E&Y_Farmer_Loans_with_NN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a69d13d2",
   "metadata": {},
   "source": [
    "## Creating a Neural Network to deal with the Farmer's Dataset to incresase the accuraccy of Loan Granting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b27090b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#IMporting Required Libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e4004379",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Lets import the data\n",
    "F_data = pd.read_csv('./Farmers_Database.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fd85e517",
   "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>names of farmers</th>\n",
       "      <th>Aadhar Card</th>\n",
       "      <th>Registered on Farmer Portal</th>\n",
       "      <th>Government Scheme Beneficiary</th>\n",
       "      <th>Land Class</th>\n",
       "      <th>Land Area</th>\n",
       "      <th>Land Owners</th>\n",
       "      <th>Land Value - Crs</th>\n",
       "      <th>Soil Health</th>\n",
       "      <th>Crop Type</th>\n",
       "      <th>...</th>\n",
       "      <th>Advanced Agriculture Equipment</th>\n",
       "      <th>Mode of Harvesting</th>\n",
       "      <th>Storage Types</th>\n",
       "      <th>MSP-Qunitol</th>\n",
       "      <th>Total Possible Yeild</th>\n",
       "      <th>Total Crop Cost</th>\n",
       "      <th>Total Revenue</th>\n",
       "      <th>Total Profit</th>\n",
       "      <th>Loan Request</th>\n",
       "      <th>Loan Granted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dihan</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Small</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.359050</td>\n",
       "      <td>Medium</td>\n",
       "      <td>CottonL</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Hand-Held Tools</td>\n",
       "      <td>SWC</td>\n",
       "      <td>6380.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>243180.0</td>\n",
       "      <td>344520.0</td>\n",
       "      <td>101340.0</td>\n",
       "      <td>1266804.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Manosh</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Marginal</td>\n",
       "      <td>0.95</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.291098</td>\n",
       "      <td>Medium</td>\n",
       "      <td>JowarH</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Manual</td>\n",
       "      <td>SWC</td>\n",
       "      <td>2970.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>112689.0</td>\n",
       "      <td>152361.0</td>\n",
       "      <td>39672.0</td>\n",
       "      <td>4105530.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dipta</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Medium</td>\n",
       "      <td>7.00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>9.513350</td>\n",
       "      <td>Good</td>\n",
       "      <td>CottonM</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Hand-Held Tools</td>\n",
       "      <td>Gramin Bhandaran</td>\n",
       "      <td>6080.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>1560405.0</td>\n",
       "      <td>2106720.0</td>\n",
       "      <td>546315.0</td>\n",
       "      <td>129492.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Maladhi</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Semi-Medium</td>\n",
       "      <td>2.90</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.299540</td>\n",
       "      <td>Low</td>\n",
       "      <td>Maize</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Gramin Bhandaran</td>\n",
       "      <td>1962.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>227592.0</td>\n",
       "      <td>307249.2</td>\n",
       "      <td>79657.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Janasha</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Marginal</td>\n",
       "      <td>0.95</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.408470</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Moong</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Open Land</td>\n",
       "      <td>7755.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>294519.0</td>\n",
       "      <td>397831.5</td>\n",
       "      <td>103312.5</td>\n",
       "      <td>680904.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  names of farmers Aadhar Card Registered on Farmer Portal  \\\n",
       "0            Dihan         Yes                          No   \n",
       "1           Manosh         Yes                          No   \n",
       "2            Dipta         Yes                         Yes   \n",
       "3          Maladhi         Yes                         Yes   \n",
       "4          Janasha          No                         Yes   \n",
       "\n",
       "  Government Scheme Beneficiary   Land Class  Land Area Land Owners  \\\n",
       "0                            No        Small       1.00         Yes   \n",
       "1                            No     Marginal       0.95         Yes   \n",
       "2                           Yes       Medium       7.00         Yes   \n",
       "3                           Yes  Semi-Medium       2.90         Yes   \n",
       "4                            No     Marginal       0.95         Yes   \n",
       "\n",
       "   Land Value - Crs Soil Health Crop Type  ...  \\\n",
       "0          1.359050      Medium   CottonL  ...   \n",
       "1          1.291098      Medium    JowarH  ...   \n",
       "2          9.513350        Good   CottonM  ...   \n",
       "3          4.299540         Low     Maize  ...   \n",
       "4          1.408470      Medium     Moong  ...   \n",
       "\n",
       "   Advanced Agriculture Equipment Mode of Harvesting     Storage Types  \\\n",
       "0                              No    Hand-Held Tools               SWC   \n",
       "1                              No             Manual               SWC   \n",
       "2                              No    Hand-Held Tools  Gramin Bhandaran   \n",
       "3                             Yes             Manual  Gramin Bhandaran   \n",
       "4                              No             Manual         Open Land   \n",
       "\n",
       "  MSP-Qunitol Total Possible Yeild Total Crop Cost Total Revenue  \\\n",
       "0      6380.0                 60.0        243180.0      344520.0   \n",
       "1      2970.0                 57.0        112689.0      152361.0   \n",
       "2      6080.0                385.0       1560405.0     2106720.0   \n",
       "3      1962.0                174.0        227592.0      307249.2   \n",
       "4      7755.0                 57.0        294519.0      397831.5   \n",
       "\n",
       "   Total Profit  Loan Request  Loan Granted  \n",
       "0      101340.0     1266804.0            No  \n",
       "1       39672.0     4105530.0            No  \n",
       "2      546315.0      129492.0           Yes  \n",
       "3       79657.2           NaN           Yes  \n",
       "4      103312.5      680904.0            No  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lets look at how it is structured\n",
    "F_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0ea77bd3",
   "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>names of farmers</th>\n",
       "      <th>Aadhar Card</th>\n",
       "      <th>Registered on Farmer Portal</th>\n",
       "      <th>Government Scheme Beneficiary</th>\n",
       "      <th>Land Class</th>\n",
       "      <th>Land Area</th>\n",
       "      <th>Land Owners</th>\n",
       "      <th>Land Value - Crs</th>\n",
       "      <th>Soil Health</th>\n",
       "      <th>Crop Type</th>\n",
       "      <th>...</th>\n",
       "      <th>Advanced Agriculture Equipment</th>\n",
       "      <th>Mode of Harvesting</th>\n",
       "      <th>Storage Types</th>\n",
       "      <th>MSP-Qunitol</th>\n",
       "      <th>Total Possible Yeild</th>\n",
       "      <th>Total Crop Cost</th>\n",
       "      <th>Total Revenue</th>\n",
       "      <th>Total Profit</th>\n",
       "      <th>Loan Request</th>\n",
       "      <th>Loan Granted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24995</th>\n",
       "      <td>Bhavin</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>6.00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>8.154300</td>\n",
       "      <td>Good</td>\n",
       "      <td>Soyabean</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Machinery</td>\n",
       "      <td>CWC</td>\n",
       "      <td>4300.0</td>\n",
       "      <td>330.00</td>\n",
       "      <td>925650.00</td>\n",
       "      <td>1277100.0</td>\n",
       "      <td>351450.00</td>\n",
       "      <td>345492.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24996</th>\n",
       "      <td>Yahya</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Semi-Medium</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.718100</td>\n",
       "      <td>Good</td>\n",
       "      <td>Moong</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Hand-Held Tools</td>\n",
       "      <td>Open Land</td>\n",
       "      <td>7755.0</td>\n",
       "      <td>110.00</td>\n",
       "      <td>568370.00</td>\n",
       "      <td>767745.0</td>\n",
       "      <td>199375.00</td>\n",
       "      <td>268438.50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24997</th>\n",
       "      <td>Navesha</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Small</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.965200</td>\n",
       "      <td>Low</td>\n",
       "      <td>Mustard</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Machinery</td>\n",
       "      <td>SWC</td>\n",
       "      <td>5450.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>320400.00</td>\n",
       "      <td>588600.0</td>\n",
       "      <td>268200.00</td>\n",
       "      <td>170212.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24998</th>\n",
       "      <td>Taranghini</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Marginal</td>\n",
       "      <td>0.95</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.408470</td>\n",
       "      <td>Low</td>\n",
       "      <td>Lentil</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Hand-Held Tools</td>\n",
       "      <td>FCI</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>52.25</td>\n",
       "      <td>169237.75</td>\n",
       "      <td>282150.0</td>\n",
       "      <td>112912.25</td>\n",
       "      <td>3069198.00</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24999</th>\n",
       "      <td>Virup</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Small</td>\n",
       "      <td>1.08</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.467774</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Mustard</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Gramin Bhandaran</td>\n",
       "      <td>5450.0</td>\n",
       "      <td>64.80</td>\n",
       "      <td>173016.00</td>\n",
       "      <td>317844.0</td>\n",
       "      <td>144828.00</td>\n",
       "      <td>724473.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      names of farmers Aadhar Card Registered on Farmer Portal  \\\n",
       "24995           Bhavin         Yes                         Yes   \n",
       "24996            Yahya          No                          No   \n",
       "24997          Navesha         Yes                          No   \n",
       "24998       Taranghini         Yes                          No   \n",
       "24999            Virup         Yes                          No   \n",
       "\n",
       "      Government Scheme Beneficiary   Land Class  Land Area Land Owners  \\\n",
       "24995                            No       Medium       6.00         Yes   \n",
       "24996                            No  Semi-Medium       2.00         Yes   \n",
       "24997                            No        Small       2.00         Yes   \n",
       "24998                           Yes     Marginal       0.95         Yes   \n",
       "24999                           Yes        Small       1.08         Yes   \n",
       "\n",
       "       Land Value - Crs Soil Health Crop Type  ...  \\\n",
       "24995          8.154300        Good  Soyabean  ...   \n",
       "24996          2.718100        Good     Moong  ...   \n",
       "24997          2.965200         Low   Mustard  ...   \n",
       "24998          1.408470         Low    Lentil  ...   \n",
       "24999          1.467774      Medium   Mustard  ...   \n",
       "\n",
       "       Advanced Agriculture Equipment Mode of Harvesting     Storage Types  \\\n",
       "24995                             Yes          Machinery               CWC   \n",
       "24996                              No    Hand-Held Tools         Open Land   \n",
       "24997                              No          Machinery               SWC   \n",
       "24998                              No    Hand-Held Tools               FCI   \n",
       "24999                              No             Manual  Gramin Bhandaran   \n",
       "\n",
       "      MSP-Qunitol Total Possible Yeild Total Crop Cost Total Revenue  \\\n",
       "24995      4300.0               330.00       925650.00     1277100.0   \n",
       "24996      7755.0               110.00       568370.00      767745.0   \n",
       "24997      5450.0               120.00       320400.00      588600.0   \n",
       "24998      6000.0                52.25       169237.75      282150.0   \n",
       "24999      5450.0                64.80       173016.00      317844.0   \n",
       "\n",
       "       Total Profit  Loan Request  Loan Granted  \n",
       "24995     351450.00     345492.00           Yes  \n",
       "24996     199375.00     268438.50            No  \n",
       "24997     268200.00     170212.50           Yes  \n",
       "24998     112912.25    3069198.00            No  \n",
       "24999     144828.00     724473.75            No  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "43ffa2cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Lets create a dummy of the datset, just to have a backup\n",
    "dummy = F_data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d4e4d6f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['names of farmers', 'Aadhar Card', 'Registered on Farmer Portal',\n",
       "       'Government Scheme Beneficiary', 'Land Class', 'Land Area',\n",
       "       'Land Owners', 'Land Value - Crs', 'Soil Health', 'Crop Type',\n",
       "       'Crop Cost - Qtl', 'Type of Farming', 'Employes', 'Irrigation Mode',\n",
       "       'Advanced Agriculture Equipment', 'Mode of Harvesting', 'Storage Types',\n",
       "       'MSP-Qunitol', 'Total Possible Yeild', 'Total Crop Cost',\n",
       "       'Total Revenue', 'Total Profit', 'Loan Request', 'Loan Granted'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets take a look at all the Feature names in the dataset\n",
    "F_data.columns\n",
    "#It can be inferred that 'Loan Granted' is the Target variable, while all other columns are the features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2c4fa028",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "names of farmers                     0\n",
       "Aadhar Card                          0\n",
       "Registered on Farmer Portal          0\n",
       "Government Scheme Beneficiary        0\n",
       "Land Class                           0\n",
       "Land Area                            0\n",
       "Land Owners                          0\n",
       "Land Value - Crs                     0\n",
       "Soil Health                          0\n",
       "Crop Type                            0\n",
       "Crop Cost - Qtl                   1027\n",
       "Type of Farming                      0\n",
       "Employes                             0\n",
       "Irrigation Mode                      0\n",
       "Advanced Agriculture Equipment       0\n",
       "Mode of Harvesting                   0\n",
       "Storage Types                        0\n",
       "MSP-Qunitol                       1027\n",
       "Total Possible Yeild                 0\n",
       "Total Crop Cost                   1027\n",
       "Total Revenue                     1027\n",
       "Total Profit                      1027\n",
       "Loan Request                      1027\n",
       "Loan Granted                         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking if there are any nan values\n",
    "F_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d4bbde41",
   "metadata": {},
   "outputs": [],
   "source": [
    "#As we understand from the table, all the NAN values are numericals, so i will be filling them \n",
    "def fillna(input_col):\n",
    "    input_col.fillna((np.sum(input_col))//len(input_col), inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "874352f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Lets run a loop through columns, then apply the fillna function on any column that has null values\n",
    "for column in F_data:\n",
    "    if F_data[column].isnull().sum()>0:\n",
    "        fillna(F_data[column])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a248ec37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "names of farmers                  0\n",
       "Aadhar Card                       0\n",
       "Registered on Farmer Portal       0\n",
       "Government Scheme Beneficiary     0\n",
       "Land Class                        0\n",
       "Land Area                         0\n",
       "Land Owners                       0\n",
       "Land Value - Crs                  0\n",
       "Soil Health                       0\n",
       "Crop Type                         0\n",
       "Crop Cost - Qtl                   0\n",
       "Type of Farming                   0\n",
       "Employes                          0\n",
       "Irrigation Mode                   0\n",
       "Advanced Agriculture Equipment    0\n",
       "Mode of Harvesting                0\n",
       "Storage Types                     0\n",
       "MSP-Qunitol                       0\n",
       "Total Possible Yeild              0\n",
       "Total Crop Cost                   0\n",
       "Total Revenue                     0\n",
       "Total Profit                      0\n",
       "Loan Request                      0\n",
       "Loan Granted                      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#LEts check if it worked\n",
    "F_data.isnull().sum() #Worked perfectly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cebfe075",
   "metadata": {},
   "outputs": [],
   "source": [
    "#time to perfrom one-hot-encoding to covert strings to numericals, as to ensure the dataset can be fed to the Neural network.\n",
    "from collections import defaultdict\n",
    "def encoder(input_column):\n",
    "    data_dic = defaultdict(list)\n",
    "    count=0\n",
    "    for uniq in F_data[input_column].unique():\n",
    "        data_dic[uniq].append(count)\n",
    "        count+=1\n",
    "    def solver(a,dic=data_dic):\n",
    "        return dic[a][0]\n",
    "    naming_system = input_column.split()\n",
    "    new_name = '-'.join(naming_system)\n",
    "    F_data[new_name] = F_data[input_column].apply(solver)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7b0f312e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Lets run through all the columns present in pur dataset, that have values in strings, to encode them into ints.\n",
    "for column in F_data:\n",
    "    if isinstance(F_data[column].iloc[0], str):\n",
    "        encoder(column)\n",
    "    else: continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "db9d7939",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38 Index(['names of farmers', 'Aadhar Card', 'Registered on Farmer Portal',\n",
      "       'Government Scheme Beneficiary', 'Land Class', 'Land Area',\n",
      "       'Land Owners', 'Land Value - Crs', 'Soil Health', 'Crop Type',\n",
      "       'Crop Cost - Qtl', 'Type of Farming', 'Employes', 'Irrigation Mode',\n",
      "       'Advanced Agriculture Equipment', 'Mode of Harvesting', 'Storage Types',\n",
      "       'MSP-Qunitol', 'Total Possible Yeild', 'Total Crop Cost',\n",
      "       'Total Revenue', 'Total Profit', 'Loan Request', 'Loan Granted',\n",
      "       'names-of-farmers', 'Aadhar-Card', 'Registered-on-Farmer-Portal',\n",
      "       'Government-Scheme-Beneficiary', 'Land-Class', 'Land-Owners',\n",
      "       'Soil-Health', 'Crop-Type', 'Type-of-Farming', 'Irrigation-Mode',\n",
      "       'Advanced-Agriculture-Equipment', 'Mode-of-Harvesting', 'Storage-Types',\n",
      "       'Loan-Granted'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#Lets see if it worked properly\n",
    "# this will help us see if new column names were added or not\n",
    "print(len(F_data.columns),F_data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cef41798",
   "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>names of farmers</th>\n",
       "      <th>Aadhar Card</th>\n",
       "      <th>Registered on Farmer Portal</th>\n",
       "      <th>Government Scheme Beneficiary</th>\n",
       "      <th>Land Class</th>\n",
       "      <th>Land Area</th>\n",
       "      <th>Land Owners</th>\n",
       "      <th>Land Value - Crs</th>\n",
       "      <th>Soil Health</th>\n",
       "      <th>Crop Type</th>\n",
       "      <th>...</th>\n",
       "      <th>Land-Class</th>\n",
       "      <th>Land-Owners</th>\n",
       "      <th>Soil-Health</th>\n",
       "      <th>Crop-Type</th>\n",
       "      <th>Type-of-Farming</th>\n",
       "      <th>Irrigation-Mode</th>\n",
       "      <th>Advanced-Agriculture-Equipment</th>\n",
       "      <th>Mode-of-Harvesting</th>\n",
       "      <th>Storage-Types</th>\n",
       "      <th>Loan-Granted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dihan</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Small</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.359050</td>\n",
       "      <td>Medium</td>\n",
       "      <td>CottonL</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Manosh</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Marginal</td>\n",
       "      <td>0.95</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.291098</td>\n",
       "      <td>Medium</td>\n",
       "      <td>JowarH</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dipta</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Medium</td>\n",
       "      <td>7.00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>9.513350</td>\n",
       "      <td>Good</td>\n",
       "      <td>CottonM</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Maladhi</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Semi-Medium</td>\n",
       "      <td>2.90</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.299540</td>\n",
       "      <td>Low</td>\n",
       "      <td>Maize</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Janasha</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Marginal</td>\n",
       "      <td>0.95</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.408470</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Moong</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  names of farmers Aadhar Card Registered on Farmer Portal  \\\n",
       "0            Dihan         Yes                          No   \n",
       "1           Manosh         Yes                          No   \n",
       "2            Dipta         Yes                         Yes   \n",
       "3          Maladhi         Yes                         Yes   \n",
       "4          Janasha          No                         Yes   \n",
       "\n",
       "  Government Scheme Beneficiary   Land Class  Land Area Land Owners  \\\n",
       "0                            No        Small       1.00         Yes   \n",
       "1                            No     Marginal       0.95         Yes   \n",
       "2                           Yes       Medium       7.00         Yes   \n",
       "3                           Yes  Semi-Medium       2.90         Yes   \n",
       "4                            No     Marginal       0.95         Yes   \n",
       "\n",
       "   Land Value - Crs Soil Health Crop Type  ...  Land-Class Land-Owners  \\\n",
       "0          1.359050      Medium   CottonL  ...           0           0   \n",
       "1          1.291098      Medium    JowarH  ...           1           0   \n",
       "2          9.513350        Good   CottonM  ...           2           0   \n",
       "3          4.299540         Low     Maize  ...           3           0   \n",
       "4          1.408470      Medium     Moong  ...           1           0   \n",
       "\n",
       "   Soil-Health Crop-Type Type-of-Farming Irrigation-Mode  \\\n",
       "0            0         0               0               0   \n",
       "1            0         1               1               1   \n",
       "2            1         2               1               2   \n",
       "3            2         3               2               0   \n",
       "4            0         4               2               0   \n",
       "\n",
       "  Advanced-Agriculture-Equipment  Mode-of-Harvesting  Storage-Types  \\\n",
       "0                              0                   0              0   \n",
       "1                              0                   1              0   \n",
       "2                              0                   0              1   \n",
       "3                              1                   1              1   \n",
       "4                              0                   1              2   \n",
       "\n",
       "   Loan-Granted  \n",
       "0             0  \n",
       "1             0  \n",
       "2             1  \n",
       "3             1  \n",
       "4             0  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cb8a2674",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now that we have encoded every string to a integer value, let's drop the string containing columsn\n",
    "def dropper(F_data):\n",
    "    for column in F_data:\n",
    "        if isinstance(F_data[column].iloc[0], str):\n",
    "            F_data.drop(column,inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4b852bb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Lets run the function on our dataset\n",
    "dropper(F_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4c729fed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24 Index(['Land Area', 'Land Value - Crs', 'Crop Cost - Qtl', 'Employes',\n",
      "       'MSP-Qunitol', 'Total Possible Yeild', 'Total Crop Cost',\n",
      "       'Total Revenue', 'Total Profit', 'Loan Request', 'names-of-farmers',\n",
      "       'Aadhar-Card', 'Registered-on-Farmer-Portal',\n",
      "       'Government-Scheme-Beneficiary', 'Land-Class', 'Land-Owners',\n",
      "       'Soil-Health', 'Crop-Type', 'Type-of-Farming', 'Irrigation-Mode',\n",
      "       'Advanced-Agriculture-Equipment', 'Mode-of-Harvesting', 'Storage-Types',\n",
      "       'Loan-Granted'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#To see if it worked\n",
    "print(len(F_data.columns),F_data.columns) #It has worked perfectly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "23df5074",
   "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>Land Area</th>\n",
       "      <th>Land Value - Crs</th>\n",
       "      <th>Crop Cost - Qtl</th>\n",
       "      <th>Employes</th>\n",
       "      <th>MSP-Qunitol</th>\n",
       "      <th>Total Possible Yeild</th>\n",
       "      <th>Total Crop Cost</th>\n",
       "      <th>Total Revenue</th>\n",
       "      <th>Total Profit</th>\n",
       "      <th>Loan Request</th>\n",
       "      <th>...</th>\n",
       "      <th>Land-Class</th>\n",
       "      <th>Land-Owners</th>\n",
       "      <th>Soil-Health</th>\n",
       "      <th>Crop-Type</th>\n",
       "      <th>Type-of-Farming</th>\n",
       "      <th>Irrigation-Mode</th>\n",
       "      <th>Advanced-Agriculture-Equipment</th>\n",
       "      <th>Mode-of-Harvesting</th>\n",
       "      <th>Storage-Types</th>\n",
       "      <th>Loan-Granted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.359050</td>\n",
       "      <td>4053.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6380.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>243180.0</td>\n",
       "      <td>344520.0</td>\n",
       "      <td>101340.0</td>\n",
       "      <td>1266804.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.95</td>\n",
       "      <td>1.291098</td>\n",
       "      <td>1977.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2970.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>112689.0</td>\n",
       "      <td>152361.0</td>\n",
       "      <td>39672.0</td>\n",
       "      <td>4105530.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.00</td>\n",
       "      <td>9.513350</td>\n",
       "      <td>4053.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6080.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>1560405.0</td>\n",
       "      <td>2106720.0</td>\n",
       "      <td>546315.0</td>\n",
       "      <td>129492.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.90</td>\n",
       "      <td>4.299540</td>\n",
       "      <td>1308.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1962.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>227592.0</td>\n",
       "      <td>307249.2</td>\n",
       "      <td>79657.2</td>\n",
       "      <td>1015690.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.95</td>\n",
       "      <td>1.408470</td>\n",
       "      <td>5167.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7755.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>294519.0</td>\n",
       "      <td>397831.5</td>\n",
       "      <td>103312.5</td>\n",
       "      <td>680904.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Land Area  Land Value - Crs  Crop Cost - Qtl  Employes  MSP-Qunitol  \\\n",
       "0       1.00          1.359050           4053.0         0       6380.0   \n",
       "1       0.95          1.291098           1977.0         0       2970.0   \n",
       "2       7.00          9.513350           4053.0         1       6080.0   \n",
       "3       2.90          4.299540           1308.0         1       1962.0   \n",
       "4       0.95          1.408470           5167.0         0       7755.0   \n",
       "\n",
       "   Total Possible Yeild  Total Crop Cost  Total Revenue  Total Profit  \\\n",
       "0                  60.0         243180.0       344520.0      101340.0   \n",
       "1                  57.0         112689.0       152361.0       39672.0   \n",
       "2                 385.0        1560405.0      2106720.0      546315.0   \n",
       "3                 174.0         227592.0       307249.2       79657.2   \n",
       "4                  57.0         294519.0       397831.5      103312.5   \n",
       "\n",
       "   Loan Request  ...  Land-Class  Land-Owners  Soil-Health  Crop-Type  \\\n",
       "0     1266804.0  ...           0            0            0          0   \n",
       "1     4105530.0  ...           1            0            0          1   \n",
       "2      129492.0  ...           2            0            1          2   \n",
       "3     1015690.0  ...           3            0            2          3   \n",
       "4      680904.0  ...           1            0            0          4   \n",
       "\n",
       "   Type-of-Farming  Irrigation-Mode  Advanced-Agriculture-Equipment  \\\n",
       "0                0                0                               0   \n",
       "1                1                1                               0   \n",
       "2                1                2                               0   \n",
       "3                2                0                               1   \n",
       "4                2                0                               0   \n",
       "\n",
       "   Mode-of-Harvesting  Storage-Types  Loan-Granted  \n",
       "0                   0              0             0  \n",
       "1                   1              0             0  \n",
       "2                   0              1             1  \n",
       "3                   1              1             1  \n",
       "4                   1              2             0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets look at the new Farmer_dataset\n",
    "F_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "903e8acd",
   "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>Land Area</th>\n",
       "      <th>Land Value - Crs</th>\n",
       "      <th>Crop Cost - Qtl</th>\n",
       "      <th>Employes</th>\n",
       "      <th>MSP-Qunitol</th>\n",
       "      <th>Total Possible Yeild</th>\n",
       "      <th>Total Crop Cost</th>\n",
       "      <th>Total Revenue</th>\n",
       "      <th>Total Profit</th>\n",
       "      <th>Loan Request</th>\n",
       "      <th>...</th>\n",
       "      <th>Land-Class</th>\n",
       "      <th>Land-Owners</th>\n",
       "      <th>Soil-Health</th>\n",
       "      <th>Crop-Type</th>\n",
       "      <th>Type-of-Farming</th>\n",
       "      <th>Irrigation-Mode</th>\n",
       "      <th>Advanced-Agriculture-Equipment</th>\n",
       "      <th>Mode-of-Harvesting</th>\n",
       "      <th>Storage-Types</th>\n",
       "      <th>Loan-Granted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Land Area</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998110</td>\n",
       "      <td>0.006505</td>\n",
       "      <td>0.002526</td>\n",
       "      <td>0.005723</td>\n",
       "      <td>0.998110</td>\n",
       "      <td>0.797570</td>\n",
       "      <td>0.807844</td>\n",
       "      <td>0.800135</td>\n",
       "      <td>-0.003755</td>\n",
       "      <td>...</td>\n",
       "      <td>0.750289</td>\n",
       "      <td>-0.008628</td>\n",
       "      <td>0.001850</td>\n",
       "      <td>-0.007133</td>\n",
       "      <td>-0.004546</td>\n",
       "      <td>0.007056</td>\n",
       "      <td>0.005856</td>\n",
       "      <td>-0.006113</td>\n",
       "      <td>0.000929</td>\n",
       "      <td>0.263316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Land Value - Crs</th>\n",
       "      <td>0.998110</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.005908</td>\n",
       "      <td>0.002031</td>\n",
       "      <td>0.005174</td>\n",
       "      <td>0.996213</td>\n",
       "      <td>0.795577</td>\n",
       "      <td>0.805917</td>\n",
       "      <td>0.798435</td>\n",
       "      <td>-0.003401</td>\n",
       "      <td>...</td>\n",
       "      <td>0.748875</td>\n",
       "      <td>-0.008424</td>\n",
       "      <td>0.000979</td>\n",
       "      <td>-0.007156</td>\n",
       "      <td>-0.004722</td>\n",
       "      <td>0.007677</td>\n",
       "      <td>0.005878</td>\n",
       "      <td>-0.005481</td>\n",
       "      <td>0.000300</td>\n",
       "      <td>0.262755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crop Cost - Qtl</th>\n",
       "      <td>0.006505</td>\n",
       "      <td>0.005908</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001169</td>\n",
       "      <td>0.985123</td>\n",
       "      <td>0.006358</td>\n",
       "      <td>0.414151</td>\n",
       "      <td>0.397564</td>\n",
       "      <td>0.343127</td>\n",
       "      <td>0.008099</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008816</td>\n",
       "      <td>0.001158</td>\n",
       "      <td>-0.003289</td>\n",
       "      <td>-0.389022</td>\n",
       "      <td>-0.015207</td>\n",
       "      <td>-0.010552</td>\n",
       "      <td>0.003879</td>\n",
       "      <td>-0.007721</td>\n",
       "      <td>-0.000826</td>\n",
       "      <td>0.174048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Employes</th>\n",
       "      <td>0.002526</td>\n",
       "      <td>0.002031</td>\n",
       "      <td>0.001169</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000923</td>\n",
       "      <td>0.001794</td>\n",
       "      <td>0.002494</td>\n",
       "      <td>0.002415</td>\n",
       "      <td>0.002135</td>\n",
       "      <td>0.012363</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005684</td>\n",
       "      <td>0.011324</td>\n",
       "      <td>0.006115</td>\n",
       "      <td>0.000843</td>\n",
       "      <td>0.005786</td>\n",
       "      <td>-0.013208</td>\n",
       "      <td>-0.000348</td>\n",
       "      <td>0.002382</td>\n",
       "      <td>-0.007844</td>\n",
       "      <td>-0.084771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSP-Qunitol</th>\n",
       "      <td>0.005723</td>\n",
       "      <td>0.005174</td>\n",
       "      <td>0.985123</td>\n",
       "      <td>0.000923</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.005644</td>\n",
       "      <td>0.407435</td>\n",
       "      <td>0.402748</td>\n",
       "      <td>0.375950</td>\n",
       "      <td>0.006704</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008241</td>\n",
       "      <td>0.002692</td>\n",
       "      <td>-0.003763</td>\n",
       "      <td>-0.338970</td>\n",
       "      <td>-0.015155</td>\n",
       "      <td>-0.009132</td>\n",
       "      <td>0.004962</td>\n",
       "      <td>-0.007824</td>\n",
       "      <td>0.000806</td>\n",
       "      <td>0.172482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Possible Yeild</th>\n",
       "      <td>0.998110</td>\n",
       "      <td>0.996213</td>\n",
       "      <td>0.006358</td>\n",
       "      <td>0.001794</td>\n",
       "      <td>0.005644</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.798969</td>\n",
       "      <td>0.809383</td>\n",
       "      <td>0.801939</td>\n",
       "      <td>-0.003646</td>\n",
       "      <td>...</td>\n",
       "      <td>0.748966</td>\n",
       "      <td>-0.008486</td>\n",
       "      <td>0.002125</td>\n",
       "      <td>-0.007265</td>\n",
       "      <td>-0.004854</td>\n",
       "      <td>0.006322</td>\n",
       "      <td>0.005479</td>\n",
       "      <td>-0.005640</td>\n",
       "      <td>0.001023</td>\n",
       "      <td>0.263909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Crop Cost</th>\n",
       "      <td>0.797570</td>\n",
       "      <td>0.795577</td>\n",
       "      <td>0.414151</td>\n",
       "      <td>0.002494</td>\n",
       "      <td>0.407435</td>\n",
       "      <td>0.798969</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.995011</td>\n",
       "      <td>0.944228</td>\n",
       "      <td>0.003418</td>\n",
       "      <td>...</td>\n",
       "      <td>0.598899</td>\n",
       "      <td>-0.003769</td>\n",
       "      <td>0.002144</td>\n",
       "      <td>-0.164109</td>\n",
       "      <td>-0.009930</td>\n",
       "      <td>-0.002343</td>\n",
       "      <td>0.008338</td>\n",
       "      <td>-0.008068</td>\n",
       "      <td>0.001559</td>\n",
       "      <td>0.283949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Revenue</th>\n",
       "      <td>0.807844</td>\n",
       "      <td>0.805917</td>\n",
       "      <td>0.397564</td>\n",
       "      <td>0.002415</td>\n",
       "      <td>0.402748</td>\n",
       "      <td>0.809383</td>\n",
       "      <td>0.995011</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.972369</td>\n",
       "      <td>0.002872</td>\n",
       "      <td>...</td>\n",
       "      <td>0.606477</td>\n",
       "      <td>-0.003567</td>\n",
       "      <td>0.002133</td>\n",
       "      <td>-0.139963</td>\n",
       "      <td>-0.009883</td>\n",
       "      <td>-0.001824</td>\n",
       "      <td>0.007915</td>\n",
       "      <td>-0.008091</td>\n",
       "      <td>0.001949</td>\n",
       "      <td>0.285762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Profit</th>\n",
       "      <td>0.800135</td>\n",
       "      <td>0.798435</td>\n",
       "      <td>0.343127</td>\n",
       "      <td>0.002135</td>\n",
       "      <td>0.375950</td>\n",
       "      <td>0.801939</td>\n",
       "      <td>0.944228</td>\n",
       "      <td>0.972369</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001482</td>\n",
       "      <td>...</td>\n",
       "      <td>0.600374</td>\n",
       "      <td>-0.002955</td>\n",
       "      <td>0.002023</td>\n",
       "      <td>-0.077959</td>\n",
       "      <td>-0.009384</td>\n",
       "      <td>-0.000539</td>\n",
       "      <td>0.006615</td>\n",
       "      <td>-0.007827</td>\n",
       "      <td>0.002787</td>\n",
       "      <td>0.278772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan Request</th>\n",
       "      <td>-0.003755</td>\n",
       "      <td>-0.003401</td>\n",
       "      <td>0.008099</td>\n",
       "      <td>0.012363</td>\n",
       "      <td>0.006704</td>\n",
       "      <td>-0.003646</td>\n",
       "      <td>0.003418</td>\n",
       "      <td>0.002872</td>\n",
       "      <td>0.001482</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.003901</td>\n",
       "      <td>0.002388</td>\n",
       "      <td>0.013089</td>\n",
       "      <td>-0.011063</td>\n",
       "      <td>0.001858</td>\n",
       "      <td>-0.003311</td>\n",
       "      <td>0.004830</td>\n",
       "      <td>-0.010501</td>\n",
       "      <td>-0.002389</td>\n",
       "      <td>-0.283901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>names-of-farmers</th>\n",
       "      <td>-0.000949</td>\n",
       "      <td>-0.000834</td>\n",
       "      <td>-0.016139</td>\n",
       "      <td>-0.001010</td>\n",
       "      <td>-0.015457</td>\n",
       "      <td>-0.001352</td>\n",
       "      <td>-0.008416</td>\n",
       "      <td>-0.007502</td>\n",
       "      <td>-0.005069</td>\n",
       "      <td>0.006873</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002404</td>\n",
       "      <td>0.009575</td>\n",
       "      <td>0.000538</td>\n",
       "      <td>0.008483</td>\n",
       "      <td>0.009989</td>\n",
       "      <td>-0.011227</td>\n",
       "      <td>0.002278</td>\n",
       "      <td>-0.006508</td>\n",
       "      <td>0.004576</td>\n",
       "      <td>-0.004801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aadhar-Card</th>\n",
       "      <td>-0.005529</td>\n",
       "      <td>-0.006338</td>\n",
       "      <td>0.000324</td>\n",
       "      <td>-0.008499</td>\n",
       "      <td>-0.000180</td>\n",
       "      <td>-0.005678</td>\n",
       "      <td>-0.006276</td>\n",
       "      <td>-0.007539</td>\n",
       "      <td>-0.010197</td>\n",
       "      <td>0.010419</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002214</td>\n",
       "      <td>-0.008333</td>\n",
       "      <td>0.002527</td>\n",
       "      <td>0.000671</td>\n",
       "      <td>0.002588</td>\n",
       "      <td>0.001486</td>\n",
       "      <td>0.000640</td>\n",
       "      <td>-0.014209</td>\n",
       "      <td>0.001591</td>\n",
       "      <td>-0.238799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Registered-on-Farmer-Portal</th>\n",
       "      <td>0.000123</td>\n",
       "      <td>0.000378</td>\n",
       "      <td>-0.007662</td>\n",
       "      <td>-0.002828</td>\n",
       "      <td>-0.009187</td>\n",
       "      <td>0.000558</td>\n",
       "      <td>-0.001176</td>\n",
       "      <td>-0.000916</td>\n",
       "      <td>-0.000272</td>\n",
       "      <td>0.003055</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>-0.000960</td>\n",
       "      <td>0.002320</td>\n",
       "      <td>-0.003023</td>\n",
       "      <td>-0.000390</td>\n",
       "      <td>0.000621</td>\n",
       "      <td>0.010398</td>\n",
       "      <td>0.004195</td>\n",
       "      <td>0.002687</td>\n",
       "      <td>-0.005141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Government-Scheme-Beneficiary</th>\n",
       "      <td>-0.005485</td>\n",
       "      <td>-0.006063</td>\n",
       "      <td>-0.001391</td>\n",
       "      <td>-0.006659</td>\n",
       "      <td>-0.002420</td>\n",
       "      <td>-0.005501</td>\n",
       "      <td>-0.007467</td>\n",
       "      <td>-0.008130</td>\n",
       "      <td>-0.009362</td>\n",
       "      <td>-0.007182</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005017</td>\n",
       "      <td>-0.000320</td>\n",
       "      <td>0.007602</td>\n",
       "      <td>-0.003294</td>\n",
       "      <td>-0.008369</td>\n",
       "      <td>-0.004374</td>\n",
       "      <td>0.000718</td>\n",
       "      <td>0.003218</td>\n",
       "      <td>-0.003420</td>\n",
       "      <td>0.065382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Land-Class</th>\n",
       "      <td>0.750289</td>\n",
       "      <td>0.748875</td>\n",
       "      <td>0.008816</td>\n",
       "      <td>0.005684</td>\n",
       "      <td>0.008241</td>\n",
       "      <td>0.748966</td>\n",
       "      <td>0.598899</td>\n",
       "      <td>0.606477</td>\n",
       "      <td>0.600374</td>\n",
       "      <td>-0.003901</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.002967</td>\n",
       "      <td>0.004452</td>\n",
       "      <td>-0.001368</td>\n",
       "      <td>-0.002401</td>\n",
       "      <td>-0.001088</td>\n",
       "      <td>0.000882</td>\n",
       "      <td>0.002207</td>\n",
       "      <td>-0.000789</td>\n",
       "      <td>0.245082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Land-Owners</th>\n",
       "      <td>-0.008628</td>\n",
       "      <td>-0.008424</td>\n",
       "      <td>0.001158</td>\n",
       "      <td>0.011324</td>\n",
       "      <td>0.002692</td>\n",
       "      <td>-0.008486</td>\n",
       "      <td>-0.003769</td>\n",
       "      <td>-0.003567</td>\n",
       "      <td>-0.002955</td>\n",
       "      <td>0.002388</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002967</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.003791</td>\n",
       "      <td>-0.002575</td>\n",
       "      <td>-0.007925</td>\n",
       "      <td>-0.001935</td>\n",
       "      <td>0.008560</td>\n",
       "      <td>0.007528</td>\n",
       "      <td>-0.009137</td>\n",
       "      <td>-0.358199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Soil-Health</th>\n",
       "      <td>0.001850</td>\n",
       "      <td>0.000979</td>\n",
       "      <td>-0.003289</td>\n",
       "      <td>0.006115</td>\n",
       "      <td>-0.003763</td>\n",
       "      <td>0.002125</td>\n",
       "      <td>0.002144</td>\n",
       "      <td>0.002133</td>\n",
       "      <td>0.002023</td>\n",
       "      <td>0.013089</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004452</td>\n",
       "      <td>-0.003791</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.000438</td>\n",
       "      <td>0.000628</td>\n",
       "      <td>-0.006322</td>\n",
       "      <td>0.002106</td>\n",
       "      <td>-0.007098</td>\n",
       "      <td>-0.000335</td>\n",
       "      <td>0.004483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crop-Type</th>\n",
       "      <td>-0.007133</td>\n",
       "      <td>-0.007156</td>\n",
       "      <td>-0.389022</td>\n",
       "      <td>0.000843</td>\n",
       "      <td>-0.338970</td>\n",
       "      <td>-0.007265</td>\n",
       "      <td>-0.164109</td>\n",
       "      <td>-0.139963</td>\n",
       "      <td>-0.077959</td>\n",
       "      <td>-0.011063</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001368</td>\n",
       "      <td>-0.002575</td>\n",
       "      <td>-0.000438</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003427</td>\n",
       "      <td>0.005624</td>\n",
       "      <td>0.002131</td>\n",
       "      <td>-0.002767</td>\n",
       "      <td>-0.001731</td>\n",
       "      <td>-0.085824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type-of-Farming</th>\n",
       "      <td>-0.004546</td>\n",
       "      <td>-0.004722</td>\n",
       "      <td>-0.015207</td>\n",
       "      <td>0.005786</td>\n",
       "      <td>-0.015155</td>\n",
       "      <td>-0.004854</td>\n",
       "      <td>-0.009930</td>\n",
       "      <td>-0.009883</td>\n",
       "      <td>-0.009384</td>\n",
       "      <td>0.001858</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002401</td>\n",
       "      <td>-0.007925</td>\n",
       "      <td>0.000628</td>\n",
       "      <td>0.003427</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.004180</td>\n",
       "      <td>0.000680</td>\n",
       "      <td>0.007436</td>\n",
       "      <td>-0.003092</td>\n",
       "      <td>-0.006147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Irrigation-Mode</th>\n",
       "      <td>0.007056</td>\n",
       "      <td>0.007677</td>\n",
       "      <td>-0.010552</td>\n",
       "      <td>-0.013208</td>\n",
       "      <td>-0.009132</td>\n",
       "      <td>0.006322</td>\n",
       "      <td>-0.002343</td>\n",
       "      <td>-0.001824</td>\n",
       "      <td>-0.000539</td>\n",
       "      <td>-0.003311</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001088</td>\n",
       "      <td>-0.001935</td>\n",
       "      <td>-0.006322</td>\n",
       "      <td>0.005624</td>\n",
       "      <td>-0.004180</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000733</td>\n",
       "      <td>0.001366</td>\n",
       "      <td>0.006285</td>\n",
       "      <td>0.003085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Advanced-Agriculture-Equipment</th>\n",
       "      <td>0.005856</td>\n",
       "      <td>0.005878</td>\n",
       "      <td>0.003879</td>\n",
       "      <td>-0.000348</td>\n",
       "      <td>0.004962</td>\n",
       "      <td>0.005479</td>\n",
       "      <td>0.008338</td>\n",
       "      <td>0.007915</td>\n",
       "      <td>0.006615</td>\n",
       "      <td>0.004830</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000882</td>\n",
       "      <td>0.008560</td>\n",
       "      <td>0.002106</td>\n",
       "      <td>0.002131</td>\n",
       "      <td>0.000680</td>\n",
       "      <td>0.000733</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.019067</td>\n",
       "      <td>0.007584</td>\n",
       "      <td>-0.005329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mode-of-Harvesting</th>\n",
       "      <td>-0.006113</td>\n",
       "      <td>-0.005481</td>\n",
       "      <td>-0.007721</td>\n",
       "      <td>0.002382</td>\n",
       "      <td>-0.007824</td>\n",
       "      <td>-0.005640</td>\n",
       "      <td>-0.008068</td>\n",
       "      <td>-0.008091</td>\n",
       "      <td>-0.007827</td>\n",
       "      <td>-0.010501</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002207</td>\n",
       "      <td>0.007528</td>\n",
       "      <td>-0.007098</td>\n",
       "      <td>-0.002767</td>\n",
       "      <td>0.007436</td>\n",
       "      <td>0.001366</td>\n",
       "      <td>-0.019067</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003560</td>\n",
       "      <td>0.008402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Storage-Types</th>\n",
       "      <td>0.000929</td>\n",
       "      <td>0.000300</td>\n",
       "      <td>-0.000826</td>\n",
       "      <td>-0.007844</td>\n",
       "      <td>0.000806</td>\n",
       "      <td>0.001023</td>\n",
       "      <td>0.001559</td>\n",
       "      <td>0.001949</td>\n",
       "      <td>0.002787</td>\n",
       "      <td>-0.002389</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000789</td>\n",
       "      <td>-0.009137</td>\n",
       "      <td>-0.000335</td>\n",
       "      <td>-0.001731</td>\n",
       "      <td>-0.003092</td>\n",
       "      <td>0.006285</td>\n",
       "      <td>0.007584</td>\n",
       "      <td>0.003560</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.030803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan-Granted</th>\n",
       "      <td>0.263316</td>\n",
       "      <td>0.262755</td>\n",
       "      <td>0.174048</td>\n",
       "      <td>-0.084771</td>\n",
       "      <td>0.172482</td>\n",
       "      <td>0.263909</td>\n",
       "      <td>0.283949</td>\n",
       "      <td>0.285762</td>\n",
       "      <td>0.278772</td>\n",
       "      <td>-0.283901</td>\n",
       "      <td>...</td>\n",
       "      <td>0.245082</td>\n",
       "      <td>-0.358199</td>\n",
       "      <td>0.004483</td>\n",
       "      <td>-0.085824</td>\n",
       "      <td>-0.006147</td>\n",
       "      <td>0.003085</td>\n",
       "      <td>-0.005329</td>\n",
       "      <td>0.008402</td>\n",
       "      <td>0.030803</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Land Area  Land Value - Crs  Crop Cost - Qtl  \\\n",
       "Land Area                        1.000000          0.998110         0.006505   \n",
       "Land Value - Crs                 0.998110          1.000000         0.005908   \n",
       "Crop Cost - Qtl                  0.006505          0.005908         1.000000   \n",
       "Employes                         0.002526          0.002031         0.001169   \n",
       "MSP-Qunitol                      0.005723          0.005174         0.985123   \n",
       "Total Possible Yeild             0.998110          0.996213         0.006358   \n",
       "Total Crop Cost                  0.797570          0.795577         0.414151   \n",
       "Total Revenue                    0.807844          0.805917         0.397564   \n",
       "Total Profit                     0.800135          0.798435         0.343127   \n",
       "Loan Request                    -0.003755         -0.003401         0.008099   \n",
       "names-of-farmers                -0.000949         -0.000834        -0.016139   \n",
       "Aadhar-Card                     -0.005529         -0.006338         0.000324   \n",
       "Registered-on-Farmer-Portal      0.000123          0.000378        -0.007662   \n",
       "Government-Scheme-Beneficiary   -0.005485         -0.006063        -0.001391   \n",
       "Land-Class                       0.750289          0.748875         0.008816   \n",
       "Land-Owners                     -0.008628         -0.008424         0.001158   \n",
       "Soil-Health                      0.001850          0.000979        -0.003289   \n",
       "Crop-Type                       -0.007133         -0.007156        -0.389022   \n",
       "Type-of-Farming                 -0.004546         -0.004722        -0.015207   \n",
       "Irrigation-Mode                  0.007056          0.007677        -0.010552   \n",
       "Advanced-Agriculture-Equipment   0.005856          0.005878         0.003879   \n",
       "Mode-of-Harvesting              -0.006113         -0.005481        -0.007721   \n",
       "Storage-Types                    0.000929          0.000300        -0.000826   \n",
       "Loan-Granted                     0.263316          0.262755         0.174048   \n",
       "\n",
       "                                Employes  MSP-Qunitol  Total Possible Yeild  \\\n",
       "Land Area                       0.002526     0.005723              0.998110   \n",
       "Land Value - Crs                0.002031     0.005174              0.996213   \n",
       "Crop Cost - Qtl                 0.001169     0.985123              0.006358   \n",
       "Employes                        1.000000     0.000923              0.001794   \n",
       "MSP-Qunitol                     0.000923     1.000000              0.005644   \n",
       "Total Possible Yeild            0.001794     0.005644              1.000000   \n",
       "Total Crop Cost                 0.002494     0.407435              0.798969   \n",
       "Total Revenue                   0.002415     0.402748              0.809383   \n",
       "Total Profit                    0.002135     0.375950              0.801939   \n",
       "Loan Request                    0.012363     0.006704             -0.003646   \n",
       "names-of-farmers               -0.001010    -0.015457             -0.001352   \n",
       "Aadhar-Card                    -0.008499    -0.000180             -0.005678   \n",
       "Registered-on-Farmer-Portal    -0.002828    -0.009187              0.000558   \n",
       "Government-Scheme-Beneficiary  -0.006659    -0.002420             -0.005501   \n",
       "Land-Class                      0.005684     0.008241              0.748966   \n",
       "Land-Owners                     0.011324     0.002692             -0.008486   \n",
       "Soil-Health                     0.006115    -0.003763              0.002125   \n",
       "Crop-Type                       0.000843    -0.338970             -0.007265   \n",
       "Type-of-Farming                 0.005786    -0.015155             -0.004854   \n",
       "Irrigation-Mode                -0.013208    -0.009132              0.006322   \n",
       "Advanced-Agriculture-Equipment -0.000348     0.004962              0.005479   \n",
       "Mode-of-Harvesting              0.002382    -0.007824             -0.005640   \n",
       "Storage-Types                  -0.007844     0.000806              0.001023   \n",
       "Loan-Granted                   -0.084771     0.172482              0.263909   \n",
       "\n",
       "                                Total Crop Cost  Total Revenue  Total Profit  \\\n",
       "Land Area                              0.797570       0.807844      0.800135   \n",
       "Land Value - Crs                       0.795577       0.805917      0.798435   \n",
       "Crop Cost - Qtl                        0.414151       0.397564      0.343127   \n",
       "Employes                               0.002494       0.002415      0.002135   \n",
       "MSP-Qunitol                            0.407435       0.402748      0.375950   \n",
       "Total Possible Yeild                   0.798969       0.809383      0.801939   \n",
       "Total Crop Cost                        1.000000       0.995011      0.944228   \n",
       "Total Revenue                          0.995011       1.000000      0.972369   \n",
       "Total Profit                           0.944228       0.972369      1.000000   \n",
       "Loan Request                           0.003418       0.002872      0.001482   \n",
       "names-of-farmers                      -0.008416      -0.007502     -0.005069   \n",
       "Aadhar-Card                           -0.006276      -0.007539     -0.010197   \n",
       "Registered-on-Farmer-Portal           -0.001176      -0.000916     -0.000272   \n",
       "Government-Scheme-Beneficiary         -0.007467      -0.008130     -0.009362   \n",
       "Land-Class                             0.598899       0.606477      0.600374   \n",
       "Land-Owners                           -0.003769      -0.003567     -0.002955   \n",
       "Soil-Health                            0.002144       0.002133      0.002023   \n",
       "Crop-Type                             -0.164109      -0.139963     -0.077959   \n",
       "Type-of-Farming                       -0.009930      -0.009883     -0.009384   \n",
       "Irrigation-Mode                       -0.002343      -0.001824     -0.000539   \n",
       "Advanced-Agriculture-Equipment         0.008338       0.007915      0.006615   \n",
       "Mode-of-Harvesting                    -0.008068      -0.008091     -0.007827   \n",
       "Storage-Types                          0.001559       0.001949      0.002787   \n",
       "Loan-Granted                           0.283949       0.285762      0.278772   \n",
       "\n",
       "                                Loan Request  ...  Land-Class  Land-Owners  \\\n",
       "Land Area                          -0.003755  ...    0.750289    -0.008628   \n",
       "Land Value - Crs                   -0.003401  ...    0.748875    -0.008424   \n",
       "Crop Cost - Qtl                     0.008099  ...    0.008816     0.001158   \n",
       "Employes                            0.012363  ...    0.005684     0.011324   \n",
       "MSP-Qunitol                         0.006704  ...    0.008241     0.002692   \n",
       "Total Possible Yeild               -0.003646  ...    0.748966    -0.008486   \n",
       "Total Crop Cost                     0.003418  ...    0.598899    -0.003769   \n",
       "Total Revenue                       0.002872  ...    0.606477    -0.003567   \n",
       "Total Profit                        0.001482  ...    0.600374    -0.002955   \n",
       "Loan Request                        1.000000  ...   -0.003901     0.002388   \n",
       "names-of-farmers                    0.006873  ...    0.002404     0.009575   \n",
       "Aadhar-Card                         0.010419  ...    0.002214    -0.008333   \n",
       "Registered-on-Farmer-Portal         0.003055  ...    0.000072    -0.000960   \n",
       "Government-Scheme-Beneficiary      -0.007182  ...   -0.005017    -0.000320   \n",
       "Land-Class                         -0.003901  ...    1.000000    -0.002967   \n",
       "Land-Owners                         0.002388  ...   -0.002967     1.000000   \n",
       "Soil-Health                         0.013089  ...    0.004452    -0.003791   \n",
       "Crop-Type                          -0.011063  ...   -0.001368    -0.002575   \n",
       "Type-of-Farming                     0.001858  ...   -0.002401    -0.007925   \n",
       "Irrigation-Mode                    -0.003311  ...   -0.001088    -0.001935   \n",
       "Advanced-Agriculture-Equipment      0.004830  ...    0.000882     0.008560   \n",
       "Mode-of-Harvesting                 -0.010501  ...    0.002207     0.007528   \n",
       "Storage-Types                      -0.002389  ...   -0.000789    -0.009137   \n",
       "Loan-Granted                       -0.283901  ...    0.245082    -0.358199   \n",
       "\n",
       "                                Soil-Health  Crop-Type  Type-of-Farming  \\\n",
       "Land Area                          0.001850  -0.007133        -0.004546   \n",
       "Land Value - Crs                   0.000979  -0.007156        -0.004722   \n",
       "Crop Cost - Qtl                   -0.003289  -0.389022        -0.015207   \n",
       "Employes                           0.006115   0.000843         0.005786   \n",
       "MSP-Qunitol                       -0.003763  -0.338970        -0.015155   \n",
       "Total Possible Yeild               0.002125  -0.007265        -0.004854   \n",
       "Total Crop Cost                    0.002144  -0.164109        -0.009930   \n",
       "Total Revenue                      0.002133  -0.139963        -0.009883   \n",
       "Total Profit                       0.002023  -0.077959        -0.009384   \n",
       "Loan Request                       0.013089  -0.011063         0.001858   \n",
       "names-of-farmers                   0.000538   0.008483         0.009989   \n",
       "Aadhar-Card                        0.002527   0.000671         0.002588   \n",
       "Registered-on-Farmer-Portal        0.002320  -0.003023        -0.000390   \n",
       "Government-Scheme-Beneficiary      0.007602  -0.003294        -0.008369   \n",
       "Land-Class                         0.004452  -0.001368        -0.002401   \n",
       "Land-Owners                       -0.003791  -0.002575        -0.007925   \n",
       "Soil-Health                        1.000000  -0.000438         0.000628   \n",
       "Crop-Type                         -0.000438   1.000000         0.003427   \n",
       "Type-of-Farming                    0.000628   0.003427         1.000000   \n",
       "Irrigation-Mode                   -0.006322   0.005624        -0.004180   \n",
       "Advanced-Agriculture-Equipment     0.002106   0.002131         0.000680   \n",
       "Mode-of-Harvesting                -0.007098  -0.002767         0.007436   \n",
       "Storage-Types                     -0.000335  -0.001731        -0.003092   \n",
       "Loan-Granted                       0.004483  -0.085824        -0.006147   \n",
       "\n",
       "                                Irrigation-Mode  \\\n",
       "Land Area                              0.007056   \n",
       "Land Value - Crs                       0.007677   \n",
       "Crop Cost - Qtl                       -0.010552   \n",
       "Employes                              -0.013208   \n",
       "MSP-Qunitol                           -0.009132   \n",
       "Total Possible Yeild                   0.006322   \n",
       "Total Crop Cost                       -0.002343   \n",
       "Total Revenue                         -0.001824   \n",
       "Total Profit                          -0.000539   \n",
       "Loan Request                          -0.003311   \n",
       "names-of-farmers                      -0.011227   \n",
       "Aadhar-Card                            0.001486   \n",
       "Registered-on-Farmer-Portal            0.000621   \n",
       "Government-Scheme-Beneficiary         -0.004374   \n",
       "Land-Class                            -0.001088   \n",
       "Land-Owners                           -0.001935   \n",
       "Soil-Health                           -0.006322   \n",
       "Crop-Type                              0.005624   \n",
       "Type-of-Farming                       -0.004180   \n",
       "Irrigation-Mode                        1.000000   \n",
       "Advanced-Agriculture-Equipment         0.000733   \n",
       "Mode-of-Harvesting                     0.001366   \n",
       "Storage-Types                          0.006285   \n",
       "Loan-Granted                           0.003085   \n",
       "\n",
       "                                Advanced-Agriculture-Equipment  \\\n",
       "Land Area                                             0.005856   \n",
       "Land Value - Crs                                      0.005878   \n",
       "Crop Cost - Qtl                                       0.003879   \n",
       "Employes                                             -0.000348   \n",
       "MSP-Qunitol                                           0.004962   \n",
       "Total Possible Yeild                                  0.005479   \n",
       "Total Crop Cost                                       0.008338   \n",
       "Total Revenue                                         0.007915   \n",
       "Total Profit                                          0.006615   \n",
       "Loan Request                                          0.004830   \n",
       "names-of-farmers                                      0.002278   \n",
       "Aadhar-Card                                           0.000640   \n",
       "Registered-on-Farmer-Portal                           0.010398   \n",
       "Government-Scheme-Beneficiary                         0.000718   \n",
       "Land-Class                                            0.000882   \n",
       "Land-Owners                                           0.008560   \n",
       "Soil-Health                                           0.002106   \n",
       "Crop-Type                                             0.002131   \n",
       "Type-of-Farming                                       0.000680   \n",
       "Irrigation-Mode                                       0.000733   \n",
       "Advanced-Agriculture-Equipment                        1.000000   \n",
       "Mode-of-Harvesting                                   -0.019067   \n",
       "Storage-Types                                         0.007584   \n",
       "Loan-Granted                                         -0.005329   \n",
       "\n",
       "                                Mode-of-Harvesting  Storage-Types  \\\n",
       "Land Area                                -0.006113       0.000929   \n",
       "Land Value - Crs                         -0.005481       0.000300   \n",
       "Crop Cost - Qtl                          -0.007721      -0.000826   \n",
       "Employes                                  0.002382      -0.007844   \n",
       "MSP-Qunitol                              -0.007824       0.000806   \n",
       "Total Possible Yeild                     -0.005640       0.001023   \n",
       "Total Crop Cost                          -0.008068       0.001559   \n",
       "Total Revenue                            -0.008091       0.001949   \n",
       "Total Profit                             -0.007827       0.002787   \n",
       "Loan Request                             -0.010501      -0.002389   \n",
       "names-of-farmers                         -0.006508       0.004576   \n",
       "Aadhar-Card                              -0.014209       0.001591   \n",
       "Registered-on-Farmer-Portal               0.004195       0.002687   \n",
       "Government-Scheme-Beneficiary             0.003218      -0.003420   \n",
       "Land-Class                                0.002207      -0.000789   \n",
       "Land-Owners                               0.007528      -0.009137   \n",
       "Soil-Health                              -0.007098      -0.000335   \n",
       "Crop-Type                                -0.002767      -0.001731   \n",
       "Type-of-Farming                           0.007436      -0.003092   \n",
       "Irrigation-Mode                           0.001366       0.006285   \n",
       "Advanced-Agriculture-Equipment           -0.019067       0.007584   \n",
       "Mode-of-Harvesting                        1.000000       0.003560   \n",
       "Storage-Types                             0.003560       1.000000   \n",
       "Loan-Granted                              0.008402       0.030803   \n",
       "\n",
       "                                Loan-Granted  \n",
       "Land Area                           0.263316  \n",
       "Land Value - Crs                    0.262755  \n",
       "Crop Cost - Qtl                     0.174048  \n",
       "Employes                           -0.084771  \n",
       "MSP-Qunitol                         0.172482  \n",
       "Total Possible Yeild                0.263909  \n",
       "Total Crop Cost                     0.283949  \n",
       "Total Revenue                       0.285762  \n",
       "Total Profit                        0.278772  \n",
       "Loan Request                       -0.283901  \n",
       "names-of-farmers                   -0.004801  \n",
       "Aadhar-Card                        -0.238799  \n",
       "Registered-on-Farmer-Portal        -0.005141  \n",
       "Government-Scheme-Beneficiary       0.065382  \n",
       "Land-Class                          0.245082  \n",
       "Land-Owners                        -0.358199  \n",
       "Soil-Health                         0.004483  \n",
       "Crop-Type                          -0.085824  \n",
       "Type-of-Farming                    -0.006147  \n",
       "Irrigation-Mode                     0.003085  \n",
       "Advanced-Agriculture-Equipment     -0.005329  \n",
       "Mode-of-Harvesting                  0.008402  \n",
       "Storage-Types                       0.030803  \n",
       "Loan-Granted                        1.000000  \n",
       "\n",
       "[24 rows x 24 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets look athe correlation of all columns\n",
    "corr = F_data.corr()\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "15090ab6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Data Correlation'}>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1800x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Lets visualize this correlation using a heatmap\n",
    "import seaborn as sns\n",
    "plt.figure(figsize=(25,15))\n",
    "plt.title('Data Correlation')\n",
    "sns.heatmap(corr,annot=True,cmap='coolwarm',vmin=-1,vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "69518bbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#as we can infer from the visualization, the names-of-farmers column is not valuable or contibuting anything so, i am dropping it\n",
    "F_data.drop('names-of-farmers',inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f276abef",
   "metadata": {},
   "source": [
    "## Data Splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a66c727a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#now its time to split the dataset into Features and Target\n",
    "X = F_data.drop('Loan-Granted',axis=1)\n",
    "Y = F_data['Loan-Granted']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2fc9efba",
   "metadata": {},
   "outputs": [],
   "source": [
    "#now its time to split the X,Y dataset into train and test data\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "861481a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18750 6250\n"
     ]
    }
   ],
   "source": [
    "#Lets look at the split size\n",
    "print(len(x_train),len(x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5b83242e",
   "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>Land Area</th>\n",
       "      <th>Land Value - Crs</th>\n",
       "      <th>Crop Cost - Qtl</th>\n",
       "      <th>Employes</th>\n",
       "      <th>MSP-Qunitol</th>\n",
       "      <th>Total Possible Yeild</th>\n",
       "      <th>Total Crop Cost</th>\n",
       "      <th>Total Revenue</th>\n",
       "      <th>Total Profit</th>\n",
       "      <th>Loan Request</th>\n",
       "      <th>...</th>\n",
       "      <th>Government-Scheme-Beneficiary</th>\n",
       "      <th>Land-Class</th>\n",
       "      <th>Land-Owners</th>\n",
       "      <th>Soil-Health</th>\n",
       "      <th>Crop-Type</th>\n",
       "      <th>Type-of-Farming</th>\n",
       "      <th>Irrigation-Mode</th>\n",
       "      <th>Advanced-Agriculture-Equipment</th>\n",
       "      <th>Mode-of-Harvesting</th>\n",
       "      <th>Storage-Types</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7091</th>\n",
       "      <td>3.9</td>\n",
       "      <td>5.300295</td>\n",
       "      <td>2805.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4300.0</td>\n",
       "      <td>214.5</td>\n",
       "      <td>601672.5</td>\n",
       "      <td>830115.00</td>\n",
       "      <td>228442.50</td>\n",
       "      <td>2139984.00</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13982</th>\n",
       "      <td>2.5</td>\n",
       "      <td>3.397625</td>\n",
       "      <td>5167.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7755.0</td>\n",
       "      <td>137.5</td>\n",
       "      <td>710462.5</td>\n",
       "      <td>959681.25</td>\n",
       "      <td>249218.75</td>\n",
       "      <td>4686330.00</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9532</th>\n",
       "      <td>0.9</td>\n",
       "      <td>1.334340</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2125.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>57510.0</td>\n",
       "      <td>103275.00</td>\n",
       "      <td>45765.00</td>\n",
       "      <td>297619.20</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5224</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.965200</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2125.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>127800.0</td>\n",
       "      <td>229500.00</td>\n",
       "      <td>101700.00</td>\n",
       "      <td>1246050.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14449</th>\n",
       "      <td>0.8</td>\n",
       "      <td>1.186080</td>\n",
       "      <td>3206.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5335.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>141064.0</td>\n",
       "      <td>211266.00</td>\n",
       "      <td>70202.00</td>\n",
       "      <td>624813.75</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Land Area  Land Value - Crs  Crop Cost - Qtl  Employes  MSP-Qunitol  \\\n",
       "7091         3.9          5.300295           2805.0         2       4300.0   \n",
       "13982        2.5          3.397625           5167.0         0       7755.0   \n",
       "9532         0.9          1.334340           1065.0         0       2125.0   \n",
       "5224         2.0          2.965200           1065.0         1       2125.0   \n",
       "14449        0.8          1.186080           3206.0         2       5335.0   \n",
       "\n",
       "       Total Possible Yeild  Total Crop Cost  Total Revenue  Total Profit  \\\n",
       "7091                  214.5         601672.5      830115.00     228442.50   \n",
       "13982                 137.5         710462.5      959681.25     249218.75   \n",
       "9532                   54.0          57510.0      103275.00      45765.00   \n",
       "5224                  120.0         127800.0      229500.00     101700.00   \n",
       "14449                  44.0         141064.0      211266.00      70202.00   \n",
       "\n",
       "       Loan Request  ...  Government-Scheme-Beneficiary  Land-Class  \\\n",
       "7091     2139984.00  ...                              1           3   \n",
       "13982    4686330.00  ...                              1           3   \n",
       "9532      297619.20  ...                              0           1   \n",
       "5224     1246050.00  ...                              0           3   \n",
       "14449     624813.75  ...                              1           1   \n",
       "\n",
       "       Land-Owners  Soil-Health  Crop-Type  Type-of-Farming  Irrigation-Mode  \\\n",
       "7091             1            2          7                3                0   \n",
       "13982            0            1          4                2                0   \n",
       "9532             0            1         19                2                0   \n",
       "5224             0            2         19                1                0   \n",
       "14449            0            0         10                2                2   \n",
       "\n",
       "       Advanced-Agriculture-Equipment  Mode-of-Harvesting  Storage-Types  \n",
       "7091                                0                   2              0  \n",
       "13982                               0                   0              1  \n",
       "9532                                0                   1              4  \n",
       "5224                                0                   1              1  \n",
       "14449                               0                   1              0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets look at x_train\n",
    "x_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0cf7f6b1",
   "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>Land Area</th>\n",
       "      <th>Land Value - Crs</th>\n",
       "      <th>Crop Cost - Qtl</th>\n",
       "      <th>Employes</th>\n",
       "      <th>MSP-Qunitol</th>\n",
       "      <th>Total Possible Yeild</th>\n",
       "      <th>Total Crop Cost</th>\n",
       "      <th>Total Revenue</th>\n",
       "      <th>Total Profit</th>\n",
       "      <th>Loan Request</th>\n",
       "      <th>...</th>\n",
       "      <th>Government-Scheme-Beneficiary</th>\n",
       "      <th>Land-Class</th>\n",
       "      <th>Land-Owners</th>\n",
       "      <th>Soil-Health</th>\n",
       "      <th>Crop-Type</th>\n",
       "      <th>Type-of-Farming</th>\n",
       "      <th>Irrigation-Mode</th>\n",
       "      <th>Advanced-Agriculture-Equipment</th>\n",
       "      <th>Mode-of-Harvesting</th>\n",
       "      <th>Storage-Types</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11505</th>\n",
       "      <td>3.90</td>\n",
       "      <td>5.300295</td>\n",
       "      <td>157.0</td>\n",
       "      <td>2</td>\n",
       "      <td>315.0</td>\n",
       "      <td>214.5</td>\n",
       "      <td>33676.5</td>\n",
       "      <td>60810.75</td>\n",
       "      <td>27134.25</td>\n",
       "      <td>489600.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8026</th>\n",
       "      <td>11.00</td>\n",
       "      <td>16.308600</td>\n",
       "      <td>3873.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5850.0</td>\n",
       "      <td>660.0</td>\n",
       "      <td>2556180.0</td>\n",
       "      <td>3474900.00</td>\n",
       "      <td>918720.00</td>\n",
       "      <td>139497.12</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16555</th>\n",
       "      <td>1.08</td>\n",
       "      <td>1.601208</td>\n",
       "      <td>3873.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5850.0</td>\n",
       "      <td>64.8</td>\n",
       "      <td>250970.4</td>\n",
       "      <td>341172.00</td>\n",
       "      <td>90201.60</td>\n",
       "      <td>76041.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12889</th>\n",
       "      <td>4.00</td>\n",
       "      <td>5.930400</td>\n",
       "      <td>1977.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2970.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>434940.0</td>\n",
       "      <td>588060.00</td>\n",
       "      <td>153120.00</td>\n",
       "      <td>2728176.00</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15487</th>\n",
       "      <td>0.95</td>\n",
       "      <td>1.291098</td>\n",
       "      <td>2670.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5450.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>152190.0</td>\n",
       "      <td>279585.00</td>\n",
       "      <td>127395.00</td>\n",
       "      <td>151121.88</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Land Area  Land Value - Crs  Crop Cost - Qtl  Employes  MSP-Qunitol  \\\n",
       "11505       3.90          5.300295            157.0         2        315.0   \n",
       "8026       11.00         16.308600           3873.0         1       5850.0   \n",
       "16555       1.08          1.601208           3873.0         2       5850.0   \n",
       "12889       4.00          5.930400           1977.0         2       2970.0   \n",
       "15487       0.95          1.291098           2670.0         2       5450.0   \n",
       "\n",
       "       Total Possible Yeild  Total Crop Cost  Total Revenue  Total Profit  \\\n",
       "11505                 214.5          33676.5       60810.75      27134.25   \n",
       "8026                  660.0        2556180.0     3474900.00     918720.00   \n",
       "16555                  64.8         250970.4      341172.00      90201.60   \n",
       "12889                 220.0         434940.0      588060.00     153120.00   \n",
       "15487                  57.0         152190.0      279585.00     127395.00   \n",
       "\n",
       "       Loan Request  ...  Government-Scheme-Beneficiary  Land-Class  \\\n",
       "11505     489600.00  ...                              0           3   \n",
       "8026      139497.12  ...                              0           4   \n",
       "16555      76041.00  ...                              0           0   \n",
       "12889    2728176.00  ...                              1           2   \n",
       "15487     151121.88  ...                              1           1   \n",
       "\n",
       "       Land-Owners  Soil-Health  Crop-Type  Type-of-Farming  Irrigation-Mode  \\\n",
       "11505            0            0         22                2                0   \n",
       "8026             1            1         15                2                1   \n",
       "16555            0            1         15                0                2   \n",
       "12889            0            2          1                3                1   \n",
       "15487            0            1         12                0                0   \n",
       "\n",
       "       Advanced-Agriculture-Equipment  Mode-of-Harvesting  Storage-Types  \n",
       "11505                               0                   1              5  \n",
       "8026                                1                   1              0  \n",
       "16555                               0                   1              2  \n",
       "12889                               1                   2              3  \n",
       "15487                               0                   2              2  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets look at x_test\n",
    "x_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67058b94",
   "metadata": {},
   "source": [
    "## Neural Network Creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "0f469d67",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Importing required libraries\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import Model\n",
    "from tensorflow.keras.layers import Dense, Input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ccb49780",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_3 (InputLayer)        [(None, 22)]              0         \n",
      "                                                                 \n",
      " dense_18 (Dense)            (None, 64)                1472      \n",
      "                                                                 \n",
      " dense_19 (Dense)            (None, 128)               8320      \n",
      "                                                                 \n",
      " dense_20 (Dense)            (None, 256)               33024     \n",
      "                                                                 \n",
      " dense_21 (Dense)            (None, 256)               65792     \n",
      "                                                                 \n",
      " dense_22 (Dense)            (None, 256)               65792     \n",
      "                                                                 \n",
      " dense_23 (Dense)            (None, 256)               65792     \n",
      "                                                                 \n",
      " dense_24 (Dense)            (None, 1)                 257       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 240,449\n",
      "Trainable params: 240,449\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Create a Sequential model\n",
    "model = Sequential()\n",
    "\n",
    "# Input layer (based on 22 features/columns from the dataframe)\n",
    "input_layer = Input(shape=(22,))\n",
    "\n",
    "# Six dense layers\n",
    "dense1 = Dense(units=64, activation='relu')(input_layer)\n",
    "dense2 = Dense(units=128, activation='relu')(dense1)\n",
    "dense3 = Dense(units=256, activation='relu')(dense2)\n",
    "dense4 = Dense(units=256, activation='relu')(dense3)\n",
    "dense5 = Dense(units=256, activation='relu')(dense4)\n",
    "dense6 = Dense(units=256, activation='relu')(dense5)\n",
    "\n",
    "# Output layer\n",
    "output_layer = Dense(units=1, activation='sigmoid')(dense6)\n",
    "\n",
    "# Construct the model\n",
    "model = Model(inputs=input_layer, outputs=output_layer)\n",
    "\n",
    "# Compile the model (assuming binary classification)\n",
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# Print model summary\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "3da939cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "586/586 [==============================] - 1s 2ms/step - loss: 0.6403 - accuracy: 0.6619\n",
      "Epoch 2/10\n",
      "586/586 [==============================] - 1s 2ms/step - loss: 0.6407 - accuracy: 0.6619\n",
      "Epoch 3/10\n",
      "586/586 [==============================] - 1s 2ms/step - loss: 0.6405 - accuracy: 0.6619\n",
      "Epoch 4/10\n",
      "586/586 [==============================] - 1s 2ms/step - loss: 0.6406 - accuracy: 0.6619\n",
      "Epoch 5/10\n",
      "586/586 [==============================] - 1s 2ms/step - loss: 0.6403 - accuracy: 0.6619\n",
      "Epoch 6/10\n",
      "586/586 [==============================] - 1s 2ms/step - loss: 0.6406 - accuracy: 0.6619\n",
      "Epoch 7/10\n",
      "586/586 [==============================] - 1s 2ms/step - loss: 0.6404 - accuracy: 0.6619\n",
      "Epoch 8/10\n",
      "586/586 [==============================] - 1s 2ms/step - loss: 0.6402 - accuracy: 0.6619\n",
      "Epoch 9/10\n",
      "586/586 [==============================] - 1s 2ms/step - loss: 0.6404 - accuracy: 0.6619\n",
      "Epoch 10/10\n",
      "586/586 [==============================] - 1s 2ms/step - loss: 0.6405 - accuracy: 0.6619\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1d58612ef10>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Time to train the neural network\n",
    "model.fit(x_train,y_train,epochs=10,batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "97b2e7e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now that we are stuck at only 66.19% accuraccy. I am going to try standard scaler to imporove acuracy\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a8b58c35",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "x_train_scaled = scaler.fit_transform(x_train)\n",
    "x_test_scaled = scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "cbf683cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "586/586 [==============================] - 2s 2ms/step - loss: 0.3109 - accuracy: 0.8635\n",
      "Epoch 2/10\n",
      "586/586 [==============================] - 1s 2ms/step - loss: 0.2191 - accuracy: 0.9160\n",
      "Epoch 3/10\n",
      "586/586 [==============================] - 1s 2ms/step - loss: 0.1839 - accuracy: 0.9284\n",
      "Epoch 4/10\n",
      "586/586 [==============================] - 1s 2ms/step - loss: 0.1341 - accuracy: 0.9508\n",
      "Epoch 5/10\n",
      "586/586 [==============================] - 1s 2ms/step - loss: 0.1097 - accuracy: 0.9594\n",
      "Epoch 6/10\n",
      "586/586 [==============================] - 1s 2ms/step - loss: 0.0951 - accuracy: 0.9616\n",
      "Epoch 7/10\n",
      "586/586 [==============================] - 1s 2ms/step - loss: 0.0841 - accuracy: 0.9650\n",
      "Epoch 8/10\n",
      "586/586 [==============================] - 1s 2ms/step - loss: 0.0815 - accuracy: 0.9656\n",
      "Epoch 9/10\n",
      "586/586 [==============================] - 1s 2ms/step - loss: 0.0687 - accuracy: 0.9691\n",
      "Epoch 10/10\n",
      "586/586 [==============================] - 1s 2ms/step - loss: 0.0890 - accuracy: 0.9599\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1d58614f3d0>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets run our neural network using the scaled vales\n",
    "model.fit(x_train_scaled,y_train,epochs=10,batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "069558e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "196/196 [==============================] - 0s 1ms/step - loss: 0.1127 - accuracy: 0.9525\n",
      "Test Loss: 0.11265601217746735\n",
      "Test Accuracy: 0.9524800181388855\n"
     ]
    }
   ],
   "source": [
    "# As theres beter accuracy after scaling. Let's see the test prediction scores\n",
    "loss, accuracy = model.evaluate(x_test_scaled, y_test)\n",
    "print(f\"Test Loss: {loss}\")\n",
    "print(f\"Test Accuracy: {accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "4784a538",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "196/196 [==============================] - 0s 1ms/step\n"
     ]
    }
   ],
   "source": [
    "#As 95% is a good accuraccy to obtain, I will no be saving the predicted values\n",
    "Loan_preds = model.predict(x_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "03ca9290",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.7040394e-05],\n",
       "       [0.0000000e+00],\n",
       "       [1.0341342e-03],\n",
       "       ...,\n",
       "       [9.9990559e-01],\n",
       "       [9.9995244e-01],\n",
       "       [2.6423329e-01]], dtype=float32)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets take a look at our predicted values\n",
    "Loan_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "48aa587a",
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6245</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6246</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6247</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6248</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6249</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6250 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      0\n",
       "0     0\n",
       "1     0\n",
       "2     0\n",
       "3     0\n",
       "4     0\n",
       "...  ..\n",
       "6245  0\n",
       "6246  0\n",
       "6247  1\n",
       "6248  1\n",
       "6249  0\n",
       "\n",
       "[6250 rows x 1 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Loan_yn = (Loan_preds > 0.5).astype(int)\n",
    "Loan_yn = pd.DataFrame(Loan_yn)\n",
    "Loan_yn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "1838c029",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's  decode the 0,1 outputs into yes and no's\n",
    "def decoder(preds):\n",
    "    if preds == 1: return 'No'\n",
    "    else: return 'Yes'\n",
    "Loan_yn['Loan_Granted'] = Loan_yn[0].apply(decoder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "c89f1a79",
   "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>0</th>\n",
       "      <th>Loan_Granted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6245</th>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6246</th>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6247</th>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6248</th>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6249</th>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6250 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      0 Loan_Granted\n",
       "0     0          Yes\n",
       "1     0          Yes\n",
       "2     0          Yes\n",
       "3     0          Yes\n",
       "4     0          Yes\n",
       "...  ..          ...\n",
       "6245  0          Yes\n",
       "6246  0          Yes\n",
       "6247  1           No\n",
       "6248  1           No\n",
       "6249  0          Yes\n",
       "\n",
       "[6250 rows x 2 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets take a look at decoded predictions\n",
    "Loan_yn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "86e57403",
   "metadata": {},
   "outputs": [],
   "source": [
    "#time to drop the first column\n",
    "Loan_yn.drop(0,axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "f5318ddb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Saving the Predictions\n",
    "Loan_yn.to_csv('./E&Y - Model/Loan_Predictions_with_NN.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "d376571e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Saving the model\n",
    "model.save('Loan_granter.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "e2ae4999",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Saving just the weights of the model\n",
    "model.save_weights('Loan_granter_weights.h5')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}