Shark Tank India.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "2cf89146",
   "metadata": {},
   "outputs": [],
   "source": [
    "#IMPORTING LIBRARIES\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "1f57e0b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#LOADING DATA\n",
    "data = pd.read_csv(\"Shark Tank India (impure).csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "6be20bbb",
   "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>Season_Number</th>\n",
       "      <th>Startup_Name</th>\n",
       "      <th>Episode_Number</th>\n",
       "      <th>Pitch_Number</th>\n",
       "      <th>Season_Start</th>\n",
       "      <th>Season_End</th>\n",
       "      <th>Original_Air_Date</th>\n",
       "      <th>Episode_Title</th>\n",
       "      <th>Anchor</th>\n",
       "      <th>Industry</th>\n",
       "      <th>...</th>\n",
       "      <th>Peyush_Investment_Amount((in_lakhs)</th>\n",
       "      <th>Amit_Investment_Amount(in_lakhs)</th>\n",
       "      <th>Ashneer_Investment_Amount</th>\n",
       "      <th>Namita_Present</th>\n",
       "      <th>Vineeta_Present</th>\n",
       "      <th>Anupam_Present</th>\n",
       "      <th>Aman_Present</th>\n",
       "      <th>Peyush_Present</th>\n",
       "      <th>Amit_Present</th>\n",
       "      <th>Ashneer_Present</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>BluePineFoods</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-12-20</td>\n",
       "      <td>2022-02-04</td>\n",
       "      <td>2021-12-20</td>\n",
       "      <td>Badlegi0Business0Ki0Tasveer</td>\n",
       "      <td>Rannvijay0Singh</td>\n",
       "      <td>Food</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>BoozScooters</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2021-12-20</td>\n",
       "      <td>2022-02-04</td>\n",
       "      <td>2021-12-20</td>\n",
       "      <td>Badlegi0Business0Ki0Tasveer</td>\n",
       "      <td>Rannvijay0Singh</td>\n",
       "      <td>Vehicles/Electrical0Vehicles</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>HeartUpMySleeves</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2021-12-20</td>\n",
       "      <td>2022-02-04</td>\n",
       "      <td>2021-12-20</td>\n",
       "      <td>Badlegi0Business0Ki0Tasveer</td>\n",
       "      <td>Rannvijay0Singh</td>\n",
       "      <td>Beauty/Fashion</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>TagzFoods</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2021-12-20</td>\n",
       "      <td>2022-02-04</td>\n",
       "      <td>2021-12-21</td>\n",
       "      <td>Insaan,0Ideas0Aur0Sapne</td>\n",
       "      <td>Rannvijay0Singh</td>\n",
       "      <td>Food</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>HeadAndHeart</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2021-12-20</td>\n",
       "      <td>2022-02-04</td>\n",
       "      <td>2021-12-21</td>\n",
       "      <td>Insaan,0Ideas0Aur0Sapne</td>\n",
       "      <td>Rannvijay0Singh</td>\n",
       "      <td>Education</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>3</td>\n",
       "      <td>$KrishnaRama</td>\n",
       "      <td>51</td>\n",
       "      <td>474</td>\n",
       "      <td>2024-01-22</td>\n",
       "      <td>2024-03-31</td>\n",
       "      <td>2024-03-30</td>\n",
       "      <td>Brilliant0Businesses</td>\n",
       "      <td>Snehil0Dixit0Mehra</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>3</td>\n",
       "      <td>Rentit4me</td>\n",
       "      <td>51</td>\n",
       "      <td>475</td>\n",
       "      <td>2024-01-22</td>\n",
       "      <td>2024-03-31</td>\n",
       "      <td>2024-03-30</td>\n",
       "      <td>Brilliant0Businesses</td>\n",
       "      <td>Snehil0Dixit0Mehra</td>\n",
       "      <td>Services</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>3</td>\n",
       "      <td>CoolTheGlobe</td>\n",
       "      <td>52</td>\n",
       "      <td>476</td>\n",
       "      <td>2024-01-22</td>\n",
       "      <td>2024-03-31</td>\n",
       "      <td>2024-03-31</td>\n",
       "      <td>Ecopreneur0Special</td>\n",
       "      <td>Snehil0Dixit0Mehra</td>\n",
       "      <td>Others</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>3</td>\n",
       "      <td>Canvaloop</td>\n",
       "      <td>52</td>\n",
       "      <td>477</td>\n",
       "      <td>2024-01-22</td>\n",
       "      <td>2024-03-31</td>\n",
       "      <td>2024-03-31</td>\n",
       "      <td>Ecopreneur0Special</td>\n",
       "      <td>Snehil0Dixit0Mehra</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>3</td>\n",
       "      <td>DigitalPaani</td>\n",
       "      <td>52</td>\n",
       "      <td>478</td>\n",
       "      <td>2024-01-22</td>\n",
       "      <td>2024-03-31</td>\n",
       "      <td>2024-03-31</td>\n",
       "      <td>Ecopreneur0Special</td>\n",
       "      <td>Snehil0Dixit0Mehra</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>478 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Season_Number      Startup_Name  Episode_Number  Pitch_Number  \\\n",
       "0                1     BluePineFoods               1             1   \n",
       "1                1      BoozScooters               1             2   \n",
       "2                1  HeartUpMySleeves               1             3   \n",
       "3                1         TagzFoods               2             4   \n",
       "4                1      HeadAndHeart               2             5   \n",
       "..             ...               ...             ...           ...   \n",
       "473              3      $KrishnaRama              51           474   \n",
       "474              3         Rentit4me              51           475   \n",
       "475              3      CoolTheGlobe              52           476   \n",
       "476              3         Canvaloop              52           477   \n",
       "477              3      DigitalPaani              52           478   \n",
       "\n",
       "    Season_Start  Season_End Original_Air_Date                Episode_Title  \\\n",
       "0     2021-12-20  2022-02-04        2021-12-20  Badlegi0Business0Ki0Tasveer   \n",
       "1     2021-12-20  2022-02-04        2021-12-20  Badlegi0Business0Ki0Tasveer   \n",
       "2     2021-12-20  2022-02-04        2021-12-20  Badlegi0Business0Ki0Tasveer   \n",
       "3     2021-12-20  2022-02-04        2021-12-21      Insaan,0Ideas0Aur0Sapne   \n",
       "4     2021-12-20  2022-02-04        2021-12-21      Insaan,0Ideas0Aur0Sapne   \n",
       "..           ...         ...               ...                          ...   \n",
       "473   2024-01-22  2024-03-31        2024-03-30         Brilliant0Businesses   \n",
       "474   2024-01-22  2024-03-31        2024-03-30         Brilliant0Businesses   \n",
       "475   2024-01-22  2024-03-31        2024-03-31           Ecopreneur0Special   \n",
       "476   2024-01-22  2024-03-31        2024-03-31           Ecopreneur0Special   \n",
       "477   2024-01-22  2024-03-31        2024-03-31           Ecopreneur0Special   \n",
       "\n",
       "                 Anchor                      Industry  ...  \\\n",
       "0       Rannvijay0Singh                          Food  ...   \n",
       "1       Rannvijay0Singh  Vehicles/Electrical0Vehicles  ...   \n",
       "2       Rannvijay0Singh                Beauty/Fashion  ...   \n",
       "3       Rannvijay0Singh                          Food  ...   \n",
       "4       Rannvijay0Singh                     Education  ...   \n",
       "..                  ...                           ...  ...   \n",
       "473  Snehil0Dixit0Mehra                 Entertainment  ...   \n",
       "474  Snehil0Dixit0Mehra                      Services  ...   \n",
       "475  Snehil0Dixit0Mehra                        Others  ...   \n",
       "476  Snehil0Dixit0Mehra                 Manufacturing  ...   \n",
       "477  Snehil0Dixit0Mehra                 Manufacturing  ...   \n",
       "\n",
       "    Peyush_Investment_Amount((in_lakhs) Amit_Investment_Amount(in_lakhs)  \\\n",
       "0                                   NaN                              NaN   \n",
       "1                                   NaN                              NaN   \n",
       "2                                   NaN                              NaN   \n",
       "3                                   NaN                              NaN   \n",
       "4                                   NaN                              NaN   \n",
       "..                                  ...                              ...   \n",
       "473                                 NaN                              NaN   \n",
       "474                                 NaN                              NaN   \n",
       "475                                 NaN                              NaN   \n",
       "476                                 NaN                              NaN   \n",
       "477                                 NaN                              NaN   \n",
       "\n",
       "     Ashneer_Investment_Amount  Namita_Present  Vineeta_Present  \\\n",
       "0                         25.0             Yes              Yes   \n",
       "1                         20.0             Yes              Yes   \n",
       "2                          NaN             Yes              Yes   \n",
       "3                         70.0             Yes              Yes   \n",
       "4                          NaN             Yes              Yes   \n",
       "..                         ...             ...              ...   \n",
       "473                        NaN             Yes              Yes   \n",
       "474                        NaN             Yes              Yes   \n",
       "475                        NaN             Yes              NaN   \n",
       "476                        NaN             Yes              NaN   \n",
       "477                        NaN             Yes              NaN   \n",
       "\n",
       "     Anupam_Present  Aman_Present  Peyush_Present Amit_Present Ashneer_Present  \n",
       "0               Yes           Yes             NaN          NaN             Yes  \n",
       "1               Yes           Yes             NaN          NaN             Yes  \n",
       "2               Yes           Yes             NaN          NaN             Yes  \n",
       "3               Yes           Yes             NaN          NaN             Yes  \n",
       "4               Yes           Yes             NaN          NaN             Yes  \n",
       "..              ...           ...             ...          ...             ...  \n",
       "473             Yes           Yes             NaN          NaN             NaN  \n",
       "474             Yes           Yes             NaN          NaN             NaN  \n",
       "475             Yes           Yes             NaN          NaN             NaN  \n",
       "476             Yes           Yes             NaN          NaN             NaN  \n",
       "477             Yes           Yes             NaN          NaN             NaN  \n",
       "\n",
       "[478 rows x 52 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "97e773e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 478 entries, 0 to 477\n",
      "Data columns (total 52 columns):\n",
      " #   Column                               Non-Null Count  Dtype  \n",
      "---  ------                               --------------  -----  \n",
      " 0   Season_Number                        478 non-null    int64  \n",
      " 1   Startup_Name                         478 non-null    object \n",
      " 2   Episode_Number                       478 non-null    int64  \n",
      " 3   Pitch_Number                         478 non-null    int64  \n",
      " 4   Season_Start                         478 non-null    object \n",
      " 5   Season_End                           478 non-null    object \n",
      " 6   Original_Air_Date                    447 non-null    object \n",
      " 7   Episode_Title                        478 non-null    object \n",
      " 8   Anchor                               478 non-null    object \n",
      " 9   Industry                             478 non-null    object \n",
      " 10  Business_Description                 478 non-null    object \n",
      " 11  Company_Website                      466 non-null    object \n",
      " 12  Started_in                           355 non-null    float64\n",
      " 13  Number_of_Presenters                 478 non-null    int64  \n",
      " 14  Male_Presenters                      412 non-null    float64\n",
      " 15  Female_Presenters                    226 non-null    float64\n",
      " 16  Transgender_Presenters               3 non-null      float64\n",
      " 17  Couple_Presenters                    473 non-null    float64\n",
      " 18  Pitchers_Average_Age                 478 non-null    object \n",
      " 19  Pitchers_City                        473 non-null    object \n",
      " 20  Pitchers_State                       474 non-null    object \n",
      " 21  Yearly_Revenue(in_lakhs)             241 non-null    float64\n",
      " 22  Monthly_Sales(in_lakhs)              225 non-null    float64\n",
      " 23  Gross_Margin                         129 non-null    float64\n",
      " 24  Net_Margin                           73 non-null     float64\n",
      " 25  EBITDA                               23 non-null     float64\n",
      " 26  Cash_Burn                            64 non-null     object \n",
      " 27  SKUs                                 28 non-null     float64\n",
      " 28  Has_Patents                          43 non-null     object \n",
      " 29  Bootstrapped                         48 non-null     object \n",
      " 30  Original_Ask_Amount                  478 non-null    float64\n",
      " 31  Original_Offered_Equity(in_%)        478 non-null    float64\n",
      " 32  Valuation_Requested(in_lakhs)        478 non-null    float64\n",
      " 33  Received_Offer                       478 non-null    object \n",
      " 34  Accepted_Offer                       321 non-null    object \n",
      " 35  Total_Deal_Amount(in_lakhs)          268 non-null    float64\n",
      " 36  Total_Deal_Equity(%)                 268 non-null    float64\n",
      " 37  Number_of_Sharks_in_Deal             268 non-null    float64\n",
      " 38  Namita_Investment_Amount(in lakhs)   89 non-null     float64\n",
      " 39  Vineeta_Investment_Amount(in_lakhs)  68 non-null     float64\n",
      " 40  Anupam_Investment_Amount(in_lakhs)   75 non-null     float64\n",
      " 41  Aman_Investment_Amount(in_lakhs)     109 non-null    float64\n",
      " 42  Peyush_Investment_Amount((in_lakhs)  89 non-null     float64\n",
      " 43  Amit_Investment_Amount(in_lakhs)     35 non-null     float64\n",
      " 44  Ashneer_Investment_Amount            21 non-null     float64\n",
      " 45  Namita_Present                       389 non-null    object \n",
      " 46  Vineeta_Present                      318 non-null    object \n",
      " 47  Anupam_Present                       424 non-null    object \n",
      " 48  Aman_Present                         420 non-null    object \n",
      " 49  Peyush_Present                       307 non-null    object \n",
      " 50  Amit_Present                         137 non-null    object \n",
      " 51  Ashneer_Present                      99 non-null     object \n",
      "dtypes: float64(24), int64(4), object(24)\n",
      "memory usage: 194.3+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "6b3a5b57",
   "metadata": {},
   "outputs": [],
   "source": [
    "#CONVETTING DATA COLUMN IN DATE TIME DATATYPE.\n",
    "data['Season_Start']=pd.to_datetime(data['Season_Start'])\n",
    "data['Season_End']=pd.to_datetime(data['Season_End'])\n",
    "data['Original_Air_Date']=pd.to_datetime(data['Original_Air_Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56b1a45a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "4e51fa4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Season_Number                            0\n",
       "Startup_Name                             0\n",
       "Episode_Number                           0\n",
       "Pitch_Number                             0\n",
       "Season_Start                             0\n",
       "Season_End                               0\n",
       "Original_Air_Date                       31\n",
       "Episode_Title                            0\n",
       "Anchor                                   0\n",
       "Industry                                 0\n",
       "Business_Description                     0\n",
       "Company_Website                         12\n",
       "Started_in                             123\n",
       "Number_of_Presenters                     0\n",
       "Male_Presenters                         66\n",
       "Female_Presenters                      252\n",
       "Transgender_Presenters                 475\n",
       "Couple_Presenters                        5\n",
       "Pitchers_Average_Age                     0\n",
       "Pitchers_City                            5\n",
       "Pitchers_State                           4\n",
       "Yearly_Revenue(in_lakhs)               237\n",
       "Monthly_Sales(in_lakhs)                253\n",
       "Gross_Margin                           349\n",
       "Net_Margin                             405\n",
       "EBITDA                                 455\n",
       "Cash_Burn                              414\n",
       "SKUs                                   450\n",
       "Has_Patents                            435\n",
       "Bootstrapped                           430\n",
       "Original_Ask_Amount                      0\n",
       "Original_Offered_Equity(in_%)            0\n",
       "Valuation_Requested(in_lakhs)            0\n",
       "Received_Offer                           0\n",
       "Accepted_Offer                         157\n",
       "Total_Deal_Amount(in_lakhs)            210\n",
       "Total_Deal_Equity(%)                   210\n",
       "Number_of_Sharks_in_Deal               210\n",
       "Namita_Investment_Amount(in lakhs)     389\n",
       "Vineeta_Investment_Amount(in_lakhs)    410\n",
       "Anupam_Investment_Amount(in_lakhs)     403\n",
       "Aman_Investment_Amount(in_lakhs)       369\n",
       "Peyush_Investment_Amount((in_lakhs)    389\n",
       "Amit_Investment_Amount(in_lakhs)       443\n",
       "Ashneer_Investment_Amount              457\n",
       "Namita_Present                          89\n",
       "Vineeta_Present                        160\n",
       "Anupam_Present                          54\n",
       "Aman_Present                            58\n",
       "Peyush_Present                         171\n",
       "Amit_Present                           341\n",
       "Ashneer_Present                        379\n",
       "dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#CHECKING FOR NULL VALUES\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "e2c8aad1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#DROP UN-NECESSARY COLUMNS\n",
    "data.drop(columns=['Company_Website','Original_Air_Date','Episode_Title','Gross_Margin','Net_Margin','EBITDA','Cash_Burn','SKUs','Has_Patents','Bootstrapped'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "3f389b68",
   "metadata": {},
   "outputs": [],
   "source": [
    "#TREATING NULL VALUES OF STARTED_IN COLUMN\n",
    "data['Started_in']=data['Started_in'].fillna(\"Not Mentioned\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73152183",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "87c9e6d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#TREATING NULL VALUES FOR COLUMN.\n",
    "data['Male_Presenters']=data['Male_Presenters'].fillna(0)\n",
    "data['Female_Presenters']=data['Female_Presenters'].fillna(0)\n",
    "data['Transgender_Presenters']=data['Transgender_Presenters'].fillna(0)\n",
    "data['Couple_Presenters']=data['Couple_Presenters'].fillna(0)\n",
    "data['Pitchers_City']=data['Pitchers_City'].fillna('Not Mentioned')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "a83f67d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#TREATING NULL VALUES FOR COLUMN.\n",
    "data['Pitchers_State']=data['Pitchers_State'].fillna('Not Mentioned')\n",
    "data['Accepted_Offer']= data['Accepted_Offer'].fillna(\"No Offer Received.\")\n",
    "data['Total_Deal_Amount(in_lakhs)']= data['Total_Deal_Amount(in_lakhs)'].fillna(0)\n",
    "data['Total_Deal_Equity(%)']= data['Total_Deal_Equity(%)'].fillna(0)\n",
    "data['Number_of_Sharks_in_Deal']= data['Number_of_Sharks_in_Deal'].fillna(0)\n",
    "data['Namita_Investment_Amount(in lakhs)']= data['Namita_Investment_Amount(in lakhs)'].fillna(0)\n",
    "data['Vineeta_Investment_Amount(in_lakhs)']= data['Vineeta_Investment_Amount(in_lakhs)'].fillna(0)\n",
    "data['Anupam_Investment_Amount(in_lakhs)']= data['Anupam_Investment_Amount(in_lakhs)'].fillna(0)\n",
    "data['Aman_Investment_Amount(in_lakhs)']= data['Aman_Investment_Amount(in_lakhs)'].fillna(0)\n",
    "data['Peyush_Investment_Amount((in_lakhs)']= data['Peyush_Investment_Amount((in_lakhs)'].fillna(0)\n",
    "data['Amit_Investment_Amount(in_lakhs)']= data['Amit_Investment_Amount(in_lakhs)'].fillna(0)\n",
    "data['Ashneer_Investment_Amount']= data['Ashneer_Investment_Amount'].fillna(0)\n",
    "data['Namita_Present']= data['Namita_Present'].fillna('No')\n",
    "data['Vineeta_Present']= data['Vineeta_Present'].fillna('No')\n",
    "data['Anupam_Present']= data['Anupam_Present'].fillna('No')\n",
    "data['Aman_Present']= data['Aman_Present'].fillna('No')\n",
    "data['Peyush_Present']= data['Peyush_Present'].fillna('No')\n",
    "data['Amit_Present']= data['Amit_Present'].fillna('No')\n",
    "data['Ashneer_Present']= data['Ashneer_Present'].fillna('No')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "b0713ae4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#TREATING NULL VALUES FOR  Yearly_Revenue(in_lakhs)  AND  Monthly_Sales(in_lakhs)\n",
    "\n",
    "data['Yearly_Revenue(in_lakhs)']=data['Yearly_Revenue(in_lakhs)'].fillna('Not Mentioned')\n",
    "data['Monthly_Sales(in_lakhs)']=data['Monthly_Sales(in_lakhs)'].fillna('Not Mentioned')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "b91e82c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#CLEANING  ANCHOR , INDUSTRY BUSINESS DESCRIPTION\n",
    "data['Anchor']= data['Anchor'].str.replace('0',' ')\n",
    "data['Industry']= data['Industry'].str.replace('0',' ')\n",
    "data['Business_Description']=data['Business_Description'].str.replace('0',' ')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "ba404994",
   "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>Season_Number</th>\n",
       "      <th>Startup_Name</th>\n",
       "      <th>Episode_Number</th>\n",
       "      <th>Pitch_Number</th>\n",
       "      <th>Season_Start</th>\n",
       "      <th>Season_End</th>\n",
       "      <th>Anchor</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Business_Description</th>\n",
       "      <th>Started_in</th>\n",
       "      <th>...</th>\n",
       "      <th>Peyush_Investment_Amount((in_lakhs)</th>\n",
       "      <th>Amit_Investment_Amount(in_lakhs)</th>\n",
       "      <th>Ashneer_Investment_Amount</th>\n",
       "      <th>Namita_Present</th>\n",
       "      <th>Vineeta_Present</th>\n",
       "      <th>Anupam_Present</th>\n",
       "      <th>Aman_Present</th>\n",
       "      <th>Peyush_Present</th>\n",
       "      <th>Amit_Present</th>\n",
       "      <th>Ashneer_Present</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>BluePineFoods</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-12-20</td>\n",
       "      <td>2022-02-04</td>\n",
       "      <td>Rannvijay Singh</td>\n",
       "      <td>Food</td>\n",
       "      <td>Frozen Momos</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>BoozScooters</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2021-12-20</td>\n",
       "      <td>2022-02-04</td>\n",
       "      <td>Rannvijay Singh</td>\n",
       "      <td>Vehicles/Electrical Vehicles</td>\n",
       "      <td>Renting e-bike for mobility in private spaces</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>HeartUpMySleeves</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2021-12-20</td>\n",
       "      <td>2022-02-04</td>\n",
       "      <td>Rannvijay Singh</td>\n",
       "      <td>Beauty/Fashion</td>\n",
       "      <td>Detachable Sleeves</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>TagzFoods</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2021-12-20</td>\n",
       "      <td>2022-02-04</td>\n",
       "      <td>Rannvijay Singh</td>\n",
       "      <td>Food</td>\n",
       "      <td>Healthy Potato Chips Snacks</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>HeadAndHeart</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2021-12-20</td>\n",
       "      <td>2022-02-04</td>\n",
       "      <td>Rannvijay Singh</td>\n",
       "      <td>Education</td>\n",
       "      <td>Brain Development Course</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>3</td>\n",
       "      <td>$KrishnaRama</td>\n",
       "      <td>51</td>\n",
       "      <td>474</td>\n",
       "      <td>2024-01-22</td>\n",
       "      <td>2024-03-31</td>\n",
       "      <td>Snehil Dixit Mehra</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>Movie</td>\n",
       "      <td>2023.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>3</td>\n",
       "      <td>Rentit4me</td>\n",
       "      <td>51</td>\n",
       "      <td>475</td>\n",
       "      <td>2024-01-22</td>\n",
       "      <td>2024-03-31</td>\n",
       "      <td>Snehil Dixit Mehra</td>\n",
       "      <td>Services</td>\n",
       "      <td>Marketplace where people can “Give on Rent” &amp; ...</td>\n",
       "      <td>2023.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>3</td>\n",
       "      <td>CoolTheGlobe</td>\n",
       "      <td>52</td>\n",
       "      <td>476</td>\n",
       "      <td>2024-01-22</td>\n",
       "      <td>2024-03-31</td>\n",
       "      <td>Snehil Dixit Mehra</td>\n",
       "      <td>Others</td>\n",
       "      <td>Movement to combat climate change</td>\n",
       "      <td>Not Mentioned</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>3</td>\n",
       "      <td>Canvaloop</td>\n",
       "      <td>52</td>\n",
       "      <td>477</td>\n",
       "      <td>2024-01-22</td>\n",
       "      <td>2024-03-31</td>\n",
       "      <td>Snehil Dixit Mehra</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Sustainable fabrics manufacturer</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>3</td>\n",
       "      <td>DigitalPaani</td>\n",
       "      <td>52</td>\n",
       "      <td>478</td>\n",
       "      <td>2024-01-22</td>\n",
       "      <td>2024-03-31</td>\n",
       "      <td>Snehil Dixit Mehra</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Manage plants, deliver safe, compliant operations</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>478 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Season_Number      Startup_Name  Episode_Number  Pitch_Number  \\\n",
       "0                1     BluePineFoods               1             1   \n",
       "1                1      BoozScooters               1             2   \n",
       "2                1  HeartUpMySleeves               1             3   \n",
       "3                1         TagzFoods               2             4   \n",
       "4                1      HeadAndHeart               2             5   \n",
       "..             ...               ...             ...           ...   \n",
       "473              3      $KrishnaRama              51           474   \n",
       "474              3         Rentit4me              51           475   \n",
       "475              3      CoolTheGlobe              52           476   \n",
       "476              3         Canvaloop              52           477   \n",
       "477              3      DigitalPaani              52           478   \n",
       "\n",
       "    Season_Start Season_End              Anchor                      Industry  \\\n",
       "0     2021-12-20 2022-02-04     Rannvijay Singh                          Food   \n",
       "1     2021-12-20 2022-02-04     Rannvijay Singh  Vehicles/Electrical Vehicles   \n",
       "2     2021-12-20 2022-02-04     Rannvijay Singh                Beauty/Fashion   \n",
       "3     2021-12-20 2022-02-04     Rannvijay Singh                          Food   \n",
       "4     2021-12-20 2022-02-04     Rannvijay Singh                     Education   \n",
       "..           ...        ...                 ...                           ...   \n",
       "473   2024-01-22 2024-03-31  Snehil Dixit Mehra                 Entertainment   \n",
       "474   2024-01-22 2024-03-31  Snehil Dixit Mehra                      Services   \n",
       "475   2024-01-22 2024-03-31  Snehil Dixit Mehra                        Others   \n",
       "476   2024-01-22 2024-03-31  Snehil Dixit Mehra                 Manufacturing   \n",
       "477   2024-01-22 2024-03-31  Snehil Dixit Mehra                 Manufacturing   \n",
       "\n",
       "                                  Business_Description     Started_in  ...  \\\n",
       "0                                         Frozen Momos         2016.0  ...   \n",
       "1        Renting e-bike for mobility in private spaces         2017.0  ...   \n",
       "2                                   Detachable Sleeves         2021.0  ...   \n",
       "3                          Healthy Potato Chips Snacks         2019.0  ...   \n",
       "4                             Brain Development Course         2015.0  ...   \n",
       "..                                                 ...            ...  ...   \n",
       "473                                              Movie         2023.0  ...   \n",
       "474  Marketplace where people can “Give on Rent” & ...         2023.0  ...   \n",
       "475                  Movement to combat climate change  Not Mentioned  ...   \n",
       "476                   Sustainable fabrics manufacturer         2020.0  ...   \n",
       "477  Manage plants, deliver safe, compliant operations         2020.0  ...   \n",
       "\n",
       "     Peyush_Investment_Amount((in_lakhs)  Amit_Investment_Amount(in_lakhs)  \\\n",
       "0                                    0.0                               0.0   \n",
       "1                                    0.0                               0.0   \n",
       "2                                    0.0                               0.0   \n",
       "3                                    0.0                               0.0   \n",
       "4                                    0.0                               0.0   \n",
       "..                                   ...                               ...   \n",
       "473                                  0.0                               0.0   \n",
       "474                                  0.0                               0.0   \n",
       "475                                  0.0                               0.0   \n",
       "476                                  0.0                               0.0   \n",
       "477                                  0.0                               0.0   \n",
       "\n",
       "     Ashneer_Investment_Amount  Namita_Present  Vineeta_Present  \\\n",
       "0                         25.0             Yes              Yes   \n",
       "1                         20.0             Yes              Yes   \n",
       "2                          0.0             Yes              Yes   \n",
       "3                         70.0             Yes              Yes   \n",
       "4                          0.0             Yes              Yes   \n",
       "..                         ...             ...              ...   \n",
       "473                        0.0             Yes              Yes   \n",
       "474                        0.0             Yes              Yes   \n",
       "475                        0.0             Yes               No   \n",
       "476                        0.0             Yes               No   \n",
       "477                        0.0             Yes               No   \n",
       "\n",
       "    Anupam_Present Aman_Present Peyush_Present Amit_Present Ashneer_Present  \n",
       "0              Yes          Yes             No           No             Yes  \n",
       "1              Yes          Yes             No           No             Yes  \n",
       "2              Yes          Yes             No           No             Yes  \n",
       "3              Yes          Yes             No           No             Yes  \n",
       "4              Yes          Yes             No           No             Yes  \n",
       "..             ...          ...            ...          ...             ...  \n",
       "473            Yes          Yes             No           No              No  \n",
       "474            Yes          Yes             No           No              No  \n",
       "475            Yes          Yes             No           No              No  \n",
       "476            Yes          Yes             No           No              No  \n",
       "477            Yes          Yes             No           No              No  \n",
       "\n",
       "[478 rows x 42 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "7658c412",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Season_Number                          0\n",
       "Startup_Name                           0\n",
       "Episode_Number                         0\n",
       "Pitch_Number                           0\n",
       "Season_Start                           0\n",
       "Season_End                             0\n",
       "Anchor                                 0\n",
       "Industry                               0\n",
       "Business_Description                   0\n",
       "Started_in                             0\n",
       "Number_of_Presenters                   0\n",
       "Male_Presenters                        0\n",
       "Female_Presenters                      0\n",
       "Transgender_Presenters                 0\n",
       "Couple_Presenters                      0\n",
       "Pitchers_Average_Age                   0\n",
       "Pitchers_City                          0\n",
       "Pitchers_State                         0\n",
       "Yearly_Revenue(in_lakhs)               0\n",
       "Monthly_Sales(in_lakhs)                0\n",
       "Original_Ask_Amount                    0\n",
       "Original_Offered_Equity(in_%)          0\n",
       "Valuation_Requested(in_lakhs)          0\n",
       "Received_Offer                         0\n",
       "Accepted_Offer                         0\n",
       "Total_Deal_Amount(in_lakhs)            0\n",
       "Total_Deal_Equity(%)                   0\n",
       "Number_of_Sharks_in_Deal               0\n",
       "Namita_Investment_Amount(in lakhs)     0\n",
       "Vineeta_Investment_Amount(in_lakhs)    0\n",
       "Anupam_Investment_Amount(in_lakhs)     0\n",
       "Aman_Investment_Amount(in_lakhs)       0\n",
       "Peyush_Investment_Amount((in_lakhs)    0\n",
       "Amit_Investment_Amount(in_lakhs)       0\n",
       "Ashneer_Investment_Amount              0\n",
       "Namita_Present                         0\n",
       "Vineeta_Present                        0\n",
       "Anupam_Present                         0\n",
       "Aman_Present                           0\n",
       "Peyush_Present                         0\n",
       "Amit_Present                           0\n",
       "Ashneer_Present                        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#CHECKING FOR NULL VALUES.\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "a3fc7096",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 478 entries, 0 to 477\n",
      "Data columns (total 42 columns):\n",
      " #   Column                               Non-Null Count  Dtype         \n",
      "---  ------                               --------------  -----         \n",
      " 0   Season_Number                        478 non-null    int64         \n",
      " 1   Startup_Name                         478 non-null    object        \n",
      " 2   Episode_Number                       478 non-null    int64         \n",
      " 3   Pitch_Number                         478 non-null    int64         \n",
      " 4   Season_Start                         478 non-null    datetime64[ns]\n",
      " 5   Season_End                           478 non-null    datetime64[ns]\n",
      " 6   Anchor                               478 non-null    object        \n",
      " 7   Industry                             478 non-null    object        \n",
      " 8   Business_Description                 478 non-null    object        \n",
      " 9   Started_in                           478 non-null    object        \n",
      " 10  Number_of_Presenters                 478 non-null    int64         \n",
      " 11  Male_Presenters                      478 non-null    float64       \n",
      " 12  Female_Presenters                    478 non-null    float64       \n",
      " 13  Transgender_Presenters               478 non-null    float64       \n",
      " 14  Couple_Presenters                    478 non-null    float64       \n",
      " 15  Pitchers_Average_Age                 478 non-null    object        \n",
      " 16  Pitchers_City                        478 non-null    object        \n",
      " 17  Pitchers_State                       478 non-null    object        \n",
      " 18  Yearly_Revenue(in_lakhs)             478 non-null    object        \n",
      " 19  Monthly_Sales(in_lakhs)              478 non-null    object        \n",
      " 20  Original_Ask_Amount                  478 non-null    float64       \n",
      " 21  Original_Offered_Equity(in_%)        478 non-null    float64       \n",
      " 22  Valuation_Requested(in_lakhs)        478 non-null    float64       \n",
      " 23  Received_Offer                       478 non-null    object        \n",
      " 24  Accepted_Offer                       478 non-null    object        \n",
      " 25  Total_Deal_Amount(in_lakhs)          478 non-null    float64       \n",
      " 26  Total_Deal_Equity(%)                 478 non-null    float64       \n",
      " 27  Number_of_Sharks_in_Deal             478 non-null    float64       \n",
      " 28  Namita_Investment_Amount(in lakhs)   478 non-null    float64       \n",
      " 29  Vineeta_Investment_Amount(in_lakhs)  478 non-null    float64       \n",
      " 30  Anupam_Investment_Amount(in_lakhs)   478 non-null    float64       \n",
      " 31  Aman_Investment_Amount(in_lakhs)     478 non-null    float64       \n",
      " 32  Peyush_Investment_Amount((in_lakhs)  478 non-null    float64       \n",
      " 33  Amit_Investment_Amount(in_lakhs)     478 non-null    float64       \n",
      " 34  Ashneer_Investment_Amount            478 non-null    float64       \n",
      " 35  Namita_Present                       478 non-null    object        \n",
      " 36  Vineeta_Present                      478 non-null    object        \n",
      " 37  Anupam_Present                       478 non-null    object        \n",
      " 38  Aman_Present                         478 non-null    object        \n",
      " 39  Peyush_Present                       478 non-null    object        \n",
      " 40  Amit_Present                         478 non-null    object        \n",
      " 41  Ashneer_Present                      478 non-null    object        \n",
      "dtypes: datetime64[ns](2), float64(17), int64(4), object(19)\n",
      "memory usage: 157.0+ KB\n"
     ]
    }
   ],
   "source": [
    "#CHECKING FOR DATATYPES.\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "287080fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#NOW OUR DATA IS CLEAN AND READY TO USE , SO WE WILL DUMP THIS FILE.\n",
    "data.to_csv('sharktank.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af797a0e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}