  2               0       3                0   \n",
       "1                  2               0       4                2   \n",
       "2                  5               2       4                2   \n",
       "3                  8               2       1                2   \n",
       "4                  2               0       4                2   \n",
       "\n",
       "   Elapsed Days In Sales Stage  Sales Stage Change Count  \\\n",
       "0                           76                        13   \n",
       "1                           63                         2   \n",
       "2                           24                         7   \n",
       "3                           16                         5   \n",
       "4                           69                        11   \n",
       "\n",
       "   Total Days Identified Through Closing  \\\n",
       "0                                    104   \n",
       "1                                    163   \n",
       "2                                     82   \n",
       "3                                    124   \n",
       "4                                     91   \n",
       "\n",
       "   Total Days Identified Through Qualified  Opportunity Amount USD  \\\n",
       "0                                      101                       0   \n",
       "1                                      163                       0   \n",
       "2                                       82                    7750   \n",
       "3                                      124                       0   \n",
       "4                                       13                   69756   \n",
       "\n",
       "   Client Size By Revenue  Client Size By Employee Count  \\\n",
       "0                       5                              5   \n",
       "1                       3                              5   \n",
       "2                       1                              1   \n",
       "3                       1                              1   \n",
       "4                       1                              1   \n",
       "\n",
       "   Revenue From Client Past Two Years  Competitor Type  \\\n",
       "0                                   0                2   \n",
       "1                                   0                2   \n",
       "2                                   0                2   \n",
       "3                                   0                0   \n",
       "4                                   0                2   \n",
       "\n",
       "   Ratio Days Identified To Total Days  Ratio Days Validated To Total Days  \\\n",
       "0                              0.69636                            0.113985   \n",
       "1                              0.00000                            1.000000   \n",
       "2                              1.00000                            0.000000   \n",
       "3                              1.00000                            0.000000   \n",
       "4                              0.00000                            0.141125   \n",
       "\n",
       "   Ratio Days Qualified To Total Days  Deal Size Category  \n",
       "0                            0.154215                   1  \n",
       "1                            0.000000                   1  \n",
       "2                            0.000000                   1  \n",
       "3                            0.000000                   1  \n",
       "4                            0.000000                   4  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We've removed the Opportunity Number and OPP result columns as they arent needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The column 'Opportunity Result' is the target so we store it in  a target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = sales_data['Opportunity Result']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For this data,we'll split the dataset into 70:30 ratio for training and testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train,data_test,target_train,target_test = train_test_split(data,target,test_size=0.30,random_state=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gaussian Naive Bayes \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes accuracy :  0.7590567327409433\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "gnb = GaussianNB()\n",
    "\n",
    "pred = gnb.fit(data_train,target_train).predict(data_test)\n",
    "\n",
    "print(\"Naive Bayes accuracy : \",accuracy_score(target_test,pred,normalize=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Support Vector Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearSVC :  0.777811004784689\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "svc_model = LinearSVC(random_state=0)\n",
    "pred = svc_model.fit(data_train,target_train).predict(data_test)\n",
    "print(\"LinearSVC : \",accuracy_score(target_test,pred,normalize=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-Neighbours Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNeighboursClassifier :  0.8145505809979494\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "neigh = KNeighborsClassifier(n_neighbors=3)\n",
    "\n",
    "pred = neigh.fit(data_train,target_train).predict(data_test)\n",
    "\n",
    "print(\"KNeighboursClassifier : \",accuracy_score(target_test,pred,normalize=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### So, the best prediction we have got is from K-Neighbors Classifier of 0.8145505809979494"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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