Payment date prediction (3).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "wcq6dWzy1ZR0",
   "metadata": {
    "id": "wcq6dWzy1ZR0"
   },
   "source": [
    "# Payment Date Prediction "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2778654e",
   "metadata": {
    "id": "2778654e"
   },
   "source": [
    "\n",
    "### Importing related Libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "304c9e38",
   "metadata": {
    "id": "304c9e38"
   },
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8724f5ee",
   "metadata": {
    "id": "8724f5ee"
   },
   "source": [
    "### Store the dataset into the Dataframe\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "415db50a",
   "metadata": {
    "id": "415db50a"
   },
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"dataset.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42e37f05",
   "metadata": {
    "id": "42e37f05"
   },
   "source": [
    "### Check the shape of the dataframe\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "27cc0907",
   "metadata": {
    "id": "27cc0907"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 19)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b68c955d",
   "metadata": {
    "id": "b68c955d"
   },
   "source": [
    "### Check the Detail information of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e092ec9e",
   "metadata": {
    "id": "e092ec9e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50000 entries, 0 to 49999\n",
      "Data columns (total 19 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   business_code           50000 non-null  object \n",
      " 1   cust_number             50000 non-null  object \n",
      " 2   name_customer           50000 non-null  object \n",
      " 3   clear_date              40000 non-null  object \n",
      " 4   buisness_year           50000 non-null  float64\n",
      " 5   doc_id                  50000 non-null  float64\n",
      " 6   posting_date            50000 non-null  object \n",
      " 7   document_create_date    50000 non-null  int64  \n",
      " 8   document_create_date.1  50000 non-null  int64  \n",
      " 9   due_in_date             50000 non-null  float64\n",
      " 10  invoice_currency        50000 non-null  object \n",
      " 11  document type           50000 non-null  object \n",
      " 12  posting_id              50000 non-null  float64\n",
      " 13  area_business           0 non-null      float64\n",
      " 14  total_open_amount       50000 non-null  float64\n",
      " 15  baseline_create_date    50000 non-null  float64\n",
      " 16  cust_payment_terms      50000 non-null  object \n",
      " 17  invoice_id              49994 non-null  float64\n",
      " 18  isOpen                  50000 non-null  int64  \n",
      "dtypes: float64(8), int64(3), object(8)\n",
      "memory usage: 7.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "112f2d0e",
   "metadata": {
    "id": "112f2d0e"
   },
   "source": [
    "### Display All the column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1416e2fd",
   "metadata": {
    "id": "1416e2fd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "business_code\n",
      "cust_number\n",
      "name_customer\n",
      "clear_date\n",
      "buisness_year\n",
      "doc_id\n",
      "posting_date\n",
      "document_create_date\n",
      "document_create_date.1\n",
      "due_in_date\n",
      "invoice_currency\n",
      "document type\n",
      "posting_id\n",
      "area_business\n",
      "total_open_amount\n",
      "baseline_create_date\n",
      "cust_payment_terms\n",
      "invoice_id\n",
      "isOpen\n"
     ]
    }
   ],
   "source": [
    "for col in df.columns:\n",
    "    print(col)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d465ed7a",
   "metadata": {
    "id": "d465ed7a"
   },
   "source": [
    "### Describe the entire dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "25f65e1b",
   "metadata": {
    "id": "25f65e1b"
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>buisness_year</th>\n",
       "      <th>doc_id</th>\n",
       "      <th>document_create_date</th>\n",
       "      <th>document_create_date.1</th>\n",
       "      <th>due_in_date</th>\n",
       "      <th>posting_id</th>\n",
       "      <th>area_business</th>\n",
       "      <th>total_open_amount</th>\n",
       "      <th>baseline_create_date</th>\n",
       "      <th>invoice_id</th>\n",
       "      <th>isOpen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50000.000000</td>\n",
       "      <td>5.000000e+04</td>\n",
       "      <td>5.000000e+04</td>\n",
       "      <td>5.000000e+04</td>\n",
       "      <td>5.000000e+04</td>\n",
       "      <td>50000.0</td>\n",
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       "      <td>4.999400e+04</td>\n",
       "      <td>50000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2019.305700</td>\n",
       "      <td>2.012238e+09</td>\n",
       "      <td>2.019351e+07</td>\n",
       "      <td>2.019354e+07</td>\n",
       "      <td>2.019368e+07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32337.021651</td>\n",
       "      <td>2.019354e+07</td>\n",
       "      <td>2.011340e+09</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.460708</td>\n",
       "      <td>2.885235e+08</td>\n",
       "      <td>4.496041e+03</td>\n",
       "      <td>4.482134e+03</td>\n",
       "      <td>4.470614e+03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39205.975231</td>\n",
       "      <td>4.482701e+03</td>\n",
       "      <td>2.766335e+08</td>\n",
       "      <td>0.400004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>1.928502e+09</td>\n",
       "      <td>2.018123e+07</td>\n",
       "      <td>2.018123e+07</td>\n",
       "      <td>2.018122e+07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>2.018121e+07</td>\n",
       "      <td>1.928502e+09</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>1.929342e+09</td>\n",
       "      <td>2.019050e+07</td>\n",
       "      <td>2.019051e+07</td>\n",
       "      <td>2.019052e+07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4928.312500</td>\n",
       "      <td>2.019050e+07</td>\n",
       "      <td>1.929342e+09</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>1.929964e+09</td>\n",
       "      <td>2.019091e+07</td>\n",
       "      <td>2.019091e+07</td>\n",
       "      <td>2.019093e+07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17609.010000</td>\n",
       "      <td>2.019091e+07</td>\n",
       "      <td>1.929964e+09</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2020.000000</td>\n",
       "      <td>1.930619e+09</td>\n",
       "      <td>2.020013e+07</td>\n",
       "      <td>2.020013e+07</td>\n",
       "      <td>2.020022e+07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47133.635000</td>\n",
       "      <td>2.020013e+07</td>\n",
       "      <td>1.930619e+09</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2020.000000</td>\n",
       "      <td>9.500000e+09</td>\n",
       "      <td>2.020052e+07</td>\n",
       "      <td>2.020052e+07</td>\n",
       "      <td>2.020071e+07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>668593.360000</td>\n",
       "      <td>2.020052e+07</td>\n",
       "      <td>2.960636e+09</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       buisness_year        doc_id  document_create_date  \\\n",
       "count   50000.000000  5.000000e+04          5.000000e+04   \n",
       "mean     2019.305700  2.012238e+09          2.019351e+07   \n",
       "std         0.460708  2.885235e+08          4.496041e+03   \n",
       "min      2019.000000  1.928502e+09          2.018123e+07   \n",
       "25%      2019.000000  1.929342e+09          2.019050e+07   \n",
       "50%      2019.000000  1.929964e+09          2.019091e+07   \n",
       "75%      2020.000000  1.930619e+09          2.020013e+07   \n",
       "max      2020.000000  9.500000e+09          2.020052e+07   \n",
       "\n",
       "       document_create_date.1   due_in_date  posting_id  area_business  \\\n",
       "count            5.000000e+04  5.000000e+04     50000.0            0.0   \n",
       "mean             2.019354e+07  2.019368e+07         1.0            NaN   \n",
       "std              4.482134e+03  4.470614e+03         0.0            NaN   \n",
       "min              2.018123e+07  2.018122e+07         1.0            NaN   \n",
       "25%              2.019051e+07  2.019052e+07         1.0            NaN   \n",
       "50%              2.019091e+07  2.019093e+07         1.0            NaN   \n",
       "75%              2.020013e+07  2.020022e+07         1.0            NaN   \n",
       "max              2.020052e+07  2.020071e+07         1.0            NaN   \n",
       "\n",
       "       total_open_amount  baseline_create_date    invoice_id        isOpen  \n",
       "count       50000.000000          5.000000e+04  4.999400e+04  50000.000000  \n",
       "mean        32337.021651          2.019354e+07  2.011340e+09      0.200000  \n",
       "std         39205.975231          4.482701e+03  2.766335e+08      0.400004  \n",
       "min             0.720000          2.018121e+07  1.928502e+09      0.000000  \n",
       "25%          4928.312500          2.019050e+07  1.929342e+09      0.000000  \n",
       "50%         17609.010000          2.019091e+07  1.929964e+09      0.000000  \n",
       "75%         47133.635000          2.020013e+07  1.930619e+09      0.000000  \n",
       "max        668593.360000          2.020052e+07  2.960636e+09      1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f2c8d02",
   "metadata": {
    "id": "0f2c8d02"
   },
   "source": [
    "# Data Cleaning\n",
    "\n",
    "- Show top 5 records from the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8f876212",
   "metadata": {
    "id": "8f876212"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>business_code</th>\n",
       "      <th>cust_number</th>\n",
       "      <th>name_customer</th>\n",
       "      <th>clear_date</th>\n",
       "      <th>buisness_year</th>\n",
       "      <th>doc_id</th>\n",
       "      <th>posting_date</th>\n",
       "      <th>document_create_date</th>\n",
       "      <th>document_create_date.1</th>\n",
       "      <th>due_in_date</th>\n",
       "      <th>invoice_currency</th>\n",
       "      <th>document type</th>\n",
       "      <th>posting_id</th>\n",
       "      <th>area_business</th>\n",
       "      <th>total_open_amount</th>\n",
       "      <th>baseline_create_date</th>\n",
       "      <th>cust_payment_terms</th>\n",
       "      <th>invoice_id</th>\n",
       "      <th>isOpen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200769623</td>\n",
       "      <td>WAL-MAR corp</td>\n",
       "      <td>2020-02-11 00:00:00</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930438e+09</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>20200125</td>\n",
       "      <td>20200126</td>\n",
       "      <td>20200210.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>RV</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54273.28</td>\n",
       "      <td>20200126.0</td>\n",
       "      <td>NAH4</td>\n",
       "      <td>1.930438e+09</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200980828</td>\n",
       "      <td>BEN E</td>\n",
       "      <td>2019-08-08 00:00:00</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1.929646e+09</td>\n",
       "      <td>2019-07-22</td>\n",
       "      <td>20190722</td>\n",
       "      <td>20190722</td>\n",
       "      <td>20190811.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>RV</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>79656.60</td>\n",
       "      <td>20190722.0</td>\n",
       "      <td>NAD1</td>\n",
       "      <td>1.929646e+09</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200792734</td>\n",
       "      <td>MDV/ trust</td>\n",
       "      <td>2019-12-30 00:00:00</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1.929874e+09</td>\n",
       "      <td>2019-09-14</td>\n",
       "      <td>20190914</td>\n",
       "      <td>20190914</td>\n",
       "      <td>20190929.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>RV</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2253.86</td>\n",
       "      <td>20190914.0</td>\n",
       "      <td>NAA8</td>\n",
       "      <td>1.929874e+09</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CA02</td>\n",
       "      <td>0140105686</td>\n",
       "      <td>SYSC llc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2.960623e+09</td>\n",
       "      <td>2020-03-30</td>\n",
       "      <td>20200330</td>\n",
       "      <td>20200330</td>\n",
       "      <td>20200410.0</td>\n",
       "      <td>CAD</td>\n",
       "      <td>RV</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3299.70</td>\n",
       "      <td>20200331.0</td>\n",
       "      <td>CA10</td>\n",
       "      <td>2.960623e+09</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200769623</td>\n",
       "      <td>WAL-MAR foundation</td>\n",
       "      <td>2019-11-25 00:00:00</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1.930148e+09</td>\n",
       "      <td>2019-11-13</td>\n",
       "      <td>20191113</td>\n",
       "      <td>20191113</td>\n",
       "      <td>20191128.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>RV</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33133.29</td>\n",
       "      <td>20191113.0</td>\n",
       "      <td>NAH4</td>\n",
       "      <td>1.930148e+09</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  business_code cust_number       name_customer           clear_date  \\\n",
       "0          U001  0200769623        WAL-MAR corp  2020-02-11 00:00:00   \n",
       "1          U001  0200980828              BEN E   2019-08-08 00:00:00   \n",
       "2          U001  0200792734          MDV/ trust  2019-12-30 00:00:00   \n",
       "3          CA02  0140105686            SYSC llc                  NaN   \n",
       "4          U001  0200769623  WAL-MAR foundation  2019-11-25 00:00:00   \n",
       "\n",
       "   buisness_year        doc_id posting_date  document_create_date  \\\n",
       "0         2020.0  1.930438e+09   2020-01-26              20200125   \n",
       "1         2019.0  1.929646e+09   2019-07-22              20190722   \n",
       "2         2019.0  1.929874e+09   2019-09-14              20190914   \n",
       "3         2020.0  2.960623e+09   2020-03-30              20200330   \n",
       "4         2019.0  1.930148e+09   2019-11-13              20191113   \n",
       "\n",
       "   document_create_date.1  due_in_date invoice_currency document type  \\\n",
       "0                20200126   20200210.0              USD            RV   \n",
       "1                20190722   20190811.0              USD            RV   \n",
       "2                20190914   20190929.0              USD            RV   \n",
       "3                20200330   20200410.0              CAD            RV   \n",
       "4                20191113   20191128.0              USD            RV   \n",
       "\n",
       "   posting_id  area_business  total_open_amount  baseline_create_date  \\\n",
       "0         1.0            NaN           54273.28            20200126.0   \n",
       "1         1.0            NaN           79656.60            20190722.0   \n",
       "2         1.0            NaN            2253.86            20190914.0   \n",
       "3         1.0            NaN            3299.70            20200331.0   \n",
       "4         1.0            NaN           33133.29            20191113.0   \n",
       "\n",
       "  cust_payment_terms    invoice_id  isOpen  \n",
       "0               NAH4  1.930438e+09       0  \n",
       "1               NAD1  1.929646e+09       0  \n",
       "2               NAA8  1.929874e+09       0  \n",
       "3               CA10  2.960623e+09       1  \n",
       "4               NAH4  1.930148e+09       0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df.head(n=5)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92b044e4",
   "metadata": {
    "id": "92b044e4"
   },
   "source": [
    "### Display the Null values percentage against every columns (compare to the total number of records)\n",
    "\n",
    "- Output expected : area_business - 100% null, clear_data = 20% null, invoice_id = 0.12% null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "24c7b13d",
   "metadata": {
    "id": "24c7b13d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "business_code               0.000\n",
       "cust_number                 0.000\n",
       "name_customer               0.000\n",
       "clear_date                 20.000\n",
       "buisness_year               0.000\n",
       "doc_id                      0.000\n",
       "posting_date                0.000\n",
       "document_create_date        0.000\n",
       "document_create_date.1      0.000\n",
       "due_in_date                 0.000\n",
       "invoice_currency            0.000\n",
       "document type               0.000\n",
       "posting_id                  0.000\n",
       "area_business             100.000\n",
       "total_open_amount           0.000\n",
       "baseline_create_date        0.000\n",
       "cust_payment_terms          0.000\n",
       "invoice_id                  0.012\n",
       "isOpen                      0.000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().mean()*100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c46a98b",
   "metadata": {
    "id": "2c46a98b"
   },
   "source": [
    "### Display Invoice_id and Doc_Id\n",
    "\n",
    "- Note - Many of the would have same invoice_id and doc_id\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "038f24bb",
   "metadata": {
    "id": "038f24bb"
   },
   "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>invoice_id</th>\n",
       "      <th>doc_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.930438e+09</td>\n",
       "      <td>1.930438e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.929646e+09</td>\n",
       "      <td>1.929646e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.929874e+09</td>\n",
       "      <td>1.929874e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.960623e+09</td>\n",
       "      <td>2.960623e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.930148e+09</td>\n",
       "      <td>1.930148e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49995</th>\n",
       "      <td>1.930797e+09</td>\n",
       "      <td>1.930797e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>1.929744e+09</td>\n",
       "      <td>1.929744e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>1.930537e+09</td>\n",
       "      <td>1.930537e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>1.930199e+09</td>\n",
       "      <td>1.930199e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>1.928576e+09</td>\n",
       "      <td>1.928576e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         invoice_id        doc_id\n",
       "0      1.930438e+09  1.930438e+09\n",
       "1      1.929646e+09  1.929646e+09\n",
       "2      1.929874e+09  1.929874e+09\n",
       "3      2.960623e+09  2.960623e+09\n",
       "4      1.930148e+09  1.930148e+09\n",
       "...             ...           ...\n",
       "49995  1.930797e+09  1.930797e+09\n",
       "49996  1.929744e+09  1.929744e+09\n",
       "49997  1.930537e+09  1.930537e+09\n",
       "49998  1.930199e+09  1.930199e+09\n",
       "49999  1.928576e+09  1.928576e+09\n",
       "\n",
       "[50000 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['invoice_id', 'doc_id']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18cfe10a",
   "metadata": {
    "id": "18cfe10a"
   },
   "source": [
    "#### Write a code to check - 'baseline_create_date',\"document_create_date\",'document_create_date.1' - these columns are almost same.\n",
    "\n",
    "- Please note, if they are same, we need to drop them later\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cf5b40ff",
   "metadata": {
    "id": "cf5b40ff"
   },
   "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>baseline_create_date</th>\n",
       "      <th>document_create_date</th>\n",
       "      <th>document_create_date.1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20200126.0</td>\n",
       "      <td>20200125</td>\n",
       "      <td>20200126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20190722.0</td>\n",
       "      <td>20190722</td>\n",
       "      <td>20190722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20190914.0</td>\n",
       "      <td>20190914</td>\n",
       "      <td>20190914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20200331.0</td>\n",
       "      <td>20200330</td>\n",
       "      <td>20200330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20191113.0</td>\n",
       "      <td>20191113</td>\n",
       "      <td>20191113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49995</th>\n",
       "      <td>20200421.0</td>\n",
       "      <td>20200417</td>\n",
       "      <td>20200421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>20190815.0</td>\n",
       "      <td>20190814</td>\n",
       "      <td>20190815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>20200219.0</td>\n",
       "      <td>20200218</td>\n",
       "      <td>20200219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>20191127.0</td>\n",
       "      <td>20191126</td>\n",
       "      <td>20191127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>20190101.0</td>\n",
       "      <td>20190105</td>\n",
       "      <td>20190105</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       baseline_create_date  document_create_date  document_create_date.1\n",
       "0                20200126.0              20200125                20200126\n",
       "1                20190722.0              20190722                20190722\n",
       "2                20190914.0              20190914                20190914\n",
       "3                20200331.0              20200330                20200330\n",
       "4                20191113.0              20191113                20191113\n",
       "...                     ...                   ...                     ...\n",
       "49995            20200421.0              20200417                20200421\n",
       "49996            20190815.0              20190814                20190815\n",
       "49997            20200219.0              20200218                20200219\n",
       "49998            20191127.0              20191126                20191127\n",
       "49999            20190101.0              20190105                20190105\n",
       "\n",
       "[50000 rows x 3 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['baseline_create_date',\"document_create_date\",'document_create_date.1']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33110576",
   "metadata": {
    "id": "33110576"
   },
   "source": [
    "#### Please check, Column 'posting_id' is constant columns or not\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ecce2664",
   "metadata": {
    "id": "ecce2664"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Yes posting_id is constant columns \n"
     ]
    }
   ],
   "source": [
    "x = df.columns[df.nunique() <= 1]\n",
    "i =0\n",
    "y = len(x)\n",
    "for i in (0,y-1):\n",
    "    if x[i] == 'posting_id':\n",
    "        print('Yes posting_id is constant columns ')\n",
    "        break;\n",
    "    else:\n",
    "        print('No posting_id is not a constant columns')\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5fb8daf",
   "metadata": {
    "id": "e5fb8daf"
   },
   "source": [
    "#### Please check 'isOpen' is a constant column and relevant column for this project or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8db9956b",
   "metadata": {
    "id": "8db9956b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No isOpen is not a constant columns\n"
     ]
    }
   ],
   "source": [
    "x = df.columns[df.nunique() <= 1]\n",
    "i =0\n",
    "y = len(x)\n",
    "for i in (0,y-1):\n",
    "    if x[i] == 'isOpen':\n",
    "        print('Yes isOpen is constant columns ')\n",
    "        break;\n",
    "    else:\n",
    "        print('No isOpen is not a constant columns')\n",
    "        break;\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45a11a62",
   "metadata": {
    "id": "45a11a62"
   },
   "source": [
    "### Write the code to drop all the following columns from the dataframe\n",
    "\n",
    "- 'area_business'\n",
    "- \"posting_id\"\n",
    "- \"invoice_id\"\n",
    "- \"document_create_date\"\n",
    "- \"isOpen\"\n",
    "- 'document type' \n",
    "- 'document_create_date.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "97c1a83b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['business_code', 'cust_number', 'name_customer', 'clear_date',\n",
      "       'buisness_year', 'doc_id', 'posting_date', 'document_create_date',\n",
      "       'document_create_date.1', 'due_in_date', 'invoice_currency',\n",
      "       'document type', 'posting_id', 'area_business', 'total_open_amount',\n",
      "       'baseline_create_date', 'cust_payment_terms', 'invoice_id', 'isOpen'],\n",
      "      dtype='object')\n",
      "19\n"
     ]
    }
   ],
   "source": [
    "print(df.columns)\n",
    "print(df.columns.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "270d85d1",
   "metadata": {
    "id": "270d85d1"
   },
   "outputs": [],
   "source": [
    "df.drop(columns=['area_business',\n",
    "\"posting_id\",\n",
    "\"invoice_id\",\n",
    "\"document_create_date\",\n",
    "\"isOpen\",\n",
    "'document type',\n",
    "'document_create_date.1'],inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "K5LHAM2XVGnk",
   "metadata": {
    "id": "K5LHAM2XVGnk"
   },
   "source": [
    "### Please check from the dataframe whether all the columns are removed or not "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ef3f7d2b",
   "metadata": {
    "id": "ef3f7d2b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['business_code', 'cust_number', 'name_customer', 'clear_date',\n",
      "       'buisness_year', 'doc_id', 'posting_date', 'due_in_date',\n",
      "       'invoice_currency', 'total_open_amount', 'baseline_create_date',\n",
      "       'cust_payment_terms'],\n",
      "      dtype='object')\n",
      "12\n"
     ]
    }
   ],
   "source": [
    "print(df.columns)\n",
    "print(df.columns.size)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bc052c7",
   "metadata": {
    "id": "6bc052c7"
   },
   "source": [
    "### Show all the Dublicate rows from the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1ae3c7e4",
   "metadata": {
    "id": "1ae3c7e4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        False\n",
       "1        False\n",
       "2        False\n",
       "3        False\n",
       "4        False\n",
       "         ...  \n",
       "49995    False\n",
       "49996    False\n",
       "49997    False\n",
       "49998    False\n",
       "49999    False\n",
       "Length: 50000, dtype: bool"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "464fab09",
   "metadata": {
    "id": "464fab09"
   },
   "source": [
    "### Display the Number of Dublicate Rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b1ea2397",
   "metadata": {
    "id": "b1ea2397"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1161"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "827a6718",
   "metadata": {
    "id": "827a6718"
   },
   "source": [
    "### Drop all the Dublicate Rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5d10151c",
   "metadata": {
    "id": "5d10151c"
   },
   "outputs": [],
   "source": [
    "df2 = df[~df.duplicated()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e5d1f9b",
   "metadata": {
    "id": "7e5d1f9b"
   },
   "source": [
    "#### Now check for all dublicate rows now\n",
    "\n",
    "- Note - It must be 0 by now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9accc9fc",
   "metadata": {
    "id": "9accc9fc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df2.duplicated()\n",
    "df2.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0704898",
   "metadata": {
    "id": "d0704898"
   },
   "source": [
    "### Check for the number of Rows and Columns in your dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "582748a8",
   "metadata": {
    "id": "582748a8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48839, 12)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4o9c5UodWRtl",
   "metadata": {
    "id": "4o9c5UodWRtl"
   },
   "source": [
    "### Find out the total count of null values in each columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b0612cb5",
   "metadata": {
    "id": "b0612cb5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "business_code              0\n",
       "cust_number                0\n",
       "name_customer              0\n",
       "clear_date              9681\n",
       "buisness_year              0\n",
       "doc_id                     0\n",
       "posting_date               0\n",
       "due_in_date                0\n",
       "invoice_currency           0\n",
       "total_open_amount          0\n",
       "baseline_create_date       0\n",
       "cust_payment_terms         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7abdb98b",
   "metadata": {
    "id": "7abdb98b"
   },
   "source": [
    "#Data type Conversion "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "LPfSUSp-WpPj",
   "metadata": {
    "id": "LPfSUSp-WpPj"
   },
   "source": [
    "### Please check the data type of each column of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "689c8592",
   "metadata": {
    "id": "689c8592"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "business_code            object\n",
       "cust_number              object\n",
       "name_customer            object\n",
       "clear_date               object\n",
       "buisness_year           float64\n",
       "doc_id                  float64\n",
       "posting_date             object\n",
       "due_in_date             float64\n",
       "invoice_currency         object\n",
       "total_open_amount       float64\n",
       "baseline_create_date    float64\n",
       "cust_payment_terms       object\n",
       "dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0nsem0_3XzOt",
   "metadata": {
    "id": "0nsem0_3XzOt"
   },
   "source": [
    "### Check the datatype format of below columns\n",
    "\n",
    "- clear_date  \n",
    "- posting_date\n",
    "- due_in_date \n",
    "- baseline_create_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "-yyODyW3X6pL",
   "metadata": {
    "id": "-yyODyW3X6pL"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "object\n",
      "object\n",
      "float64\n",
      "float64\n"
     ]
    }
   ],
   "source": [
    "print(df2.clear_date.dtypes)\n",
    "print(df2.posting_date.dtypes)\n",
    "print(df2.due_in_date.dtypes)\n",
    "print(df2.baseline_create_date.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11cf9478",
   "metadata": {
    "id": "11cf9478"
   },
   "source": [
    "### converting date columns into date time formats\n",
    "\n",
    "- clear_date  \n",
    "- posting_date\n",
    "- due_in_date \n",
    "- baseline_create_date\n",
    "\n",
    "\n",
    "- **Note - You have to convert all these above columns into \"%Y%m%d\" format**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b0dbc9d1",
   "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>clear_date</th>\n",
       "      <th>posting_date</th>\n",
       "      <th>due_in_date</th>\n",
       "      <th>baseline_create_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-11 00:00:00</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>20200210.0</td>\n",
       "      <td>20200126.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-08-08 00:00:00</td>\n",
       "      <td>2019-07-22</td>\n",
       "      <td>20190811.0</td>\n",
       "      <td>20190722.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-12-30 00:00:00</td>\n",
       "      <td>2019-09-14</td>\n",
       "      <td>20190929.0</td>\n",
       "      <td>20190914.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-03-30</td>\n",
       "      <td>20200410.0</td>\n",
       "      <td>20200331.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-11-25 00:00:00</td>\n",
       "      <td>2019-11-13</td>\n",
       "      <td>20191128.0</td>\n",
       "      <td>20191113.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49995</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-04-21</td>\n",
       "      <td>20200506.0</td>\n",
       "      <td>20200421.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>2019-09-03 00:00:00</td>\n",
       "      <td>2019-08-15</td>\n",
       "      <td>20190830.0</td>\n",
       "      <td>20190815.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>2020-03-05 00:00:00</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>20200305.0</td>\n",
       "      <td>20200219.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>2019-12-12 00:00:00</td>\n",
       "      <td>2019-11-27</td>\n",
       "      <td>20191212.0</td>\n",
       "      <td>20191127.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>2019-01-15 00:00:00</td>\n",
       "      <td>2019-01-05</td>\n",
       "      <td>20190124.0</td>\n",
       "      <td>20190101.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48839 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                clear_date posting_date  due_in_date  baseline_create_date\n",
       "0      2020-02-11 00:00:00   2020-01-26   20200210.0            20200126.0\n",
       "1      2019-08-08 00:00:00   2019-07-22   20190811.0            20190722.0\n",
       "2      2019-12-30 00:00:00   2019-09-14   20190929.0            20190914.0\n",
       "3                      NaN   2020-03-30   20200410.0            20200331.0\n",
       "4      2019-11-25 00:00:00   2019-11-13   20191128.0            20191113.0\n",
       "...                    ...          ...          ...                   ...\n",
       "49995                  NaN   2020-04-21   20200506.0            20200421.0\n",
       "49996  2019-09-03 00:00:00   2019-08-15   20190830.0            20190815.0\n",
       "49997  2020-03-05 00:00:00   2020-02-19   20200305.0            20200219.0\n",
       "49998  2019-12-12 00:00:00   2019-11-27   20191212.0            20191127.0\n",
       "49999  2019-01-15 00:00:00   2019-01-05   20190124.0            20190101.0\n",
       "\n",
       "[48839 rows x 4 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[['clear_date', 'posting_date', 'due_in_date', 'baseline_create_date']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a5c62539",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.mode.chained_assignment=None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "55a41769",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2020-02-11\n",
       "Name: clear_date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['clear_date']=pd.to_datetime(df2['clear_date'],format='%Y-%m-%d %H:%M:%S')\n",
    "df2['clear_date'].head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "24bba825",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2020-01-26\n",
       "Name: posting_date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['posting_date']=pd.to_datetime(df2['posting_date'],format='%Y-%m-%d')\n",
    "df2['posting_date'].head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4eaaca68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2020-02-10\n",
       "Name: due_in_date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['due_in_date']=pd.to_datetime(df2['due_in_date'],format='%Y%m%d')\n",
    "df2['due_in_date'].head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a99e39d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2020-01-26\n",
       "Name: baseline_create_date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['baseline_create_date']=pd.to_datetime(df2['baseline_create_date'],format='%Y%m%d')\n",
    "df2['baseline_create_date'].head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7adq0wSIYSCS",
   "metadata": {
    "id": "7adq0wSIYSCS"
   },
   "source": [
    "### Please check the datatype of all the columns after conversion of the above 4 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f4a09b76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "datetime64[ns]\n",
      "datetime64[ns]\n",
      "datetime64[ns]\n",
      "datetime64[ns]\n"
     ]
    }
   ],
   "source": [
    "print(df2.clear_date.dtypes)\n",
    "print(df2.posting_date.dtypes)\n",
    "print(df2.due_in_date.dtypes)\n",
    "print(df2.baseline_create_date.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c9882fa",
   "metadata": {
    "id": "8c9882fa"
   },
   "source": [
    "#### the invoice_currency column contains two different categories, USD and CAD\n",
    "\n",
    "- Please do a count of each currency "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "72085397",
   "metadata": {
    "id": "72085397"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "USD    45011\n",
       "CAD     3828\n",
       "Name: invoice_currency, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[\"invoice_currency\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cbe26ee",
   "metadata": {
    "id": "6cbe26ee"
   },
   "source": [
    "#### display the \"total_open_amount\" column value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6c49f2ab",
   "metadata": {
    "id": "6c49f2ab"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        54273.28\n",
       "1        79656.60\n",
       "2         2253.86\n",
       "3         3299.70\n",
       "4        33133.29\n",
       "           ...   \n",
       "49995     3187.86\n",
       "49996     6766.54\n",
       "49997     6120.86\n",
       "49998       63.48\n",
       "49999     1790.30\n",
       "Name: total_open_amount, Length: 48839, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.total_open_amount"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df899966",
   "metadata": {
    "id": "df899966"
   },
   "source": [
    "### Convert all CAD into USD currency of \"total_open_amount\" column\n",
    "\n",
    "- 1 CAD = 0.7 USD\n",
    "- Create a new column i.e \"converted_usd\" and store USD and convered CAD to USD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8eb2f1c5",
   "metadata": {
    "id": "8eb2f1c5"
   },
   "outputs": [],
   "source": [
    "currencies = df2.invoice_currency.unique().tolist()\n",
    "currencies = dict.fromkeys(currencies, pd.NA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "855e6422",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "currencies['CAD'] = 0.7\n",
    "currencies['USD'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "daaf449d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['converted_usd'] = df2.apply(lambda x: x.total_open_amount * currencies[x.invoice_currency], axis=1 )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9f6ef1d",
   "metadata": {
    "id": "f9f6ef1d"
   },
   "source": [
    "### Display the new \"converted_usd\" column values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1fc1a178",
   "metadata": {
    "id": "1fc1a178"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0        54273.28\n",
      "1        79656.60\n",
      "2         2253.86\n",
      "3         2309.79\n",
      "4        33133.29\n",
      "           ...   \n",
      "49995     3187.86\n",
      "49996     6766.54\n",
      "49997     6120.86\n",
      "49998       63.48\n",
      "49999     1790.30\n",
      "Name: converted_usd, Length: 48839, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(df2.converted_usd)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6XLXX17kayuy",
   "metadata": {
    "id": "6XLXX17kayuy"
   },
   "source": [
    "### Display year wise total number of record \n",
    "\n",
    "- Note -  use \"buisness_year\" column for this "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "00c9f6ee",
   "metadata": {
    "id": "00c9f6ee"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "buisness_year\n",
       "2019.0    33975\n",
       "2020.0    14864\n",
       "Name: buisness_year, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[\"buisness_year\"].groupby(df2.buisness_year).agg('count')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05c35904",
   "metadata": {
    "id": "05c35904"
   },
   "source": [
    "### Write the code to delete the following columns \n",
    "\n",
    "- 'invoice_currency'\n",
    "- 'total_open_amount', "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4ac28aa5",
   "metadata": {
    "id": "4ac28aa5"
   },
   "outputs": [],
   "source": [
    "df2.drop(columns=['invoice_currency','total_open_amount'],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bDBJ_Kvwc086",
   "metadata": {
    "id": "bDBJ_Kvwc086"
   },
   "source": [
    "### Write a code to check the number of columns in dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ea360a8c",
   "metadata": {
    "id": "ea360a8c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of columns:  11\n"
     ]
    }
   ],
   "source": [
    "col = df2.shape\n",
    "print(\"Number of columns: \",col[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8f63655",
   "metadata": {
    "id": "b8f63655"
   },
   "source": [
    "# Splitting the Dataset "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a00f749d",
   "metadata": {
    "id": "a00f749d"
   },
   "source": [
    "### Look for all columns containing null value\n",
    "\n",
    "- Note - Output expected is only one column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "148c801e",
   "metadata": {
    "id": "148c801e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['clear_date'], dtype='object')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.columns[df2.isnull().any()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a094a290",
   "metadata": {
    "id": "a094a290"
   },
   "source": [
    "#### Find out the number of null values from the column that you got from the above code"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f6d939b",
   "metadata": {
    "id": "7f6d939b"
   },
   "source": [
    "### On basis of the above column we are spliting data into dataset\n",
    "\n",
    "- First dataframe (refer that as maindata) only containing the rows, that have Not NULL data in that column ( This is going to be our train dataset ) \n",
    "- Second dataframe (refer that as nulldata) that contains the columns, that have Null data in that column ( This is going to be our test dataset ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c8764c33",
   "metadata": {
    "id": "c8764c33"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>business_code</th>\n",
       "      <th>cust_number</th>\n",
       "      <th>name_customer</th>\n",
       "      <th>clear_date</th>\n",
       "      <th>buisness_year</th>\n",
       "      <th>doc_id</th>\n",
       "      <th>posting_date</th>\n",
       "      <th>due_in_date</th>\n",
       "      <th>baseline_create_date</th>\n",
       "      <th>cust_payment_terms</th>\n",
       "      <th>converted_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200769623</td>\n",
       "      <td>WAL-MAR corp</td>\n",
       "      <td>2020-02-11</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930438e+09</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>2020-02-10</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>NAH4</td>\n",
       "      <td>54273.280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200980828</td>\n",
       "      <td>BEN E</td>\n",
       "      <td>2019-08-08</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1.929646e+09</td>\n",
       "      <td>2019-07-22</td>\n",
       "      <td>2019-08-11</td>\n",
       "      <td>2019-07-22</td>\n",
       "      <td>NAD1</td>\n",
       "      <td>79656.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200792734</td>\n",
       "      <td>MDV/ trust</td>\n",
       "      <td>2019-12-30</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1.929874e+09</td>\n",
       "      <td>2019-09-14</td>\n",
       "      <td>2019-09-29</td>\n",
       "      <td>2019-09-14</td>\n",
       "      <td>NAA8</td>\n",
       "      <td>2253.860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200769623</td>\n",
       "      <td>WAL-MAR foundation</td>\n",
       "      <td>2019-11-25</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1.930148e+09</td>\n",
       "      <td>2019-11-13</td>\n",
       "      <td>2019-11-28</td>\n",
       "      <td>2019-11-13</td>\n",
       "      <td>NAH4</td>\n",
       "      <td>33133.290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA02</td>\n",
       "      <td>0140106181</td>\n",
       "      <td>THE  corporation</td>\n",
       "      <td>2019-12-04</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2.960581e+09</td>\n",
       "      <td>2019-09-20</td>\n",
       "      <td>2019-10-04</td>\n",
       "      <td>2019-09-24</td>\n",
       "      <td>CA10</td>\n",
       "      <td>15558.088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39153</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200762301</td>\n",
       "      <td>C&amp;S WH trust</td>\n",
       "      <td>2019-07-25</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1.929601e+09</td>\n",
       "      <td>2019-07-10</td>\n",
       "      <td>2019-07-25</td>\n",
       "      <td>2019-07-10</td>\n",
       "      <td>NAC6</td>\n",
       "      <td>84780.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39154</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200769623</td>\n",
       "      <td>WAL-MAR co</td>\n",
       "      <td>2019-09-03</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1.929744e+09</td>\n",
       "      <td>2019-08-15</td>\n",
       "      <td>2019-08-30</td>\n",
       "      <td>2019-08-15</td>\n",
       "      <td>NAH4</td>\n",
       "      <td>6766.540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39155</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200772595</td>\n",
       "      <td>SAFEW associates</td>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930537e+09</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>NAA8</td>\n",
       "      <td>6120.860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39156</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200726979</td>\n",
       "      <td>BJ'S  llc</td>\n",
       "      <td>2019-12-12</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1.930199e+09</td>\n",
       "      <td>2019-11-27</td>\n",
       "      <td>2019-12-12</td>\n",
       "      <td>2019-11-27</td>\n",
       "      <td>NAA8</td>\n",
       "      <td>63.480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39157</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200020431</td>\n",
       "      <td>DEC corp</td>\n",
       "      <td>2019-01-15</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1.928576e+09</td>\n",
       "      <td>2019-01-05</td>\n",
       "      <td>2019-01-24</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>NAM4</td>\n",
       "      <td>1790.300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>39158 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      business_code cust_number       name_customer clear_date  buisness_year  \\\n",
       "0              U001  0200769623        WAL-MAR corp 2020-02-11         2020.0   \n",
       "1              U001  0200980828              BEN E  2019-08-08         2019.0   \n",
       "2              U001  0200792734          MDV/ trust 2019-12-30         2019.0   \n",
       "3              U001  0200769623  WAL-MAR foundation 2019-11-25         2019.0   \n",
       "4              CA02  0140106181    THE  corporation 2019-12-04         2019.0   \n",
       "...             ...         ...                 ...        ...            ...   \n",
       "39153          U001  0200762301        C&S WH trust 2019-07-25         2019.0   \n",
       "39154          U001  0200769623          WAL-MAR co 2019-09-03         2019.0   \n",
       "39155          U001  0200772595    SAFEW associates 2020-03-05         2020.0   \n",
       "39156          U001  0200726979           BJ'S  llc 2019-12-12         2019.0   \n",
       "39157          U001  0200020431            DEC corp 2019-01-15         2019.0   \n",
       "\n",
       "             doc_id posting_date due_in_date baseline_create_date  \\\n",
       "0      1.930438e+09   2020-01-26  2020-02-10           2020-01-26   \n",
       "1      1.929646e+09   2019-07-22  2019-08-11           2019-07-22   \n",
       "2      1.929874e+09   2019-09-14  2019-09-29           2019-09-14   \n",
       "3      1.930148e+09   2019-11-13  2019-11-28           2019-11-13   \n",
       "4      2.960581e+09   2019-09-20  2019-10-04           2019-09-24   \n",
       "...             ...          ...         ...                  ...   \n",
       "39153  1.929601e+09   2019-07-10  2019-07-25           2019-07-10   \n",
       "39154  1.929744e+09   2019-08-15  2019-08-30           2019-08-15   \n",
       "39155  1.930537e+09   2020-02-19  2020-03-05           2020-02-19   \n",
       "39156  1.930199e+09   2019-11-27  2019-12-12           2019-11-27   \n",
       "39157  1.928576e+09   2019-01-05  2019-01-24           2019-01-01   \n",
       "\n",
       "      cust_payment_terms  converted_usd  \n",
       "0                   NAH4      54273.280  \n",
       "1                   NAD1      79656.600  \n",
       "2                   NAA8       2253.860  \n",
       "3                   NAH4      33133.290  \n",
       "4                   CA10      15558.088  \n",
       "...                  ...            ...  \n",
       "39153               NAC6      84780.400  \n",
       "39154               NAH4       6766.540  \n",
       "39155               NAA8       6120.860  \n",
       "39156               NAA8         63.480  \n",
       "39157               NAM4       1790.300  \n",
       "\n",
       "[39158 rows x 11 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maindata = df2[df2.clear_date.notnull()].reset_index() #train set on non-null clear date\n",
    "maindata.drop(columns=['index'],inplace=True)\n",
    "maindata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "745321bc",
   "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>business_code</th>\n",
       "      <th>cust_number</th>\n",
       "      <th>name_customer</th>\n",
       "      <th>clear_date</th>\n",
       "      <th>buisness_year</th>\n",
       "      <th>doc_id</th>\n",
       "      <th>posting_date</th>\n",
       "      <th>due_in_date</th>\n",
       "      <th>baseline_create_date</th>\n",
       "      <th>cust_payment_terms</th>\n",
       "      <th>converted_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA02</td>\n",
       "      <td>0140105686</td>\n",
       "      <td>SYSC llc</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2.960623e+09</td>\n",
       "      <td>2020-03-30</td>\n",
       "      <td>2020-04-10</td>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>CA10</td>\n",
       "      <td>2309.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200744019</td>\n",
       "      <td>TARG us</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930659e+09</td>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>2020-04-03</td>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>NAA8</td>\n",
       "      <td>11173.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200418007</td>\n",
       "      <td>AM</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930611e+09</td>\n",
       "      <td>2020-03-11</td>\n",
       "      <td>2020-03-26</td>\n",
       "      <td>2020-03-11</td>\n",
       "      <td>NAA8</td>\n",
       "      <td>3525.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200739534</td>\n",
       "      <td>OK systems</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930788e+09</td>\n",
       "      <td>2020-04-15</td>\n",
       "      <td>2020-04-30</td>\n",
       "      <td>2020-04-15</td>\n",
       "      <td>NAA8</td>\n",
       "      <td>121105.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200353024</td>\n",
       "      <td>DECA corporation</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930817e+09</td>\n",
       "      <td>2020-04-23</td>\n",
       "      <td>2020-04-26</td>\n",
       "      <td>2020-04-16</td>\n",
       "      <td>NAM2</td>\n",
       "      <td>3726.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9676</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200769623</td>\n",
       "      <td>WAL-MAR in</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930625e+09</td>\n",
       "      <td>2020-03-10</td>\n",
       "      <td>2020-03-25</td>\n",
       "      <td>2020-03-10</td>\n",
       "      <td>NAH4</td>\n",
       "      <td>13114.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9677</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200769623</td>\n",
       "      <td>WAL-MAR corporation</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930851e+09</td>\n",
       "      <td>2020-05-03</td>\n",
       "      <td>2020-05-18</td>\n",
       "      <td>2020-05-03</td>\n",
       "      <td>NAH4</td>\n",
       "      <td>8899.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9678</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200148860</td>\n",
       "      <td>DOLLA co</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930638e+09</td>\n",
       "      <td>2020-03-11</td>\n",
       "      <td>2020-03-26</td>\n",
       "      <td>2020-03-11</td>\n",
       "      <td>NAA8</td>\n",
       "      <td>4967.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9679</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200900909</td>\n",
       "      <td>SYSCO co</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930702e+09</td>\n",
       "      <td>2020-03-25</td>\n",
       "      <td>2020-04-09</td>\n",
       "      <td>2020-03-25</td>\n",
       "      <td>NAA8</td>\n",
       "      <td>1998.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9680</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200561861</td>\n",
       "      <td>CO corporation</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930797e+09</td>\n",
       "      <td>2020-04-21</td>\n",
       "      <td>2020-05-06</td>\n",
       "      <td>2020-04-21</td>\n",
       "      <td>NAA8</td>\n",
       "      <td>3187.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9681 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     business_code cust_number        name_customer clear_date  buisness_year  \\\n",
       "0             CA02  0140105686             SYSC llc        NaT         2020.0   \n",
       "1             U001  0200744019              TARG us        NaT         2020.0   \n",
       "2             U001  0200418007                  AM         NaT         2020.0   \n",
       "3             U001  0200739534           OK systems        NaT         2020.0   \n",
       "4             U001  0200353024     DECA corporation        NaT         2020.0   \n",
       "...            ...         ...                  ...        ...            ...   \n",
       "9676          U001  0200769623           WAL-MAR in        NaT         2020.0   \n",
       "9677          U001  0200769623  WAL-MAR corporation        NaT         2020.0   \n",
       "9678          U001  0200148860             DOLLA co        NaT         2020.0   \n",
       "9679          U001  0200900909             SYSCO co        NaT         2020.0   \n",
       "9680          U001  0200561861       CO corporation        NaT         2020.0   \n",
       "\n",
       "            doc_id posting_date due_in_date baseline_create_date  \\\n",
       "0     2.960623e+09   2020-03-30  2020-04-10           2020-03-31   \n",
       "1     1.930659e+09   2020-03-19  2020-04-03           2020-03-19   \n",
       "2     1.930611e+09   2020-03-11  2020-03-26           2020-03-11   \n",
       "3     1.930788e+09   2020-04-15  2020-04-30           2020-04-15   \n",
       "4     1.930817e+09   2020-04-23  2020-04-26           2020-04-16   \n",
       "...            ...          ...         ...                  ...   \n",
       "9676  1.930625e+09   2020-03-10  2020-03-25           2020-03-10   \n",
       "9677  1.930851e+09   2020-05-03  2020-05-18           2020-05-03   \n",
       "9678  1.930638e+09   2020-03-11  2020-03-26           2020-03-11   \n",
       "9679  1.930702e+09   2020-03-25  2020-04-09           2020-03-25   \n",
       "9680  1.930797e+09   2020-04-21  2020-05-06           2020-04-21   \n",
       "\n",
       "     cust_payment_terms  converted_usd  \n",
       "0                  CA10        2309.79  \n",
       "1                  NAA8       11173.02  \n",
       "2                  NAA8        3525.59  \n",
       "3                  NAA8      121105.65  \n",
       "4                  NAM2        3726.06  \n",
       "...                 ...            ...  \n",
       "9676               NAH4       13114.99  \n",
       "9677               NAH4        8899.71  \n",
       "9678               NAA8        4967.06  \n",
       "9679               NAA8        1998.64  \n",
       "9680               NAA8        3187.86  \n",
       "\n",
       "[9681 rows x 11 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nulldata = df2[df2.clear_date.isnull()].reset_index() #test dataset on null clear date\n",
    "nulldata.drop(columns=['index'],inplace=True)\n",
    "nulldata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3P8riRBHd_r6",
   "metadata": {
    "id": "3P8riRBHd_r6"
   },
   "source": [
    "### Check the number of Rows and Columns for both the dataframes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "22037ea3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39158, 11)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maindata.shape #train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "0693a464",
   "metadata": {
    "id": "0693a464"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9681, 11)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nulldata.shape #test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0747165d",
   "metadata": {
    "id": "0747165d"
   },
   "source": [
    "### Display the 5 records from maindata and nulldata dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "dec2ec36",
   "metadata": {
    "id": "dec2ec36"
   },
   "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>business_code</th>\n",
       "      <th>cust_number</th>\n",
       "      <th>name_customer</th>\n",
       "      <th>clear_date</th>\n",
       "      <th>buisness_year</th>\n",
       "      <th>doc_id</th>\n",
       "      <th>posting_date</th>\n",
       "      <th>due_in_date</th>\n",
       "      <th>baseline_create_date</th>\n",
       "      <th>cust_payment_terms</th>\n",
       "      <th>converted_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200769623</td>\n",
       "      <td>WAL-MAR corp</td>\n",
       "      <td>2020-02-11</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930438e+09</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>2020-02-10</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>NAH4</td>\n",
       "      <td>54273.280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200980828</td>\n",
       "      <td>BEN E</td>\n",
       "      <td>2019-08-08</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1.929646e+09</td>\n",
       "      <td>2019-07-22</td>\n",
       "      <td>2019-08-11</td>\n",
       "      <td>2019-07-22</td>\n",
       "      <td>NAD1</td>\n",
       "      <td>79656.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200792734</td>\n",
       "      <td>MDV/ trust</td>\n",
       "      <td>2019-12-30</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1.929874e+09</td>\n",
       "      <td>2019-09-14</td>\n",
       "      <td>2019-09-29</td>\n",
       "      <td>2019-09-14</td>\n",
       "      <td>NAA8</td>\n",
       "      <td>2253.860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200769623</td>\n",
       "      <td>WAL-MAR foundation</td>\n",
       "      <td>2019-11-25</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1.930148e+09</td>\n",
       "      <td>2019-11-13</td>\n",
       "      <td>2019-11-28</td>\n",
       "      <td>2019-11-13</td>\n",
       "      <td>NAH4</td>\n",
       "      <td>33133.290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA02</td>\n",
       "      <td>0140106181</td>\n",
       "      <td>THE  corporation</td>\n",
       "      <td>2019-12-04</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2.960581e+09</td>\n",
       "      <td>2019-09-20</td>\n",
       "      <td>2019-10-04</td>\n",
       "      <td>2019-09-24</td>\n",
       "      <td>CA10</td>\n",
       "      <td>15558.088</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  business_code cust_number       name_customer clear_date  buisness_year  \\\n",
       "0          U001  0200769623        WAL-MAR corp 2020-02-11         2020.0   \n",
       "1          U001  0200980828              BEN E  2019-08-08         2019.0   \n",
       "2          U001  0200792734          MDV/ trust 2019-12-30         2019.0   \n",
       "3          U001  0200769623  WAL-MAR foundation 2019-11-25         2019.0   \n",
       "4          CA02  0140106181    THE  corporation 2019-12-04         2019.0   \n",
       "\n",
       "         doc_id posting_date due_in_date baseline_create_date  \\\n",
       "0  1.930438e+09   2020-01-26  2020-02-10           2020-01-26   \n",
       "1  1.929646e+09   2019-07-22  2019-08-11           2019-07-22   \n",
       "2  1.929874e+09   2019-09-14  2019-09-29           2019-09-14   \n",
       "3  1.930148e+09   2019-11-13  2019-11-28           2019-11-13   \n",
       "4  2.960581e+09   2019-09-20  2019-10-04           2019-09-24   \n",
       "\n",
       "  cust_payment_terms  converted_usd  \n",
       "0               NAH4      54273.280  \n",
       "1               NAD1      79656.600  \n",
       "2               NAA8       2253.860  \n",
       "3               NAH4      33133.290  \n",
       "4               CA10      15558.088  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maindata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "eee2d68a",
   "metadata": {
    "id": "eee2d68a"
   },
   "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>business_code</th>\n",
       "      <th>cust_number</th>\n",
       "      <th>name_customer</th>\n",
       "      <th>clear_date</th>\n",
       "      <th>buisness_year</th>\n",
       "      <th>doc_id</th>\n",
       "      <th>posting_date</th>\n",
       "      <th>due_in_date</th>\n",
       "      <th>baseline_create_date</th>\n",
       "      <th>cust_payment_terms</th>\n",
       "      <th>converted_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA02</td>\n",
       "      <td>0140105686</td>\n",
       "      <td>SYSC llc</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2.960623e+09</td>\n",
       "      <td>2020-03-30</td>\n",
       "      <td>2020-04-10</td>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>CA10</td>\n",
       "      <td>2309.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200744019</td>\n",
       "      <td>TARG us</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930659e+09</td>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>2020-04-03</td>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>NAA8</td>\n",
       "      <td>11173.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200418007</td>\n",
       "      <td>AM</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930611e+09</td>\n",
       "      <td>2020-03-11</td>\n",
       "      <td>2020-03-26</td>\n",
       "      <td>2020-03-11</td>\n",
       "      <td>NAA8</td>\n",
       "      <td>3525.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200739534</td>\n",
       "      <td>OK systems</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930788e+09</td>\n",
       "      <td>2020-04-15</td>\n",
       "      <td>2020-04-30</td>\n",
       "      <td>2020-04-15</td>\n",
       "      <td>NAA8</td>\n",
       "      <td>121105.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200353024</td>\n",
       "      <td>DECA corporation</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930817e+09</td>\n",
       "      <td>2020-04-23</td>\n",
       "      <td>2020-04-26</td>\n",
       "      <td>2020-04-16</td>\n",
       "      <td>NAM2</td>\n",
       "      <td>3726.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  business_code cust_number     name_customer clear_date  buisness_year  \\\n",
       "0          CA02  0140105686          SYSC llc        NaT         2020.0   \n",
       "1          U001  0200744019           TARG us        NaT         2020.0   \n",
       "2          U001  0200418007               AM         NaT         2020.0   \n",
       "3          U001  0200739534        OK systems        NaT         2020.0   \n",
       "4          U001  0200353024  DECA corporation        NaT         2020.0   \n",
       "\n",
       "         doc_id posting_date due_in_date baseline_create_date  \\\n",
       "0  2.960623e+09   2020-03-30  2020-04-10           2020-03-31   \n",
       "1  1.930659e+09   2020-03-19  2020-04-03           2020-03-19   \n",
       "2  1.930611e+09   2020-03-11  2020-03-26           2020-03-11   \n",
       "3  1.930788e+09   2020-04-15  2020-04-30           2020-04-15   \n",
       "4  1.930817e+09   2020-04-23  2020-04-26           2020-04-16   \n",
       "\n",
       "  cust_payment_terms  converted_usd  \n",
       "0               CA10        2309.79  \n",
       "1               NAA8       11173.02  \n",
       "2               NAA8        3525.59  \n",
       "3               NAA8      121105.65  \n",
       "4               NAM2        3726.06  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nulldata.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24aa6746",
   "metadata": {
    "id": "24aa6746"
   },
   "source": [
    "## Considering the **maindata**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f92c4aa7",
   "metadata": {
    "id": "f92c4aa7"
   },
   "source": [
    "#### Generate a new column \"Delay\" from the existing columns\n",
    "\n",
    "- Note - You are expected to create a new column 'Delay' from two existing columns, \"clear_date\" and \"due_in_date\" \n",
    "- Formula - Delay = clear_date - due_in_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a8883c7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "business_code                   object\n",
       "cust_number                     object\n",
       "name_customer                   object\n",
       "clear_date              datetime64[ns]\n",
       "buisness_year                  float64\n",
       "doc_id                         float64\n",
       "posting_date            datetime64[ns]\n",
       "due_in_date             datetime64[ns]\n",
       "baseline_create_date    datetime64[ns]\n",
       "cust_payment_terms              object\n",
       "converted_usd                  float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maindata.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8eeceb9c",
   "metadata": {
    "id": "8eeceb9c"
   },
   "outputs": [],
   "source": [
    "maindata['Delay']=maindata['clear_date']-maindata['due_in_date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "6b72bb16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "business_code                    object\n",
       "cust_number                      object\n",
       "name_customer                    object\n",
       "clear_date               datetime64[ns]\n",
       "buisness_year                   float64\n",
       "doc_id                          float64\n",
       "posting_date             datetime64[ns]\n",
       "due_in_date              datetime64[ns]\n",
       "baseline_create_date     datetime64[ns]\n",
       "cust_payment_terms               object\n",
       "converted_usd                   float64\n",
       "Delay                   timedelta64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maindata.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3491bebb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1 days\n",
       "1       -3 days\n",
       "2       92 days\n",
       "3       -3 days\n",
       "4       61 days\n",
       "          ...  \n",
       "39153    0 days\n",
       "39154    4 days\n",
       "39155    0 days\n",
       "39156    0 days\n",
       "39157   -9 days\n",
       "Name: Delay, Length: 39158, dtype: timedelta64[ns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maindata['Delay']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f482144e",
   "metadata": {
    "id": "f482144e"
   },
   "source": [
    "### Generate a new column \"avgdelay\" from the existing columns\n",
    "\n",
    "- Note - You are expected to make a new column \"avgdelay\" by grouping \"name_customer\" column with reapect to mean of the \"Delay\" column.\n",
    "- This new column \"avg_delay\" is meant to store \"customer_name\" wise delay\n",
    "- groupby('name_customer')['Delay'].mean(numeric_only=False)\n",
    "- Display the new \"avg_delay\" column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d18d2f8d",
   "metadata": {
    "id": "d18d2f8d"
   },
   "outputs": [],
   "source": [
    " maindata[ 'avg_delay']= maindata.groupby('name_customer')['Delay'].mean(numeric_only=False)\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64b995e8",
   "metadata": {
    "id": "64b995e8"
   },
   "source": [
    "You need to add the \"avg_delay\" column with the maindata, mapped with \"name_customer\" column\n",
    "\n",
    " - Note - You need to use map function to map the avgdelay with respect to \"name_customer\" column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e1e1f3d9",
   "metadata": {
    "id": "e1e1f3d9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "business_code                    object\n",
       "cust_number                      object\n",
       "name_customer                    object\n",
       "clear_date               datetime64[ns]\n",
       "buisness_year                   float64\n",
       "doc_id                          float64\n",
       "posting_date             datetime64[ns]\n",
       "due_in_date              datetime64[ns]\n",
       "baseline_create_date     datetime64[ns]\n",
       "cust_payment_terms               object\n",
       "converted_usd                   float64\n",
       "Delay                   timedelta64[ns]\n",
       "avg_delay               timedelta64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_num=maindata.groupby(\"name_customer\")['Delay'].mean(numeric_only=False)\n",
    "maindata['avg_delay']=maindata['name_customer'].map(cust_num)\n",
    "maindata.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "8257bbbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>business_code</th>\n",
       "      <th>cust_number</th>\n",
       "      <th>name_customer</th>\n",
       "      <th>clear_date</th>\n",
       "      <th>buisness_year</th>\n",
       "      <th>doc_id</th>\n",
       "      <th>posting_date</th>\n",
       "      <th>due_in_date</th>\n",
       "      <th>baseline_create_date</th>\n",
       "      <th>cust_payment_terms</th>\n",
       "      <th>converted_usd</th>\n",
       "      <th>Delay</th>\n",
       "      <th>avg_delay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200769623</td>\n",
       "      <td>WAL-MAR corp</td>\n",
       "      <td>2020-02-11</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930438e+09</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>2020-02-10</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>NAH4</td>\n",
       "      <td>54273.280</td>\n",
       "      <td>1 days</td>\n",
       "      <td>-3 days +07:08:49.779837776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200980828</td>\n",
       "      <td>BEN E</td>\n",
       "      <td>2019-08-08</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1.929646e+09</td>\n",
       "      <td>2019-07-22</td>\n",
       "      <td>2019-08-11</td>\n",
       "      <td>2019-07-22</td>\n",
       "      <td>NAD1</td>\n",
       "      <td>79656.600</td>\n",
       "      <td>-3 days</td>\n",
       "      <td>19 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200792734</td>\n",
       "      <td>MDV/ trust</td>\n",
       "      <td>2019-12-30</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1.929874e+09</td>\n",
       "      <td>2019-09-14</td>\n",
       "      <td>2019-09-29</td>\n",
       "      <td>2019-09-14</td>\n",
       "      <td>NAA8</td>\n",
       "      <td>2253.860</td>\n",
       "      <td>92 days</td>\n",
       "      <td>8 days 02:10:54.545454545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200769623</td>\n",
       "      <td>WAL-MAR foundation</td>\n",
       "      <td>2019-11-25</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1.930148e+09</td>\n",
       "      <td>2019-11-13</td>\n",
       "      <td>2019-11-28</td>\n",
       "      <td>2019-11-13</td>\n",
       "      <td>NAH4</td>\n",
       "      <td>33133.290</td>\n",
       "      <td>-3 days</td>\n",
       "      <td>-3 days +19:33:27.692307693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA02</td>\n",
       "      <td>0140106181</td>\n",
       "      <td>THE  corporation</td>\n",
       "      <td>2019-12-04</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2.960581e+09</td>\n",
       "      <td>2019-09-20</td>\n",
       "      <td>2019-10-04</td>\n",
       "      <td>2019-09-24</td>\n",
       "      <td>CA10</td>\n",
       "      <td>15558.088</td>\n",
       "      <td>61 days</td>\n",
       "      <td>71 days 10:40:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  business_code cust_number       name_customer clear_date  buisness_year  \\\n",
       "0          U001  0200769623        WAL-MAR corp 2020-02-11         2020.0   \n",
       "1          U001  0200980828              BEN E  2019-08-08         2019.0   \n",
       "2          U001  0200792734          MDV/ trust 2019-12-30         2019.0   \n",
       "3          U001  0200769623  WAL-MAR foundation 2019-11-25         2019.0   \n",
       "4          CA02  0140106181    THE  corporation 2019-12-04         2019.0   \n",
       "\n",
       "         doc_id posting_date due_in_date baseline_create_date  \\\n",
       "0  1.930438e+09   2020-01-26  2020-02-10           2020-01-26   \n",
       "1  1.929646e+09   2019-07-22  2019-08-11           2019-07-22   \n",
       "2  1.929874e+09   2019-09-14  2019-09-29           2019-09-14   \n",
       "3  1.930148e+09   2019-11-13  2019-11-28           2019-11-13   \n",
       "4  2.960581e+09   2019-09-20  2019-10-04           2019-09-24   \n",
       "\n",
       "  cust_payment_terms  converted_usd   Delay                   avg_delay  \n",
       "0               NAH4      54273.280  1 days -3 days +07:08:49.779837776  \n",
       "1               NAD1      79656.600 -3 days            19 days 00:00:00  \n",
       "2               NAA8       2253.860 92 days   8 days 02:10:54.545454545  \n",
       "3               NAH4      33133.290 -3 days -3 days +19:33:27.692307693  \n",
       "4               CA10      15558.088 61 days            71 days 10:40:00  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maindata.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d332525",
   "metadata": {
    "id": "1d332525"
   },
   "source": [
    "### Observe that the \"avg_delay\" column is in days format. You need to change the format into seconds\n",
    "\n",
    "- Days_format :  17 days 00:00:00\n",
    "- Format in seconds : 1641600.0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "OvgtHSsx_O-n",
   "metadata": {
    "id": "OvgtHSsx_O-n"
   },
   "source": [
    "### Display the maindata dataframe "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "97ca9c45",
   "metadata": {
    "id": "97ca9c45"
   },
   "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>business_code</th>\n",
       "      <th>cust_number</th>\n",
       "      <th>name_customer</th>\n",
       "      <th>clear_date</th>\n",
       "      <th>buisness_year</th>\n",
       "      <th>doc_id</th>\n",
       "      <th>posting_date</th>\n",
       "      <th>due_in_date</th>\n",
       "      <th>baseline_create_date</th>\n",
       "      <th>cust_payment_terms</th>\n",
       "      <th>converted_usd</th>\n",
       "      <th>Delay</th>\n",
       "      <th>avg_delay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200769623</td>\n",
       "      <td>WAL-MAR corp</td>\n",
       "      <td>2020-02-11</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930438e+09</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>2020-02-10</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>NAH4</td>\n",
       "      <td>54273.280</td>\n",
       "      <td>1 days</td>\n",
       "      <td>-2.334702e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200980828</td>\n",
       "      <td>BEN E</td>\n",
       "      <td>2019-08-08</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1.929646e+09</td>\n",
       "      <td>2019-07-22</td>\n",
       "      <td>2019-08-11</td>\n",
       "      <td>2019-07-22</td>\n",
       "      <td>NAD1</td>\n",
       "      <td>79656.600</td>\n",
       "      <td>-3 days</td>\n",
       "      <td>1.641600e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200792734</td>\n",
       "      <td>MDV/ trust</td>\n",
       "      <td>2019-12-30</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1.929874e+09</td>\n",
       "      <td>2019-09-14</td>\n",
       "      <td>2019-09-29</td>\n",
       "      <td>2019-09-14</td>\n",
       "      <td>NAA8</td>\n",
       "      <td>2253.860</td>\n",
       "      <td>92 days</td>\n",
       "      <td>6.990545e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200769623</td>\n",
       "      <td>WAL-MAR foundation</td>\n",
       "      <td>2019-11-25</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1.930148e+09</td>\n",
       "      <td>2019-11-13</td>\n",
       "      <td>2019-11-28</td>\n",
       "      <td>2019-11-13</td>\n",
       "      <td>NAH4</td>\n",
       "      <td>33133.290</td>\n",
       "      <td>-3 days</td>\n",
       "      <td>-1.887923e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA02</td>\n",
       "      <td>0140106181</td>\n",
       "      <td>THE  corporation</td>\n",
       "      <td>2019-12-04</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2.960581e+09</td>\n",
       "      <td>2019-09-20</td>\n",
       "      <td>2019-10-04</td>\n",
       "      <td>2019-09-24</td>\n",
       "      <td>CA10</td>\n",
       "      <td>15558.088</td>\n",
       "      <td>61 days</td>\n",
       "      <td>6.172800e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  business_code cust_number       name_customer clear_date  buisness_year  \\\n",
       "0          U001  0200769623        WAL-MAR corp 2020-02-11         2020.0   \n",
       "1          U001  0200980828              BEN E  2019-08-08         2019.0   \n",
       "2          U001  0200792734          MDV/ trust 2019-12-30         2019.0   \n",
       "3          U001  0200769623  WAL-MAR foundation 2019-11-25         2019.0   \n",
       "4          CA02  0140106181    THE  corporation 2019-12-04         2019.0   \n",
       "\n",
       "         doc_id posting_date due_in_date baseline_create_date  \\\n",
       "0  1.930438e+09   2020-01-26  2020-02-10           2020-01-26   \n",
       "1  1.929646e+09   2019-07-22  2019-08-11           2019-07-22   \n",
       "2  1.929874e+09   2019-09-14  2019-09-29           2019-09-14   \n",
       "3  1.930148e+09   2019-11-13  2019-11-28           2019-11-13   \n",
       "4  2.960581e+09   2019-09-20  2019-10-04           2019-09-24   \n",
       "\n",
       "  cust_payment_terms  converted_usd   Delay     avg_delay  \n",
       "0               NAH4      54273.280  1 days -2.334702e+05  \n",
       "1               NAD1      79656.600 -3 days  1.641600e+06  \n",
       "2               NAA8       2253.860 92 days  6.990545e+05  \n",
       "3               NAH4      33133.290 -3 days -1.887923e+05  \n",
       "4               CA10      15558.088 61 days  6.172800e+06  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maindata['avg_delay'] = pd.to_timedelta(maindata['avg_delay']).view(np.int64) / 1e9\n",
    "#maindata['avg_delay']=maindata['avg_delay'].dt.total_seconds()\n",
    "maindata.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae24c7bb",
   "metadata": {
    "id": "ae24c7bb"
   },
   "source": [
    "### Since you have created the \"avg_delay\" column from \"Delay\" and \"clear_date\" column, there is no need of these two columns anymore \n",
    "\n",
    "- You are expected to drop \"Delay\" and \"clear_date\" columns from maindata dataframe "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "78a61ab9",
   "metadata": {
    "id": "78a61ab9"
   },
   "outputs": [],
   "source": [
    "maindata.drop(columns=[ \"Delay\", \"clear_date\"],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "78eb13cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['business_code', 'cust_number', 'name_customer', 'buisness_year',\n",
       "       'doc_id', 'posting_date', 'due_in_date', 'baseline_create_date',\n",
       "       'cust_payment_terms', 'converted_usd', 'avg_delay'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maindata.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae724bfc",
   "metadata": {
    "id": "ae724bfc"
   },
   "source": [
    "# Splitting of Train and the Test Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb6f0264",
   "metadata": {
    "id": "cb6f0264"
   },
   "source": [
    "### You need to split the \"maindata\" columns into X and y dataframe\n",
    "\n",
    "- Note - y should have the target column i.e. \"avg_delay\" and the other column should be in X\n",
    "\n",
    "- X is going to hold the source fields and y will be going to hold the target fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "75ab29ab",
   "metadata": {
    "id": "75ab29ab"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_delay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.334702e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.641600e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.990545e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.887923e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.172800e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39153</th>\n",
       "      <td>-3.330000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39154</th>\n",
       "      <td>-2.135915e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39155</th>\n",
       "      <td>9.051429e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39156</th>\n",
       "      <td>1.354030e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39157</th>\n",
       "      <td>-3.371478e+05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>39158 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          avg_delay\n",
       "0     -2.334702e+05\n",
       "1      1.641600e+06\n",
       "2      6.990545e+05\n",
       "3     -1.887923e+05\n",
       "4      6.172800e+06\n",
       "...             ...\n",
       "39153 -3.330000e+04\n",
       "39154 -2.135915e+05\n",
       "39155  9.051429e+04\n",
       "39156  1.354030e+05\n",
       "39157 -3.371478e+05\n",
       "\n",
       "[39158 rows x 1 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = maindata.drop(columns= maindata.columns[:-1])\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "6412c62b",
   "metadata": {
    "id": "6412c62b"
   },
   "outputs": [
    {
     "data": {
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       "      <th>cust_number</th>\n",
       "      <th>name_customer</th>\n",
       "      <th>buisness_year</th>\n",
       "      <th>doc_id</th>\n",
       "      <th>posting_date</th>\n",
       "      <th>due_in_date</th>\n",
       "      <th>baseline_create_date</th>\n",
       "      <th>cust_payment_terms</th>\n",
       "      <th>converted_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200769623</td>\n",
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       "      <td>2020.0</td>\n",
       "      <td>1.930438e+09</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>2020-02-10</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>NAH4</td>\n",
       "      <td>54273.280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200980828</td>\n",
       "      <td>BEN E</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1.929646e+09</td>\n",
       "      <td>2019-07-22</td>\n",
       "      <td>2019-08-11</td>\n",
       "      <td>2019-07-22</td>\n",
       "      <td>NAD1</td>\n",
       "      <td>79656.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200792734</td>\n",
       "      <td>MDV/ trust</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1.929874e+09</td>\n",
       "      <td>2019-09-14</td>\n",
       "      <td>2019-09-29</td>\n",
       "      <td>2019-09-14</td>\n",
       "      <td>NAA8</td>\n",
       "      <td>2253.860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200769623</td>\n",
       "      <td>WAL-MAR foundation</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1.930148e+09</td>\n",
       "      <td>2019-11-13</td>\n",
       "      <td>2019-11-28</td>\n",
       "      <td>2019-11-13</td>\n",
       "      <td>NAH4</td>\n",
       "      <td>33133.290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA02</td>\n",
       "      <td>0140106181</td>\n",
       "      <td>THE  corporation</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2.960581e+09</td>\n",
       "      <td>2019-09-20</td>\n",
       "      <td>2019-10-04</td>\n",
       "      <td>2019-09-24</td>\n",
       "      <td>CA10</td>\n",
       "      <td>15558.088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>39153</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200762301</td>\n",
       "      <td>C&amp;S WH trust</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1.929601e+09</td>\n",
       "      <td>2019-07-10</td>\n",
       "      <td>2019-07-25</td>\n",
       "      <td>2019-07-10</td>\n",
       "      <td>NAC6</td>\n",
       "      <td>84780.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39154</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200769623</td>\n",
       "      <td>WAL-MAR co</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1.929744e+09</td>\n",
       "      <td>2019-08-15</td>\n",
       "      <td>2019-08-30</td>\n",
       "      <td>2019-08-15</td>\n",
       "      <td>NAH4</td>\n",
       "      <td>6766.540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39155</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200772595</td>\n",
       "      <td>SAFEW associates</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930537e+09</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>NAA8</td>\n",
       "      <td>6120.860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39156</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200726979</td>\n",
       "      <td>BJ'S  llc</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1.930199e+09</td>\n",
       "      <td>2019-11-27</td>\n",
       "      <td>2019-12-12</td>\n",
       "      <td>2019-11-27</td>\n",
       "      <td>NAA8</td>\n",
       "      <td>63.480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39157</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200020431</td>\n",
       "      <td>DEC corp</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1.928576e+09</td>\n",
       "      <td>2019-01-05</td>\n",
       "      <td>2019-01-24</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>NAM4</td>\n",
       "      <td>1790.300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>39158 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      business_code cust_number       name_customer  buisness_year  \\\n",
       "0              U001  0200769623        WAL-MAR corp         2020.0   \n",
       "1              U001  0200980828              BEN E          2019.0   \n",
       "2              U001  0200792734          MDV/ trust         2019.0   \n",
       "3              U001  0200769623  WAL-MAR foundation         2019.0   \n",
       "4              CA02  0140106181    THE  corporation         2019.0   \n",
       "...             ...         ...                 ...            ...   \n",
       "39153          U001  0200762301        C&S WH trust         2019.0   \n",
       "39154          U001  0200769623          WAL-MAR co         2019.0   \n",
       "39155          U001  0200772595    SAFEW associates         2020.0   \n",
       "39156          U001  0200726979           BJ'S  llc         2019.0   \n",
       "39157          U001  0200020431            DEC corp         2019.0   \n",
       "\n",
       "             doc_id posting_date due_in_date baseline_create_date  \\\n",
       "0      1.930438e+09   2020-01-26  2020-02-10           2020-01-26   \n",
       "1      1.929646e+09   2019-07-22  2019-08-11           2019-07-22   \n",
       "2      1.929874e+09   2019-09-14  2019-09-29           2019-09-14   \n",
       "3      1.930148e+09   2019-11-13  2019-11-28           2019-11-13   \n",
       "4      2.960581e+09   2019-09-20  2019-10-04           2019-09-24   \n",
       "...             ...          ...         ...                  ...   \n",
       "39153  1.929601e+09   2019-07-10  2019-07-25           2019-07-10   \n",
       "39154  1.929744e+09   2019-08-15  2019-08-30           2019-08-15   \n",
       "39155  1.930537e+09   2020-02-19  2020-03-05           2020-02-19   \n",
       "39156  1.930199e+09   2019-11-27  2019-12-12           2019-11-27   \n",
       "39157  1.928576e+09   2019-01-05  2019-01-24           2019-01-01   \n",
       "\n",
       "      cust_payment_terms  converted_usd  \n",
       "0                   NAH4      54273.280  \n",
       "1                   NAD1      79656.600  \n",
       "2                   NAA8       2253.860  \n",
       "3                   NAH4      33133.290  \n",
       "4                   CA10      15558.088  \n",
       "...                  ...            ...  \n",
       "39153               NAC6      84780.400  \n",
       "39154               NAH4       6766.540  \n",
       "39155               NAA8       6120.860  \n",
       "39156               NAA8         63.480  \n",
       "39157               NAM4       1790.300  \n",
       "\n",
       "[39158 rows x 10 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = maindata.iloc[:,:-1].copy()\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c2942bf",
   "metadata": {
    "id": "1c2942bf"
   },
   "source": [
    "#### You are expected to split both the dataframes into train and test format in 60:40 ratio \n",
    "\n",
    "- Note - The expected output should be in \"X_train\", \"X_loc_test\", \"y_train\", \"y_loc_test\" format "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "cefafc10",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "b43ee03c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_loc_test, y_train, y_loc_test = train_test_split(X,y,train_size = 0.6,random_state=0) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "p4OME62pDufR",
   "metadata": {
    "id": "p4OME62pDufR"
   },
   "source": [
    "### Please check for the number of rows and columns of all the new dataframes (all 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "48328d0a",
   "metadata": {
    "id": "48328d0a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23494, 10)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e302af41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15664, 10)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_loc_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "5a1ebab8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23494, 1)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "329bb80b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15664, 1)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_loc_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a68ed71",
   "metadata": {
    "id": "4a68ed71"
   },
   "source": [
    "### Now you are expected to split the \"X_loc_test\" and \"y_loc_test\" dataset into \"Test\" and \"Validation\" (as the names given below) dataframe with 50:50 format \n",
    "\n",
    "- Note - The expected output should be in \"X_val\", \"X_test\", \"y_val\", \"y_test\" format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "1b370a2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_val, X_test, y_val, y_test  = train_test_split(X_loc_test,y_loc_test,train_size = 0.5,random_state=0) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bJTSAskvERH1",
   "metadata": {
    "id": "bJTSAskvERH1"
   },
   "source": [
    "### Please check for the number of rows and columns of all the 4 dataframes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "845d7564",
   "metadata": {
    "id": "845d7564"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7832, 10)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_val.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "b9594d28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7832, 10)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "53b0a7d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7832, 1)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_val.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "d1831603",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7832, 1)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "110fa872",
   "metadata": {
    "id": "110fa872"
   },
   "source": [
    "# Exploratory Data Analysis (EDA) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffc8fe0f",
   "metadata": {
    "id": "ffc8fe0f"
   },
   "source": [
    "### Distribution Plot of the target variable (use the dataframe which contains the target field)\n",
    "\n",
    "- Note - You are expected to make a distribution plot for the target variable "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "62c36fa6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "ba2bf8ed",
   "metadata": {
    "id": "ba2bf8ed"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([5.0000e+00, 1.0000e+01, 2.3000e+01, 3.0480e+03, 3.5171e+04,\n",
       "        1.9400e+02, 6.2500e+02, 6.5000e+01, 1.0000e+01, 7.0000e+00]),\n",
       " array([-7689600., -5857920., -4026240., -2194560.,  -362880.,  1468800.,\n",
       "         3300480.,  5132160.,  6963840.,  8795520., 10627200.]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(15,6))\n",
    "plt.hist(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0e323a3",
   "metadata": {
    "id": "d0e323a3"
   },
   "source": [
    "### You are expected to group the X_train dataset on 'name_customer' column with 'doc_id' in the x_train set\n",
    "\n",
    "### Need to store the outcome into a new dataframe \n",
    "\n",
    "- Note code given for groupby statement- X_train.groupby(by=['name_customer'], as_index=False)['doc_id'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "f7acf0ee",
   "metadata": {
    "id": "f7acf0ee"
   },
   "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>name_customer</th>\n",
       "      <th>doc_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17135 associates</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>236008 associates</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>99 CE associates</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>99 CE corporation</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>99 CE foundation</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3097</th>\n",
       "      <td>YAEGER corp</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3098</th>\n",
       "      <td>YEN BROS</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3099</th>\n",
       "      <td>YEN BROS co</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3100</th>\n",
       "      <td>YEN BROS corp</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3101</th>\n",
       "      <td>ZIYAD  us</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3102 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          name_customer  doc_id\n",
       "0      17135 associates       1\n",
       "1     236008 associates       1\n",
       "2      99 CE associates       2\n",
       "3     99 CE corporation       2\n",
       "4      99 CE foundation       2\n",
       "...                 ...     ...\n",
       "3097        YAEGER corp       1\n",
       "3098          YEN BROS        1\n",
       "3099        YEN BROS co       1\n",
       "3100      YEN BROS corp       1\n",
       "3101          ZIYAD  us       1\n",
       "\n",
       "[3102 rows x 2 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.groupby(by=['name_customer'], as_index=False)['doc_id'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cA43bFffFt6i",
   "metadata": {
    "id": "cA43bFffFt6i"
   },
   "source": [
    "### You can make another distribution plot of the \"doc_id\" column from x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "9576bf33",
   "metadata": {
    "id": "9576bf33"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([2.1617e+04, 1.8730e+03, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 4.0000e+00]),\n",
       " array([1.92850176e+09, 2.68565159e+09, 3.44280143e+09, 4.19995126e+09,\n",
       "        4.95710110e+09, 5.71425093e+09, 6.47140077e+09, 7.22855060e+09,\n",
       "        7.98570044e+09, 8.74285027e+09, 9.50000011e+09]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(10,7))\n",
    "plt.hist(X_train[\"doc_id\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fba2c44f",
   "metadata": {
    "id": "fba2c44f"
   },
   "source": [
    "#### Create a Distribution plot only for business_year and a seperate distribution plot of \"business_year\" column along with the doc_id\" column\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "4fecec77",
   "metadata": {
    "id": "4fecec77"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([20387.,     0.,     0.,     0.,     0.,     0.,     0.,     0.,\n",
       "            0.,  3107.]),\n",
       " array([2019. , 2019.1, 2019.2, 2019.3, 2019.4, 2019.5, 2019.6, 2019.7,\n",
       "        2019.8, 2019.9, 2020. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(10,8))\n",
    "plt.hist(X_train['buisness_year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "qr1jGhfOKjnw",
   "metadata": {
    "id": "qr1jGhfOKjnw"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 23494 artists>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(X_train['buisness_year'],X_train['doc_id'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "968fbcc9",
   "metadata": {
    "id": "968fbcc9"
   },
   "source": [
    "# Feature Engineering "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "jbh6CyGqH3XE",
   "metadata": {
    "id": "jbh6CyGqH3XE"
   },
   "source": [
    "### Display and describe the X_train dataframe "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "e6bcf307",
   "metadata": {
    "id": "e6bcf307"
   },
   "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>business_code</th>\n",
       "      <th>cust_number</th>\n",
       "      <th>name_customer</th>\n",
       "      <th>buisness_year</th>\n",
       "      <th>doc_id</th>\n",
       "      <th>posting_date</th>\n",
       "      <th>due_in_date</th>\n",
       "      <th>baseline_create_date</th>\n",
       "      <th>cust_payment_terms</th>\n",
       "      <th>converted_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35886</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200794332</td>\n",
       "      <td>COST foundation</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930448e+09</td>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>NAAX</td>\n",
       "      <td>36273.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14338</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200706844</td>\n",
       "      <td>WINC trust</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930517e+09</td>\n",
       "      <td>2020-02-13</td>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>2020-02-13</td>\n",
       "      <td>NAA8</td>\n",
       "      <td>6349.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17006</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200803720</td>\n",
       "      <td>DEC us</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1.928590e+09</td>\n",
       "      <td>2019-01-08</td>\n",
       "      <td>2019-01-24</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>NAM4</td>\n",
       "      <td>282.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30683</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200752302</td>\n",
       "      <td>KROGER</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930509e+09</td>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>NAA8</td>\n",
       "      <td>64749.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20038</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200974851</td>\n",
       "      <td>RESTA co</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1.929483e+09</td>\n",
       "      <td>2019-06-12</td>\n",
       "      <td>2019-06-27</td>\n",
       "      <td>2019-06-12</td>\n",
       "      <td>NAA8</td>\n",
       "      <td>2066.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      business_code cust_number    name_customer  buisness_year        doc_id  \\\n",
       "35886          U001  0200794332  COST foundation         2020.0  1.930448e+09   \n",
       "14338          U001  0200706844       WINC trust         2020.0  1.930517e+09   \n",
       "17006          U001  0200803720           DEC us         2019.0  1.928590e+09   \n",
       "30683          U001  0200752302          KROGER          2020.0  1.930509e+09   \n",
       "20038          U001  0200974851         RESTA co         2019.0  1.929483e+09   \n",
       "\n",
       "      posting_date due_in_date baseline_create_date cust_payment_terms  \\\n",
       "35886   2020-01-28  2020-02-12           2020-01-28               NAAX   \n",
       "14338   2020-02-13  2020-02-28           2020-02-13               NAA8   \n",
       "17006   2019-01-08  2019-01-24           2019-01-01               NAM4   \n",
       "30683   2020-02-12  2020-02-27           2020-02-12               NAA8   \n",
       "20038   2019-06-12  2019-06-27           2019-06-12               NAA8   \n",
       "\n",
       "       converted_usd  \n",
       "35886       36273.03  \n",
       "14338        6349.27  \n",
       "17006         282.84  \n",
       "30683       64749.18  \n",
       "20038        2066.63  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "08ccc819",
   "metadata": {
    "id": "08ccc819"
   },
   "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>buisness_year</th>\n",
       "      <th>doc_id</th>\n",
       "      <th>converted_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>23494.000000</td>\n",
       "      <td>2.349400e+04</td>\n",
       "      <td>23494.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2019.132247</td>\n",
       "      <td>2.014021e+09</td>\n",
       "      <td>30999.755826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.338766</td>\n",
       "      <td>2.956842e+08</td>\n",
       "      <td>36630.432880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>1.928502e+09</td>\n",
       "      <td>4.380000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>1.929174e+09</td>\n",
       "      <td>4622.179500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>1.929731e+09</td>\n",
       "      <td>16919.885000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>1.930209e+09</td>\n",
       "      <td>45911.302500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2020.000000</td>\n",
       "      <td>9.500000e+09</td>\n",
       "      <td>632134.240000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       buisness_year        doc_id  converted_usd\n",
       "count   23494.000000  2.349400e+04   23494.000000\n",
       "mean     2019.132247  2.014021e+09   30999.755826\n",
       "std         0.338766  2.956842e+08   36630.432880\n",
       "min      2019.000000  1.928502e+09       4.380000\n",
       "25%      2019.000000  1.929174e+09    4622.179500\n",
       "50%      2019.000000  1.929731e+09   16919.885000\n",
       "75%      2019.000000  1.930209e+09   45911.302500\n",
       "max      2020.000000  9.500000e+09  632134.240000"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.describe()\n",
    "#X_train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c009419",
   "metadata": {},
   "source": [
    "### The \"business_code\" column inside X_train, is a categorical column, so you need to perform Labelencoder on that particular column\n",
    "Note - call the Label Encoder from sklearn library and use the fit() function on \"business_code\" column\n",
    "\n",
    "Note - Please fill in the blanks (two) to complete this code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "e1cda380",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "7c223545",
   "metadata": {
    "id": "7c223545"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LabelEncoder()"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "business_coder = LabelEncoder()\n",
    "business_coder.fit(X_train[ \"business_code\"])\n",
    "#business_coder.___(X_train[__________])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f86f7d9c",
   "metadata": {
    "id": "f86f7d9c"
   },
   "source": [
    "#### You are expected to store the value into a new column i.e. \"business_code_enc\"\n",
    "\n",
    "- Note - For Training set you are expected to use fit_trainsform()\n",
    "- Note - For Test set you are expected to use the trainsform()\n",
    "- Note - For Validation set you are expected to use the trainsform()\n",
    "\n",
    "\n",
    "- Partial code is provided, please fill in the blanks "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "4269c307",
   "metadata": {
    "id": "4269c307"
   },
   "outputs": [],
   "source": [
    "#X_train['business_code_enc'] = business_coder._____________(X_train['business_code'])\n",
    "X_train['business_code_enc'] = business_coder.fit_transform(X_train['business_code'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "70a53712",
   "metadata": {
    "id": "70a53712"
   },
   "outputs": [],
   "source": [
    "#X_val['business_code_enc'] = business_coder.__________(X_val['business_code'])\n",
    "#X_test['business_code_enc'] = business_coder.____________(X_test['business_code'])\n",
    "X_val['business_code_enc'] = business_coder.transform(X_val['business_code'])\n",
    "X_test['business_code_enc'] = business_coder.transform(X_test['business_code'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "gdNYxTkqNfmz",
   "metadata": {
    "id": "gdNYxTkqNfmz"
   },
   "source": [
    "### Display \"business_code\" and \"business_code_enc\" together from X_train dataframe "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "1196a002",
   "metadata": {
    "id": "1196a002"
   },
   "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>business_code</th>\n",
       "      <th>business_code_enc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35886</th>\n",
       "      <td>U001</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14338</th>\n",
       "      <td>U001</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17006</th>\n",
       "      <td>U001</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30683</th>\n",
       "      <td>U001</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20038</th>\n",
       "      <td>U001</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20757</th>\n",
       "      <td>U001</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32103</th>\n",
       "      <td>U001</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30403</th>\n",
       "      <td>U001</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21243</th>\n",
       "      <td>U001</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2732</th>\n",
       "      <td>U001</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23494 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      business_code  business_code_enc\n",
       "35886          U001                  1\n",
       "14338          U001                  1\n",
       "17006          U001                  1\n",
       "30683          U001                  1\n",
       "20038          U001                  1\n",
       "...             ...                ...\n",
       "20757          U001                  1\n",
       "32103          U001                  1\n",
       "30403          U001                  1\n",
       "21243          U001                  1\n",
       "2732           U001                  1\n",
       "\n",
       "[23494 rows x 2 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[[\"business_code\" , \"business_code_enc\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11477224",
   "metadata": {
    "id": "11477224"
   },
   "source": [
    "#### Create a function called \"custom\" for dropping the columns 'business_code' from train, test and validation dataframe\n",
    "\n",
    "- Note - Fill in the blank to complete the code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "1052868a",
   "metadata": {
    "id": "1052868a"
   },
   "outputs": [],
   "source": [
    "#def ________(col ,traindf = X_train,valdf = X_val,testdf = X_test):\n",
    "def custom(col ,traindf = X_train,valdf = X_val,testdf = X_test):\n",
    "    traindf.drop(col, axis =1,inplace=True)\n",
    "    valdf.drop(col,axis=1 , inplace=True)\n",
    "    testdf.drop(col,axis=1 , inplace=True)\n",
    "\n",
    "    return traindf,valdf ,testdf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rI--ZuMbNLne",
   "metadata": {
    "id": "rI--ZuMbNLne"
   },
   "source": [
    "### Call the function by passing the column name which needed to be dropped from train, test and validation dataframes. Return updated dataframes to be stored in X_train ,X_val, X_test  \n",
    "\n",
    "- Note = Fill in the blank to complete the code "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "1a0f955c",
   "metadata": {
    "id": "1a0f955c"
   },
   "outputs": [],
   "source": [
    "#________ , ______ , _______ = ______(['business_code'])\n",
    "X_train ,X_val, X_test = custom(['business_code'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28b5b27e",
   "metadata": {
    "id": "28b5b27e"
   },
   "source": [
    "### Manually replacing str values with numbers, Here we are trying manually replace the customer numbers with some specific values like, 'CCCA' as 1, 'CCU' as 2 and so on. Also we are converting the datatype \"cust_number\" field to int type.\n",
    "\n",
    "- We are doing it for all the three dataframes as shown below. This is fully completed code. No need to modify anything here \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "85dd129e",
   "metadata": {
    "id": "85dd129e"
   },
   "outputs": [],
   "source": [
    "X_train['cust_number'] = X_train['cust_number'].str.replace('CCCA',\"1\").str.replace('CCU',\"2\").str.replace('CC',\"3\").astype(int)\n",
    "X_test['cust_number'] = X_test['cust_number'].str.replace('CCCA',\"1\").str.replace('CCU',\"2\").str.replace('CC',\"3\").astype(int)\n",
    "X_val['cust_number'] = X_val['cust_number'].str.replace('CCCA',\"1\").str.replace('CCU',\"2\").str.replace('CC',\"3\").astype(int)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "U8vA-zmdPnJ8",
   "metadata": {
    "id": "U8vA-zmdPnJ8"
   },
   "source": [
    "#### It differs from LabelEncoder by handling new classes and providing a value for it [Unknown]. Unknown will be added in fit and transform will take care of new item. It gives unknown class id.\n",
    "\n",
    "#### This will fit the encoder for all the unique values and introduce unknown value\n",
    "\n",
    "- Note - Keep this code as it is, we will be using this later on.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "151f48ba",
   "metadata": {
    "id": "151f48ba"
   },
   "outputs": [],
   "source": [
    "#For encoding unseen labels\n",
    "class EncoderExt(object):\n",
    "    def __init__(self):\n",
    "        self.label_encoder = LabelEncoder()\n",
    "    def fit(self, data_list):\n",
    "        self.label_encoder = self.label_encoder.fit(list(data_list) + ['Unknown'])\n",
    "        self.classes_ = self.label_encoder.classes_\n",
    "        return self\n",
    "    def transform(self, data_list):\n",
    "        new_data_list = list(data_list)\n",
    "        for unique_item in np.unique(data_list):\n",
    "            if unique_item not in self.label_encoder.classes_:\n",
    "                new_data_list = ['Unknown' if x==unique_item else x for x in new_data_list]\n",
    "        return self.label_encoder.transform(new_data_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "254c64e6",
   "metadata": {
    "id": "254c64e6"
   },
   "source": [
    "### Use the user define Label Encoder function called \"EncoderExt\" for the \"name_customer\" column\n",
    "\n",
    "- Note - Keep the code as it is, no need to change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "62b17eff",
   "metadata": {
    "id": "62b17eff"
   },
   "outputs": [],
   "source": [
    "label_encoder = EncoderExt()\n",
    "label_encoder.fit(X_train['name_customer'])\n",
    "X_train['name_customer_enc']=label_encoder.transform(X_train['name_customer'])\n",
    "X_val['name_customer_enc']=label_encoder.transform(X_val['name_customer'])\n",
    "X_test['name_customer_enc']=label_encoder.transform(X_test['name_customer'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mK7LMoy2QZhy",
   "metadata": {
    "id": "mK7LMoy2QZhy"
   },
   "source": [
    "### As we have created the a new column \"name_customer_enc\", so now drop \"name_customer\" column from all three dataframes\n",
    "\n",
    "- Note - Keep the code as it is, no need to change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "ef85f1c0",
   "metadata": {
    "id": "ef85f1c0"
   },
   "outputs": [],
   "source": [
    "X_train ,X_val, X_test = custom(['name_customer'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3aa09d22",
   "metadata": {
    "id": "3aa09d22"
   },
   "source": [
    "### Using Label Encoder for the \"cust_payment_terms\" column\n",
    "\n",
    "- Note - Keep the code as it is, no need to change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "6f9ab642",
   "metadata": {
    "id": "6f9ab642"
   },
   "outputs": [],
   "source": [
    "label_encoder1 = EncoderExt()\n",
    "label_encoder1.fit(X_train['cust_payment_terms'])\n",
    "X_train['cust_payment_terms_enc']=label_encoder1.transform(X_train['cust_payment_terms'])\n",
    "X_val['cust_payment_terms_enc']=label_encoder1.transform(X_val['cust_payment_terms'])\n",
    "X_test['cust_payment_terms_enc']=label_encoder1.transform(X_test['cust_payment_terms'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "55f9a7c2",
   "metadata": {
    "id": "55f9a7c2"
   },
   "outputs": [],
   "source": [
    "X_train ,X_val, X_test = custom(['cust_payment_terms'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0788f42b",
   "metadata": {
    "id": "0788f42b"
   },
   "source": [
    "## Check the datatype of all the columns of Train, Test and Validation dataframes realted to X\n",
    "\n",
    "- Note - You are expected yo use dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "bc79a316",
   "metadata": {
    "id": "bc79a316"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cust_number                        int32\n",
       "buisness_year                    float64\n",
       "doc_id                           float64\n",
       "posting_date              datetime64[ns]\n",
       "due_in_date               datetime64[ns]\n",
       "baseline_create_date      datetime64[ns]\n",
       "converted_usd                    float64\n",
       "business_code_enc                  int32\n",
       "name_customer_enc                  int32\n",
       "cust_payment_terms_enc             int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "b33242d8",
   "metadata": {
    "id": "b33242d8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cust_number                        int32\n",
       "buisness_year                    float64\n",
       "doc_id                           float64\n",
       "posting_date              datetime64[ns]\n",
       "due_in_date               datetime64[ns]\n",
       "baseline_create_date      datetime64[ns]\n",
       "converted_usd                    float64\n",
       "business_code_enc                  int32\n",
       "name_customer_enc                  int32\n",
       "cust_payment_terms_enc             int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_val.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "6bd4da71",
   "metadata": {
    "id": "6bd4da71"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cust_number                        int32\n",
       "buisness_year                    float64\n",
       "doc_id                           float64\n",
       "posting_date              datetime64[ns]\n",
       "due_in_date               datetime64[ns]\n",
       "baseline_create_date      datetime64[ns]\n",
       "converted_usd                    float64\n",
       "business_code_enc                  int32\n",
       "name_customer_enc                  int32\n",
       "cust_payment_terms_enc             int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "LVfvuPiWPeMB",
   "metadata": {
    "id": "LVfvuPiWPeMB"
   },
   "source": [
    "### From the above output you can notice their are multiple date columns with datetime format\n",
    "\n",
    "### In order to pass it into our model, we need to convert it into float format"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d344db9",
   "metadata": {
    "id": "9d344db9"
   },
   "source": [
    "### You need to extract day, month and year from the \"posting_date\" column \n",
    "\n",
    "1.   Extract days from \"posting_date\" column and store it into a new column \"day_of_postingdate\" for train, test and validation dataset \n",
    "2.   Extract months from \"posting_date\" column and store it into a new column \"month_of_postingdate\" for train, test and validation dataset\n",
    "3.   Extract year from \"posting_date\" column and store it into a new column \"year_of_postingdate\" for train, test and validation dataset \n",
    "\n",
    "\n",
    "\n",
    "- Note - You are supposed yo use \n",
    "\n",
    "*   dt.day\n",
    "*   dt.month\n",
    "*   dt.year\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "6e3cdfd6",
   "metadata": {
    "id": "6e3cdfd6"
   },
   "outputs": [],
   "source": [
    "X_train['day_of_postingdate'] = X_train['posting_date'].dt.day\n",
    "X_train['month_of_postingdate'] = X_train['posting_date'].dt.month\n",
    "X_train['year_of_postingdate'] = X_train['posting_date'].dt.year\n",
    "\n",
    "X_val['day_of_postingdate'] = X_val['posting_date'].dt.day\n",
    "X_val['month_of_postingdate'] = X_val['posting_date'].dt.month\n",
    "X_val['year_of_postingdate'] = X_val['posting_date'].dt.year\n",
    "\n",
    "\n",
    "X_test['day_of_postingdate'] = X_test['posting_date'].dt.day\n",
    "X_test['month_of_postingdate'] = X_test['posting_date'].dt.month\n",
    "X_test['year_of_postingdate'] = X_test['posting_date'].dt.year\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "GyI-F853Rxa7",
   "metadata": {
    "id": "GyI-F853Rxa7"
   },
   "source": [
    "### pass the \"posting_date\" column into the Custom function for train, test and validation dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "FQHtQkrnRx_V",
   "metadata": {
    "id": "FQHtQkrnRx_V"
   },
   "outputs": [],
   "source": [
    "X_train ,X_val, X_test = custom(['posting_date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "GMnCaEcKReSw",
   "metadata": {
    "id": "GMnCaEcKReSw"
   },
   "source": [
    "### You need to extract day, month and year from the \"baseline_create_date\" column \n",
    "\n",
    "1.   Extract days from \"baseline_create_date\" column and store it into a new column \"day_of_createdate\" for train, test and validation dataset \n",
    "2.   Extract months from \"baseline_create_date\" column and store it into a new column \"month_of_createdate\" for train, test and validation dataset\n",
    "3.   Extract year from \"baseline_create_date\" column and store it into a new column \"year_of_createdate\" for train, test and validation dataset \n",
    "\n",
    "\n",
    "\n",
    "- Note - You are supposed yo use \n",
    "\n",
    "*   dt.day\n",
    "*   dt.month\n",
    "*   dt.year\n",
    "\n",
    "\n",
    "- Note - Do as it is been shown in the previous two code boxes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee4d83d0",
   "metadata": {
    "id": "ee4d83d0"
   },
   "source": [
    "### Extracting Day, Month, Year for 'baseline_create_date' column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "6c296b39",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train[\"day_of_createdate\"] = X_train['baseline_create_date'].dt.day\n",
    "X_train[\"month_of_createdate\"] = X_train['baseline_create_date'].dt.month\n",
    "X_train[\"year_of_createdate\"] = X_train['baseline_create_date'].dt.year\n",
    "\n",
    "X_val[\"day_of_createdate\"] = X_val['baseline_create_date'].dt.day\n",
    "X_val[\"month_of_createdate\"] = X_val['baseline_create_date'].dt.month\n",
    "X_val[\"year_of_createdate\"] = X_val['baseline_create_date'].dt.year\n",
    "\n",
    "\n",
    "X_test[\"day_of_createdate\"] = X_test['baseline_create_date'].dt.day\n",
    "X_test[\"month_of_createdate\"] = X_test['baseline_create_date'].dt.month\n",
    "X_test[\"year_of_createdate\"] = X_test['baseline_create_date'].dt.year\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cFgwkS5rSDDs",
   "metadata": {
    "id": "cFgwkS5rSDDs"
   },
   "source": [
    "### pass the \"baseline_create_date\" column into the Custom function for train, test and validation dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "RGYa2BEQSDg3",
   "metadata": {
    "id": "RGYa2BEQSDg3"
   },
   "outputs": [],
   "source": [
    "X_train, X_val, X_test = custom(\"baseline_create_date\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77c7a0df",
   "metadata": {
    "id": "77c7a0df"
   },
   "source": [
    "### You need to extract day, month and year from the \"due_in_date\" column \n",
    "\n",
    "1.   Extract days from \"due_in_date\" column and store it into a new column \"day_of_due\" for train, test and validation dataset \n",
    "2.   Extract months from \"due_in_date\" column and store it into a new column \"month_of_due\" for train, test and validation dataset\n",
    "3.   Extract year from \"due_in_date\" column and store it into a new column \"year_of_due\" for train, test and validation dataset \n",
    "\n",
    "\n",
    "\n",
    "- Note - You are supposed yo use \n",
    "\n",
    "*   dt.day\n",
    "*   dt.month\n",
    "*   dt.year\n",
    "\n",
    "- Note - Do as it is been shown in the previous code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "e6d78cf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train[\"day_of_due\"] = X_train['due_in_date'].dt.day\n",
    "X_train[\"month_of_due\"] = X_train['due_in_date'].dt.month\n",
    "X_train[\"year_of_due\"] = X_train['due_in_date'].dt.year\n",
    "\n",
    "X_val[\"day_of_due\"] = X_val['due_in_date'].dt.day\n",
    "X_val[\"month_of_due\"] = X_val['due_in_date'].dt.month\n",
    "X_val[\"year_of_due\"] = X_val['due_in_date'].dt.year\n",
    "\n",
    "\n",
    "X_test[\"day_of_due\"] = X_test['due_in_date'].dt.day\n",
    "X_test[\"month_of_due\"] = X_test['due_in_date'].dt.month\n",
    "X_test[\"year_of_due\"] = X_test['due_in_date'].dt.year\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "FYLLzulGSvRd",
   "metadata": {
    "id": "FYLLzulGSvRd"
   },
   "source": [
    "pass the \"due_in_date\" column into the Custom function for train, test and validation dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "1-s6QuY9Svrh",
   "metadata": {
    "id": "1-s6QuY9Svrh"
   },
   "outputs": [],
   "source": [
    "X_train,X_val,X_test = custom(\"due_in_date\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ae5d052",
   "metadata": {
    "id": "1ae5d052"
   },
   "source": [
    "### Check for the datatypes for train, test and validation set again\n",
    "\n",
    "- Note - all the data type should be in either int64 or float64 format \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "aee9d828",
   "metadata": {
    "id": "aee9d828"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cust_number                 int32\n",
       "buisness_year             float64\n",
       "doc_id                    float64\n",
       "converted_usd             float64\n",
       "business_code_enc           int32\n",
       "name_customer_enc           int32\n",
       "cust_payment_terms_enc      int32\n",
       "day_of_postingdate          int64\n",
       "month_of_postingdate        int64\n",
       "year_of_postingdate         int64\n",
       "day_of_createdate           int64\n",
       "month_of_createdate         int64\n",
       "year_of_createdate          int64\n",
       "day_of_due                  int64\n",
       "month_of_due                int64\n",
       "year_of_due                 int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "2e763a04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cust_number                 int32\n",
       "buisness_year             float64\n",
       "doc_id                    float64\n",
       "converted_usd             float64\n",
       "business_code_enc           int32\n",
       "name_customer_enc           int32\n",
       "cust_payment_terms_enc      int32\n",
       "day_of_postingdate          int64\n",
       "month_of_postingdate        int64\n",
       "year_of_postingdate         int64\n",
       "day_of_createdate           int64\n",
       "month_of_createdate         int64\n",
       "year_of_createdate          int64\n",
       "day_of_due                  int64\n",
       "month_of_due                int64\n",
       "year_of_due                 int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "c1f2c38c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cust_number                 int32\n",
       "buisness_year             float64\n",
       "doc_id                    float64\n",
       "converted_usd             float64\n",
       "business_code_enc           int32\n",
       "name_customer_enc           int32\n",
       "cust_payment_terms_enc      int32\n",
       "day_of_postingdate          int64\n",
       "month_of_postingdate        int64\n",
       "year_of_postingdate         int64\n",
       "day_of_createdate           int64\n",
       "month_of_createdate         int64\n",
       "year_of_createdate          int64\n",
       "day_of_due                  int64\n",
       "month_of_due                int64\n",
       "year_of_due                 int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_val.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65810f55",
   "metadata": {
    "id": "65810f55"
   },
   "source": [
    "# Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bb1ad9f",
   "metadata": {
    "id": "4bb1ad9f"
   },
   "source": [
    "### Filter Method\n",
    "\n",
    "- Calling the VarianceThreshold Function \n",
    "- Note - Keep the code as it is, no need to change "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "e882509f",
   "metadata": {
    "id": "e882509f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import VarianceThreshold\n",
    "constant_filter = VarianceThreshold(threshold=0)\n",
    "constant_filter.fit(X_train)\n",
    "len(X_train.columns[constant_filter.get_support()])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "V9531H3jR-W2",
   "metadata": {
    "id": "V9531H3jR-W2"
   },
   "source": [
    "- Note - Keep the code as it is, no need to change \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "c77c12e1",
   "metadata": {
    "id": "c77c12e1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "constant_columns = [column for column in X_train.columns\n",
    "                    if column not in X_train.columns[constant_filter.get_support()]]\n",
    "print(len(constant_columns))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d9b8610",
   "metadata": {
    "id": "6d9b8610"
   },
   "source": [
    "- transpose the feature matrice\n",
    "- print the number of duplicated features\n",
    "- select the duplicated features columns names\n",
    "\n",
    "- Note - Keep the code as it is, no need to change \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "0fb7db95",
   "metadata": {
    "id": "0fb7db95"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "x_train_T = X_train.T\n",
    "print(x_train_T.duplicated().sum())\n",
    "duplicated_columns = x_train_T[x_train_T.duplicated()].index.values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "510fa831",
   "metadata": {
    "id": "510fa831"
   },
   "source": [
    "### Filtering depending upon correlation matrix value\n",
    "- We have created a function called handling correlation which is going to return fields based on the correlation matrix value with a threshold of 0.8\n",
    "\n",
    "- Note - Keep the code as it is, no need to change "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "67731abc",
   "metadata": {
    "id": "67731abc"
   },
   "outputs": [],
   "source": [
    "def handling_correlation(X_train,threshold=0.8):\n",
    "    corr_features = set()\n",
    "    corr_matrix = X_train.corr()\n",
    "    for i in range(len(corr_matrix .columns)):\n",
    "        for j in range(i):\n",
    "            if abs(corr_matrix.iloc[i, j]) >threshold:\n",
    "                colname = corr_matrix.columns[i]\n",
    "                corr_features.add(colname)\n",
    "    return list(corr_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "JaE_6qVgSXl3",
   "metadata": {
    "id": "JaE_6qVgSXl3"
   },
   "source": [
    "- Note : Here we are trying to find out the relevant fields, from X_train\n",
    "- Please fill in the blanks to call handling_correlation() function with a threshold value of 0.85"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "dd91d1a2",
   "metadata": {
    "id": "dd91d1a2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['month_of_createdate',\n",
       " 'year_of_due',\n",
       " 'day_of_createdate',\n",
       " 'year_of_createdate',\n",
       " 'month_of_due',\n",
       " 'year_of_postingdate']"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train=X_train.copy()\n",
    "handling_correlation(train.copy(), 0.85)\n",
    "#____________________(train.copy(),____)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "154da511",
   "metadata": {
    "id": "154da511"
   },
   "source": [
    "### Heatmap for X_train\n",
    "\n",
    "- Note - Keep the code as it is, no need to change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "2e8f2fe4",
   "metadata": {
    "id": "2e8f2fe4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Pearson Correlation of Features'}>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colormap = plt.cm.RdBu\n",
    "plt.figure(figsize=(14,12))\n",
    "plt.title('Pearson Correlation of Features', y=1.05, size=20)\n",
    "sns.heatmap(X_train.merge(y_train , on = X_train.index ).corr(),linewidths=0.1,vmax=1.0, \n",
    "            square=True, cmap='gist_rainbow_r', linecolor='white', annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3b0d745",
   "metadata": {
    "id": "e3b0d745"
   },
   "source": [
    "#### Calling variance threshold for threshold value = 0.8\n",
    "\n",
    "- Note -  Fill in the blanks to call the appropriate method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "a9b2080f",
   "metadata": {
    "id": "a9b2080f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VarianceThreshold(threshold=0.8)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import VarianceThreshold\n",
    "sel = VarianceThreshold(0.8)\n",
    "#sel = _________________(0.8)\n",
    "sel.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "6cb8c3dc",
   "metadata": {
    "id": "6cb8c3dc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.81954912e+15, 1.14757386e-01, 8.74254278e+16, 1.34173150e+09,\n",
       "       2.87927752e-01, 1.07015118e+06, 1.35481309e+02, 7.55282009e+01,\n",
       "       1.22549606e+01, 1.14798749e-01, 7.70243713e+01, 1.22619699e+01,\n",
       "       1.14937418e-01, 7.66173615e+01, 1.20665594e+01, 1.18125269e-01])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel.variances_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62633a84",
   "metadata": {
    "id": "62633a84"
   },
   "source": [
    "### Important features columns are \n",
    "- 'year_of_createdate' \n",
    "- 'year_of_due'\n",
    "- 'day_of_createdate'\n",
    "- 'year_of_postingdate'\n",
    "- 'month_of_due'\n",
    "- 'month_of_createdate'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "651f1ad0",
   "metadata": {
    "id": "651f1ad0"
   },
   "source": [
    "# Modelling \n",
    "\n",
    "#### Now you need to compare with different machine learning models, and needs to find out the best predicted model\n",
    "\n",
    "- Linear Regression\n",
    "- Decision Tree Regression\n",
    "- Random Forest Regression\n",
    "- Support Vector Regression\n",
    "- Extreme Gradient Boost Regression "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "PicEhSuUUOkt",
   "metadata": {
    "id": "PicEhSuUUOkt"
   },
   "source": [
    "### You need to make different blank list for different evaluation matrix \n",
    "\n",
    "- MSE\n",
    "- R2\n",
    "- Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "701e12b0",
   "metadata": {
    "id": "701e12b0"
   },
   "outputs": [],
   "source": [
    "MSE_Score = []\n",
    "R2_Score = []\n",
    "Algorithm = []\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29310119",
   "metadata": {
    "id": "29310119"
   },
   "source": [
    "### You need to start with the baseline model Linear Regression\n",
    "\n",
    "- Step 1 : Call the Linear Regression from sklearn library\n",
    "- Step 2 : make an object of Linear Regression \n",
    "- Step 3 : fit the X_train and y_train dataframe into the object \n",
    "- Step 4 : Predict the output by passing the X_test Dataset into predict function\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "- Note - Append the Algorithm name into the algorithm list for tracking purpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "6bdea395",
   "metadata": {
    "id": "6bdea395"
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "Algorithm.append('LinearRegression')\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n",
    "predicted= regressor.predict(X_test)\n",
    "#print(regressor.predict(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "G02cpnBhXJ14",
   "metadata": {
    "id": "G02cpnBhXJ14"
   },
   "source": [
    "### Check for the \n",
    "\n",
    "- Mean Square Error\n",
    "- R Square Error \n",
    "\n",
    "for y_test and predicted dataset and store those data inside respective list for comparison "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "0f69ca19",
   "metadata": {
    "id": "0f69ca19"
   },
   "outputs": [],
   "source": [
    "MSE_Score.append(mean_squared_error(y_test, predicted))\n",
    "R2_Score.append(r2_score(y_test, predicted))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "CsmScbHjYMv1",
   "metadata": {
    "id": "CsmScbHjYMv1"
   },
   "source": [
    "### Check the same for the Validation set also "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "fe653295",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1643093994094,
     "user": {
      "displayName": "Chandramouli Das",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgsMuDXf6PZsS77v0Q5x8metxFPnlLXsBC6Y3O7=s64",
      "userId": "13777762579346461395"
     },
     "user_tz": -330
    },
    "id": "fe653295",
    "outputId": "0c7429ca-50d0-42a2-96a1-effaa92f549e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "521974.13181915326"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_test= regressor.predict(X_val)\n",
    "mean_squared_error(y_val, predict_test, squared=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "LokxV2LGYUVh",
   "metadata": {
    "id": "LokxV2LGYUVh"
   },
   "source": [
    "### Display The Comparison Lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "9c405bd3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1643093994095,
     "user": {
      "displayName": "Chandramouli Das",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgsMuDXf6PZsS77v0Q5x8metxFPnlLXsBC6Y3O7=s64",
      "userId": "13777762579346461395"
     },
     "user_tz": -330
    },
    "id": "9c405bd3",
    "outputId": "9d78f4a9-33fc-48d1-edc8-c997eca38de0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['LinearRegression'],[289680195988.1249],[0.31983221802929696],"
     ]
    }
   ],
   "source": [
    "for i in Algorithm, MSE_Score, R2_Score:\n",
    "    print(i,end=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0e65c86",
   "metadata": {
    "id": "b0e65c86"
   },
   "source": [
    "### You need to start with the baseline model Support Vector Regression\n",
    "\n",
    "- Step 1 : Call the Support Vector Regressor from sklearn library\n",
    "- Step 2 : make an object of SVR\n",
    "- Step 3 : fit the X_train and y_train dataframe into the object \n",
    "- Step 4 : Predict the output by passing the X_test Dataset into predict function\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "- Note - Append the Algorithm name into the algorithm list for tracking purpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "31612018",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR\n",
    "Algorithm.append('Support Vector Regression')\n",
    "regressor = SVR()\n",
    "regressor.fit(X_train, y_train.values.ravel())\n",
    "\n",
    "# Predicting the Test Set Results\n",
    "predicted= regressor.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "zz9kcrViYt7e",
   "metadata": {
    "id": "zz9kcrViYt7e"
   },
   "source": [
    "### Check for the \n",
    "\n",
    "- Mean Square Error\n",
    "- R Square Error \n",
    "\n",
    "for \"y_test\" and \"predicted\" dataset and store those data inside respective list for comparison "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "195d6892",
   "metadata": {},
   "outputs": [],
   "source": [
    "MSE_Score.append(mean_squared_error(y_test, predicted))\n",
    "R2_Score.append(r2_score(y_test, predicted))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0YAxd8N9Y0hJ",
   "metadata": {
    "id": "0YAxd8N9Y0hJ"
   },
   "source": [
    "### Check the same for the Validation set also "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "5b96cbeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "631968.1689215614"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_test= regressor.predict(X_val)\n",
    "mean_squared_error(y_val, predict_test, squared=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eGcqS5EcY4BI",
   "metadata": {
    "id": "eGcqS5EcY4BI"
   },
   "source": [
    "### Display The Comparison Lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "aa72c1ec",
   "metadata": {
    "id": "aa72c1ec"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['LinearRegression', 'Support Vector Regression'],[289680195988.1249, 427979886589.643],[0.31983221802929696, -0.004894826160928734],"
     ]
    }
   ],
   "source": [
    "for i in Algorithm, MSE_Score, R2_Score:\n",
    "    print(i,end=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dad18bb3",
   "metadata": {
    "id": "dad18bb3"
   },
   "source": [
    "### Your next model would be Decision Tree Regression\n",
    "\n",
    "- Step 1 : Call the Decision Tree Regressor from sklearn library\n",
    "- Step 2 : make an object of Decision Tree\n",
    "- Step 3 : fit the X_train and y_train dataframe into the object \n",
    "- Step 4 : Predict the output by passing the X_test Dataset into predict function\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "- Note - Append the Algorithm name into the algorithm list for tracking purpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "1b6a51eb",
   "metadata": {
    "id": "1b6a51eb"
   },
   "outputs": [],
   "source": [
    "# Fitting Decision Tree to the Training Set\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "Algorithm.append('DecisionTreeRegression')\n",
    "reg = DecisionTreeRegressor()\n",
    "reg.fit(X_train, y_train)\n",
    "\n",
    "# Predicting the Test Set Results\n",
    "predicted = reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "AOzfgfeOZo3F",
   "metadata": {
    "id": "AOzfgfeOZo3F"
   },
   "source": [
    "### Check for the \n",
    "\n",
    "- Mean Square Error\n",
    "- R Square Error \n",
    "\n",
    "for y_test and predicted dataset and store those data inside respective list for comparison "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "776e6983",
   "metadata": {
    "id": "776e6983"
   },
   "outputs": [],
   "source": [
    "MSE_Score.append(mean_squared_error(y_test, predicted))\n",
    "R2_Score.append(r2_score(y_test, predicted))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eI6d49DQZrhW",
   "metadata": {
    "id": "eI6d49DQZrhW"
   },
   "source": [
    "### Check the same for the Validation set also "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "155fb55c",
   "metadata": {
    "id": "155fb55c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "502030.00501590874"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_test= reg.predict(X_val)\n",
    "mean_squared_error(y_val, predict_test, squared=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sbGXvBLQZw5E",
   "metadata": {
    "id": "sbGXvBLQZw5E"
   },
   "source": [
    "### Display The Comparison Lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "1d74d515",
   "metadata": {
    "id": "1d74d515"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['LinearRegression', 'Support Vector Regression', 'DecisionTreeRegression'],[289680195988.1249, 427979886589.643, 409140761460.1264],[0.31983221802929696, -0.004894826160928734, 0.03933935391445731],"
     ]
    }
   ],
   "source": [
    "for i in Algorithm, MSE_Score, R2_Score:\n",
    "    print(i,end=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ae9979b",
   "metadata": {
    "id": "4ae9979b"
   },
   "source": [
    "### Your next model would be Random Forest Regression\n",
    "\n",
    "- Step 1 : Call the Random Forest Regressor from sklearn library\n",
    "- Step 2 : make an object of Random Forest\n",
    "- Step 3 : fit the X_train and y_train dataframe into the object \n",
    "- Step 4 : Predict the output by passing the X_test Dataset into predict function\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "- Note - Append the Algorithm name into the algorithm list for tracking purpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "a69e476a",
   "metadata": {
    "id": "a69e476a"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "Algorithm.append('RandomForestRegression')\n",
    "rf = RandomForestRegressor()\n",
    "rf.fit(X_train,y_train.values.ravel())\n",
    "\n",
    "# Predicting the Test Set Results\n",
    "predicted=rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "XNcEJF-6anof",
   "metadata": {
    "id": "XNcEJF-6anof"
   },
   "source": [
    "### Check for the \n",
    "\n",
    "- Mean Square Error\n",
    "- R Square Error \n",
    "\n",
    "for y_test and predicted dataset and store those data inside respective list for comparison "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "826f63f4",
   "metadata": {
    "id": "826f63f4"
   },
   "outputs": [],
   "source": [
    "MSE_Score.append(mean_squared_error(y_test, predicted))\n",
    "R2_Score.append(r2_score(y_test, predicted))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "yMbyr9V4ati1",
   "metadata": {
    "id": "yMbyr9V4ati1"
   },
   "source": [
    "### Check the same for the Validation set also "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "55b9fb54",
   "metadata": {
    "id": "55b9fb54"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "336437.91485047725"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_test= rf.predict(X_val)\n",
    "mean_squared_error(y_val, predict_test, squared=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tiBawcCsaw_Z",
   "metadata": {
    "id": "tiBawcCsaw_Z"
   },
   "source": [
    "### Display The Comparison Lists\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "8277c13e",
   "metadata": {
    "id": "8277c13e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['LinearRegression', 'Support Vector Regression', 'DecisionTreeRegression', 'RandomForestRegression'],[289680195988.1249, 427979886589.643, 409140761460.1264, 156297699183.94452],[0.31983221802929696, -0.004894826160928734, 0.03933935391445731, 0.6330137135593981],"
     ]
    }
   ],
   "source": [
    "for i in Algorithm, MSE_Score, R2_Score:\n",
    "    print(i,end=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6b21881",
   "metadata": {
    "id": "e6b21881"
   },
   "source": [
    "### The last but not the least model would be XGBoost or Extreme Gradient Boost Regression\n",
    "\n",
    "- Step 1 : Call the XGBoost Regressor from xgb library\n",
    "- Step 2 : make an object of Xgboost\n",
    "- Step 3 : fit the X_train and y_train dataframe into the object \n",
    "- Step 4 : Predict the output by passing the X_test Dataset into predict function\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "- Note - Append the Algorithm name into the algorithm list for tracking purpose### Extreme Gradient Boost Regression\n",
    "- Note -  No need to change the code "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "705a38ec",
   "metadata": {
    "id": "705a38ec"
   },
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "Algorithm.append('XGB Regressor')\n",
    "regressor = xgb.XGBRegressor()\n",
    "regressor.fit(X_train, y_train)\n",
    "predicted = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ierNZkb9bQDD",
   "metadata": {
    "id": "ierNZkb9bQDD"
   },
   "source": [
    "### Check for the \n",
    "\n",
    "- Mean Square Error\n",
    "- R Square Error \n",
    "\n",
    "for y_test and predicted dataset and store those data inside respective list for comparison "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "507a9d2f",
   "metadata": {
    "id": "507a9d2f"
   },
   "outputs": [],
   "source": [
    "MSE_Score.append(mean_squared_error(y_test, predicted))\n",
    "R2_Score.append(r2_score(y_test, predicted))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84UZ2ojsbWaH",
   "metadata": {
    "id": "84UZ2ojsbWaH"
   },
   "source": [
    "### Check the same for the Validation set also "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "e78ac250",
   "metadata": {
    "id": "e78ac250"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "350656.3387120784"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_test= regressor.predict(X_val)\n",
    "mean_squared_error(y_val, predict_test, squared=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9FJFyaVbbbAH",
   "metadata": {
    "id": "9FJFyaVbbbAH"
   },
   "source": [
    "### Display The Comparison Lists\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "f765ba35",
   "metadata": {
    "id": "f765ba35"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['LinearRegression', 'Support Vector Regression', 'DecisionTreeRegression', 'RandomForestRegression', 'XGB Regressor'],[289680195988.1249, 427979886589.643, 409140761460.1264, 156297699183.94452, 153265909840.83405],[0.31983221802929696, -0.004894826160928734, 0.03933935391445731, 0.6330137135593981, 0.6401323411406581],"
     ]
    }
   ],
   "source": [
    "for i in Algorithm, MSE_Score, R2_Score:\n",
    "    print(i,end=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a71bc90f",
   "metadata": {
    "id": "a71bc90f"
   },
   "source": [
    "## You need to make the comparison list into a comparison dataframe "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "ff5159a7",
   "metadata": {
    "id": "ff5159a7"
   },
   "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>Algorithm</th>\n",
       "      <th>MSE_Score</th>\n",
       "      <th>R2_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinearRegression</td>\n",
       "      <td>2.896802e+11</td>\n",
       "      <td>0.319832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Support Vector Regression</td>\n",
       "      <td>4.279799e+11</td>\n",
       "      <td>-0.004895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DecisionTreeRegression</td>\n",
       "      <td>4.091408e+11</td>\n",
       "      <td>0.039339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RandomForestRegression</td>\n",
       "      <td>1.562977e+11</td>\n",
       "      <td>0.633014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>XGB Regressor</td>\n",
       "      <td>1.532659e+11</td>\n",
       "      <td>0.640132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Algorithm     MSE_Score  R2_Score\n",
       "0           LinearRegression  2.896802e+11  0.319832\n",
       "1  Support Vector Regression  4.279799e+11 -0.004895\n",
       "2     DecisionTreeRegression  4.091408e+11  0.039339\n",
       "3     RandomForestRegression  1.562977e+11  0.633014\n",
       "4              XGB Regressor  1.532659e+11  0.640132"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Comparison = pd.DataFrame(list(zip(Algorithm, MSE_Score, R2_Score)), columns = ['Algorithm', 'MSE_Score', 'R2_Score'])\n",
    "Comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62e61c60",
   "metadata": {
    "id": "62e61c60"
   },
   "source": [
    "## Now from the Comparison table, you need to choose the best fit model\n",
    "\n",
    "- Step 1 - Fit X_train and y_train inside the model \n",
    "- Step 2 - Predict the X_test dataset\n",
    "- Step 3 - Predict the X_val dataset\n",
    "\n",
    "\n",
    "- Note - No need to change the code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "3e07c258",
   "metadata": {
    "id": "3e07c258"
   },
   "outputs": [],
   "source": [
    "regressorfinal = xgb.XGBRegressor()\n",
    "regressorfinal.fit(X_train, y_train)\n",
    "predictedfinal = regressorfinal.predict(X_test)\n",
    "predict_testfinal = regressorfinal.predict(X_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e4df6c4",
   "metadata": {
    "id": "8e4df6c4"
   },
   "source": [
    "### Calculate the Mean Square Error for test dataset\n",
    "\n",
    "- Note - No need to change the code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "5fb466d0",
   "metadata": {
    "id": "5fb466d0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "391491.90265040484"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_test,predictedfinal,squared=False)  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce27f87f",
   "metadata": {
    "id": "ce27f87f"
   },
   "source": [
    "### Calculate the mean Square Error for validation dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "b47978ea",
   "metadata": {
    "id": "b47978ea"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "350656.3387120784"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_val,predict_testfinal,squared=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30014dbd",
   "metadata": {
    "id": "30014dbd"
   },
   "source": [
    "### Calculate the R2 score for test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "8a162737",
   "metadata": {
    "id": "8a162737"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6401323411406581"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test, predictedfinal)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c9853b0",
   "metadata": {
    "id": "1c9853b0"
   },
   "source": [
    "### Calculate the R2 score for Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "1a6dc77c",
   "metadata": {
    "id": "1a6dc77c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6905912864894136"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_val, predict_testfinal)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "499522d9",
   "metadata": {
    "id": "499522d9"
   },
   "source": [
    "### Calculate the Accuracy for train Dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "efbea8f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "7a4f1ce8",
   "metadata": {
    "id": "7a4f1ce8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95.61689299223204"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressorfinal.score(X_train, y_train)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12a1c921",
   "metadata": {
    "id": "12a1c921"
   },
   "source": [
    "### Calculate the accuracy for validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "d2579b4f",
   "metadata": {
    "id": "d2579b4f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "69.05912864894135"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressorfinal.score(X_val, y_val)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79b82e84",
   "metadata": {
    "id": "79b82e84"
   },
   "source": [
    "### Calculate the accuracy for test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "f09e6431",
   "metadata": {
    "id": "f09e6431"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64.01323411406581"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressorfinal.score(X_test, y_test)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9488a5d9",
   "metadata": {
    "id": "9488a5d9"
   },
   "source": [
    "## Specify the reason behind choosing your machine learning model \n",
    "\n",
    "- Note : Provide your answer as a text here"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e11dcc46",
   "metadata": {},
   "source": [
    "# XGboost Model\n",
    "The accuracy in case of XGboost model is better than other models.\n",
    "The value of MSE is less in case to this model.\n",
    "The value or R2 score is closest to 1 while using XGBoost model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "387a6519",
   "metadata": {
    "id": "387a6519"
   },
   "source": [
    "## Now you need to pass the Nulldata dataframe into this machine learning model\n",
    "\n",
    "#### In order to pass this Nulldata dataframe into the ML model, we need to perform the following\n",
    "\n",
    "- Step 1 : Label Encoding \n",
    "- Step 2 : Day, Month and Year extraction \n",
    "- Step 3 : Change all the column data type into int64 or float64\n",
    "- Step 4 : Need to drop the useless columns "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "I7JuxAkdiAdI",
   "metadata": {
    "id": "I7JuxAkdiAdI"
   },
   "source": [
    "### Display the Nulldata "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "6d6a51d2",
   "metadata": {
    "id": "6d6a51d2"
   },
   "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>business_code</th>\n",
       "      <th>cust_number</th>\n",
       "      <th>name_customer</th>\n",
       "      <th>clear_date</th>\n",
       "      <th>buisness_year</th>\n",
       "      <th>doc_id</th>\n",
       "      <th>posting_date</th>\n",
       "      <th>due_in_date</th>\n",
       "      <th>baseline_create_date</th>\n",
       "      <th>cust_payment_terms</th>\n",
       "      <th>converted_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA02</td>\n",
       "      <td>0140105686</td>\n",
       "      <td>SYSC llc</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2.960623e+09</td>\n",
       "      <td>2020-03-30</td>\n",
       "      <td>2020-04-10</td>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>CA10</td>\n",
       "      <td>2309.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200744019</td>\n",
       "      <td>TARG us</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930659e+09</td>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>2020-04-03</td>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>NAA8</td>\n",
       "      <td>11173.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200418007</td>\n",
       "      <td>AM</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930611e+09</td>\n",
       "      <td>2020-03-11</td>\n",
       "      <td>2020-03-26</td>\n",
       "      <td>2020-03-11</td>\n",
       "      <td>NAA8</td>\n",
       "      <td>3525.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200739534</td>\n",
       "      <td>OK systems</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930788e+09</td>\n",
       "      <td>2020-04-15</td>\n",
       "      <td>2020-04-30</td>\n",
       "      <td>2020-04-15</td>\n",
       "      <td>NAA8</td>\n",
       "      <td>121105.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200353024</td>\n",
       "      <td>DECA corporation</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930817e+09</td>\n",
       "      <td>2020-04-23</td>\n",
       "      <td>2020-04-26</td>\n",
       "      <td>2020-04-16</td>\n",
       "      <td>NAM2</td>\n",
       "      <td>3726.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  business_code cust_number     name_customer clear_date  buisness_year  \\\n",
       "0          CA02  0140105686          SYSC llc        NaT         2020.0   \n",
       "1          U001  0200744019           TARG us        NaT         2020.0   \n",
       "2          U001  0200418007               AM         NaT         2020.0   \n",
       "3          U001  0200739534        OK systems        NaT         2020.0   \n",
       "4          U001  0200353024  DECA corporation        NaT         2020.0   \n",
       "\n",
       "         doc_id posting_date due_in_date baseline_create_date  \\\n",
       "0  2.960623e+09   2020-03-30  2020-04-10           2020-03-31   \n",
       "1  1.930659e+09   2020-03-19  2020-04-03           2020-03-19   \n",
       "2  1.930611e+09   2020-03-11  2020-03-26           2020-03-11   \n",
       "3  1.930788e+09   2020-04-15  2020-04-30           2020-04-15   \n",
       "4  1.930817e+09   2020-04-23  2020-04-26           2020-04-16   \n",
       "\n",
       "  cust_payment_terms  converted_usd  \n",
       "0               CA10        2309.79  \n",
       "1               NAA8       11173.02  \n",
       "2               NAA8        3525.59  \n",
       "3               NAA8      121105.65  \n",
       "4               NAM2        3726.06  "
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nulldata.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Vamx5xqtiHCH",
   "metadata": {
    "id": "Vamx5xqtiHCH"
   },
   "source": [
    "### Check for the number of rows and columns in the nulldata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "59de1092",
   "metadata": {
    "id": "59de1092"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9681, 11)"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nulldata.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "BxzHNbBjpqXL",
   "metadata": {
    "id": "BxzHNbBjpqXL"
   },
   "source": [
    "### Check the Description and Information of the nulldata "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "a6294d29",
   "metadata": {
    "id": "a6294d29"
   },
   "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>buisness_year</th>\n",
       "      <th>doc_id</th>\n",
       "      <th>converted_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9681.0</td>\n",
       "      <td>9.681000e+03</td>\n",
       "      <td>9681.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>2.006165e+09</td>\n",
       "      <td>32065.681125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.673629e+08</td>\n",
       "      <td>35419.613688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930535e+09</td>\n",
       "      <td>0.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930658e+09</td>\n",
       "      <td>5607.190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930731e+09</td>\n",
       "      <td>19024.190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930818e+09</td>\n",
       "      <td>47752.640000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>2.960636e+09</td>\n",
       "      <td>653644.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       buisness_year        doc_id  converted_usd\n",
       "count         9681.0  9.681000e+03    9681.000000\n",
       "mean          2020.0  2.006165e+09   32065.681125\n",
       "std              0.0  2.673629e+08   35419.613688\n",
       "min           2020.0  1.930535e+09       0.720000\n",
       "25%           2020.0  1.930658e+09    5607.190000\n",
       "50%           2020.0  1.930731e+09   19024.190000\n",
       "75%           2020.0  1.930818e+09   47752.640000\n",
       "max           2020.0  2.960636e+09  653644.800000"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nulldata.describe()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe860d94",
   "metadata": {
    "id": "fe860d94"
   },
   "source": [
    "### Storing the Nulldata into a different dataset \n",
    "# for BACKUP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "16352034",
   "metadata": {
    "id": "16352034"
   },
   "outputs": [],
   "source": [
    "nulldata1 = nulldata.copy(deep=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00f35b8c",
   "metadata": {
    "id": "00f35b8c"
   },
   "source": [
    "### Call the Label Encoder for Nulldata\n",
    "\n",
    "- Note - you are expected to fit \"business_code\" as it is a categorical variable\n",
    "- Note - No need to change the code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "baf04b17",
   "metadata": {
    "id": "baf04b17"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "business_codern = LabelEncoder()\n",
    "business_codern.fit(nulldata['business_code'])\n",
    "nulldata['business_code_enc'] = business_codern.transform(nulldata['business_code'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ZCPBK9karIR-",
   "metadata": {
    "id": "ZCPBK9karIR-"
   },
   "source": [
    "### Now you need to manually replacing str values with numbers\n",
    "- Note - No need to change the code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "5822a49b",
   "metadata": {},
   "outputs": [],
   "source": [
    "nulldata['cust_number'] = nulldata['cust_number'].str.replace('CCCA',\"1\").str.replace('CCU',\"2\").str.replace('CC',\"3\").astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a55f5f6",
   "metadata": {
    "id": "9a55f5f6"
   },
   "source": [
    "## You need to extract day, month and year from the \"clear_date\", \"posting_date\", \"due_in_date\", \"baseline_create_date\" columns\n",
    "\n",
    "\n",
    "##### 1.   Extract day from \"clear_date\" column and store it into 'day_of_cleardate'\n",
    "##### 2.   Extract month from \"clear_date\" column and store it into 'month_of_cleardate'\n",
    "##### 3.   Extract year from \"clear_date\" column and store it into 'year_of_cleardate'\n",
    "\n",
    "\n",
    "\n",
    "##### 4.   Extract day from \"posting_date\" column and store it into 'day_of_postingdate'\n",
    "##### 5.   Extract month from \"posting_date\" column and store it into 'month_of_postingdate'\n",
    "##### 6.   Extract year from \"posting_date\" column and store it into 'year_of_postingdate'\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "##### 7.   Extract day from \"due_in_date\" column and store it into 'day_of_due'\n",
    "##### 8.   Extract month from \"due_in_date\" column and store it into 'month_of_due'\n",
    "##### 9.   Extract year from \"due_in_date\" column and store it into 'year_of_due'\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "##### 10.   Extract day from \"baseline_create_date\" column and store it into 'day_of_createdate'\n",
    "##### 11.   Extract month from \"baseline_create_date\" column and store it into 'month_of_createdate'\n",
    "##### 12.   Extract year from \"baseline_create_date\" column and store it into 'year_of_createdate'\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "- Note - You are supposed To use - \n",
    "\n",
    "*   dt.day\n",
    "*   dt.month\n",
    "*   dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "4166fbe4",
   "metadata": {
    "id": "4166fbe4"
   },
   "outputs": [],
   "source": [
    "nulldata['day_of_cleardate'] = nulldata[\"clear_date\"].dt.day\n",
    "nulldata['month_of_cleardate' ] = nulldata[\"clear_date\"].dt.month\n",
    "nulldata['year_of_cleardate'] =nulldata[\"clear_date\"].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "e7ab1fbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "nulldata['day_of_postingdate'] = nulldata[\"posting_date\"].dt.day\n",
    "nulldata['month_of_postingdate' ] = nulldata[\"posting_date\"].dt.month\n",
    "nulldata['year_of_postingdate'] =nulldata[\"posting_date\"].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "a3e09fcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "nulldata['day_of_due'] = nulldata[\"due_in_date\"].dt.day\n",
    "nulldata['month_of_due' ] = nulldata[\"due_in_date\"].dt.month\n",
    "nulldata['year_of_due'] =nulldata[\"due_in_date\"].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "c585fd42",
   "metadata": {},
   "outputs": [],
   "source": [
    "nulldata['day_of_createdate'] = nulldata[\"baseline_create_date\"].dt.day\n",
    "nulldata['month_of_createdate' ] = nulldata[\"baseline_create_date\"].dt.month\n",
    "nulldata['year_of_createdate'] =nulldata[\"baseline_create_date\"].dt.year"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "QeHWJYrAvOC6",
   "metadata": {
    "id": "QeHWJYrAvOC6"
   },
   "source": [
    "### Use Label Encoder1 of all the following columns - \n",
    "- 'cust_payment_terms' and store into 'cust_payment_terms_enc'\n",
    "- 'business_code' and store into 'business_code_enc'\n",
    "- 'name_customer' and store into 'name_customer_enc'\n",
    "\n",
    "Note - No need to change the code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "bac330e2",
   "metadata": {
    "id": "bac330e2"
   },
   "outputs": [],
   "source": [
    "nulldata['cust_payment_terms_enc']=label_encoder1.transform(nulldata['cust_payment_terms'])\n",
    "nulldata['business_code_enc']=label_encoder1.transform(nulldata['business_code'])\n",
    "nulldata['name_customer_enc']=label_encoder.transform(nulldata['name_customer'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "zD9I-XqQwC28",
   "metadata": {
    "id": "zD9I-XqQwC28"
   },
   "source": [
    "### Check for the datatypes of all the columns of Nulldata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "d4f72517",
   "metadata": {
    "id": "d4f72517"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "business_code                     object\n",
       "cust_number                        int32\n",
       "name_customer                     object\n",
       "clear_date                datetime64[ns]\n",
       "buisness_year                    float64\n",
       "doc_id                           float64\n",
       "posting_date              datetime64[ns]\n",
       "due_in_date               datetime64[ns]\n",
       "baseline_create_date      datetime64[ns]\n",
       "cust_payment_terms                object\n",
       "converted_usd                    float64\n",
       "business_code_enc                  int32\n",
       "day_of_cleardate                 float64\n",
       "month_of_cleardate               float64\n",
       "year_of_cleardate                float64\n",
       "day_of_postingdate                 int64\n",
       "month_of_postingdate               int64\n",
       "year_of_postingdate                int64\n",
       "day_of_due                         int64\n",
       "month_of_due                       int64\n",
       "year_of_due                        int64\n",
       "day_of_createdate                  int64\n",
       "month_of_createdate                int64\n",
       "year_of_createdate                 int64\n",
       "cust_payment_terms_enc             int32\n",
       "name_customer_enc                  int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nulldata.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17cd5452",
   "metadata": {
    "id": "17cd5452"
   },
   "source": [
    "### Now you need to drop all the unnecessary columns - \n",
    "\n",
    "- 'business_code'\n",
    "- \"baseline_create_date\"\n",
    "- \"due_in_date\"\n",
    "- \"posting_date\"\n",
    "- \"name_customer\"\n",
    "- \"clear_date\"\n",
    "- \"cust_payment_terms\"\n",
    "- 'day_of_cleardate'\n",
    "- \"month_of_cleardate\"\n",
    "- \"year_of_cleardate\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "d7c82076",
   "metadata": {
    "id": "d7c82076"
   },
   "outputs": [],
   "source": [
    "nulldata.drop(columns=['business_code',\n",
    "\"baseline_create_date\",\n",
    "\"due_in_date\",\n",
    "\"posting_date\",\n",
    "\"name_customer\",\n",
    "\"clear_date\",\n",
    "\"cust_payment_terms\",\n",
    "'day_of_cleardate',\n",
    "\"month_of_cleardate\",\n",
    "\"year_of_cleardate\"],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Q_NCr9IPweVq",
   "metadata": {
    "id": "Q_NCr9IPweVq"
   },
   "source": [
    "### Check the information of the \"nulldata\" dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "4e7ffee0",
   "metadata": {
    "id": "4e7ffee0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9681 entries, 0 to 9680\n",
      "Data columns (total 16 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   cust_number             9681 non-null   int32  \n",
      " 1   buisness_year           9681 non-null   float64\n",
      " 2   doc_id                  9681 non-null   float64\n",
      " 3   converted_usd           9681 non-null   float64\n",
      " 4   business_code_enc       9681 non-null   int32  \n",
      " 5   day_of_postingdate      9681 non-null   int64  \n",
      " 6   month_of_postingdate    9681 non-null   int64  \n",
      " 7   year_of_postingdate     9681 non-null   int64  \n",
      " 8   day_of_due              9681 non-null   int64  \n",
      " 9   month_of_due            9681 non-null   int64  \n",
      " 10  year_of_due             9681 non-null   int64  \n",
      " 11  day_of_createdate       9681 non-null   int64  \n",
      " 12  month_of_createdate     9681 non-null   int64  \n",
      " 13  year_of_createdate      9681 non-null   int64  \n",
      " 14  cust_payment_terms_enc  9681 non-null   int32  \n",
      " 15  name_customer_enc       9681 non-null   int32  \n",
      "dtypes: float64(3), int32(4), int64(9)\n",
      "memory usage: 1.0 MB\n"
     ]
    }
   ],
   "source": [
    "nulldata.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "-XvjhWqmwi-C",
   "metadata": {
    "id": "-XvjhWqmwi-C"
   },
   "source": [
    "### Compare \"nulldata\" with the \"X_test\" dataframe \n",
    "\n",
    "- use info() method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "02f4b62d",
   "metadata": {
    "id": "02f4b62d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9681 entries, 0 to 9680\n",
      "Data columns (total 16 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   cust_number             9681 non-null   int32  \n",
      " 1   buisness_year           9681 non-null   float64\n",
      " 2   doc_id                  9681 non-null   float64\n",
      " 3   converted_usd           9681 non-null   float64\n",
      " 4   business_code_enc       9681 non-null   int32  \n",
      " 5   day_of_postingdate      9681 non-null   int64  \n",
      " 6   month_of_postingdate    9681 non-null   int64  \n",
      " 7   year_of_postingdate     9681 non-null   int64  \n",
      " 8   day_of_due              9681 non-null   int64  \n",
      " 9   month_of_due            9681 non-null   int64  \n",
      " 10  year_of_due             9681 non-null   int64  \n",
      " 11  day_of_createdate       9681 non-null   int64  \n",
      " 12  month_of_createdate     9681 non-null   int64  \n",
      " 13  year_of_createdate      9681 non-null   int64  \n",
      " 14  cust_payment_terms_enc  9681 non-null   int32  \n",
      " 15  name_customer_enc       9681 non-null   int32  \n",
      "dtypes: float64(3), int32(4), int64(9)\n",
      "memory usage: 1.0 MB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 7832 entries, 18813 to 4112\n",
      "Data columns (total 16 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   cust_number             7832 non-null   int32  \n",
      " 1   buisness_year           7832 non-null   float64\n",
      " 2   doc_id                  7832 non-null   float64\n",
      " 3   converted_usd           7832 non-null   float64\n",
      " 4   business_code_enc       7832 non-null   int32  \n",
      " 5   name_customer_enc       7832 non-null   int32  \n",
      " 6   cust_payment_terms_enc  7832 non-null   int32  \n",
      " 7   day_of_postingdate      7832 non-null   int64  \n",
      " 8   month_of_postingdate    7832 non-null   int64  \n",
      " 9   year_of_postingdate     7832 non-null   int64  \n",
      " 10  day_of_createdate       7832 non-null   int64  \n",
      " 11  month_of_createdate     7832 non-null   int64  \n",
      " 12  year_of_createdate      7832 non-null   int64  \n",
      " 13  day_of_due              7832 non-null   int64  \n",
      " 14  month_of_due            7832 non-null   int64  \n",
      " 15  year_of_due             7832 non-null   int64  \n",
      "dtypes: float64(3), int32(4), int64(9)\n",
      "memory usage: 917.8 KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nulldata.info() == X_test.info()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Us3ey-9zwqjq",
   "metadata": {
    "id": "Us3ey-9zwqjq"
   },
   "source": [
    "### You must have noticed that there is a mismatch in the column sequence while compairing the dataframes\n",
    "\n",
    "- Note - In order to fed into the machine learning model, you need to edit the sequence of \"nulldata\", similar to the \"X_test\" dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "vduVNt1kxPW-",
   "metadata": {
    "id": "vduVNt1kxPW-"
   },
   "source": [
    "- Display all the columns of the X_test dataframe \n",
    "- Display all the columns of the Nulldata dataframe \n",
    "- Store the Nulldata with new sequence into a new dataframe \n",
    "\n",
    "\n",
    "- Note - The code is given below, no need to change "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "6729353e",
   "metadata": {
    "id": "6729353e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['cust_number', 'buisness_year', 'doc_id', 'converted_usd',\n",
       "       'business_code_enc', 'name_customer_enc', 'cust_payment_terms_enc',\n",
       "       'day_of_postingdate', 'month_of_postingdate', 'year_of_postingdate',\n",
       "       'day_of_createdate', 'month_of_createdate', 'year_of_createdate',\n",
       "       'day_of_due', 'month_of_due', 'year_of_due'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "47bd9c5e",
   "metadata": {
    "id": "47bd9c5e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['cust_number', 'buisness_year', 'doc_id', 'converted_usd',\n",
       "       'business_code_enc', 'day_of_postingdate', 'month_of_postingdate',\n",
       "       'year_of_postingdate', 'day_of_due', 'month_of_due', 'year_of_due',\n",
       "       'day_of_createdate', 'month_of_createdate', 'year_of_createdate',\n",
       "       'cust_payment_terms_enc', 'name_customer_enc'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nulldata.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "aa5a2103",
   "metadata": {
    "id": "aa5a2103"
   },
   "outputs": [],
   "source": [
    "nulldata2=nulldata[['cust_number', 'buisness_year', 'doc_id', 'converted_usd',\n",
    "       'business_code_enc', 'name_customer_enc', 'cust_payment_terms_enc',\n",
    "       'day_of_postingdate', 'month_of_postingdate', 'year_of_postingdate',\n",
    "       'day_of_createdate', 'month_of_createdate', 'year_of_createdate',\n",
    "       'day_of_due', 'month_of_due', 'year_of_due']]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dc8b021",
   "metadata": {
    "id": "1dc8b021"
   },
   "source": [
    "### Display the Final Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "2f39785a",
   "metadata": {
    "id": "2f39785a"
   },
   "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>cust_number</th>\n",
       "      <th>buisness_year</th>\n",
       "      <th>doc_id</th>\n",
       "      <th>converted_usd</th>\n",
       "      <th>business_code_enc</th>\n",
       "      <th>name_customer_enc</th>\n",
       "      <th>cust_payment_terms_enc</th>\n",
       "      <th>day_of_postingdate</th>\n",
       "      <th>month_of_postingdate</th>\n",
       "      <th>year_of_postingdate</th>\n",
       "      <th>day_of_createdate</th>\n",
       "      <th>month_of_createdate</th>\n",
       "      <th>year_of_createdate</th>\n",
       "      <th>day_of_due</th>\n",
       "      <th>month_of_due</th>\n",
       "      <th>year_of_due</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>140105686</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2.960623e+09</td>\n",
       "      <td>2309.79</td>\n",
       "      <td>66</td>\n",
       "      <td>2714</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200744019</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930659e+09</td>\n",
       "      <td>11173.02</td>\n",
       "      <td>66</td>\n",
       "      <td>2798</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200418007</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930611e+09</td>\n",
       "      <td>3525.59</td>\n",
       "      <td>66</td>\n",
       "      <td>89</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>200739534</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930788e+09</td>\n",
       "      <td>121105.65</td>\n",
       "      <td>66</td>\n",
       "      <td>2043</td>\n",
       "      <td>18</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>2020</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>2020</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>200353024</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930817e+09</td>\n",
       "      <td>3726.06</td>\n",
       "      <td>66</td>\n",
       "      <td>733</td>\n",
       "      <td>34</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>2020</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>2020</td>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cust_number  buisness_year        doc_id  converted_usd  business_code_enc  \\\n",
       "0    140105686         2020.0  2.960623e+09        2309.79                 66   \n",
       "1    200744019         2020.0  1.930659e+09       11173.02                 66   \n",
       "2    200418007         2020.0  1.930611e+09        3525.59                 66   \n",
       "3    200739534         2020.0  1.930788e+09      121105.65                 66   \n",
       "4    200353024         2020.0  1.930817e+09        3726.06                 66   \n",
       "\n",
       "   name_customer_enc  cust_payment_terms_enc  day_of_postingdate  \\\n",
       "0               2714                       5                  30   \n",
       "1               2798                      18                  19   \n",
       "2                 89                      18                  11   \n",
       "3               2043                      18                  15   \n",
       "4                733                      34                  23   \n",
       "\n",
       "   month_of_postingdate  year_of_postingdate  day_of_createdate  \\\n",
       "0                     3                 2020                 31   \n",
       "1                     3                 2020                 19   \n",
       "2                     3                 2020                 11   \n",
       "3                     4                 2020                 15   \n",
       "4                     4                 2020                 16   \n",
       "\n",
       "   month_of_createdate  year_of_createdate  day_of_due  month_of_due  \\\n",
       "0                    3                2020          10             4   \n",
       "1                    3                2020           3             4   \n",
       "2                    3                2020          26             3   \n",
       "3                    4                2020          30             4   \n",
       "4                    4                2020          26             4   \n",
       "\n",
       "   year_of_due  \n",
       "0         2020  \n",
       "1         2020  \n",
       "2         2020  \n",
       "3         2020  \n",
       "4         2020  "
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nulldata2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "e9e50ea4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23494, 16)"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "b86d84ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23494, 1)"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "5bccb848",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9681, 16)"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nulldata2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27b88c5a",
   "metadata": {
    "id": "27b88c5a"
   },
   "source": [
    "### Now you can pass this dataset into you final model and store it into \"final_result\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "9e0b6388",
   "metadata": {
    "id": "9e0b6388"
   },
   "outputs": [],
   "source": [
    "regressorfinal = xgb.XGBRegressor()\n",
    "regressorfinal.fit(X_train, y_train)\n",
    "predictedfinal = regressorfinal.predict(nulldata2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "be977e2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_result = predictedfinal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9653d3c6",
   "metadata": {
    "id": "9653d3c6"
   },
   "source": [
    "### you need to make the final_result as dataframe, with a column name \"avg_delay\"\n",
    "\n",
    "- Note - No need to change the code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "25ef814d",
   "metadata": {
    "id": "25ef814d"
   },
   "outputs": [],
   "source": [
    "final_result = pd.Series(final_result,name='avg_delay')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "C86staIhyf2C",
   "metadata": {
    "id": "C86staIhyf2C"
   },
   "source": [
    "### Display the \"avg_delay\" column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "4fd46406",
   "metadata": {
    "id": "4fd46406"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    882522.125000\n",
       "1    654349.750000\n",
       "2     91307.375000\n",
       "3   -250995.546875\n",
       "4   -208052.328125\n",
       "Name: avg_delay, dtype: float32"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_result.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44f71a7e",
   "metadata": {
    "id": "44f71a7e"
   },
   "source": [
    "### Now you need to merge this final_result dataframe with the BACKUP of \"nulldata\" Dataframe which we have created in earlier steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "e8f0969d",
   "metadata": {
    "id": "e8f0969d"
   },
   "outputs": [],
   "source": [
    "nulldata1.reset_index(drop=True,inplace=True)\n",
    "Final = nulldata1.merge(final_result , on = nulldata.index )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "G-hLtxXgy4GZ",
   "metadata": {
    "id": "G-hLtxXgy4GZ"
   },
   "source": [
    "### Display the \"Final\" dataframe "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "71fb4dc0",
   "metadata": {
    "id": "71fb4dc0"
   },
   "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>key_0</th>\n",
       "      <th>business_code</th>\n",
       "      <th>cust_number</th>\n",
       "      <th>name_customer</th>\n",
       "      <th>clear_date</th>\n",
       "      <th>buisness_year</th>\n",
       "      <th>doc_id</th>\n",
       "      <th>posting_date</th>\n",
       "      <th>due_in_date</th>\n",
       "      <th>baseline_create_date</th>\n",
       "      <th>cust_payment_terms</th>\n",
       "      <th>converted_usd</th>\n",
       "      <th>avg_delay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>CA02</td>\n",
       "      <td>0140105686</td>\n",
       "      <td>SYSC llc</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2.960623e+09</td>\n",
       "      <td>2020-03-30</td>\n",
       "      <td>2020-04-10</td>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>CA10</td>\n",
       "      <td>2309.79</td>\n",
       "      <td>882522.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>U001</td>\n",
       "      <td>0200744019</td>\n",
       "      <td>TARG us</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930659e+09</td>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>2020-04-03</td>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>NAA8</td>\n",
       "      <td>11173.02</td>\n",
       "      <td>654349.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>U001</td>\n",
       "      <td>0200418007</td>\n",
       "      <td>AM</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930611e+09</td>\n",
       "      <td>2020-03-11</td>\n",
       "      <td>2020-03-26</td>\n",
       "      <td>2020-03-11</td>\n",
       "      <td>NAA8</td>\n",
       "      <td>3525.59</td>\n",
       "      <td>91307.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>U001</td>\n",
       "      <td>0200739534</td>\n",
       "      <td>OK systems</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930788e+09</td>\n",
       "      <td>2020-04-15</td>\n",
       "      <td>2020-04-30</td>\n",
       "      <td>2020-04-15</td>\n",
       "      <td>NAA8</td>\n",
       "      <td>121105.65</td>\n",
       "      <td>-250995.546875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>U001</td>\n",
       "      <td>0200353024</td>\n",
       "      <td>DECA corporation</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930817e+09</td>\n",
       "      <td>2020-04-23</td>\n",
       "      <td>2020-04-26</td>\n",
       "      <td>2020-04-16</td>\n",
       "      <td>NAM2</td>\n",
       "      <td>3726.06</td>\n",
       "      <td>-208052.328125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key_0 business_code cust_number     name_customer clear_date  \\\n",
       "0      0          CA02  0140105686          SYSC llc        NaT   \n",
       "1      1          U001  0200744019           TARG us        NaT   \n",
       "2      2          U001  0200418007               AM         NaT   \n",
       "3      3          U001  0200739534        OK systems        NaT   \n",
       "4      4          U001  0200353024  DECA corporation        NaT   \n",
       "\n",
       "   buisness_year        doc_id posting_date due_in_date baseline_create_date  \\\n",
       "0         2020.0  2.960623e+09   2020-03-30  2020-04-10           2020-03-31   \n",
       "1         2020.0  1.930659e+09   2020-03-19  2020-04-03           2020-03-19   \n",
       "2         2020.0  1.930611e+09   2020-03-11  2020-03-26           2020-03-11   \n",
       "3         2020.0  1.930788e+09   2020-04-15  2020-04-30           2020-04-15   \n",
       "4         2020.0  1.930817e+09   2020-04-23  2020-04-26           2020-04-16   \n",
       "\n",
       "  cust_payment_terms  converted_usd      avg_delay  \n",
       "0               CA10        2309.79  882522.125000  \n",
       "1               NAA8       11173.02  654349.750000  \n",
       "2               NAA8        3525.59   91307.375000  \n",
       "3               NAA8      121105.65 -250995.546875  \n",
       "4               NAM2        3726.06 -208052.328125  "
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Final.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4sc27Uz-y-0O",
   "metadata": {
    "id": "4sc27Uz-y-0O"
   },
   "source": [
    "### Check for the Number of Rows and Columns in your \"Final\" dataframe "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "5iUXOIhzy_HR",
   "metadata": {
    "id": "5iUXOIhzy_HR"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9681, 13)"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Final.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48886d2c",
   "metadata": {
    "id": "48886d2c"
   },
   "source": [
    "### Now, you need to do convert the below fields back into date and time format \n",
    "\n",
    "- Convert \"due_in_date\" into datetime format\n",
    "- Convert \"avg_delay\" into datetime format\n",
    "- Create a new column \"clear_date\" and store the sum of \"due_in_date\" and \"avg_delay\"\n",
    "- display the new \"clear_date\" column\n",
    "- Note - Code is given below, no need to change "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "243abc2d",
   "metadata": {
    "id": "243abc2d"
   },
   "outputs": [],
   "source": [
    "Final['clear_date'] = pd.to_datetime(Final['due_in_date']) + pd.to_timedelta(Final['avg_delay'], unit='s')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9QcX_fAjIkYR",
   "metadata": {
    "id": "9QcX_fAjIkYR"
   },
   "source": [
    "### Display the \"clear_date\" column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "740e1486",
   "metadata": {
    "id": "740e1486"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2020-04-20 05:08:42.125000000\n",
       "1      2020-04-10 13:45:49.750000000\n",
       "2      2020-03-27 01:21:47.375000000\n",
       "3      2020-04-27 02:16:44.453125000\n",
       "4      2020-04-23 14:12:27.671875000\n",
       "                    ...             \n",
       "9676   2020-04-01 01:47:59.875000000\n",
       "9677   2020-05-24 21:25:23.250000000\n",
       "9678   2020-03-23 00:07:01.921875000\n",
       "9679   2020-04-08 04:06:19.585937500\n",
       "9680   2020-05-04 14:38:59.132812500\n",
       "Name: clear_date, Length: 9681, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Final['clear_date']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "MSkNLq6-z7rZ",
   "metadata": {
    "id": "MSkNLq6-z7rZ"
   },
   "source": [
    "### Convert the average delay into number of days format \n",
    "\n",
    "- Note - Formula = avg_delay//(24 * 3600)\n",
    "- Note - full code is given for this, no need to change "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "ce6b618a",
   "metadata": {
    "id": "ce6b618a"
   },
   "outputs": [],
   "source": [
    "Final['avg_delay'] = Final.apply(lambda row: row.avg_delay//(24 * 3600), axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "wbBBZPjP0W7o",
   "metadata": {
    "id": "wbBBZPjP0W7o"
   },
   "source": [
    "### Display the \"avg_delay\" column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "a494982f",
   "metadata": {
    "id": "a494982f",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       10.0\n",
       "1        7.0\n",
       "2        1.0\n",
       "3       -3.0\n",
       "4       -3.0\n",
       "        ... \n",
       "9676     7.0\n",
       "9677     6.0\n",
       "9678    -3.0\n",
       "9679    -1.0\n",
       "9680    -2.0\n",
       "Name: avg_delay, Length: 9681, dtype: float64"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Final['avg_delay']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "815d8811",
   "metadata": {
    "id": "815d8811"
   },
   "source": [
    "### Now you need to convert average delay column into bucket\n",
    "\n",
    "- Need to perform binning \n",
    "- create a list of bins i.e. bins= [0,15,30,45,60,100]\n",
    "- create a list of labels i.e. labels = ['0-15','16-30','31-45','46-60','Greatar than 60']\n",
    "- perform binning by using cut() function from \"Final\" dataframe\n",
    "\n",
    "\n",
    "- Please fill up the first two rows of the code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "c797e4b5",
   "metadata": {
    "id": "c797e4b5"
   },
   "outputs": [],
   "source": [
    "\n",
    "#bins= ___________________\n",
    "bins= [0,15,30,45,60,100]\n",
    "#labels =__________________________\n",
    "labels = ['0-15','16-30','31-45','46-60','Greatar than 60']\n",
    "Final['Aging Bucket'] = pd.cut(Final['avg_delay'], bins=bins, labels=labels, right=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c35725f",
   "metadata": {
    "id": "1c35725f"
   },
   "source": [
    "### Now you need to drop \"key_0\" and \"avg_delay\" columns from the \"Final\" Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "b31bc6a3",
   "metadata": {
    "id": "b31bc6a3"
   },
   "outputs": [],
   "source": [
    "Final.drop(columns=[\"key_0\",\"avg_delay\"],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Ui-tyIvU0-5u",
   "metadata": {
    "id": "Ui-tyIvU0-5u"
   },
   "source": [
    "### Display the count of each categoty of new \"Aging Bucket\" column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "a6e16218",
   "metadata": {
    "id": "a6e16218"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0-15               6783\n",
       "16-30               328\n",
       "31-45                70\n",
       "46-60                12\n",
       "Greatar than 60       6\n",
       "Name: Aging Bucket, dtype: int64"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " Final[\"Aging Bucket\"]. value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "kgYegy551GKJ",
   "metadata": {
    "id": "kgYegy551GKJ"
   },
   "source": [
    "### Display your final dataset with aging buckets "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "c4bc87ec",
   "metadata": {
    "id": "c4bc87ec"
   },
   "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>business_code</th>\n",
       "      <th>cust_number</th>\n",
       "      <th>name_customer</th>\n",
       "      <th>clear_date</th>\n",
       "      <th>buisness_year</th>\n",
       "      <th>doc_id</th>\n",
       "      <th>posting_date</th>\n",
       "      <th>due_in_date</th>\n",
       "      <th>baseline_create_date</th>\n",
       "      <th>cust_payment_terms</th>\n",
       "      <th>converted_usd</th>\n",
       "      <th>Aging Bucket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA02</td>\n",
       "      <td>0140105686</td>\n",
       "      <td>SYSC llc</td>\n",
       "      <td>2020-04-20 05:08:42.125000</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2.960623e+09</td>\n",
       "      <td>2020-03-30</td>\n",
       "      <td>2020-04-10</td>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>CA10</td>\n",
       "      <td>2309.79</td>\n",
       "      <td>0-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200744019</td>\n",
       "      <td>TARG us</td>\n",
       "      <td>2020-04-10 13:45:49.750000</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930659e+09</td>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>2020-04-03</td>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>NAA8</td>\n",
       "      <td>11173.02</td>\n",
       "      <td>0-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200418007</td>\n",
       "      <td>AM</td>\n",
       "      <td>2020-03-27 01:21:47.375000</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930611e+09</td>\n",
       "      <td>2020-03-11</td>\n",
       "      <td>2020-03-26</td>\n",
       "      <td>2020-03-11</td>\n",
       "      <td>NAA8</td>\n",
       "      <td>3525.59</td>\n",
       "      <td>0-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200739534</td>\n",
       "      <td>OK systems</td>\n",
       "      <td>2020-04-27 02:16:44.453125</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930788e+09</td>\n",
       "      <td>2020-04-15</td>\n",
       "      <td>2020-04-30</td>\n",
       "      <td>2020-04-15</td>\n",
       "      <td>NAA8</td>\n",
       "      <td>121105.65</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200353024</td>\n",
       "      <td>DECA corporation</td>\n",
       "      <td>2020-04-23 14:12:27.671875</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.930817e+09</td>\n",
       "      <td>2020-04-23</td>\n",
       "      <td>2020-04-26</td>\n",
       "      <td>2020-04-16</td>\n",
       "      <td>NAM2</td>\n",
       "      <td>3726.06</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  business_code cust_number     name_customer                 clear_date  \\\n",
       "0          CA02  0140105686          SYSC llc 2020-04-20 05:08:42.125000   \n",
       "1          U001  0200744019           TARG us 2020-04-10 13:45:49.750000   \n",
       "2          U001  0200418007               AM  2020-03-27 01:21:47.375000   \n",
       "3          U001  0200739534        OK systems 2020-04-27 02:16:44.453125   \n",
       "4          U001  0200353024  DECA corporation 2020-04-23 14:12:27.671875   \n",
       "\n",
       "   buisness_year        doc_id posting_date due_in_date baseline_create_date  \\\n",
       "0         2020.0  2.960623e+09   2020-03-30  2020-04-10           2020-03-31   \n",
       "1         2020.0  1.930659e+09   2020-03-19  2020-04-03           2020-03-19   \n",
       "2         2020.0  1.930611e+09   2020-03-11  2020-03-26           2020-03-11   \n",
       "3         2020.0  1.930788e+09   2020-04-15  2020-04-30           2020-04-15   \n",
       "4         2020.0  1.930817e+09   2020-04-23  2020-04-26           2020-04-16   \n",
       "\n",
       "  cust_payment_terms  converted_usd Aging Bucket  \n",
       "0               CA10        2309.79         0-15  \n",
       "1               NAA8       11173.02         0-15  \n",
       "2               NAA8        3525.59         0-15  \n",
       "3               NAA8      121105.65          NaN  \n",
       "4               NAM2        3726.06          NaN  "
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Final.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Ji7AoDCB1L_x",
   "metadata": {
    "id": "Ji7AoDCB1L_x"
   },
   "source": [
    "### Store this dataframe into the .csv format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "727d0b8d",
   "metadata": {
    "id": "727d0b8d"
   },
   "outputs": [],
   "source": [
    "Final.to_csv('Final.csv', date_format='%Y-%m-%d %H:%M:%S')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "FK0fabl61SkC",
   "metadata": {
    "id": "FK0fabl61SkC"
   },
   "source": [
    "# END OF THE PROJECT"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [
    "62633a84"
   ],
   "name": "Payment date prediction.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
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