ReducingMarketWaste.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reducing Market Waste ( Hackerearth ML Competition )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem Statement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The company has products which can be used for hiring assessments. The task is to predict the probability percentage that a client will purchase a product from the features provided in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset Description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset contains the following files :\n",
    "\n",
    "           1. train.csv : 7007 x 23  \n",
    "           2. test.csv  : 2093 x 22\n",
    "           3. sample_submission.csv : 5 x 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing neccessary modules\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt \n",
    "import numpy as np \n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import confusion_matrix, classification_report "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Currently at C:\\Users\\Sarthak\\Desktop\\Reducing Market Waste\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "path = os.getcwd()\n",
    "\n",
    "print(\"Currently at\", path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the training dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Deal_title</th>\n",
       "      <th>Lead_name</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Deal_value</th>\n",
       "      <th>Weighted_amount</th>\n",
       "      <th>Date_of_creation</th>\n",
       "      <th>Pitch</th>\n",
       "      <th>Contact_no</th>\n",
       "      <th>Lead_revenue</th>\n",
       "      <th>Fund_category</th>\n",
       "      <th>...</th>\n",
       "      <th>Designation</th>\n",
       "      <th>Lead_POC_email</th>\n",
       "      <th>Hiring_candidate_role</th>\n",
       "      <th>Lead_source</th>\n",
       "      <th>Level_of_meeting</th>\n",
       "      <th>Last_lead_update</th>\n",
       "      <th>Internal_POC</th>\n",
       "      <th>Resource</th>\n",
       "      <th>Internal_rating</th>\n",
       "      <th>Success_probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TitleM5DZY</td>\n",
       "      <td>Davis, Perkins and Bishop Inc</td>\n",
       "      <td>Restaurants</td>\n",
       "      <td>320506$</td>\n",
       "      <td>2067263.7$</td>\n",
       "      <td>2020-03-29</td>\n",
       "      <td>Product_2</td>\n",
       "      <td>607.447.7883</td>\n",
       "      <td>50 - 100 Million</td>\n",
       "      <td>Category 2</td>\n",
       "      <td>...</td>\n",
       "      <td>Executive Vice President</td>\n",
       "      <td>charlenewerner@davis.com</td>\n",
       "      <td>Community pharmacist</td>\n",
       "      <td>Website</td>\n",
       "      <td>Level 3</td>\n",
       "      <td>No track</td>\n",
       "      <td>Davis,Sharrice A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>73.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TitleKIW18</td>\n",
       "      <td>Bender PLC LLC</td>\n",
       "      <td>Construction Services</td>\n",
       "      <td>39488$</td>\n",
       "      <td>240876.8$</td>\n",
       "      <td>2019-07-10</td>\n",
       "      <td>Product_2</td>\n",
       "      <td>892-938-9493</td>\n",
       "      <td>500 Million - 1 Billion</td>\n",
       "      <td>Category 4</td>\n",
       "      <td>...</td>\n",
       "      <td>Chairman/CEO/President</td>\n",
       "      <td>terrylogan@bender.com</td>\n",
       "      <td>Recruitment consultant</td>\n",
       "      <td>Others</td>\n",
       "      <td>Level 1</td>\n",
       "      <td>Did not hear back after Level 1</td>\n",
       "      <td>Brown,Maxine A</td>\n",
       "      <td>No</td>\n",
       "      <td>5</td>\n",
       "      <td>58.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TitleFXSDN</td>\n",
       "      <td>Carter-Henry and Sons</td>\n",
       "      <td>Hospitals/Clinics</td>\n",
       "      <td>359392$</td>\n",
       "      <td>2407926.4$</td>\n",
       "      <td>2019-07-27</td>\n",
       "      <td>Product_1</td>\n",
       "      <td>538.748.2271</td>\n",
       "      <td>500 Million - 1 Billion</td>\n",
       "      <td>Category 4</td>\n",
       "      <td>...</td>\n",
       "      <td>SVP/General Counsel</td>\n",
       "      <td>arielhamilton@carterhenry.com</td>\n",
       "      <td>Health service manager</td>\n",
       "      <td>Marketing Event</td>\n",
       "      <td>Level 1</td>\n",
       "      <td>?</td>\n",
       "      <td>Georgakopoulos,Vasilios T</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>68.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TitlePSK4Y</td>\n",
       "      <td>Garcia Ltd Ltd</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>76774$</td>\n",
       "      <td>468321.4$</td>\n",
       "      <td>2021-01-30</td>\n",
       "      <td>Product_2</td>\n",
       "      <td>(692)052-1389x75188</td>\n",
       "      <td>500 Million - 1 Billion</td>\n",
       "      <td>Category 3</td>\n",
       "      <td>...</td>\n",
       "      <td>CEO/Co-Founder/Chairman</td>\n",
       "      <td>erinwilson@garcia.com</td>\n",
       "      <td>Therapist, speech and language</td>\n",
       "      <td>Contact Email</td>\n",
       "      <td>Level 2</td>\n",
       "      <td>Did not hear back after Level 1</td>\n",
       "      <td>Brown,Maxine A</td>\n",
       "      <td>We have all the requirements</td>\n",
       "      <td>1</td>\n",
       "      <td>64.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Title904GV</td>\n",
       "      <td>Lee and Sons PLC</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>483896$</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-05-22</td>\n",
       "      <td>Product_2</td>\n",
       "      <td>001-878-814-6134x015</td>\n",
       "      <td>50 - 100 Million</td>\n",
       "      <td>Category 3</td>\n",
       "      <td>...</td>\n",
       "      <td>Executive Vice President</td>\n",
       "      <td>mr.christopher@lee.com</td>\n",
       "      <td>Media planner</td>\n",
       "      <td>Website</td>\n",
       "      <td>Level 2</td>\n",
       "      <td>Up-to-date</td>\n",
       "      <td>Thomas,Lori E</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>62.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Deal_title                      Lead_name               Industry  \\\n",
       "0  TitleM5DZY  Davis, Perkins and Bishop Inc            Restaurants   \n",
       "1  TitleKIW18                 Bender PLC LLC  Construction Services   \n",
       "2  TitleFXSDN          Carter-Henry and Sons      Hospitals/Clinics   \n",
       "3  TitlePSK4Y                 Garcia Ltd Ltd            Real Estate   \n",
       "4  Title904GV               Lee and Sons PLC     Financial Services   \n",
       "\n",
       "  Deal_value Weighted_amount Date_of_creation      Pitch  \\\n",
       "0    320506$      2067263.7$       2020-03-29  Product_2   \n",
       "1     39488$       240876.8$       2019-07-10  Product_2   \n",
       "2    359392$      2407926.4$       2019-07-27  Product_1   \n",
       "3     76774$       468321.4$       2021-01-30  Product_2   \n",
       "4    483896$             NaN       2019-05-22  Product_2   \n",
       "\n",
       "             Contact_no             Lead_revenue Fund_category  ...  \\\n",
       "0          607.447.7883         50 - 100 Million    Category 2  ...   \n",
       "1          892-938-9493  500 Million - 1 Billion    Category 4  ...   \n",
       "2          538.748.2271  500 Million - 1 Billion    Category 4  ...   \n",
       "3   (692)052-1389x75188  500 Million - 1 Billion    Category 3  ...   \n",
       "4  001-878-814-6134x015         50 - 100 Million    Category 3  ...   \n",
       "\n",
       "                Designation                 Lead_POC_email  \\\n",
       "0  Executive Vice President       charlenewerner@davis.com   \n",
       "1    Chairman/CEO/President          terrylogan@bender.com   \n",
       "2      SVP/General Counsel   arielhamilton@carterhenry.com   \n",
       "3   CEO/Co-Founder/Chairman          erinwilson@garcia.com   \n",
       "4  Executive Vice President         mr.christopher@lee.com   \n",
       "\n",
       "            Hiring_candidate_role      Lead_source Level_of_meeting  \\\n",
       "0            Community pharmacist          Website          Level 3   \n",
       "1          Recruitment consultant           Others          Level 1   \n",
       "2          Health service manager  Marketing Event          Level 1   \n",
       "3  Therapist, speech and language    Contact Email          Level 2   \n",
       "4                   Media planner          Website          Level 2   \n",
       "\n",
       "                  Last_lead_update               Internal_POC  \\\n",
       "0                         No track           Davis,Sharrice A   \n",
       "1  Did not hear back after Level 1             Brown,Maxine A   \n",
       "2                                ?  Georgakopoulos,Vasilios T   \n",
       "3  Did not hear back after Level 1             Brown,Maxine A   \n",
       "4                       Up-to-date              Thomas,Lori E   \n",
       "\n",
       "                       Resource Internal_rating Success_probability  \n",
       "0                           NaN               3                73.6  \n",
       "1                            No               5                58.9  \n",
       "2                            No               4                68.8  \n",
       "3  We have all the requirements               1                64.5  \n",
       "4                            No               4                62.4  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train = pd.read_csv(path+\"/dataset/train.csv\")\n",
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<bound method DataFrame.info of       Deal_title                         Lead_name               Industry  \\\n",
      "0     TitleM5DZY     Davis, Perkins and Bishop Inc            Restaurants   \n",
      "1     TitleKIW18                    Bender PLC LLC  Construction Services   \n",
      "2     TitleFXSDN             Carter-Henry and Sons      Hospitals/Clinics   \n",
      "3     TitlePSK4Y                    Garcia Ltd Ltd            Real Estate   \n",
      "4     Title904GV                  Lee and Sons PLC     Financial Services   \n",
      "...          ...                               ...                    ...   \n",
      "7002  TitleJ7TDY                 Flowers-Adams PLC                  Banks   \n",
      "7003  TitleO1IIN  Kelly, Smith and Benton and Sons      Hospitals/Clinics   \n",
      "7004  TitleF1FWY                 Romero-Juarez PLC         Semiconductors   \n",
      "7005  TitleXVBNJ  Sullivan, Mooney and Elliott LLC                  Banks   \n",
      "7006  TitleKXU3H                Jones LLC and Sons     Financial Services   \n",
      "\n",
      "     Deal_value Weighted_amount Date_of_creation      Pitch  \\\n",
      "0       320506$      2067263.7$       2020-03-29  Product_2   \n",
      "1        39488$       240876.8$       2019-07-10  Product_2   \n",
      "2       359392$      2407926.4$       2019-07-27  Product_1   \n",
      "3        76774$       468321.4$       2021-01-30  Product_2   \n",
      "4       483896$             NaN       2019-05-22  Product_2   \n",
      "...         ...             ...              ...        ...   \n",
      "7002    192800$      1195360.0$       2020-12-07  Product_1   \n",
      "7003    220208$      1453372.8$       2020-03-13  Product_2   \n",
      "7004    253608$             NaN       2020-03-10  Product_1   \n",
      "7005    118615$       794720.5$       2019-12-26  Product_1   \n",
      "7006    258627$     1642281.45$       2020-04-27  Product_2   \n",
      "\n",
      "                Contact_no             Lead_revenue Fund_category  ...  \\\n",
      "0             607.447.7883         50 - 100 Million    Category 2  ...   \n",
      "1             892-938-9493  500 Million - 1 Billion    Category 4  ...   \n",
      "2             538.748.2271  500 Million - 1 Billion    Category 4  ...   \n",
      "3      (692)052-1389x75188  500 Million - 1 Billion    Category 3  ...   \n",
      "4     001-878-814-6134x015         50 - 100 Million    Category 3  ...   \n",
      "...                    ...                      ...           ...  ...   \n",
      "7002            1734434912        100 - 500 Million    Category 4  ...   \n",
      "7003   (002)106-0243x03346        100 - 500 Million    Category 1  ...   \n",
      "7004      777-231-4109x712        100 - 500 Million    Category 2  ...   \n",
      "7005  001-212-631-5467x377  500 Million - 1 Billion    Category 2  ...   \n",
      "7006       +1-240-958-7728  500 Million - 1 Billion    Category 2  ...   \n",
      "\n",
      "                                            Designation  \\\n",
      "0                              Executive Vice President   \n",
      "1                                Chairman/CEO/President   \n",
      "2                                  SVP/General Counsel    \n",
      "3                               CEO/Co-Founder/Chairman   \n",
      "4                              Executive Vice President   \n",
      "...                                                 ...   \n",
      "7002                            CEO/Co-Founder/Chairman   \n",
      "7003                                                CEO   \n",
      "7004                               SVP/General Counsel    \n",
      "7005                           Executive Vice President   \n",
      "7006  Vice President / GM (04-present) : VP Sales an...   \n",
      "\n",
      "                       Lead_POC_email               Hiring_candidate_role  \\\n",
      "0            charlenewerner@davis.com                Community pharmacist   \n",
      "1               terrylogan@bender.com              Recruitment consultant   \n",
      "2       arielhamilton@carterhenry.com              Health service manager   \n",
      "3               erinwilson@garcia.com      Therapist, speech and language   \n",
      "4              mr.christopher@lee.com                       Media planner   \n",
      "...                               ...                                 ...   \n",
      "7002  clairewilliams@flowersadams.com                 Call centre manager   \n",
      "7003        vanessaanderson@kelly.com              Financial risk analyst   \n",
      "7004  nicholasduncan@romerojuarez.com         Nature conservation officer   \n",
      "7005       waynewilliams@sullivan.com                   Designer, textile   \n",
      "7006        carolstrickland@jones.com  Government social research officer   \n",
      "\n",
      "          Lead_source Level_of_meeting                 Last_lead_update  \\\n",
      "0             Website          Level 3                         No track   \n",
      "1              Others          Level 1  Did not hear back after Level 1   \n",
      "2     Marketing Event          Level 1                                ?   \n",
      "3       Contact Email          Level 2  Did not hear back after Level 1   \n",
      "4             Website          Level 2                       Up-to-date   \n",
      "...               ...              ...                              ...   \n",
      "7002    Contact Email          Level 1            More than a week back   \n",
      "7003  Marketing Event          Level 2                                ?   \n",
      "7004  Marketing Event          Level 3                              NaN   \n",
      "7005          Website          Level 3                              NaN   \n",
      "7006    Contact Email          Level 3            More than a week back   \n",
      "\n",
      "                   Internal_POC                      Resource Internal_rating  \\\n",
      "0              Davis,Sharrice A                           NaN               3   \n",
      "1                Brown,Maxine A                            No               5   \n",
      "2     Georgakopoulos,Vasilios T                            No               4   \n",
      "3                Brown,Maxine A  We have all the requirements               1   \n",
      "4                 Thomas,Lori E                            No               4   \n",
      "...                         ...                           ...             ...   \n",
      "7002            Jones,Eyvette W  We have all the requirements               4   \n",
      "7003             Brown,Maxine A  We have all the requirements               3   \n",
      "7004              Logan,Kevin N                           NaN               1   \n",
      "7005              Cashin,Marc C  We have all the requirements               3   \n",
      "7006           Massiah,Gerard F  We have all the requirements               1   \n",
      "\n",
      "     Success_probability  \n",
      "0                  73.60  \n",
      "1                  58.90  \n",
      "2                  68.80  \n",
      "3                  64.50  \n",
      "4                  62.40  \n",
      "...                  ...  \n",
      "7002              107.34  \n",
      "7003               26.35  \n",
      "7004               70.60  \n",
      "7005               -5.00  \n",
      "7006               68.70  \n",
      "\n",
      "[7007 rows x 23 columns]>\n"
     ]
    }
   ],
   "source": [
    "print(data_train.info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Internal_rating</th>\n",
       "      <th>Success_probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7007.000000</td>\n",
       "      <td>7007.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.009562</td>\n",
       "      <td>64.745133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.418666</td>\n",
       "      <td>17.931635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>60.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>65.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>69.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>107.340000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Internal_rating  Success_probability\n",
       "count      7007.000000          7007.000000\n",
       "mean          3.009562            64.745133\n",
       "std           1.418666            17.931635\n",
       "min           1.000000            -5.000000\n",
       "25%           2.000000            60.600000\n",
       "50%           3.000000            65.300000\n",
       "75%           4.000000            69.600000\n",
       "max           5.000000           107.340000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Deal_title</th>\n",
       "      <th>Lead_name</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Deal_value</th>\n",
       "      <th>Weighted_amount</th>\n",
       "      <th>Date_of_creation</th>\n",
       "      <th>Pitch</th>\n",
       "      <th>Contact_no</th>\n",
       "      <th>Lead_revenue</th>\n",
       "      <th>Fund_category</th>\n",
       "      <th>...</th>\n",
       "      <th>Designation</th>\n",
       "      <th>Lead_POC_email</th>\n",
       "      <th>Hiring_candidate_role</th>\n",
       "      <th>Lead_source</th>\n",
       "      <th>Level_of_meeting</th>\n",
       "      <th>Last_lead_update</th>\n",
       "      <th>Internal_POC</th>\n",
       "      <th>Resource</th>\n",
       "      <th>Internal_rating</th>\n",
       "      <th>Success_probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7007</td>\n",
       "      <td>7007</td>\n",
       "      <td>7006</td>\n",
       "      <td>6956</td>\n",
       "      <td>6482</td>\n",
       "      <td>7007</td>\n",
       "      <td>7007</td>\n",
       "      <td>7007</td>\n",
       "      <td>7007</td>\n",
       "      <td>7007</td>\n",
       "      <td>...</td>\n",
       "      <td>7007</td>\n",
       "      <td>7007</td>\n",
       "      <td>7007</td>\n",
       "      <td>7007</td>\n",
       "      <td>7007</td>\n",
       "      <td>6374</td>\n",
       "      <td>7007</td>\n",
       "      <td>6858</td>\n",
       "      <td>7007.000000</td>\n",
       "      <td>7007.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>7007</td>\n",
       "      <td>7007</td>\n",
       "      <td>171</td>\n",
       "      <td>6907</td>\n",
       "      <td>6480</td>\n",
       "      <td>777</td>\n",
       "      <td>2</td>\n",
       "      <td>7007</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>7007</td>\n",
       "      <td>639</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>60</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Title2LXRP</td>\n",
       "      <td>Rodriguez, Schultz and Lewis Group</td>\n",
       "      <td>Banks</td>\n",
       "      <td>445634$</td>\n",
       "      <td>684031.5$</td>\n",
       "      <td>2019-01-22</td>\n",
       "      <td>Product_1</td>\n",
       "      <td>001-352-388-7465x187</td>\n",
       "      <td>500 Million - 1 Billion</td>\n",
       "      <td>Category 3</td>\n",
       "      <td>...</td>\n",
       "      <td>CEO</td>\n",
       "      <td>stevenmejia@marquezjohnson.com</td>\n",
       "      <td>Film/video editor</td>\n",
       "      <td>Marketing Event</td>\n",
       "      <td>Level 1</td>\n",
       "      <td>Following up but lead not responding</td>\n",
       "      <td>Hebron,Artenia D</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1480</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>3504</td>\n",
       "      <td>1</td>\n",
       "      <td>2377</td>\n",
       "      <td>1791</td>\n",
       "      <td>...</td>\n",
       "      <td>748</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>1824</td>\n",
       "      <td>2409</td>\n",
       "      <td>627</td>\n",
       "      <td>136</td>\n",
       "      <td>1179</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.009562</td>\n",
       "      <td>64.745133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.418666</td>\n",
       "      <td>17.931635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>60.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>65.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>69.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>107.340000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Deal_title                           Lead_name Industry Deal_value  \\\n",
       "count         7007                                7007     7006       6956   \n",
       "unique        7007                                7007      171       6907   \n",
       "top     Title2LXRP  Rodriguez, Schultz and Lewis Group    Banks    445634$   \n",
       "freq             1                                   1     1480          2   \n",
       "mean           NaN                                 NaN      NaN        NaN   \n",
       "std            NaN                                 NaN      NaN        NaN   \n",
       "min            NaN                                 NaN      NaN        NaN   \n",
       "25%            NaN                                 NaN      NaN        NaN   \n",
       "50%            NaN                                 NaN      NaN        NaN   \n",
       "75%            NaN                                 NaN      NaN        NaN   \n",
       "max            NaN                                 NaN      NaN        NaN   \n",
       "\n",
       "       Weighted_amount Date_of_creation      Pitch            Contact_no  \\\n",
       "count             6482             7007       7007                  7007   \n",
       "unique            6480              777          2                  7007   \n",
       "top          684031.5$       2019-01-22  Product_1  001-352-388-7465x187   \n",
       "freq                 2               18       3504                     1   \n",
       "mean               NaN              NaN        NaN                   NaN   \n",
       "std                NaN              NaN        NaN                   NaN   \n",
       "min                NaN              NaN        NaN                   NaN   \n",
       "25%                NaN              NaN        NaN                   NaN   \n",
       "50%                NaN              NaN        NaN                   NaN   \n",
       "75%                NaN              NaN        NaN                   NaN   \n",
       "max                NaN              NaN        NaN                   NaN   \n",
       "\n",
       "                   Lead_revenue Fund_category  ... Designation  \\\n",
       "count                      7007          7007  ...        7007   \n",
       "unique                        3             4  ...          10   \n",
       "top     500 Million - 1 Billion    Category 3  ...         CEO   \n",
       "freq                       2377          1791  ...         748   \n",
       "mean                        NaN           NaN  ...         NaN   \n",
       "std                         NaN           NaN  ...         NaN   \n",
       "min                         NaN           NaN  ...         NaN   \n",
       "25%                         NaN           NaN  ...         NaN   \n",
       "50%                         NaN           NaN  ...         NaN   \n",
       "75%                         NaN           NaN  ...         NaN   \n",
       "max                         NaN           NaN  ...         NaN   \n",
       "\n",
       "                        Lead_POC_email Hiring_candidate_role      Lead_source  \\\n",
       "count                             7007                  7007             7007   \n",
       "unique                            7007                   639                4   \n",
       "top     stevenmejia@marquezjohnson.com     Film/video editor  Marketing Event   \n",
       "freq                                 1                    22             1824   \n",
       "mean                               NaN                   NaN              NaN   \n",
       "std                                NaN                   NaN              NaN   \n",
       "min                                NaN                   NaN              NaN   \n",
       "25%                                NaN                   NaN              NaN   \n",
       "50%                                NaN                   NaN              NaN   \n",
       "75%                                NaN                   NaN              NaN   \n",
       "max                                NaN                   NaN              NaN   \n",
       "\n",
       "       Level_of_meeting                      Last_lead_update  \\\n",
       "count              7007                                  6374   \n",
       "unique                3                                    11   \n",
       "top             Level 1  Following up but lead not responding   \n",
       "freq               2409                                   627   \n",
       "mean                NaN                                   NaN   \n",
       "std                 NaN                                   NaN   \n",
       "min                 NaN                                   NaN   \n",
       "25%                 NaN                                   NaN   \n",
       "50%                 NaN                                   NaN   \n",
       "75%                 NaN                                   NaN   \n",
       "max                 NaN                                   NaN   \n",
       "\n",
       "            Internal_POC Resource Internal_rating Success_probability  \n",
       "count               7007     6858     7007.000000         7007.000000  \n",
       "unique                60        6             NaN                 NaN  \n",
       "top     Hebron,Artenia D       No             NaN                 NaN  \n",
       "freq                 136     1179             NaN                 NaN  \n",
       "mean                 NaN      NaN        3.009562           64.745133  \n",
       "std                  NaN      NaN        1.418666           17.931635  \n",
       "min                  NaN      NaN        1.000000           -5.000000  \n",
       "25%                  NaN      NaN        2.000000           60.600000  \n",
       "50%                  NaN      NaN        3.000000           65.300000  \n",
       "75%                  NaN      NaN        4.000000           69.600000  \n",
       "max                  NaN      NaN        5.000000          107.340000  \n",
       "\n",
       "[11 rows x 23 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check for missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Deal_title                 0\n",
       "Lead_name                  0\n",
       "Industry                   1\n",
       "Deal_value                51\n",
       "Weighted_amount          525\n",
       "Date_of_creation           0\n",
       "Pitch                      0\n",
       "Contact_no                 0\n",
       "Lead_revenue               0\n",
       "Fund_category              0\n",
       "Geography                972\n",
       "Location                  11\n",
       "POC_name                   8\n",
       "Designation                0\n",
       "Lead_POC_email             0\n",
       "Hiring_candidate_role      0\n",
       "Lead_source                0\n",
       "Level_of_meeting           0\n",
       "Last_lead_update         633\n",
       "Internal_POC               0\n",
       "Resource                 149\n",
       "Internal_rating            0\n",
       "Success_probability        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the output, we can clearly see that the data contains a lot of **missing values** particularly in columns such as **Deal_value, Weighted_amount, Geography, Location, Last_lead_update** and **Resource**"
   ]
  },
  {
   "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",
       "\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>Deal_title</th>\n",
       "      <th>Lead_name</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Deal_value</th>\n",
       "      <th>Weighted_amount</th>\n",
       "      <th>Date_of_creation</th>\n",
       "      <th>Pitch</th>\n",
       "      <th>Contact_no</th>\n",
       "      <th>Lead_revenue</th>\n",
       "      <th>Fund_category</th>\n",
       "      <th>...</th>\n",
       "      <th>Designation</th>\n",
       "      <th>Lead_POC_email</th>\n",
       "      <th>Hiring_candidate_role</th>\n",
       "      <th>Lead_source</th>\n",
       "      <th>Level_of_meeting</th>\n",
       "      <th>Last_lead_update</th>\n",
       "      <th>Internal_POC</th>\n",
       "      <th>Resource</th>\n",
       "      <th>Internal_rating</th>\n",
       "      <th>Success_probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TitleM5DZY</td>\n",
       "      <td>Davis, Perkins and Bishop Inc</td>\n",
       "      <td>Restaurants</td>\n",
       "      <td>320506.0</td>\n",
       "      <td>2067263.7</td>\n",
       "      <td>2020-03-29</td>\n",
       "      <td>Product_2</td>\n",
       "      <td>607.447.7883</td>\n",
       "      <td>50 - 100 Million</td>\n",
       "      <td>Category 2</td>\n",
       "      <td>...</td>\n",
       "      <td>Executive Vice President</td>\n",
       "      <td>charlenewerner@davis.com</td>\n",
       "      <td>Community pharmacist</td>\n",
       "      <td>Website</td>\n",
       "      <td>Level 3</td>\n",
       "      <td>No track</td>\n",
       "      <td>Davis,Sharrice A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>73.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TitleKIW18</td>\n",
       "      <td>Bender PLC LLC</td>\n",
       "      <td>Construction Services</td>\n",
       "      <td>39488.0</td>\n",
       "      <td>240876.8</td>\n",
       "      <td>2019-07-10</td>\n",
       "      <td>Product_2</td>\n",
       "      <td>892-938-9493</td>\n",
       "      <td>500 Million - 1 Billion</td>\n",
       "      <td>Category 4</td>\n",
       "      <td>...</td>\n",
       "      <td>Chairman/CEO/President</td>\n",
       "      <td>terrylogan@bender.com</td>\n",
       "      <td>Recruitment consultant</td>\n",
       "      <td>Others</td>\n",
       "      <td>Level 1</td>\n",
       "      <td>Did not hear back after Level 1</td>\n",
       "      <td>Brown,Maxine A</td>\n",
       "      <td>No</td>\n",
       "      <td>5</td>\n",
       "      <td>58.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TitleFXSDN</td>\n",
       "      <td>Carter-Henry and Sons</td>\n",
       "      <td>Hospitals/Clinics</td>\n",
       "      <td>359392.0</td>\n",
       "      <td>2407926.4</td>\n",
       "      <td>2019-07-27</td>\n",
       "      <td>Product_1</td>\n",
       "      <td>538.748.2271</td>\n",
       "      <td>500 Million - 1 Billion</td>\n",
       "      <td>Category 4</td>\n",
       "      <td>...</td>\n",
       "      <td>SVP/General Counsel</td>\n",
       "      <td>arielhamilton@carterhenry.com</td>\n",
       "      <td>Health service manager</td>\n",
       "      <td>Marketing Event</td>\n",
       "      <td>Level 1</td>\n",
       "      <td>?</td>\n",
       "      <td>Georgakopoulos,Vasilios T</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>68.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TitlePSK4Y</td>\n",
       "      <td>Garcia Ltd Ltd</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>76774.0</td>\n",
       "      <td>468321.4</td>\n",
       "      <td>2021-01-30</td>\n",
       "      <td>Product_2</td>\n",
       "      <td>(692)052-1389x75188</td>\n",
       "      <td>500 Million - 1 Billion</td>\n",
       "      <td>Category 3</td>\n",
       "      <td>...</td>\n",
       "      <td>CEO/Co-Founder/Chairman</td>\n",
       "      <td>erinwilson@garcia.com</td>\n",
       "      <td>Therapist, speech and language</td>\n",
       "      <td>Contact Email</td>\n",
       "      <td>Level 2</td>\n",
       "      <td>Did not hear back after Level 1</td>\n",
       "      <td>Brown,Maxine A</td>\n",
       "      <td>We have all the requirements</td>\n",
       "      <td>1</td>\n",
       "      <td>64.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Title904GV</td>\n",
       "      <td>Lee and Sons PLC</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>483896.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-05-22</td>\n",
       "      <td>Product_2</td>\n",
       "      <td>001-878-814-6134x015</td>\n",
       "      <td>50 - 100 Million</td>\n",
       "      <td>Category 3</td>\n",
       "      <td>...</td>\n",
       "      <td>Executive Vice President</td>\n",
       "      <td>mr.christopher@lee.com</td>\n",
       "      <td>Media planner</td>\n",
       "      <td>Website</td>\n",
       "      <td>Level 2</td>\n",
       "      <td>Up-to-date</td>\n",
       "      <td>Thomas,Lori E</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>62.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Deal_title                      Lead_name               Industry  \\\n",
       "0  TitleM5DZY  Davis, Perkins and Bishop Inc            Restaurants   \n",
       "1  TitleKIW18                 Bender PLC LLC  Construction Services   \n",
       "2  TitleFXSDN          Carter-Henry and Sons      Hospitals/Clinics   \n",
       "3  TitlePSK4Y                 Garcia Ltd Ltd            Real Estate   \n",
       "4  Title904GV               Lee and Sons PLC     Financial Services   \n",
       "\n",
       "   Deal_value  Weighted_amount Date_of_creation      Pitch  \\\n",
       "0    320506.0        2067263.7       2020-03-29  Product_2   \n",
       "1     39488.0         240876.8       2019-07-10  Product_2   \n",
       "2    359392.0        2407926.4       2019-07-27  Product_1   \n",
       "3     76774.0         468321.4       2021-01-30  Product_2   \n",
       "4    483896.0              NaN       2019-05-22  Product_2   \n",
       "\n",
       "             Contact_no             Lead_revenue Fund_category  ...  \\\n",
       "0          607.447.7883         50 - 100 Million    Category 2  ...   \n",
       "1          892-938-9493  500 Million - 1 Billion    Category 4  ...   \n",
       "2          538.748.2271  500 Million - 1 Billion    Category 4  ...   \n",
       "3   (692)052-1389x75188  500 Million - 1 Billion    Category 3  ...   \n",
       "4  001-878-814-6134x015         50 - 100 Million    Category 3  ...   \n",
       "\n",
       "                Designation                 Lead_POC_email  \\\n",
       "0  Executive Vice President       charlenewerner@davis.com   \n",
       "1    Chairman/CEO/President          terrylogan@bender.com   \n",
       "2      SVP/General Counsel   arielhamilton@carterhenry.com   \n",
       "3   CEO/Co-Founder/Chairman          erinwilson@garcia.com   \n",
       "4  Executive Vice President         mr.christopher@lee.com   \n",
       "\n",
       "            Hiring_candidate_role      Lead_source Level_of_meeting  \\\n",
       "0            Community pharmacist          Website          Level 3   \n",
       "1          Recruitment consultant           Others          Level 1   \n",
       "2          Health service manager  Marketing Event          Level 1   \n",
       "3  Therapist, speech and language    Contact Email          Level 2   \n",
       "4                   Media planner          Website          Level 2   \n",
       "\n",
       "                  Last_lead_update               Internal_POC  \\\n",
       "0                         No track           Davis,Sharrice A   \n",
       "1  Did not hear back after Level 1             Brown,Maxine A   \n",
       "2                                ?  Georgakopoulos,Vasilios T   \n",
       "3  Did not hear back after Level 1             Brown,Maxine A   \n",
       "4                       Up-to-date              Thomas,Lori E   \n",
       "\n",
       "                       Resource Internal_rating Success_probability  \n",
       "0                           NaN               3                73.6  \n",
       "1                            No               5                58.9  \n",
       "2                            No               4                68.8  \n",
       "3  We have all the requirements               1                64.5  \n",
       "4                            No               4                62.4  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Data formatting\n",
    "data_train['Deal_value'] = data_train['Deal_value'].str.replace('$', '')\n",
    "data_train['Weighted_amount'] = data_train['Weighted_amount'].str.replace('$', '')\n",
    "data_train['Deal_value']=data_train['Deal_value'].astype('float')\n",
    "data_train['Weighted_amount']=data_train['Weighted_amount'].astype('float')\n",
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Dropping the insignificant columns\n",
    "data_train.drop([\"Lead_name\",\"Industry\",\"Date_of_creation\",\"Contact_no\",\"Location\",\"POC_name\",\"Designation\",\"Lead_POC_email\",\"Hiring_candidate_role\",\"Lead_source\",\"Last_lead_update\",\"Internal_POC\",\"Resource\"], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Deal_title</th>\n",
       "      <th>Deal_value</th>\n",
       "      <th>Weighted_amount</th>\n",
       "      <th>Pitch</th>\n",
       "      <th>Lead_revenue</th>\n",
       "      <th>Fund_category</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Level_of_meeting</th>\n",
       "      <th>Internal_rating</th>\n",
       "      <th>Success_probability</th>\n",
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       "      <th>0</th>\n",
       "      <td>TitleM5DZY</td>\n",
       "      <td>320506.0</td>\n",
       "      <td>2067263.7</td>\n",
       "      <td>Product_2</td>\n",
       "      <td>50 - 100 Million</td>\n",
       "      <td>Category 2</td>\n",
       "      <td>USA</td>\n",
       "      <td>Level 3</td>\n",
       "      <td>3</td>\n",
       "      <td>73.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TitleKIW18</td>\n",
       "      <td>39488.0</td>\n",
       "      <td>240876.8</td>\n",
       "      <td>Product_2</td>\n",
       "      <td>500 Million - 1 Billion</td>\n",
       "      <td>Category 4</td>\n",
       "      <td>India</td>\n",
       "      <td>Level 1</td>\n",
       "      <td>5</td>\n",
       "      <td>58.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TitleFXSDN</td>\n",
       "      <td>359392.0</td>\n",
       "      <td>2407926.4</td>\n",
       "      <td>Product_1</td>\n",
       "      <td>500 Million - 1 Billion</td>\n",
       "      <td>Category 4</td>\n",
       "      <td>USA</td>\n",
       "      <td>Level 1</td>\n",
       "      <td>4</td>\n",
       "      <td>68.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TitlePSK4Y</td>\n",
       "      <td>76774.0</td>\n",
       "      <td>468321.4</td>\n",
       "      <td>Product_2</td>\n",
       "      <td>500 Million - 1 Billion</td>\n",
       "      <td>Category 3</td>\n",
       "      <td>USA</td>\n",
       "      <td>Level 2</td>\n",
       "      <td>1</td>\n",
       "      <td>64.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Title904GV</td>\n",
       "      <td>483896.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Product_2</td>\n",
       "      <td>50 - 100 Million</td>\n",
       "      <td>Category 3</td>\n",
       "      <td>India</td>\n",
       "      <td>Level 2</td>\n",
       "      <td>4</td>\n",
       "      <td>62.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Deal_title  Deal_value  Weighted_amount      Pitch  \\\n",
       "0  TitleM5DZY    320506.0        2067263.7  Product_2   \n",
       "1  TitleKIW18     39488.0         240876.8  Product_2   \n",
       "2  TitleFXSDN    359392.0        2407926.4  Product_1   \n",
       "3  TitlePSK4Y     76774.0         468321.4  Product_2   \n",
       "4  Title904GV    483896.0              NaN  Product_2   \n",
       "\n",
       "              Lead_revenue Fund_category Geography Level_of_meeting  \\\n",
       "0         50 - 100 Million    Category 2       USA          Level 3   \n",
       "1  500 Million - 1 Billion    Category 4     India          Level 1   \n",
       "2  500 Million - 1 Billion    Category 4       USA          Level 1   \n",
       "3  500 Million - 1 Billion    Category 3       USA          Level 2   \n",
       "4         50 - 100 Million    Category 3     India          Level 2   \n",
       "\n",
       "   Internal_rating  Success_probability  \n",
       "0                3                 73.6  \n",
       "1                5                 58.9  \n",
       "2                4                 68.8  \n",
       "3                1                 64.5  \n",
       "4                4                 62.4  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the table, we have a feature which is categorical in nature. The **Pitch, Lead_revenue, Fund_category, Geography and Level_of_meeting** columns represent categorical values. To convert them to integral values we use a simple **get_dummies()** function in **pandas** module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Converting columns containing categorical values to integral values\n",
    "\n",
    "##Pitch\n",
    "p=pd.get_dummies(data_train['Pitch'],drop_first=True)\n",
    "\n",
    "\n",
    "##Lead_revenue\n",
    "lr=pd.get_dummies(data_train['Lead_revenue'],drop_first=True)\n",
    "\n",
    "\n",
    "##Fund_category\n",
    "fc=pd.get_dummies(data_train['Fund_category'],drop_first=True)\n",
    "\n",
    "\n",
    "##Geography\n",
    "g=pd.get_dummies(data_train['Geography'],drop_first=True)\n",
    "\n",
    "\n",
    "##Level_of_meeting\n",
    "lm=pd.get_dummies(data_train['Level_of_meeting'],drop_first=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   Product_2\n",
       "0          1\n",
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       "4          1"
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    "p.head()"
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  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   50 - 100 Million  500 Million - 1 Billion\n",
       "0                 1                        0\n",
       "1                 0                        1\n",
       "2                 0                        1\n",
       "3                 0                        1\n",
       "4                 1                        0"
      ]
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     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "   Category 2  Category 3  Category 4\n",
       "0           1           0           0\n",
       "1           0           0           1\n",
       "2           0           0           1\n",
       "3           0           1           0\n",
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      ]
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     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
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    "fc.head()"
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  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Level 2  Level 3\n",
       "0        0        1\n",
       "1        0        0\n",
       "2        0        0\n",
       "3        1        0\n",
       "4        1        0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Deal_title</th>\n",
       "      <th>Deal_value</th>\n",
       "      <th>Weighted_amount</th>\n",
       "      <th>Pitch</th>\n",
       "      <th>Lead_revenue</th>\n",
       "      <th>Fund_category</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Level_of_meeting</th>\n",
       "      <th>Internal_rating</th>\n",
       "      <th>Success_probability</th>\n",
       "      <th>Product_2</th>\n",
       "      <th>50 - 100 Million</th>\n",
       "      <th>500 Million - 1 Billion</th>\n",
       "      <th>Category 2</th>\n",
       "      <th>Category 3</th>\n",
       "      <th>Category 4</th>\n",
       "      <th>USA</th>\n",
       "      <th>Level 2</th>\n",
       "      <th>Level 3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TitleM5DZY</td>\n",
       "      <td>320506.0</td>\n",
       "      <td>2067263.7</td>\n",
       "      <td>Product_2</td>\n",
       "      <td>50 - 100 Million</td>\n",
       "      <td>Category 2</td>\n",
       "      <td>USA</td>\n",
       "      <td>Level 3</td>\n",
       "      <td>3</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TitleKIW18</td>\n",
       "      <td>39488.0</td>\n",
       "      <td>240876.8</td>\n",
       "      <td>Product_2</td>\n",
       "      <td>500 Million - 1 Billion</td>\n",
       "      <td>Category 4</td>\n",
       "      <td>India</td>\n",
       "      <td>Level 1</td>\n",
       "      <td>5</td>\n",
       "      <td>58.9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TitleFXSDN</td>\n",
       "      <td>359392.0</td>\n",
       "      <td>2407926.4</td>\n",
       "      <td>Product_1</td>\n",
       "      <td>500 Million - 1 Billion</td>\n",
       "      <td>Category 4</td>\n",
       "      <td>USA</td>\n",
       "      <td>Level 1</td>\n",
       "      <td>4</td>\n",
       "      <td>68.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TitlePSK4Y</td>\n",
       "      <td>76774.0</td>\n",
       "      <td>468321.4</td>\n",
       "      <td>Product_2</td>\n",
       "      <td>500 Million - 1 Billion</td>\n",
       "      <td>Category 3</td>\n",
       "      <td>USA</td>\n",
       "      <td>Level 2</td>\n",
       "      <td>1</td>\n",
       "      <td>64.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Title904GV</td>\n",
       "      <td>483896.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Product_2</td>\n",
       "      <td>50 - 100 Million</td>\n",
       "      <td>Category 3</td>\n",
       "      <td>India</td>\n",
       "      <td>Level 2</td>\n",
       "      <td>4</td>\n",
       "      <td>62.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Deal_title  Deal_value  Weighted_amount      Pitch  \\\n",
       "0  TitleM5DZY    320506.0        2067263.7  Product_2   \n",
       "1  TitleKIW18     39488.0         240876.8  Product_2   \n",
       "2  TitleFXSDN    359392.0        2407926.4  Product_1   \n",
       "3  TitlePSK4Y     76774.0         468321.4  Product_2   \n",
       "4  Title904GV    483896.0              NaN  Product_2   \n",
       "\n",
       "              Lead_revenue Fund_category Geography Level_of_meeting  \\\n",
       "0         50 - 100 Million    Category 2       USA          Level 3   \n",
       "1  500 Million - 1 Billion    Category 4     India          Level 1   \n",
       "2  500 Million - 1 Billion    Category 4       USA          Level 1   \n",
       "3  500 Million - 1 Billion    Category 3       USA          Level 2   \n",
       "4         50 - 100 Million    Category 3     India          Level 2   \n",
       "\n",
       "   Internal_rating  Success_probability  Product_2  50 - 100 Million  \\\n",
       "0                3                 73.6          1                 1   \n",
       "1                5                 58.9          1                 0   \n",
       "2                4                 68.8          0                 0   \n",
       "3                1                 64.5          1                 0   \n",
       "4                4                 62.4          1                 1   \n",
       "\n",
       "   500 Million - 1 Billion  Category 2  Category 3  Category 4  USA  Level 2  \\\n",
       "0                        0           1           0           0    1        0   \n",
       "1                        1           0           0           1    0        0   \n",
       "2                        1           0           0           1    1        0   \n",
       "3                        1           0           1           0    1        1   \n",
       "4                        0           0           1           0    0        1   \n",
       "\n",
       "   Level 3  \n",
       "0        1  \n",
       "1        0  \n",
       "2        0  \n",
       "3        0  \n",
       "4        0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Concatenating the converted columns in the dataframe\n",
    "data_train=pd.concat([data_train,p,lr,fc,g,lm],axis=1)\n",
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Dropping the categorical columns\n",
    "data_train.drop([\"Pitch\",\"Lead_revenue\",\"Fund_category\",\"Geography\",\"Level_of_meeting\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train.drop([\"Deal_title\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Deal_value</th>\n",
       "      <th>Weighted_amount</th>\n",
       "      <th>Internal_rating</th>\n",
       "      <th>Success_probability</th>\n",
       "      <th>Product_2</th>\n",
       "      <th>50 - 100 Million</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>359392.0</td>\n",
       "      <td>2407926.4</td>\n",
       "      <td>4</td>\n",
       "      <td>68.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>76774.0</td>\n",
       "      <td>468321.4</td>\n",
       "      <td>1</td>\n",
       "      <td>64.5</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>483896.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>62.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Deal_value  Weighted_amount  Internal_rating  Success_probability  \\\n",
       "0    320506.0        2067263.7                3                 73.6   \n",
       "1     39488.0         240876.8                5                 58.9   \n",
       "2    359392.0        2407926.4                4                 68.8   \n",
       "3     76774.0         468321.4                1                 64.5   \n",
       "4    483896.0              NaN                4                 62.4   \n",
       "\n",
       "   Product_2  50 - 100 Million  500 Million - 1 Billion  Category 2  \\\n",
       "0          1                 1                        0           1   \n",
       "1          1                 0                        1           0   \n",
       "2          0                 0                        1           0   \n",
       "3          1                 0                        1           0   \n",
       "4          1                 1                        0           0   \n",
       "\n",
       "   Category 3  Category 4  USA  Level 2  Level 3  \n",
       "0           0           0    1        0        1  \n",
       "1           0           1    0        0        0  \n",
       "2           0           1    1        0        0  \n",
       "3           1           0    1        1        0  \n",
       "4           1           0    0        1        0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7007 entries, 0 to 7006\n",
      "Data columns (total 13 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   Deal_value               6956 non-null   float64\n",
      " 1   Weighted_amount          6482 non-null   float64\n",
      " 2   Internal_rating          7007 non-null   int64  \n",
      " 3   Success_probability      7007 non-null   float64\n",
      " 4   Product_2                7007 non-null   uint8  \n",
      " 5   50 - 100 Million         7007 non-null   uint8  \n",
      " 6   500 Million - 1 Billion  7007 non-null   uint8  \n",
      " 7   Category 2               7007 non-null   uint8  \n",
      " 8   Category 3               7007 non-null   uint8  \n",
      " 9   Category 4               7007 non-null   uint8  \n",
      " 10  USA                      7007 non-null   uint8  \n",
      " 11  Level 2                  7007 non-null   uint8  \n",
      " 12  Level 3                  7007 non-null   uint8  \n",
      "dtypes: float64(3), int64(1), uint8(9)\n",
      "memory usage: 280.7 KB\n"
     ]
    }
   ],
   "source": [
    "data_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>483896.0</td>\n",
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      ],
      "text/plain": [
       "   Deal_value  Weighted_amount  Internal_rating  Success_probability  \\\n",
       "0    320506.0     2.067264e+06                3                 73.6   \n",
       "1     39488.0     2.408768e+05                5                 58.9   \n",
       "2    359392.0     2.407926e+06                4                 68.8   \n",
       "3     76774.0     4.683214e+05                1                 64.5   \n",
       "4    483896.0     1.569884e+06                4                 62.4   \n",
       "\n",
       "   Product_2  50 - 100 Million  500 Million - 1 Billion  Category 2  \\\n",
       "0          1                 1                        0           1   \n",
       "1          1                 0                        1           0   \n",
       "2          0                 0                        1           0   \n",
       "3          1                 0                        1           0   \n",
       "4          1                 1                        0           0   \n",
       "\n",
       "   Category 3  Category 4  USA  Level 2  Level 3  \n",
       "0           0           0    1        0        1  \n",
       "1           0           1    0        0        0  \n",
       "2           0           1    1        0        0  \n",
       "3           1           0    1        1        0  \n",
       "4           1           0    0        1        0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Correcting Missing Values in the dataset\n",
    "data_train['Deal_value'] = data_train['Deal_value'].fillna((data_train['Deal_value'].mean()))\n",
    "data_train['Weighted_amount'] = data_train['Weighted_amount'].fillna((data_train['Weighted_amount'].mean()))\n",
    "data_train['Internal_rating'] = data_train['Internal_rating'].fillna((data_train['Internal_rating'].mean()))\n",
    "data_train['Success_probability'] = data_train['Success_probability'].fillna((data_train['Success_probability'].mean()))\n",
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25e63454b20>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gCWV/6bNYs4Fn1fZLUuN/Fto+UIFDWv/Zy3xItP3uLWy7VupDh7QP+SJg/9xjt1f8ui/Lvs6PA9tUjdnh+7g3cCuwW4WvO/+9vLmO7+VEu1Udw1wiaU2y/ZeStgb+1W+QiDgLOEvS7hFxecUytZsKrAU8mN1fC3hKRCyV1HdZJT2btMD130itpQMi4ipJTwEuJ1V4RU1p/SMiRoBZkj5BqtDX7rdsOTOAt0q6nfTzUHqLeHbJeAskfR/4Kbmfb0T087W2rC5pjYj4Zxbj25L+QlpgvFaJeJNz/z6+7doUSoiIH0r6JfBpSYcC/0mPPcYF3EPaercpsAmpcqsSU5LWi4gHszL/StJrSMNaG5aMmR+i+3DbtVLfy4mmaoV5NPBLYJqk7wB7krrRZS2U9BEeP1nxtgox/xu4RtKvSRXHC4HPSloLOL9EvBOBrwMfiYhHc2VcXGLccYGkfSPil7k4x0haTIlxrZz9Kry2k3VJOyFelnss6O/DoeUUYFdSCy4Fijhf0utIP6t+nSRp7Yj4e0R8pfWgpKdR7ufbKtPfgQ9Ieg5wBtU+wIiIAyWtR+qVfCor3/qSdomIMmOOxwHPAObl3uM6SS8htWLL+LikJ0bEPyJi2Xhy1hD6ZsmYE0rlnT6SNiJ1AwTMi4h7K8S6DLiENL6ztPV4RHSaDOon7mak/HgCroiIrhvse8R6f0Sc0PbY4RHxxSpl7PGe+0TEeX2+ZkfgBdndSyLi2vpLtmqQJGCdiCg10TVGzE2B15PGXKdFxLQeL7EGqFRhSnphp8cj4uKS8a6JiOeULtDYcaeSBtzzrdayZbwqInZqe+zqiHhutVL29549nn848A6WtwAPAmZHxJdLvv/mpAmaPUkty0uBwyNiUZl4lmQz2w8AW0TEH4ddHuutaoX5s9zdNUituCsj4sUl430GuCwizi5dqMfHPI70SX4jafYT0njeK/uMMxN4A2l88JLcpXWApRHx0hqKO9Z791UhS7oO2D0iHsnurwVcXnYMM1te8l3gW9lDhwBvjIh9ysRbFWVj02dGxO+U1sn+EtiRtPTrDRFRevjAVp5KY5gRcUD+vqRplBuHajkc+Eg2GfMYyycr1q0Q81XA0yOi7wmeNpcBfwY2Bj6fe/xh4LqKsXvp91NN5IY0sn93SpZa1CYRcVru/umS3l8hHpImR8TS3s8cTrwBxHw98Ons32/J/t4E2JY0RuoKcxyoe6fPImCHsi+OiHVqLEvLbcDqlJi9z8u6TH8krVFrutOA30r6SXb/VcA3KsS7V9IhwPey+zNJS8CqWCjph8BpEXFTxViDiFd3zCWxvDv3cmBOVhnfnF+8X5SkL9PlgzQi3teEmBNNpQqz7Rs8ibQYufTkQt1jopl/kGbJL2DFJTF9/fAlXRoRMyQ9zIq/VJVbwZKe0N4Cbnvsjn7iRcTx2aqAGVn5Do2Iq8uWD3gbaXXAF0hf+2XZY1U8mzThcYqkScCppEqk7MRK3fHqjvkvSTsAfyWtmfxQ7tqaJeItKPGaYcScUKqOYb4ld3cEuCMiflMhXq1jolnMt3R6PCLOKBuzbmNMJPU10ZO9Zt2IeEhSx3V40dA9wdkH5fdIu1Z+CHw6IhY2JV4dMSXtSup6bwJ8ISI+kz2+P/CmiJhZsXxrtcas6zKImONdoxMIt8ZEq/4y1VSWrouBy1RGkp5MWlj/bdKEUmuccV3g5IjYrs94P4+IV2QL1ju1gp/aZ7wjIuK/x+qqVemiKZ0b/W+kTQBbkSaUvkNaCvXZiNh2mPHqjinpgyz/+UZ2uxe4NCokl8n2eH8DWDsitsiWk70zIt7VpJgTRakuuaTr6TzWUXVHSbvSY6KSzoyIfx+rrCXKeGUWZ6yT5vqqjDIvJy3035w0kdSK/RDwkX6DRcQrsr+nlyhLJzdnfw+iq3YraW/2/0TEZbnHfzjW0MxKjld3zE7j81sBH5X0yYiYU7KMJ5B+j+YCRMS1Fb7eQcacEEq1MCVt2e162TVlY4yJ3hERh5SItVlE/HmssjZp3Zuk11RdnJ/F6dqFjwGlJ+tX1nL7aNSUbq7ueIOKOcb7bAic3+/wS+71v42IXfNLzyRdGxE7VihT7TEnilItzAFWNvmWzAjwvbJjorH89Ld3RcSR+WvZ2swjH/+qsUnaLltD1/EXu2Jl9DxJF0TEA9l7bQD8Z0T0u9Xy812uBdDXWHA2ptxt1rSvtay51y2VtDdQS2VUd7xBxRzjfe7PdhKVdaekPYBQOh3xfSzvGTQp5oRQddJnN9IOkGeQNudPBh6pOGM8hbQ2DeCWiHisdAEZc0Llun675JJmR8QsSb/qcDkqTkw9bmF6mUmfukl6UbfrEXFRt+s9Yv8XsB4pReCyiYWyHzx1xxtUzA7v8WLgY2V/fyRtDHwReClpSOdc0i6s0su+BhFzoqhaYS4gLbv4AbAz8GbgaRHx0ZLx9iLNJN5B+kFNA95SZlmRpP8HvIs0tviH3KV1SOnK+u7mD0q2M+f5rWVEShmgFkTEM/uM8+pu16NcdqGBqPuDZ0AfZLXFHGMsfUNgMfDmiPhdiSIiaZOIuKfMa1dmzImicoUZETvnW2ySLouIPUrGu5K0TeyW7P62pG7580rEWg/YgJTo96jcpYerLK+RtAapIp5B+g9wCWlG+58VYh4BvJK04DxIaxznRkRfu6YkndblckSfWZ+6TO61AtY1uTfhdRhLD+C+qst2lDK2305qBf+oNazTtJgTRdUK82JSs/0U4C+krYNvLTs43KmrXKb7PEbsJ5HWdgIQEX8qGedM0nbIb2cPzQQ2iIjXVSzffsBLyLpAEXFOlXh1GNTkXhZ7PVJ6wNbs60XAMZHldxx2vEHFHARJu5B6eq8CbiItrv9291et/JgTQdUKc0vSzoUpwAdI4z1fKbtIWNKppE/eVpKHN5KypR9aoYwHkBLLPgW4m5S16OZ+u7u5eI+bLWzKDKKkQyIl5P1gp+sR0Z5gd2gk/Qi4gTQEA/AmYMeI6DqssLLiDSrmIGVjj8eTEqNM7vX8YcUcz6ruJd8JODvSVrFP1VCe/we8mzQrJ9J5LV/p+orePkPK13l+RDw3m/msshD+akm7RcQ8WLaDo/TupixGfrvlFNLe9zKTZ62M5bXsydcAt4OSzp95Te7+pyRd06B4g4pZK0nrktL3HUw65uMnpB1yjYo5UVStMF8JnJB1zecA50Q6aqGUbNLjeB5/1EAVj0XEfZImSZoUKZX/cf0GyY3nrQ68WdKfsvtbkrospUVb0hFJr6LEL2hEfC37u44PLyJiRqfy1eRRSTMi4lIApVMzH+3xmpUZb1Ax63Yt6eiQY6K+410GEXNCqCPj+uqkIxFeT5oIOS8i3l4y1itIKbBayX7rSGxxPmkc5nOk1Gx3k2ak+5qYGuR43hjvNy8iduvzNV/qdj0qZpuR9ERge9JmgtKZ9bNYraMf1iP9nO8njX+XSt5Sd7xBxaybJEVEqMZ934OIOVHUspc8qzT3Je25fUFEbFIyzkLS8brXRx0FSzHXIrUKJpHGRNcDvlN1TVldk0hZrPyY2CTSEq0XRURfqeQkLSGNuZ1JWq6ywoLo6DPhiKRXkk7uvB/4GOl44r+StvQd2W+8Md5j3axstRz/UHe8QcWsi7yXfKWqOumzL2mcY2/Sed/fJ83wluqWZ+veXhIRoz2fXCzeZNIwQW3Z0LNK5PPUNImUxcwvBxohrUP9ekTc3WecjYDXkVr7IyxfFvK3kuW6Nou3HmlP9bMj4rbsw+KCiHhWmbhZ7E4TUw+SslP1PU5Yd7xBxaybpN+SjgWeG8u3Md4QEaXz0g4i5kRRdQzzraSxy3dG9YzmAEcAZ0u6iBVzV5Ya04y0ve0fyh1HWoNPU+8kElVWAbTFuQ84GThZ6RyjmcCNko6MiG91f3VHoxHxewBJt0fEbdn73C2p9Fh1Zufs1krp92/AfOAwST/odw3qAOINKmbtIuJOrbi7snKW+EHEnAiqHlFxcLfrki7vs1v5X8DfSV3dus5B/idwvdK5NPntbWXH82qZRALak408TtkyKu13nwnsA/wfKdNSGZOU9rVPAkazf7f+F00a+2WFbATsFOk4WyQdTcoz+cKsvP1WRnXHG1TMunkv+UpU9xEV7dbo/ZQVbBgRL+v9tL78IrvV5QFJa5N2+HxH0t2k7m8ZrWQje5ImU76f3X8dJSo5SZ8CXkH65Z4DfLjKqgVSV/xKlleS+T3UVceYtwCW5O4/BmwZEY8qnek07HiDilm3w0j7vqeS0iGeS9qJ1rSYE8KgK8x+/1OdL+llEXFubQXoMTEh6Udta+16OZDUan0/yyeRSmW0aZVN0luBvSNLNCLpZNIvab8+TjrDaMfs9tmsW1UqT2lEbFWiDEV9F5gn6azs/gHA97JJujLLtOqON6iYtcpWK7wx/5jSAXUnNCnmRDHQjOvq/zzth0mLr+s8NbLXe/Z9prhSpvRdSB8I8yPiLxXLcAvpWNz7s/sbAPMi4ul9xlmpS5+qkvQ8lp87dGlEVEpUXHe8QcUcNEl/iogtmh5zPBp0C7OvPH8DWiDd8237ebKktwOfAC4kfX1flnRMRJxaoQzHknYQtbLjvAj4ZL9BmlYhFrAm8FBEnCZpE0nTo8JxDQOIN6iYg1Ylv+bKjDnuDLqFuUNE3NDnazYAtmHFNY5VTo3s9X79toJvAfZorePMlvJc1m9rsEPcJwO7kirwK6q2Wpsum0DZmXRm/LaSngL8ICL2bEK8QcVcGdzCHJyyZ/q07y1edolcF7pEZfl24HDSGTfXkJbvXE6fmcL71O8n5yJStqKWh4E7ayjHLqTDtSB9b3/W5blDJ2nt1uxxSQcBzyWbSIqIxZKq9DDqjjeomLXo8X+wzLG9A4k50ZQ9omJQvzSHA88njd/tLWk76knq0U2hoypyi5jvAn6bTQQEaRLoiioFkHQs6ev+TvbQ+yTtEREfrhJ3wG4izSKXtSTbfhewbEdWFXXHG1TMWgzi/+CQhsTGlVrGMFXfNsF/RsQ/JSHpCZHO0CnV1VXBky37mJFv/TL9gRUzuJ/V4bn92h94TmuHk6QzgKuBvipMpfyNHybtnW9tT707K+Ox0Wci2DF2ukD6Hq7dT6wOzpT0NWB9Se8gJU0+pUHxBhXTxrGqWyNr3SYo6Sek/ejvJ3XD/wasHhH7l4g10BnjrGsWFbulrVjXAXvlZsk3BH7d7zIgSeeQJqPOaI2BZmOjbwFeGhH79Bnvn8D/0Hmd6QciYv1+4nWIvw/wMlIFfE5EnNekeIOKaeNYRJS+kdJAbQRcnd3fG5hdJWYu9otI6eOm5B7boI7YFcu1A6n198fsdiXwzIoxZ2axTidlx7kdOLhEnFvKXOvymsuA541x7c6KX/NxRR4bVrxBxfRtfN+qbm97LNJs8bJtgqSzxCuLiIsiYm5E5HdaXNBvHEm7SZov6e+SlkhaKqlK1pnZwAcjYsuI2BL4T+DrZYMprSy/lDTB9ePstntEzCkR7o+SjpC0aS7+ppKOpNzE1KGkiryTnUvEy+vU2t2vQfEGFdPGsapjmHVuEyyizFqwE+lwsmWFMqyVfTAAEBG/rjIZEBEh6aeRDnqbW6FckLIUHQVclKs0/5LF/fcSZbuly7W/limgcqd5ZkMRLetQInN93fEGFdMmhqpjmAPJNdnl/fo+q1v1n2z5E9Iyk1b2n0OAnSPiVWXiZTFPAk6PiPllY4wXqvk0z7rjDSqmTQx1ZFzfEtgmIs5Xysg9OSIe7vW6ku9VpsKs+2TLDUhLnWZkD10MfCpK5pzMYt4EPJ2UB/MRSu79zmJtR1rqNJW0SmAxKa9hI7PN1LjCYiDxBhXTxqdKXfJsqcUs0oH0W5P+k55MOi52EMp0yd9EagG/h3Sy5TSgn2Qby988JST+SFQ86qGDWsbFsrHKmaRMRa21oZuTEkbMiYhj63ifOmiM0zyBsissao03qJg2zlWZMSLtxplCNkuePXZ9iTgbdrvlnzfsWTLgwhpjrUFaQnUi8E7SkcJV4v2etAyr/fEpwK01lfmqmuLUusKi7niDiunb+L5VnfT5V0QsUZaZWdJqlMuTeGX2OpF2j/wt+/f6wJ+A6QBRboxrT1Iii78kFP8AAAzlSURBVNbBamSxnlqinJCSZMwlTSLlExL/uESsM0hZmS4htTK3J+12KmuU1Bpqn9neLLtWh7qSMNSWiHlA8QYV08axqhXmRZI+AqyZLfB9FyX2QEfEdFiWB3JuRJyd3d+PNP5YxTdIXfErqSfN/obAfay4vz1Iy4H6tX1k5+JI+gYVt1iSWqsXSLqV5cuItiCtCnhPxdgtdSVjbq2wuJh6VljUHW9QMW0cqzpLPgn4D3I7IYBTomRQSVdGWl6Tf2xBRJRe8yfptxGxa9nXD1L7JFaZSa0OMSeREnlMJf1MFpFydjbqTJa6V1gMYsXGyl4FYs1Xxyz5JgARcU/lwqStfZcA3ya12g4BXhgRL68Q81hgMqkFmD9Y7aoxX9Q93lNJ6ft3y8p4OfD+KJEjUdJSlnfrWxlh/gH1J05W9exCdZXjacCmEfGbtsdfCNwVEX/o/MqVE29QMW1iKLXTR8knJd0L/A64RdI9kj5RsTwzSUkjfpLdNqHiiYykHJM7A58l7Xv/PPC/FeJ9l3Tu92ak8cIfkGal+xYRkyNi3ey2TkSslvt33VnmG3GkAumYg07Lzv5BuSMQ6o43qJg2AZRqYUr6ACnDzqxWyypreX0V+GVEfKFSoWpqDWXd09dGxJlVY+ViPq6LL2leROxW13uUpe7ZhT4aERuuzPJ0LEiX860lXR99nnVed7xBxbSJoexe8jcDM/Pd0EhnVh+SXStF0h7ZIu6bsvs7SvpK2XiR0qXVNdnR8itJR0naStKWko4AfiFpQ6UsQ8P0WdIOlXXabmtT4VhcSXtKOk/S7yXdJul2SbeVDNftJNEySWrrjjeomDYBlJ0lXz3SyXIriIh7JK1eoTxfAF5Otqc6Iq7Nxo2qOE/Sh0hH2OaXAZXd4vb67O93tj3+NtKYZtnlSnW4CvhpRDzuiF6lbPZl1bnSYL6kd0TECglLJP0H5c5PrzveoGLaBFC2Sz7mbG6Vmd5Wd1e5kxwlXRsltzFmr+80GRMV1mH2er99Ykg5E5WSLd/X6cNM0qZRPmFGbSsNlJKC/IR03ner8tmZtLj+oOjzLKO64w0qpk0MZSvM/OzuCpeANSKiVCtT0g9JW9FOJM1Cv4+U2OLgMvGGoY6lQU1T90qDLObepNyiADdGxIUVy1hrvEHFtPFtoKdG9kvSxqQlOy8lVb7nAu+r0H1GKSHIB4EtImKWpG1IpwD+vI4yd3i/vs85bzotP/43LyJikIfTmTXOoM8l79fTI+KN+QeyrY1VchCeRupWtdK5LSItBRpIhUm5raGNFhF7D7sMZk1QNeN63b5c8LF+bB0R/03as01EPEp9+6FXCZLWk3S8pAXZ7fNKOSPNVimNaGFK2p3UAtykbS3huqSxsyqWSFqTrOUnaWty43ADcMcAY/etpjHVU4EbWJ61/U2klvurK8Y1G1ea0sKcQloruBorrh98CHhtxdifBH4JTJP0HdK5QIXOIu9E0uuUToxE0sck/VjSsgopIppWidTRmt46Io6OiNuy26cY7vIps6FoRAszIi4iZT46PSoef9sh9rmSriTNugs4vNOymz58PCJ+IGkGac3o/5J2ODUywQf1ZBd6VNKMiLgUlo0rP1pDXLNxpWmz5NsCHwK2YsXclaVnYyVdEBEv6fVYH/GujojnSvocKVnydyfizHiepOeQcneuR/rQuZ90zMe1Qy2Y2UrWiBZmzg9IR1ycQsUdJZLWAJ4IbKx0Dk+ra7ouKWlGWXdJ+hpp6dNxkp5Ac4Y2BiIirgF2lLRudr/KMcVm41bTWpiPy4dZIdbhpIS6TwHuYnmF+RDw9Yg4sWTcJwL7klqXt0raDHhWRJxbQ7EbRdIhEfHtsZJ6RMTxK7tMZsPUiBZmLmnFzyS9i7QtLb+jpO+F6xHxReCLkt4bEVWXJuVtBvwiIv4laS/g2cA3a4zfJK3z1tcZainMGqIRLcxsv3frTJ92lfd9S9qDx4+LlqrkJF1D2le8FSnD/FzSgvv9q5SxDtnayA8DryLlEoV02uFZwLER8cCwymY2ETSihRnZmT6DIOlbpCOAr2H5uGhQvlU4GhEjkl4NnBARX5Z0dQ1FrcOZwIXAXq0EEZKeDLyFND68Tz/BJH2p2/Wo/7hhs0ZrRIXZklVC7R4kjRfeXTLszqTDxupqSj8maSYp7+cB2WNVUtrVaauIWOFUw6ziPE7S20rEcyozs5xGVZikA9V2B1rJHvYC5gHbSjomIr5VIuYNwJOBP9dSQjgUOAz4r4i4XdJ00hlETfDHLKHxGa1Ublmqsrey/BTJwiLijHqLZza+NWIMs0XSz4C3t/1n/yrwduDisY4N6BHzV8BzSEfY5ieSXlmhnGuSsh/dUjbGIGTLp44CDgSelD38V9I463H9Tp5lP48xf0GqfA/NxqOmVZgrnJciSaTu+A5lF4dLelGnx7PdRWXKeABpd8+UiJieLeo+ZiJWHmN971rKfg/NxqumdckvkfRz0gQFwGuAi5XOhy41wzuA/9SfJJ37/ess/jVZt7xxsu2buwA3lFkn6grRbEVNqzDfTaok9yQtMfom8KNswqavnIySHqZzd7Lqmd8jEfFgavwu04hmuqQrImKX7N/vIH0/fwIcLWmniDi2z3hnRsS/S7qeDl9jRDy7jnKbjReN6pKPB5K+Qcp4dBSpcn8f6VC4w4ZaMFbM9i5pPrB/djDdWsC86P8I280i4s+Stux0ve5EKWZN14gWpqRLI2JGh1Zh1dbgILwX+ChpAum7pMXrnxlqiZablE38TCJ9GN4DEBGPSBrpN1hE/Dn72xWjGW5hTiiS7gBGyT5ogD0i4i+S1gYujYjn9Bmv4wcYzfwgMxu4xmXZkTRD0qHZvzdu2oSKpPMkrZ+7v4Gkc4ZZppaI2CoinhoR07O/W8fBjgIHlQh5AXATqQW9Q0SsExHrtv6uq9xm40WjWpiSjibtzHl6RGwr6SnADyJizyEXbZlOy5smcj7MbH/6q4GDgTWA7wNzqpzkaTZeNa2FeRDwSrIzzyNiMc3LlDMqaYvWnWxCpDmfOjWLiAcj4jRgP1Ku0mNIO4fMVjmNmPTJWRIRIal1YNlavV4wBB8FLpXUWqP4QmDWEMszUFmmp5nAC4BLgYMi4pLhlspsOJrWJf8QsA0pq87ngLcB3605n2VlkjZm+RlBl1c8I6ixskmkB4A5pCxIK8y0R8RVQyiW2dA0osKU9H7gN8DVpAXqLyNVRudExHnDLFs7SQcBF0bEg9n99Unp1H463JLVT9KvWT7c0J6vNKqctWQ2HjWlwvxf0rnk2wHXAZeRKtDLmza5IOma9uU5E3nSx8yWa0SF2SJpCmmWfA9SmrfdgQciYvuhFixH0nXtWwLbk4aY2cTUtFnyNUmnOq6X3RYDvx1qiR5vgaTjJW0t6amSvoAT7ZqtEhrRwpQ0G3gm8DCpgpxH2vv8t6EWrINs5v7jpGN2BZwLfCYiHhlqwcxs4JqyrGgL4AnAraQjcRdRMp3boGUV41HDLsfKkuUk3QWYSpr4WQxcUeORH2bjRiNamLDsP+YzSeOXewA7APeTJn6OHmbZ8rIM7p1SnU24GWNJLwO+wvIPMoDNgacB75qIZ7GbddOYCrNF0uakfJh7AK8ANoqI9bu/auWR9Lzc3TVIKd5GIuKIIRVpYCTdDOwXEXe0PT4dODsinjGUgpkNSSO65JLeR6og9wQeI1tSBJwKXD/Eoj1ORLRP8Pwmt+tnolmNNDzS7i6ac1Km2UrTiAoT2Ar4IfCBVg7GppK0Ye7uJNIyqCcPqTiDdiowX9Iclp86OY2UiOMbQyuV2ZA0rkvedJJuZ/kY5ghwB+kQtEuHVqgBkrQ9KSHKVNKqgEXA3Ii4aagFMxuCprQwG0/S84E7I2J6dv8tpPHLO0g5IyekrGKcsF+fWT+atnC9yb4GLAGQ9EJScpAzgAeB2UMs18BIWk/SsZJ+J+m+7HZz9lhjJuLMVhZXmMVNzu1rfz0wOyJ+FBEfJy2zmYjOBP5GSi6yUURsREqO8gDLj0I2W2W4wixusqTWEMZLSOnOWibq0MZWEXFc7qgLIuIv2XG9W3R5ndmE5AqzuO8BF0k6C3gUuARA0tNI3fKJ6I+SjpC0aesBSZtKOpLls+ZmqwzPkvdB0m7AZsC5rb3jkrYF1p6IyXSzI3uPAg4EnpQ9/FdgLnBc01LvmQ2aK0wzs4Im6tibDYCkGaREHDd4H7mtijyGaWOSdEXu3+8ATiSd4nm0pFUmY5NZi7vkNqb80RuS5gP7R8Q9WU7Qec4yb6sad8mtm0nZxM8k0ofrPZBygkoa6f5Ss4nHFaZ1sx7p+A0BIenJEfEXSWuz4gmSZqsEd8mtb5KeCGwaEbcPuyxmK5MrTDOzgjxLbmZWkCtMM7OCXGGamRXkCtPMrKD/D1URWxUr2ITnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data_train.isnull(), yticklabels=False, vmin=0, vmax=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing the testing dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>Deal_title</th>\n",
       "      <th>Lead_name</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Deal_value</th>\n",
       "      <th>Weighted_amount</th>\n",
       "      <th>Date_of_creation</th>\n",
       "      <th>Pitch</th>\n",
       "      <th>Contact_no</th>\n",
       "      <th>Lead_revenue</th>\n",
       "      <th>Fund_category</th>\n",
       "      <th>...</th>\n",
       "      <th>POC_name</th>\n",
       "      <th>Designation</th>\n",
       "      <th>Lead_POC_email</th>\n",
       "      <th>Hiring_candidate_role</th>\n",
       "      <th>Lead_source</th>\n",
       "      <th>Level_of_meeting</th>\n",
       "      <th>Last_lead_update</th>\n",
       "      <th>Internal_POC</th>\n",
       "      <th>Resource</th>\n",
       "      <th>Internal_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TitleAD16O</td>\n",
       "      <td>Bonilla Ltd Inc</td>\n",
       "      <td>Investment Bank/Brokerage</td>\n",
       "      <td>200988$</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-04-15</td>\n",
       "      <td>Product_1</td>\n",
       "      <td>167.332.2751x989</td>\n",
       "      <td>100 - 500 Million</td>\n",
       "      <td>Category 4</td>\n",
       "      <td>...</td>\n",
       "      <td>sonia</td>\n",
       "      <td>Chairman/CEO/President</td>\n",
       "      <td>maureenthomas@bonilla.com</td>\n",
       "      <td>Designer, fashion/clothing</td>\n",
       "      <td>Marketing Event</td>\n",
       "      <td>Level 1</td>\n",
       "      <td>more than a month</td>\n",
       "      <td>Massiah,Gerard F</td>\n",
       "      <td>No</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TitleOW6CR</td>\n",
       "      <td>Williams, Rogers and Roach PLC</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>409961$</td>\n",
       "      <td>2541758.2$</td>\n",
       "      <td>2021-01-23</td>\n",
       "      <td>Product_1</td>\n",
       "      <td>001-486-903-0711x7831</td>\n",
       "      <td>100 - 500 Million</td>\n",
       "      <td>Category 3</td>\n",
       "      <td>...</td>\n",
       "      <td>Daniel Bell</td>\n",
       "      <td>CEO/Co-Founder/Chairman</td>\n",
       "      <td>danielbell@williams.com</td>\n",
       "      <td>Horticultural consultant</td>\n",
       "      <td>Marketing Event</td>\n",
       "      <td>Level 2</td>\n",
       "      <td>Up-to-date</td>\n",
       "      <td>Smith,Keenan H</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TitleVVJQ5</td>\n",
       "      <td>Wood, Vaughn and Morales Ltd</td>\n",
       "      <td>Banks</td>\n",
       "      <td>434433$</td>\n",
       "      <td>3041031.0$</td>\n",
       "      <td>2020-07-19</td>\n",
       "      <td>Product_1</td>\n",
       "      <td>(393)104-2610x9723</td>\n",
       "      <td>100 - 500 Million</td>\n",
       "      <td>Category 1</td>\n",
       "      <td>...</td>\n",
       "      <td>Andrew Davis</td>\n",
       "      <td>Chairman/Chief Innovation Officer</td>\n",
       "      <td>andrewdavis@wood.com</td>\n",
       "      <td>Information officer</td>\n",
       "      <td>Marketing Event</td>\n",
       "      <td>Level 2</td>\n",
       "      <td>Did not hear back after Level 1</td>\n",
       "      <td>Gilley,Janine</td>\n",
       "      <td>Deliverable</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TitleUS8NA</td>\n",
       "      <td>Durham-Crawford Inc</td>\n",
       "      <td>Music</td>\n",
       "      <td>218952$</td>\n",
       "      <td>1521716.4$</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Product_2</td>\n",
       "      <td>(817)040-4599</td>\n",
       "      <td>100 - 500 Million</td>\n",
       "      <td>Category 1</td>\n",
       "      <td>...</td>\n",
       "      <td>shital</td>\n",
       "      <td>CEO/Chairman/President</td>\n",
       "      <td>charlesrivera@durhamcrawford.com</td>\n",
       "      <td>Commercial/residential surveyor</td>\n",
       "      <td>Contact Email</td>\n",
       "      <td>Level 3</td>\n",
       "      <td>more than a month</td>\n",
       "      <td>Morsy,Omar A</td>\n",
       "      <td>No</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Title5VGWW</td>\n",
       "      <td>Simpson, Duncan and Long LLC</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>392835$</td>\n",
       "      <td>2455218.75$</td>\n",
       "      <td>2020-10-25</td>\n",
       "      <td>Product_1</td>\n",
       "      <td>718-032-5726x76098</td>\n",
       "      <td>500 Million - 1 Billion</td>\n",
       "      <td>Category 3</td>\n",
       "      <td>...</td>\n",
       "      <td>Shelly Stephenson</td>\n",
       "      <td>CEO/Co-Founder/Chairman</td>\n",
       "      <td>shellystephenson@simpson.com</td>\n",
       "      <td>Wellsite geologist</td>\n",
       "      <td>Others</td>\n",
       "      <td>Level 3</td>\n",
       "      <td>More than 2 weeks</td>\n",
       "      <td>Morsy,Omar A</td>\n",
       "      <td>Deliverable</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Deal_title                       Lead_name                   Industry  \\\n",
       "0  TitleAD16O                 Bonilla Ltd Inc  Investment Bank/Brokerage   \n",
       "1  TitleOW6CR  Williams, Rogers and Roach PLC                Electronics   \n",
       "2  TitleVVJQ5    Wood, Vaughn and Morales Ltd                      Banks   \n",
       "3  TitleUS8NA             Durham-Crawford Inc                      Music   \n",
       "4  Title5VGWW    Simpson, Duncan and Long LLC                Real Estate   \n",
       "\n",
       "  Deal_value Weighted_amount Date_of_creation      Pitch  \\\n",
       "0    200988$             NaN       2020-04-15  Product_1   \n",
       "1    409961$      2541758.2$       2021-01-23  Product_1   \n",
       "2    434433$      3041031.0$       2020-07-19  Product_1   \n",
       "3    218952$      1521716.4$       2020-02-27  Product_2   \n",
       "4    392835$     2455218.75$       2020-10-25  Product_1   \n",
       "\n",
       "              Contact_no             Lead_revenue Fund_category  ...  \\\n",
       "0       167.332.2751x989        100 - 500 Million    Category 4  ...   \n",
       "1  001-486-903-0711x7831        100 - 500 Million    Category 3  ...   \n",
       "2     (393)104-2610x9723        100 - 500 Million    Category 1  ...   \n",
       "3          (817)040-4599        100 - 500 Million    Category 1  ...   \n",
       "4     718-032-5726x76098  500 Million - 1 Billion    Category 3  ...   \n",
       "\n",
       "            POC_name                        Designation  \\\n",
       "0              sonia             Chairman/CEO/President   \n",
       "1        Daniel Bell            CEO/Co-Founder/Chairman   \n",
       "2       Andrew Davis  Chairman/Chief Innovation Officer   \n",
       "3             shital             CEO/Chairman/President   \n",
       "4  Shelly Stephenson            CEO/Co-Founder/Chairman   \n",
       "\n",
       "                     Lead_POC_email            Hiring_candidate_role  \\\n",
       "0         maureenthomas@bonilla.com       Designer, fashion/clothing   \n",
       "1           danielbell@williams.com         Horticultural consultant   \n",
       "2              andrewdavis@wood.com              Information officer   \n",
       "3  charlesrivera@durhamcrawford.com  Commercial/residential surveyor   \n",
       "4      shellystephenson@simpson.com               Wellsite geologist   \n",
       "\n",
       "       Lead_source Level_of_meeting                 Last_lead_update  \\\n",
       "0  Marketing Event          Level 1                more than a month   \n",
       "1  Marketing Event          Level 2                       Up-to-date   \n",
       "2  Marketing Event          Level 2  Did not hear back after Level 1   \n",
       "3    Contact Email          Level 3                more than a month   \n",
       "4           Others          Level 3                More than 2 weeks   \n",
       "\n",
       "       Internal_POC     Resource Internal_rating  \n",
       "0  Massiah,Gerard F           No            -1.0  \n",
       "1    Smith,Keenan H          Yes             1.0  \n",
       "2     Gilley,Janine  Deliverable             5.0  \n",
       "3      Morsy,Omar A           No             5.0  \n",
       "4      Morsy,Omar A  Deliverable             2.0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test = pd.read_csv(path+\"/dataset/test.csv\")\n",
    "data_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<bound method DataFrame.info of       Deal_title                        Lead_name                   Industry  \\\n",
      "0     TitleAD16O                  Bonilla Ltd Inc  Investment Bank/Brokerage   \n",
      "1     TitleOW6CR   Williams, Rogers and Roach PLC                Electronics   \n",
      "2     TitleVVJQ5     Wood, Vaughn and Morales Ltd                      Banks   \n",
      "3     TitleUS8NA              Durham-Crawford Inc                      Music   \n",
      "4     Title5VGWW     Simpson, Duncan and Long LLC                Real Estate   \n",
      "...          ...                              ...                        ...   \n",
      "2088  Title2R8VU    Phillips, Smith and Jones Inc              BioTech/Drugs   \n",
      "2089  Title7HCNJ              Elliott-Morales PLC                Real Estate   \n",
      "2090  TitleCD5YZ               Herrera-Santos PLC   Sales/Marketing Services   \n",
      "2091  Title8OKXL  Howard, Martinez and Jacobs PLC                      Banks   \n",
      "2092  TitleHFQT8                  Smith Group LLC   Architecture/Engineering   \n",
      "\n",
      "     Deal_value Weighted_amount Date_of_creation      Pitch  \\\n",
      "0       200988$             NaN       2020-04-15  Product_1   \n",
      "1       409961$      2541758.2$       2021-01-23  Product_1   \n",
      "2       434433$      3041031.0$       2020-07-19  Product_1   \n",
      "3       218952$      1521716.4$       2020-02-27  Product_2   \n",
      "4       392835$     2455218.75$       2020-10-25  Product_1   \n",
      "...         ...             ...              ...        ...   \n",
      "2088    417150$      2732332.5$       2020-11-25  Product_2   \n",
      "2089    488661$     2956399.05$       2019-10-20  Product_2   \n",
      "2090    421119$     2631993.75$       2019-03-23  Product_1   \n",
      "2091     59879$      350292.15$       2019-02-05  Product_1   \n",
      "2092    159677$      1021932.8$       2019-06-22  Product_1   \n",
      "\n",
      "                 Contact_no             Lead_revenue Fund_category  ...  \\\n",
      "0          167.332.2751x989        100 - 500 Million    Category 4  ...   \n",
      "1     001-486-903-0711x7831        100 - 500 Million    Category 3  ...   \n",
      "2        (393)104-2610x9723        100 - 500 Million    Category 1  ...   \n",
      "3             (817)040-4599        100 - 500 Million    Category 1  ...   \n",
      "4        718-032-5726x76098  500 Million - 1 Billion    Category 3  ...   \n",
      "...                     ...                      ...           ...  ...   \n",
      "2088      (444)658-6350x207        100 - 500 Million    Category 3  ...   \n",
      "2089             1377254815         50 - 100 Million    Category 3  ...   \n",
      "2090      695-757-3607x5834         50 - 100 Million    Category 1  ...   \n",
      "2091    +1-688-318-4079x644         50 - 100 Million    Category 2  ...   \n",
      "2092      769-766-0278x1840         50 - 100 Million    Category 4  ...   \n",
      "\n",
      "               POC_name                        Designation  \\\n",
      "0                 sonia             Chairman/CEO/President   \n",
      "1           Daniel Bell            CEO/Co-Founder/Chairman   \n",
      "2          Andrew Davis  Chairman/Chief Innovation Officer   \n",
      "3                shital             CEO/Chairman/President   \n",
      "4     Shelly Stephenson            CEO/Co-Founder/Chairman   \n",
      "...                 ...                                ...   \n",
      "2088     William Spears             Chairman/CEO/President   \n",
      "2089           Amy Page            Chief Executive Officer   \n",
      "2090              ashma            CEO/Co-Founder/Chairman   \n",
      "2091              aarti            Chief Executive Officer   \n",
      "2092              rakhi             CEO/Chairman/President   \n",
      "\n",
      "                        Lead_POC_email            Hiring_candidate_role  \\\n",
      "0            maureenthomas@bonilla.com       Designer, fashion/clothing   \n",
      "1              danielbell@williams.com         Horticultural consultant   \n",
      "2                 andrewdavis@wood.com              Information officer   \n",
      "3     charlesrivera@durhamcrawford.com  Commercial/residential surveyor   \n",
      "4         shellystephenson@simpson.com               Wellsite geologist   \n",
      "...                                ...                              ...   \n",
      "2088        williamspears@phillips.com     Community development worker   \n",
      "2089        amypage@elliottmorales.com          Forest/woodland manager   \n",
      "2090       markcombs@herrerasantos.com                          Actuary   \n",
      "2091           justinmorgan@howard.com                Designer, textile   \n",
      "2092             juansalazar@smith.com          Clinical cytogeneticist   \n",
      "\n",
      "          Lead_source Level_of_meeting                      Last_lead_update  \\\n",
      "0     Marketing Event          Level 1                     more than a month   \n",
      "1     Marketing Event          Level 2                            Up-to-date   \n",
      "2     Marketing Event          Level 2       Did not hear back after Level 1   \n",
      "3       Contact Email          Level 3                     more than a month   \n",
      "4              Others          Level 3                     More than 2 weeks   \n",
      "...               ...              ...                                   ...   \n",
      "2088  Marketing Event          Level 3                              No track   \n",
      "2089           Others          Level 2  Following up but lead not responding   \n",
      "2090    Contact Email          Level 1                                     ?   \n",
      "2091  Marketing Event          Level 1                                   NaN   \n",
      "2092          Website          Level 2                     More than 2 weeks   \n",
      "\n",
      "                  Internal_POC                      Resource Internal_rating  \n",
      "0             Massiah,Gerard F                            No            -1.0  \n",
      "1               Smith,Keenan H                           Yes             1.0  \n",
      "2                Gilley,Janine                   Deliverable             5.0  \n",
      "3                 Morsy,Omar A                            No             5.0  \n",
      "4                 Morsy,Omar A                   Deliverable             2.0  \n",
      "...                        ...                           ...             ...  \n",
      "2088               Ross,Eric L                            No             1.0  \n",
      "2089  Abdul-Hamid,Saud Muhamad                Cannot deliver             5.0  \n",
      "2090            Bannister,Joan                   Deliverable             5.0  \n",
      "2091           Murray,Younetta  We have all the requirements             5.0  \n",
      "2092             Cashin,Marc C  We have all the requirements             3.0  \n",
      "\n",
      "[2093 rows x 22 columns]>\n"
     ]
    }
   ],
   "source": [
    "print(data_test.info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Internal_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2093.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.143354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.510451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>82.340000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Internal_rating\n",
       "count      2093.000000\n",
       "mean          3.143354\n",
       "std           4.510451\n",
       "min          -1.000000\n",
       "25%           2.000000\n",
       "50%           3.000000\n",
       "75%           4.000000\n",
       "max          82.340000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Deal_title</th>\n",
       "      <th>Lead_name</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Deal_value</th>\n",
       "      <th>Weighted_amount</th>\n",
       "      <th>Date_of_creation</th>\n",
       "      <th>Pitch</th>\n",
       "      <th>Contact_no</th>\n",
       "      <th>Lead_revenue</th>\n",
       "      <th>Fund_category</th>\n",
       "      <th>...</th>\n",
       "      <th>POC_name</th>\n",
       "      <th>Designation</th>\n",
       "      <th>Lead_POC_email</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2093</td>\n",
       "      <td>2093</td>\n",
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       "      <td>2093</td>\n",
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       "      <td>2093</td>\n",
       "      <td>2093</td>\n",
       "      <td>2093</td>\n",
       "      <td>2093</td>\n",
       "      <td>2093</td>\n",
       "      <td>1925</td>\n",
       "      <td>2093</td>\n",
       "      <td>2079</td>\n",
       "      <td>2093.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2093</td>\n",
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       "      <td>138</td>\n",
       "      <td>2083</td>\n",
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       "      <td>2</td>\n",
       "      <td>2093</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>1745</td>\n",
       "      <td>10</td>\n",
       "      <td>2093</td>\n",
       "      <td>618</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>60</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Title1T6W8</td>\n",
       "      <td>Roberson-Coleman and Sons</td>\n",
       "      <td>Banks</td>\n",
       "      <td>81590$</td>\n",
       "      <td>130836.0$</td>\n",
       "      <td>2019-10-05</td>\n",
       "      <td>Product_2</td>\n",
       "      <td>895.944.5114x87917</td>\n",
       "      <td>50 - 100 Million</td>\n",
       "      <td>Category 3</td>\n",
       "      <td>...</td>\n",
       "      <td>pooja</td>\n",
       "      <td>CEO/Co-Founder/Chairman</td>\n",
       "      <td>richardochoa@arias.com</td>\n",
       "      <td>Conservation officer, historic buildings</td>\n",
       "      <td>Contact Email</td>\n",
       "      <td>Level 1</td>\n",
       "      <td>2 days back</td>\n",
       "      <td>Georgakopoulos,Vasilios T</td>\n",
       "      <td>Cannot deliver</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>462</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1063</td>\n",
       "      <td>1</td>\n",
       "      <td>718</td>\n",
       "      <td>537</td>\n",
       "      <td>...</td>\n",
       "      <td>23</td>\n",
       "      <td>230</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>540</td>\n",
       "      <td>739</td>\n",
       "      <td>204</td>\n",
       "      <td>48</td>\n",
       "      <td>384</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.143354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.510451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82.340000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Deal_title                  Lead_name Industry Deal_value  \\\n",
       "count         2093                       2093     2092       2088   \n",
       "unique        2093                       2093      138       2083   \n",
       "top     Title1T6W8  Roberson-Coleman and Sons    Banks     81590$   \n",
       "freq             1                          1      462          2   \n",
       "mean           NaN                        NaN      NaN        NaN   \n",
       "std            NaN                        NaN      NaN        NaN   \n",
       "min            NaN                        NaN      NaN        NaN   \n",
       "25%            NaN                        NaN      NaN        NaN   \n",
       "50%            NaN                        NaN      NaN        NaN   \n",
       "75%            NaN                        NaN      NaN        NaN   \n",
       "max            NaN                        NaN      NaN        NaN   \n",
       "\n",
       "       Weighted_amount Date_of_creation      Pitch          Contact_no  \\\n",
       "count             2033             2093       2093                2093   \n",
       "unique            2033              720          2                2093   \n",
       "top          130836.0$       2019-10-05  Product_2  895.944.5114x87917   \n",
       "freq                 1                9       1063                   1   \n",
       "mean               NaN              NaN        NaN                 NaN   \n",
       "std                NaN              NaN        NaN                 NaN   \n",
       "min                NaN              NaN        NaN                 NaN   \n",
       "25%                NaN              NaN        NaN                 NaN   \n",
       "50%                NaN              NaN        NaN                 NaN   \n",
       "75%                NaN              NaN        NaN                 NaN   \n",
       "max                NaN              NaN        NaN                 NaN   \n",
       "\n",
       "            Lead_revenue Fund_category  ... POC_name              Designation  \\\n",
       "count               2093          2093  ...     2091                     2093   \n",
       "unique                 3             4  ...     1745                       10   \n",
       "top     50 - 100 Million    Category 3  ...    pooja  CEO/Co-Founder/Chairman   \n",
       "freq                 718           537  ...       23                      230   \n",
       "mean                 NaN           NaN  ...      NaN                      NaN   \n",
       "std                  NaN           NaN  ...      NaN                      NaN   \n",
       "min                  NaN           NaN  ...      NaN                      NaN   \n",
       "25%                  NaN           NaN  ...      NaN                      NaN   \n",
       "50%                  NaN           NaN  ...      NaN                      NaN   \n",
       "75%                  NaN           NaN  ...      NaN                      NaN   \n",
       "max                  NaN           NaN  ...      NaN                      NaN   \n",
       "\n",
       "                Lead_POC_email                     Hiring_candidate_role  \\\n",
       "count                     2093                                      2093   \n",
       "unique                    2093                                       618   \n",
       "top     richardochoa@arias.com  Conservation officer, historic buildings   \n",
       "freq                         1                                        10   \n",
       "mean                       NaN                                       NaN   \n",
       "std                        NaN                                       NaN   \n",
       "min                        NaN                                       NaN   \n",
       "25%                        NaN                                       NaN   \n",
       "50%                        NaN                                       NaN   \n",
       "75%                        NaN                                       NaN   \n",
       "max                        NaN                                       NaN   \n",
       "\n",
       "          Lead_source Level_of_meeting Last_lead_update  \\\n",
       "count            2093             2093             1925   \n",
       "unique              4                3               11   \n",
       "top     Contact Email          Level 1      2 days back   \n",
       "freq              540              739              204   \n",
       "mean              NaN              NaN              NaN   \n",
       "std               NaN              NaN              NaN   \n",
       "min               NaN              NaN              NaN   \n",
       "25%               NaN              NaN              NaN   \n",
       "50%               NaN              NaN              NaN   \n",
       "75%               NaN              NaN              NaN   \n",
       "max               NaN              NaN              NaN   \n",
       "\n",
       "                     Internal_POC        Resource Internal_rating  \n",
       "count                        2093            2079     2093.000000  \n",
       "unique                         60               6             NaN  \n",
       "top     Georgakopoulos,Vasilios T  Cannot deliver             NaN  \n",
       "freq                           48             384             NaN  \n",
       "mean                          NaN             NaN        3.143354  \n",
       "std                           NaN             NaN        4.510451  \n",
       "min                           NaN             NaN       -1.000000  \n",
       "25%                           NaN             NaN        2.000000  \n",
       "50%                           NaN             NaN        3.000000  \n",
       "75%                           NaN             NaN        4.000000  \n",
       "max                           NaN             NaN       82.340000  \n",
       "\n",
       "[11 rows x 22 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Deal_title</th>\n",
       "      <th>Lead_name</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Deal_value</th>\n",
       "      <th>Weighted_amount</th>\n",
       "      <th>Date_of_creation</th>\n",
       "      <th>Pitch</th>\n",
       "      <th>Contact_no</th>\n",
       "      <th>Lead_revenue</th>\n",
       "      <th>Fund_category</th>\n",
       "      <th>...</th>\n",
       "      <th>POC_name</th>\n",
       "      <th>Designation</th>\n",
       "      <th>Lead_POC_email</th>\n",
       "      <th>Hiring_candidate_role</th>\n",
       "      <th>Lead_source</th>\n",
       "      <th>Level_of_meeting</th>\n",
       "      <th>Last_lead_update</th>\n",
       "      <th>Internal_POC</th>\n",
       "      <th>Resource</th>\n",
       "      <th>Internal_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TitleAD16O</td>\n",
       "      <td>Bonilla Ltd Inc</td>\n",
       "      <td>Investment Bank/Brokerage</td>\n",
       "      <td>200988</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-04-15</td>\n",
       "      <td>Product_1</td>\n",
       "      <td>167.332.2751x989</td>\n",
       "      <td>100 - 500 Million</td>\n",
       "      <td>Category 4</td>\n",
       "      <td>...</td>\n",
       "      <td>sonia</td>\n",
       "      <td>Chairman/CEO/President</td>\n",
       "      <td>maureenthomas@bonilla.com</td>\n",
       "      <td>Designer, fashion/clothing</td>\n",
       "      <td>Marketing Event</td>\n",
       "      <td>Level 1</td>\n",
       "      <td>more than a month</td>\n",
       "      <td>Massiah,Gerard F</td>\n",
       "      <td>No</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TitleOW6CR</td>\n",
       "      <td>Williams, Rogers and Roach PLC</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>409961</td>\n",
       "      <td>2541758.2</td>\n",
       "      <td>2021-01-23</td>\n",
       "      <td>Product_1</td>\n",
       "      <td>001-486-903-0711x7831</td>\n",
       "      <td>100 - 500 Million</td>\n",
       "      <td>Category 3</td>\n",
       "      <td>...</td>\n",
       "      <td>Daniel Bell</td>\n",
       "      <td>CEO/Co-Founder/Chairman</td>\n",
       "      <td>danielbell@williams.com</td>\n",
       "      <td>Horticultural consultant</td>\n",
       "      <td>Marketing Event</td>\n",
       "      <td>Level 2</td>\n",
       "      <td>Up-to-date</td>\n",
       "      <td>Smith,Keenan H</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TitleVVJQ5</td>\n",
       "      <td>Wood, Vaughn and Morales Ltd</td>\n",
       "      <td>Banks</td>\n",
       "      <td>434433</td>\n",
       "      <td>3041031.0</td>\n",
       "      <td>2020-07-19</td>\n",
       "      <td>Product_1</td>\n",
       "      <td>(393)104-2610x9723</td>\n",
       "      <td>100 - 500 Million</td>\n",
       "      <td>Category 1</td>\n",
       "      <td>...</td>\n",
       "      <td>Andrew Davis</td>\n",
       "      <td>Chairman/Chief Innovation Officer</td>\n",
       "      <td>andrewdavis@wood.com</td>\n",
       "      <td>Information officer</td>\n",
       "      <td>Marketing Event</td>\n",
       "      <td>Level 2</td>\n",
       "      <td>Did not hear back after Level 1</td>\n",
       "      <td>Gilley,Janine</td>\n",
       "      <td>Deliverable</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TitleUS8NA</td>\n",
       "      <td>Durham-Crawford Inc</td>\n",
       "      <td>Music</td>\n",
       "      <td>218952</td>\n",
       "      <td>1521716.4</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Product_2</td>\n",
       "      <td>(817)040-4599</td>\n",
       "      <td>100 - 500 Million</td>\n",
       "      <td>Category 1</td>\n",
       "      <td>...</td>\n",
       "      <td>shital</td>\n",
       "      <td>CEO/Chairman/President</td>\n",
       "      <td>charlesrivera@durhamcrawford.com</td>\n",
       "      <td>Commercial/residential surveyor</td>\n",
       "      <td>Contact Email</td>\n",
       "      <td>Level 3</td>\n",
       "      <td>more than a month</td>\n",
       "      <td>Morsy,Omar A</td>\n",
       "      <td>No</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Title5VGWW</td>\n",
       "      <td>Simpson, Duncan and Long LLC</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>392835</td>\n",
       "      <td>2455218.75</td>\n",
       "      <td>2020-10-25</td>\n",
       "      <td>Product_1</td>\n",
       "      <td>718-032-5726x76098</td>\n",
       "      <td>500 Million - 1 Billion</td>\n",
       "      <td>Category 3</td>\n",
       "      <td>...</td>\n",
       "      <td>Shelly Stephenson</td>\n",
       "      <td>CEO/Co-Founder/Chairman</td>\n",
       "      <td>shellystephenson@simpson.com</td>\n",
       "      <td>Wellsite geologist</td>\n",
       "      <td>Others</td>\n",
       "      <td>Level 3</td>\n",
       "      <td>More than 2 weeks</td>\n",
       "      <td>Morsy,Omar A</td>\n",
       "      <td>Deliverable</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Deal_title                       Lead_name                   Industry  \\\n",
       "0  TitleAD16O                 Bonilla Ltd Inc  Investment Bank/Brokerage   \n",
       "1  TitleOW6CR  Williams, Rogers and Roach PLC                Electronics   \n",
       "2  TitleVVJQ5    Wood, Vaughn and Morales Ltd                      Banks   \n",
       "3  TitleUS8NA             Durham-Crawford Inc                      Music   \n",
       "4  Title5VGWW    Simpson, Duncan and Long LLC                Real Estate   \n",
       "\n",
       "  Deal_value Weighted_amount Date_of_creation      Pitch  \\\n",
       "0     200988             NaN       2020-04-15  Product_1   \n",
       "1     409961       2541758.2       2021-01-23  Product_1   \n",
       "2     434433       3041031.0       2020-07-19  Product_1   \n",
       "3     218952       1521716.4       2020-02-27  Product_2   \n",
       "4     392835      2455218.75       2020-10-25  Product_1   \n",
       "\n",
       "              Contact_no             Lead_revenue Fund_category  ...  \\\n",
       "0       167.332.2751x989        100 - 500 Million    Category 4  ...   \n",
       "1  001-486-903-0711x7831        100 - 500 Million    Category 3  ...   \n",
       "2     (393)104-2610x9723        100 - 500 Million    Category 1  ...   \n",
       "3          (817)040-4599        100 - 500 Million    Category 1  ...   \n",
       "4     718-032-5726x76098  500 Million - 1 Billion    Category 3  ...   \n",
       "\n",
       "            POC_name                        Designation  \\\n",
       "0              sonia             Chairman/CEO/President   \n",
       "1        Daniel Bell            CEO/Co-Founder/Chairman   \n",
       "2       Andrew Davis  Chairman/Chief Innovation Officer   \n",
       "3             shital             CEO/Chairman/President   \n",
       "4  Shelly Stephenson            CEO/Co-Founder/Chairman   \n",
       "\n",
       "                     Lead_POC_email            Hiring_candidate_role  \\\n",
       "0         maureenthomas@bonilla.com       Designer, fashion/clothing   \n",
       "1           danielbell@williams.com         Horticultural consultant   \n",
       "2              andrewdavis@wood.com              Information officer   \n",
       "3  charlesrivera@durhamcrawford.com  Commercial/residential surveyor   \n",
       "4      shellystephenson@simpson.com               Wellsite geologist   \n",
       "\n",
       "       Lead_source Level_of_meeting                 Last_lead_update  \\\n",
       "0  Marketing Event          Level 1                more than a month   \n",
       "1  Marketing Event          Level 2                       Up-to-date   \n",
       "2  Marketing Event          Level 2  Did not hear back after Level 1   \n",
       "3    Contact Email          Level 3                more than a month   \n",
       "4           Others          Level 3                More than 2 weeks   \n",
       "\n",
       "       Internal_POC     Resource Internal_rating  \n",
       "0  Massiah,Gerard F           No            -1.0  \n",
       "1    Smith,Keenan H          Yes             1.0  \n",
       "2     Gilley,Janine  Deliverable             5.0  \n",
       "3      Morsy,Omar A           No             5.0  \n",
       "4      Morsy,Omar A  Deliverable             2.0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Data formatting \n",
    "data_test['Deal_value'] = data_test['Deal_value'].str.replace('$', '')\n",
    "data_test['Weighted_amount'] = data_test['Weighted_amount'].str.replace('$', '')\n",
    "data_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Dropping insignificant columns from the dataset\n",
    "data_test.drop([\"Lead_name\",\"Industry\",\"Date_of_creation\",\"Contact_no\",\"Location\",\"POC_name\",\"Designation\",\"Lead_POC_email\",\"Hiring_candidate_role\",\"Lead_source\",\"Last_lead_update\",\"Internal_POC\",\"Resource\"], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Converting columns with categorical values to integral values using pd.get_dummies() \n",
    "p=pd.get_dummies(data_test['Pitch'],drop_first=True)                     ## for Pitch\n",
    "lr=pd.get_dummies(data_test['Lead_revenue'],drop_first=True)             ## for Lead_revenue\n",
    "fc=pd.get_dummies(data_test['Fund_category'],drop_first=True)            ## for Fund_category\n",
    "g=pd.get_dummies(data_test['Geography'],drop_first=True)                 ## for Geography\n",
    "lm=pd.get_dummies(data_test['Level_of_meeting'],drop_first=True)         ## for Level_of_meeting\n",
    "data_test=pd.concat([data_test,p,lr,fc,g,lm],axis=1)                     ## Concatenation into dataframe\n",
    "data_test.drop([\"Pitch\",\"Lead_revenue\",\"Fund_category\",\"Geography\",\"Level_of_meeting\"],axis=1,inplace=True)  ## removing categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Deal_title</th>\n",
       "      <th>Deal_value</th>\n",
       "      <th>Weighted_amount</th>\n",
       "      <th>Internal_rating</th>\n",
       "      <th>Product_2</th>\n",
       "      <th>50 - 100 Million</th>\n",
       "      <th>500 Million - 1 Billion</th>\n",
       "      <th>Category 2</th>\n",
       "      <th>Category 3</th>\n",
       "      <th>Category 4</th>\n",
       "      <th>USA</th>\n",
       "      <th>Level 2</th>\n",
       "      <th>Level 3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TitleAD16O</td>\n",
       "      <td>200988</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.0</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TitleOW6CR</td>\n",
       "      <td>409961</td>\n",
       "      <td>2541758.2</td>\n",
       "      <td>1.0</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TitleVVJQ5</td>\n",
       "      <td>434433</td>\n",
       "      <td>3041031.0</td>\n",
       "      <td>5.0</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TitleUS8NA</td>\n",
       "      <td>218952</td>\n",
       "      <td>1521716.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Title5VGWW</td>\n",
       "      <td>392835</td>\n",
       "      <td>2455218.75</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Deal_title Deal_value Weighted_amount  Internal_rating  Product_2  \\\n",
       "0  TitleAD16O     200988             NaN             -1.0          0   \n",
       "1  TitleOW6CR     409961       2541758.2              1.0          0   \n",
       "2  TitleVVJQ5     434433       3041031.0              5.0          0   \n",
       "3  TitleUS8NA     218952       1521716.4              5.0          1   \n",
       "4  Title5VGWW     392835      2455218.75              2.0          0   \n",
       "\n",
       "   50 - 100 Million  500 Million - 1 Billion  Category 2  Category 3  \\\n",
       "0                 0                        0           0           0   \n",
       "1                 0                        0           0           1   \n",
       "2                 0                        0           0           0   \n",
       "3                 0                        0           0           0   \n",
       "4                 0                        1           0           1   \n",
       "\n",
       "   Category 4  USA  Level 2  Level 3  \n",
       "0           1    0        0        0  \n",
       "1           0    1        1        0  \n",
       "2           0    1        1        0  \n",
       "3           0    0        0        1  \n",
       "4           0    1        0        1  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(data_test,columns=[\"Deal_title\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>Deal_title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TitleAD16O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TitleOW6CR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TitleVVJQ5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TitleUS8NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Title5VGWW</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Deal_title\n",
       "0  TitleAD16O\n",
       "1  TitleOW6CR\n",
       "2  TitleVVJQ5\n",
       "3  TitleUS8NA\n",
       "4  Title5VGWW"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Data formatting\n",
    "data_test['Deal_value']=data_test['Deal_value'].astype('float')\n",
    "data_test['Weighted_amount']=data_test['Weighted_amount'].astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Correcting missing values\n",
    "data_test['Deal_value'] = data_test['Deal_value'].fillna((data_train['Deal_value'].mean()))\n",
    "data_test['Weighted_amount'] = data_test['Weighted_amount'].fillna((data_train['Weighted_amount'].mean()))\n",
    "data_test['Internal_rating'] = data_test['Internal_rating'].fillna((data_train['Internal_rating'].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_test.drop([\"Deal_title\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25e638b3820>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data_test.isnull(), yticklabels=False, vmin=0, vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>Deal_value</th>\n",
       "      <th>Weighted_amount</th>\n",
       "      <th>Internal_rating</th>\n",
       "      <th>Product_2</th>\n",
       "      <th>50 - 100 Million</th>\n",
       "      <th>500 Million - 1 Billion</th>\n",
       "      <th>Category 2</th>\n",
       "      <th>Category 3</th>\n",
       "      <th>Category 4</th>\n",
       "      <th>USA</th>\n",
       "      <th>Level 2</th>\n",
       "      <th>Level 3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>200988.0</td>\n",
       "      <td>1.569884e+06</td>\n",
       "      <td>-1.0</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>409961.0</td>\n",
       "      <td>2.541758e+06</td>\n",
       "      <td>1.0</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>434433.0</td>\n",
       "      <td>3.041031e+06</td>\n",
       "      <td>5.0</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>218952.0</td>\n",
       "      <td>1.521716e+06</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>392835.0</td>\n",
       "      <td>2.455219e+06</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Deal_value  Weighted_amount  Internal_rating  Product_2  50 - 100 Million  \\\n",
       "0    200988.0     1.569884e+06             -1.0          0                 0   \n",
       "1    409961.0     2.541758e+06              1.0          0                 0   \n",
       "2    434433.0     3.041031e+06              5.0          0                 0   \n",
       "3    218952.0     1.521716e+06              5.0          1                 0   \n",
       "4    392835.0     2.455219e+06              2.0          0                 0   \n",
       "\n",
       "   500 Million - 1 Billion  Category 2  Category 3  Category 4  USA  Level 2  \\\n",
       "0                        0           0           0           1    0        0   \n",
       "1                        0           0           1           0    1        1   \n",
       "2                        0           0           0           0    1        1   \n",
       "3                        0           0           0           0    0        0   \n",
       "4                        1           0           1           0    1        0   \n",
       "\n",
       "   Level 3  \n",
       "0        0  \n",
       "1        0  \n",
       "2        0  \n",
       "3        1  \n",
       "4        1  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = data_train.drop(['Success_probability'], axis=1)\n",
    "y_train = data_train['Success_probability']\n",
    "x_test=data_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training data on XGBoost model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[14:39:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/objective/regression_obj.cu:170: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Deal_title</th>\n",
       "      <th>Success_probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TitleAD16O</td>\n",
       "      <td>41.137135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TitleOW6CR</td>\n",
       "      <td>42.775093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TitleVVJQ5</td>\n",
       "      <td>43.081978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TitleUS8NA</td>\n",
       "      <td>42.441956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Title5VGWW</td>\n",
       "      <td>43.842171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Deal_title  Success_probability\n",
       "0  TitleAD16O            41.137135\n",
       "1  TitleOW6CR            42.775093\n",
       "2  TitleVVJQ5            43.081978\n",
       "3  TitleUS8NA            42.441956\n",
       "4  Title5VGWW            43.842171"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "xg_reg = xgb.XGBRegressor(objective ='reg:linear', colsample_bytree = 0.3, learning_rate = 0.1,\n",
    "                max_depth = 5, alpha = 10, n_estimators = 10)\n",
    "xg_reg.fit(x_train,y_train)\n",
    "xgbost= xg_reg.predict(x_test)\n",
    "df[\"Success_probability\"]=xgbost\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"xg_boost.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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>Deal_title</th>\n",
       "      <th>Success_probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TitleAD16O</td>\n",
       "      <td>41.137135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TitleOW6CR</td>\n",
       "      <td>42.775093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TitleVVJQ5</td>\n",
       "      <td>43.081978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TitleUS8NA</td>\n",
       "      <td>42.441956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Title5VGWW</td>\n",
       "      <td>43.842171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2088</th>\n",
       "      <td>Title2R8VU</td>\n",
       "      <td>43.646858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2089</th>\n",
       "      <td>Title7HCNJ</td>\n",
       "      <td>43.081978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2090</th>\n",
       "      <td>TitleCD5YZ</td>\n",
       "      <td>41.444019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2091</th>\n",
       "      <td>Title8OKXL</td>\n",
       "      <td>41.319359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2092</th>\n",
       "      <td>TitleHFQT8</td>\n",
       "      <td>41.570187</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2093 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Deal_title  Success_probability\n",
       "0     TitleAD16O            41.137135\n",
       "1     TitleOW6CR            42.775093\n",
       "2     TitleVVJQ5            43.081978\n",
       "3     TitleUS8NA            42.441956\n",
       "4     Title5VGWW            43.842171\n",
       "...          ...                  ...\n",
       "2088  Title2R8VU            43.646858\n",
       "2089  Title7HCNJ            43.081978\n",
       "2090  TitleCD5YZ            41.444019\n",
       "2091  Title8OKXL            41.319359\n",
       "2092  TitleHFQT8            41.570187\n",
       "\n",
       "[2093 rows x 2 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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