ml/HRC62283WK_RUDRASHIS_DUTTA_prediction.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 datetime as dt\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "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')\n",
    "#Here i am importing given dataset and getting shape of dataset."
   ]
  },
  {
   "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": [
    {
     "data": {
      "text/plain": [
       "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')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "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": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<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",
       "      <td>0.0</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>5.000000e+04</td>\n",
       "      <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": {
      "text/html": [
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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()"
   ]
  },
  {
   "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().sum() / df.shape[0] * 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",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[                    0\n",
       " Same/Different       \n",
       " Different       28768\n",
       " Same            21232,\n",
       "                     0\n",
       " Same/Different       \n",
       " Same            44452\n",
       " Different        5548]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check = np.where((df['document_create_date.1'] == df['document_create_date']), \"Same\", \"Different\")\n",
    "check1 = pd.DataFrame(check, columns=['Same/Different'])\n",
    "check = np.where((df['document_create_date.1'] == df['baseline_create_date']), \"Same\", \"Different\")\n",
    "check2 = pd.DataFrame(check, columns=['Same/Different'])\n",
    "#We found that baseline_create_date and document_create_date\t values are nearly same.\n",
    "[pd.DataFrame(check1.value_counts()), pd.DataFrame(check2.value_counts())]\n",
    "#extra cell"
   ]
  },
  {
   "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": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"posting_id\"].nunique()"
   ]
  },
  {
   "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": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"isOpen\"].nunique()"
   ]
  },
  {
   "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": "270d85d1",
   "metadata": {
    "id": "270d85d1"
   },
   "outputs": [],
   "source": [
    "cols = [\"area_business\", \"posting_id\", \"invoice_id\", \"document_create_date\", \"isOpen\", \"document type\",\n",
    "        \"document_create_date.1\"]\n",
    "df.drop(cols, axis=1, inplace=True)"
   ]
  },
  {
   "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": 14,
   "id": "ef3f7d2b",
   "metadata": {
    "id": "ef3f7d2b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bc052c7",
   "metadata": {
    "id": "6bc052c7"
   },
   "source": [
    "### Show all the Duplicate rows from the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1ae3c7e4",
   "metadata": {
    "id": "1ae3c7e4"
   },
   "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>invoice_currency</th>\n",
       "      <th>total_open_amount</th>\n",
       "      <th>baseline_create_date</th>\n",
       "      <th>cust_payment_terms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1041</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200769623</td>\n",
       "      <td>WAL-MAR in</td>\n",
       "      <td>2019-03-12 00:00:00</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1.928870e+09</td>\n",
       "      <td>2019-02-28</td>\n",
       "      <td>20190315.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>19557.41</td>\n",
       "      <td>20190228.0</td>\n",
       "      <td>NAH4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2400</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200769623</td>\n",
       "      <td>WAL-MAR trust</td>\n",
       "      <td>2019-08-28 00:00:00</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1.929758e+09</td>\n",
       "      <td>2019-08-18</td>\n",
       "      <td>20190902.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>5600.41</td>\n",
       "      <td>20190818.0</td>\n",
       "      <td>NAH4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2584</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200769623</td>\n",
       "      <td>WAL-MAR corporation</td>\n",
       "      <td>2019-12-16 00:00:00</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1.930217e+09</td>\n",
       "      <td>2019-12-04</td>\n",
       "      <td>20191219.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>35352.17</td>\n",
       "      <td>20191204.0</td>\n",
       "      <td>NAH4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3755</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200769623</td>\n",
       "      <td>WAL-MAR</td>\n",
       "      <td>2019-11-22 00:00:00</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1.930137e+09</td>\n",
       "      <td>2019-11-12</td>\n",
       "      <td>20191127.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>2982.64</td>\n",
       "      <td>20191112.0</td>\n",
       "      <td>NAH4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3873</th>\n",
       "      <td>CA02</td>\n",
       "      <td>0140104409</td>\n",
       "      <td>LOB associates</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2.960629e+09</td>\n",
       "      <td>2020-04-14</td>\n",
       "      <td>20200425.0</td>\n",
       "      <td>CAD</td>\n",
       "      <td>82975.82</td>\n",
       "      <td>20200415.0</td>\n",
       "      <td>CA10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49928</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200915438</td>\n",
       "      <td>GROC trust</td>\n",
       "      <td>2019-08-15 00:00:00</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1.929646e+09</td>\n",
       "      <td>2019-07-25</td>\n",
       "      <td>20190809.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>6969.00</td>\n",
       "      <td>20190725.0</td>\n",
       "      <td>NAA8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49963</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200759878</td>\n",
       "      <td>SA us</td>\n",
       "      <td>2019-01-29 00:00:00</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1.928614e+09</td>\n",
       "      <td>2019-01-13</td>\n",
       "      <td>20190128.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>10968.24</td>\n",
       "      <td>20190113.0</td>\n",
       "      <td>NAH4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49986</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200772670</td>\n",
       "      <td>ASSOCIAT foundation</td>\n",
       "      <td>2019-06-12 00:00:00</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1.929403e+09</td>\n",
       "      <td>2019-05-29</td>\n",
       "      <td>20190613.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>155837.53</td>\n",
       "      <td>20190529.0</td>\n",
       "      <td>NAU5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49990</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200765011</td>\n",
       "      <td>MAINES llc</td>\n",
       "      <td>2019-06-06 00:00:00</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1.929365e+09</td>\n",
       "      <td>2019-05-22</td>\n",
       "      <td>20190606.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>4008.05</td>\n",
       "      <td>20190522.0</td>\n",
       "      <td>NAA8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49991</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200704045</td>\n",
       "      <td>RA trust</td>\n",
       "      <td>2019-10-25 00:00:00</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1.930001e+09</td>\n",
       "      <td>2019-10-10</td>\n",
       "      <td>20191025.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>73002.24</td>\n",
       "      <td>20191010.0</td>\n",
       "      <td>NAA8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1161 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      business_code cust_number        name_customer           clear_date  \\\n",
       "1041           U001  0200769623           WAL-MAR in  2019-03-12 00:00:00   \n",
       "2400           U001  0200769623        WAL-MAR trust  2019-08-28 00:00:00   \n",
       "2584           U001  0200769623  WAL-MAR corporation  2019-12-16 00:00:00   \n",
       "3755           U001  0200769623             WAL-MAR   2019-11-22 00:00:00   \n",
       "3873           CA02  0140104409       LOB associates                  NaN   \n",
       "...             ...         ...                  ...                  ...   \n",
       "49928          U001  0200915438           GROC trust  2019-08-15 00:00:00   \n",
       "49963          U001  0200759878                SA us  2019-01-29 00:00:00   \n",
       "49986          U001  0200772670  ASSOCIAT foundation  2019-06-12 00:00:00   \n",
       "49990          U001  0200765011           MAINES llc  2019-06-06 00:00:00   \n",
       "49991          U001  0200704045             RA trust  2019-10-25 00:00:00   \n",
       "\n",
       "       buisness_year        doc_id posting_date  due_in_date invoice_currency  \\\n",
       "1041          2019.0  1.928870e+09   2019-02-28   20190315.0              USD   \n",
       "2400          2019.0  1.929758e+09   2019-08-18   20190902.0              USD   \n",
       "2584          2019.0  1.930217e+09   2019-12-04   20191219.0              USD   \n",
       "3755          2019.0  1.930137e+09   2019-11-12   20191127.0              USD   \n",
       "3873          2020.0  2.960629e+09   2020-04-14   20200425.0              CAD   \n",
       "...              ...           ...          ...          ...              ...   \n",
       "49928         2019.0  1.929646e+09   2019-07-25   20190809.0              USD   \n",
       "49963         2019.0  1.928614e+09   2019-01-13   20190128.0              USD   \n",
       "49986         2019.0  1.929403e+09   2019-05-29   20190613.0              USD   \n",
       "49990         2019.0  1.929365e+09   2019-05-22   20190606.0              USD   \n",
       "49991         2019.0  1.930001e+09   2019-10-10   20191025.0              USD   \n",
       "\n",
       "       total_open_amount  baseline_create_date cust_payment_terms  \n",
       "1041            19557.41            20190228.0               NAH4  \n",
       "2400             5600.41            20190818.0               NAH4  \n",
       "2584            35352.17            20191204.0               NAH4  \n",
       "3755             2982.64            20191112.0               NAH4  \n",
       "3873            82975.82            20200415.0               CA10  \n",
       "...                  ...                   ...                ...  \n",
       "49928            6969.00            20190725.0               NAA8  \n",
       "49963           10968.24            20190113.0               NAH4  \n",
       "49986          155837.53            20190529.0               NAU5  \n",
       "49990            4008.05            20190522.0               NAA8  \n",
       "49991           73002.24            20191010.0               NAA8  \n",
       "\n",
       "[1161 rows x 12 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Showing the 1st value amongst duplicated values which is denoted by 'first'\n",
    "df[df.duplicated(keep='first')]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "464fab09",
   "metadata": {
    "id": "464fab09"
   },
   "source": [
    "### Display the Number of Duplicate Rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b1ea2397",
   "metadata": {
    "id": "b1ea2397"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1161"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "827a6718",
   "metadata": {
    "id": "827a6718"
   },
   "source": [
    "### Drop all the Duplicate Rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5d10151c",
   "metadata": {
    "id": "5d10151c"
   },
   "outputs": [],
   "source": [
    "df.drop_duplicates(keep='first', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e5d1f9b",
   "metadata": {
    "id": "7e5d1f9b"
   },
   "source": [
    "#### Now check for all duplicate rows now\n",
    "\n",
    "- Note - It must be 0 by now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9accc9fc",
   "metadata": {
    "id": "9accc9fc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.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": 19,
   "id": "582748a8",
   "metadata": {
    "id": "582748a8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48839, 12)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.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": 20,
   "id": "b0612cb5",
   "metadata": {
    "id": "b0612cb5"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>business_code</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cust_number</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name_customer</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clear_date</th>\n",
       "      <td>9681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>buisness_year</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>doc_id</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>posting_date</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>due_in_date</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>invoice_currency</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_open_amount</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baseline_create_date</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cust_payment_terms</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         0\n",
       "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"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(df.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": 21,
   "id": "689c8592",
   "metadata": {
    "id": "689c8592"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>business_code</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cust_number</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name_customer</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clear_date</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>buisness_year</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>doc_id</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>posting_date</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>due_in_date</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>invoice_currency</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_open_amount</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baseline_create_date</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cust_payment_terms</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            0\n",
       "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"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(df.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": 22,
   "id": "-yyODyW3X6pL",
   "metadata": {
    "id": "-yyODyW3X6pL"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>clear_date</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>posting_date</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>due_in_date</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baseline_create_date</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            0\n",
       "clear_date             object\n",
       "posting_date           object\n",
       "due_in_date           float64\n",
       "baseline_create_date  float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(df[[\"clear_date\", \"posting_date\", \"due_in_date\", \"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": 23,
   "id": "9a8c6c71",
   "metadata": {
    "id": "9a8c6c71"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>invoice_currency</th>\n",
       "      <th>total_open_amount</th>\n",
       "      <th>baseline_create_date</th>\n",
       "      <th>cust_payment_terms</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</td>\n",
       "      <td>1.930438e+09</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>2020-02-10</td>\n",
       "      <td>USD</td>\n",
       "      <td>54273.28</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>NAH4</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</td>\n",
       "      <td>1.929646e+09</td>\n",
       "      <td>2019-07-22</td>\n",
       "      <td>2019-08-11</td>\n",
       "      <td>USD</td>\n",
       "      <td>79656.60</td>\n",
       "      <td>2019-07-22</td>\n",
       "      <td>NAD1</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</td>\n",
       "      <td>1.929874e+09</td>\n",
       "      <td>2019-09-14</td>\n",
       "      <td>2019-09-29</td>\n",
       "      <td>USD</td>\n",
       "      <td>2253.86</td>\n",
       "      <td>2019-09-14</td>\n",
       "      <td>NAA8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CA02</td>\n",
       "      <td>0140105686</td>\n",
       "      <td>SYSC llc</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020</td>\n",
       "      <td>2.960623e+09</td>\n",
       "      <td>2020-03-30</td>\n",
       "      <td>2020-04-10</td>\n",
       "      <td>CAD</td>\n",
       "      <td>3299.70</td>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>CA10</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</td>\n",
       "      <td>2019</td>\n",
       "      <td>1.930148e+09</td>\n",
       "      <td>2019-11-13</td>\n",
       "      <td>2019-11-28</td>\n",
       "      <td>USD</td>\n",
       "      <td>33133.29</td>\n",
       "      <td>2019-11-13</td>\n",
       "      <td>NAH4</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   \n",
       "1          U001  0200980828              BEN E  2019-08-08           2019   \n",
       "2          U001  0200792734          MDV/ trust 2019-12-30           2019   \n",
       "3          CA02  0140105686            SYSC llc        NaT           2020   \n",
       "4          U001  0200769623  WAL-MAR foundation 2019-11-25           2019   \n",
       "\n",
       "         doc_id posting_date due_in_date invoice_currency  total_open_amount  \\\n",
       "0  1.930438e+09   2020-01-26  2020-02-10              USD           54273.28   \n",
       "1  1.929646e+09   2019-07-22  2019-08-11              USD           79656.60   \n",
       "2  1.929874e+09   2019-09-14  2019-09-29              USD            2253.86   \n",
       "3  2.960623e+09   2020-03-30  2020-04-10              CAD            3299.70   \n",
       "4  1.930148e+09   2019-11-13  2019-11-28              USD           33133.29   \n",
       "\n",
       "  baseline_create_date cust_payment_terms  \n",
       "0           2020-01-26               NAH4  \n",
       "1           2019-07-22               NAD1  \n",
       "2           2019-09-14               NAA8  \n",
       "3           2020-03-31               CA10  \n",
       "4           2019-11-13               NAH4  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def date_format(date):\n",
    "    x = str(date)\n",
    "    year = int(x[0:4])\n",
    "    month = int(x[4:6])\n",
    "    day = int(x[6:])\n",
    "    return dt.datetime(year, month, day)\n",
    "\n",
    "\n",
    "# Down-casting date columns from float to int\n",
    "df['buisness_year'] = pd.to_numeric(df['buisness_year'], downcast='integer')\n",
    "df['due_in_date'] = pd.to_numeric(df['due_in_date'], downcast='integer')\n",
    "df['baseline_create_date'] = pd.to_numeric(df['baseline_create_date'], downcast='integer')\n",
    "\n",
    "# Applying the date formatter\n",
    "df['due_in_date'] = df['due_in_date'].apply(date_format)\n",
    "df['baseline_create_date'] = df['baseline_create_date'].apply(date_format)\n",
    "\n",
    "# Converting object type to datetime format\n",
    "df['clear_date'] = pd.to_datetime(df['clear_date'])\n",
    "df['posting_date'] = pd.to_datetime(df['posting_date'])\n",
    "\n",
    "# Printing the head of the df\n",
    "df.head()"
   ]
  },
  {
   "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": 24,
   "id": "fd028c61",
   "metadata": {
    "id": "fd028c61"
   },
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>business_code</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cust_number</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name_customer</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clear_date</th>\n",
       "      <td>datetime64[ns]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>buisness_year</th>\n",
       "      <td>int16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>doc_id</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>posting_date</th>\n",
       "      <td>datetime64[ns]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>due_in_date</th>\n",
       "      <td>datetime64[ns]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>invoice_currency</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_open_amount</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baseline_create_date</th>\n",
       "      <td>datetime64[ns]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cust_payment_terms</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   0\n",
       "business_code                 object\n",
       "cust_number                   object\n",
       "name_customer                 object\n",
       "clear_date            datetime64[ns]\n",
       "buisness_year                  int16\n",
       "doc_id                       float64\n",
       "posting_date          datetime64[ns]\n",
       "due_in_date           datetime64[ns]\n",
       "invoice_currency              object\n",
       "total_open_amount            float64\n",
       "baseline_create_date  datetime64[ns]\n",
       "cust_payment_terms            object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(df.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": 25,
   "id": "72085397",
   "metadata": {
    "id": "72085397"
   },
   "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_currency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>USD</th>\n",
       "      <td>45011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAD</th>\n",
       "      <td>3828</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     invoice_currency\n",
       "USD             45011\n",
       "CAD              3828"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(df[\"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": 26,
   "id": "6c49f2ab",
   "metadata": {
    "id": "6c49f2ab"
   },
   "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>total_open_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54273.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>79656.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2253.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3299.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33133.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49995</th>\n",
       "      <td>3187.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>6766.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>6120.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>63.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>1790.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48839 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       total_open_amount\n",
       "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",
       "\n",
       "[48839 rows x 1 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"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": 27,
   "id": "8eb2f1c5",
   "metadata": {
    "id": "8eb2f1c5"
   },
   "outputs": [],
   "source": [
    "df['converted_usd'] = np.where(df['invoice_currency'] == 'CAD', df['total_open_amount'] * 0.7, df['total_open_amount'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9f6ef1d",
   "metadata": {
    "id": "f9f6ef1d"
   },
   "source": [
    "### Display the new \"converted_usd\" column values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1fc1a178",
   "metadata": {
    "id": "1fc1a178"
   },
   "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>converted_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54273.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>79656.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2253.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2309.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33133.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49995</th>\n",
       "      <td>3187.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>6766.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>6120.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>63.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>1790.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48839 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       converted_usd\n",
       "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",
       "\n",
       "[48839 rows x 1 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['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": 29,
   "id": "00c9f6ee",
   "metadata": {
    "id": "00c9f6ee"
   },
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>33975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>14864</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      buisness_year\n",
       "2019          33975\n",
       "2020          14864"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(df[\"buisness_year\"].value_counts())"
   ]
  },
  {
   "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": 30,
   "id": "4ac28aa5",
   "metadata": {
    "id": "4ac28aa5"
   },
   "outputs": [],
   "source": [
    "df.drop(['invoice_currency', 'total_open_amount'], axis=1, 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": 31,
   "id": "ea360a8c",
   "metadata": {
    "id": "ea360a8c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.columns)"
   ]
  },
  {
   "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": 32,
   "id": "148c801e",
   "metadata": {
    "id": "148c801e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['clear_date']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = [i for i in df.isnull().any().keys() if df.isnull().any()[i] == True]\n",
    "columns\n",
    "#Output print only clear_date column name"
   ]
  },
  {
   "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": "code",
   "execution_count": 33,
   "id": "30bfb113",
   "metadata": {
    "id": "30bfb113"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "clear_date    9681\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_null_sums = pd.Series([])\n",
    "for i in columns:\n",
    "    columns_null_sums[i] = df.isnull().sum()[i]\n",
    "columns_null_sums"
   ]
  },
  {
   "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 NO 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": 34,
   "id": "c8764c33",
   "metadata": {
    "id": "c8764c33"
   },
   "outputs": [],
   "source": [
    "nulldata = df.loc[df['clear_date'].isnull()]\n",
    "maindata = df.loc[~df['clear_date'].isnull()]"
   ]
  },
  {
   "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": 35,
   "id": "0693a464",
   "metadata": {
    "id": "0693a464"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39158, 11)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maindata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7f86bc74",
   "metadata": {
    "id": "7f86bc74"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9681, 11)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nulldata.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0747165d",
   "metadata": {
    "id": "0747165d"
   },
   "source": [
    "### Display the 5 records from maindata and nulldata dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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</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</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</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>4</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200769623</td>\n",
       "      <td>WAL-MAR foundation</td>\n",
       "      <td>2019-11-25</td>\n",
       "      <td>2019</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>5</th>\n",
       "      <td>CA02</td>\n",
       "      <td>0140106181</td>\n",
       "      <td>THE  corporation</td>\n",
       "      <td>2019-12-04</td>\n",
       "      <td>2019</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   \n",
       "1          U001  0200980828              BEN E  2019-08-08           2019   \n",
       "2          U001  0200792734          MDV/ trust 2019-12-30           2019   \n",
       "4          U001  0200769623  WAL-MAR foundation 2019-11-25           2019   \n",
       "5          CA02  0140106181    THE  corporation 2019-12-04           2019   \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",
       "4  1.930148e+09   2019-11-13  2019-11-28           2019-11-13   \n",
       "5  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",
       "4               NAH4      33133.290  \n",
       "5               CA10      15558.088  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maindata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>3</th>\n",
       "      <td>CA02</td>\n",
       "      <td>0140105686</td>\n",
       "      <td>SYSC llc</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020</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>7</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200744019</td>\n",
       "      <td>TARG us</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020</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>10</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200418007</td>\n",
       "      <td>AM</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020</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>14</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200739534</td>\n",
       "      <td>OK systems</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020</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>15</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200353024</td>\n",
       "      <td>DECA corporation</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020</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",
       "3           CA02  0140105686          SYSC llc        NaT           2020   \n",
       "7           U001  0200744019           TARG us        NaT           2020   \n",
       "10          U001  0200418007               AM         NaT           2020   \n",
       "14          U001  0200739534        OK systems        NaT           2020   \n",
       "15          U001  0200353024  DECA corporation        NaT           2020   \n",
       "\n",
       "          doc_id posting_date due_in_date baseline_create_date  \\\n",
       "3   2.960623e+09   2020-03-30  2020-04-10           2020-03-31   \n",
       "7   1.930659e+09   2020-03-19  2020-04-03           2020-03-19   \n",
       "10  1.930611e+09   2020-03-11  2020-03-26           2020-03-11   \n",
       "14  1.930788e+09   2020-04-15  2020-04-30           2020-04-15   \n",
       "15  1.930817e+09   2020-04-23  2020-04-26           2020-04-16   \n",
       "\n",
       "   cust_payment_terms  converted_usd  \n",
       "3                CA10        2309.79  \n",
       "7                NAA8       11173.02  \n",
       "10               NAA8        3525.59  \n",
       "14               NAA8      121105.65  \n",
       "15               NAM2        3726.06  "
      ]
     },
     "execution_count": 38,
     "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": 39,
   "id": "8eeceb9c",
   "metadata": {
    "id": "8eeceb9c"
   },
   "outputs": [],
   "source": [
    "maindata[\"Delay\"] = maindata[\"clear_date\"] - maindata[\"due_in_date\"]"
   ]
  },
  {
   "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": 40,
   "id": "d18d2f8d",
   "metadata": {
    "id": "d18d2f8d"
   },
   "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>avg_Delay</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name_customer</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11078 us</th>\n",
       "      <td>17 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17135 associates</th>\n",
       "      <td>-10 days +00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17135 llc</th>\n",
       "      <td>-3 days +00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236008 associates</th>\n",
       "      <td>-3 days +00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99 CE</th>\n",
       "      <td>2 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEN BROS corp</th>\n",
       "      <td>0 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEN BROS corporation</th>\n",
       "      <td>-1 days +12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEN BROS llc</th>\n",
       "      <td>-2 days +00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZARCO co</th>\n",
       "      <td>-1 days +00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZIYAD  us</th>\n",
       "      <td>6 days 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3889 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              avg_Delay\n",
       "name_customer                          \n",
       "11078 us               17 days 00:00:00\n",
       "17135 associates     -10 days +00:00:00\n",
       "17135 llc             -3 days +00:00:00\n",
       "236008 associates     -3 days +00:00:00\n",
       "99 CE                   2 days 00:00:00\n",
       "...                                 ...\n",
       "YEN BROS corp           0 days 00:00:00\n",
       "YEN BROS corporation  -1 days +12:00:00\n",
       "YEN BROS llc          -2 days +00:00:00\n",
       "ZARCO co              -1 days +00:00:00\n",
       "ZIYAD  us               6 days 00:00:00\n",
       "\n",
       "[3889 rows x 1 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_delay = pd.DataFrame(maindata.groupby('name_customer')['Delay'].mean(numeric_only=False))\n",
    "avg_delay.rename(columns={'Delay': 'avg_Delay'}, inplace=True)\n",
    "avg_delay"
   ]
  },
  {
   "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": 41,
   "id": "e1e1f3d9",
   "metadata": {
    "id": "e1e1f3d9"
   },
   "outputs": [],
   "source": [
    "maindata[\"avg_delay\"] = maindata[\"name_customer\"].map(avg_delay['avg_Delay'])"
   ]
  },
  {
   "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": "code",
   "execution_count": 42,
   "id": "d5f1041e",
   "metadata": {
    "id": "d5f1041e"
   },
   "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>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>4</th>\n",
       "      <td>-1.887923e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.172800e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      avg_delay\n",
       "0 -2.334702e+05\n",
       "1  1.641600e+06\n",
       "2  6.990545e+05\n",
       "4 -1.887923e+05\n",
       "5  6.172800e+06"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maindata[\"avg_delay\"] = maindata[\"avg_delay\"] / np.timedelta64(1, 's')\n",
    "maindata[[\"avg_delay\"]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "OvgtHSsx_O-n",
   "metadata": {
    "id": "OvgtHSsx_O-n"
   },
   "source": [
    "### Display the maindata dataframe "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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</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</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</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>4</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200769623</td>\n",
       "      <td>WAL-MAR foundation</td>\n",
       "      <td>2019-11-25</td>\n",
       "      <td>2019</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>5</th>\n",
       "      <td>CA02</td>\n",
       "      <td>0140106181</td>\n",
       "      <td>THE  corporation</td>\n",
       "      <td>2019-12-04</td>\n",
       "      <td>2019</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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49994</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</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",
       "      <td>0 days</td>\n",
       "      <td>-3.330000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200769623</td>\n",
       "      <td>WAL-MAR co</td>\n",
       "      <td>2019-09-03</td>\n",
       "      <td>2019</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",
       "      <td>4 days</td>\n",
       "      <td>-2.135915e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200772595</td>\n",
       "      <td>SAFEW associates</td>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>2020</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",
       "      <td>0 days</td>\n",
       "      <td>9.051429e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200726979</td>\n",
       "      <td>BJ'S  llc</td>\n",
       "      <td>2019-12-12</td>\n",
       "      <td>2019</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",
       "      <td>0 days</td>\n",
       "      <td>1.354030e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200020431</td>\n",
       "      <td>DEC corp</td>\n",
       "      <td>2019-01-15</td>\n",
       "      <td>2019</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",
       "      <td>-9 days</td>\n",
       "      <td>-3.371478e+05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>39158 rows × 13 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   \n",
       "1              U001  0200980828              BEN E  2019-08-08           2019   \n",
       "2              U001  0200792734          MDV/ trust 2019-12-30           2019   \n",
       "4              U001  0200769623  WAL-MAR foundation 2019-11-25           2019   \n",
       "5              CA02  0140106181    THE  corporation 2019-12-04           2019   \n",
       "...             ...         ...                 ...        ...            ...   \n",
       "49994          U001  0200762301        C&S WH trust 2019-07-25           2019   \n",
       "49996          U001  0200769623          WAL-MAR co 2019-09-03           2019   \n",
       "49997          U001  0200772595    SAFEW associates 2020-03-05           2020   \n",
       "49998          U001  0200726979           BJ'S  llc 2019-12-12           2019   \n",
       "49999          U001  0200020431            DEC corp 2019-01-15           2019   \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",
       "4      1.930148e+09   2019-11-13  2019-11-28           2019-11-13   \n",
       "5      2.960581e+09   2019-09-20  2019-10-04           2019-09-24   \n",
       "...             ...          ...         ...                  ...   \n",
       "49994  1.929601e+09   2019-07-10  2019-07-25           2019-07-10   \n",
       "49996  1.929744e+09   2019-08-15  2019-08-30           2019-08-15   \n",
       "49997  1.930537e+09   2020-02-19  2020-03-05           2020-02-19   \n",
       "49998  1.930199e+09   2019-11-27  2019-12-12           2019-11-27   \n",
       "49999  1.928576e+09   2019-01-05  2019-01-24           2019-01-01   \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",
       "4                   NAH4      33133.290 -3 days -1.887923e+05  \n",
       "5                   CA10      15558.088 61 days  6.172800e+06  \n",
       "...                  ...            ...     ...           ...  \n",
       "49994               NAC6      84780.400  0 days -3.330000e+04  \n",
       "49996               NAH4       6766.540  4 days -2.135915e+05  \n",
       "49997               NAA8       6120.860  0 days  9.051429e+04  \n",
       "49998               NAA8         63.480  0 days  1.354030e+05  \n",
       "49999               NAM4       1790.300 -9 days -3.371478e+05  \n",
       "\n",
       "[39158 rows x 13 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maindata"
   ]
  },
  {
   "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": 44,
   "id": "78a61ab9",
   "metadata": {
    "id": "78a61ab9"
   },
   "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": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maindata.drop(['Delay', 'clear_date'], axis=1, inplace=True)\n",
    "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": 45,
   "id": "75ab29ab",
   "metadata": {
    "id": "75ab29ab"
   },
   "outputs": [],
   "source": [
    "X = maindata.drop(['avg_delay'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6412c62b",
   "metadata": {
    "id": "6412c62b"
   },
   "outputs": [],
   "source": [
    "y = maindata['avg_delay']"
   ]
  },
  {
   "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": 47,
   "id": "d92160a5",
   "metadata": {
    "id": "d92160a5"
   },
   "outputs": [],
   "source": [
    "X_train, X_loc_test, y_train, y_loc_test = train_test_split(X, y, test_size=0.4, random_state=1)"
   ]
  },
  {
   "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": 48,
   "id": "48328d0a",
   "metadata": {
    "id": "48328d0a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "X_train       (23494, 10)\n",
       "X_loc_test    (15664, 10)\n",
       "y_train          (23494,)\n",
       "y_loc_test       (15664,)\n",
       "dtype: object"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series({'X_train':X_train.shape,'X_loc_test':X_loc_test.shape,'y_train':y_train.shape,'y_loc_test':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": 49,
   "id": "b56c62f2",
   "metadata": {
    "id": "b56c62f2"
   },
   "outputs": [],
   "source": [
    "X_val, X_test, y_val, y_test = train_test_split(X_loc_test, y_loc_test, test_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": 50,
   "id": "845d7564",
   "metadata": {
    "id": "845d7564"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "X_val     (7832, 10)\n",
       "X_test    (7832, 10)\n",
       "y_val        (7832,)\n",
       "y_test       (7832,)\n",
       "dtype: object"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series({'X_val':X_val.shape,'X_test':X_test.shape,'y_val':y_val.shape,'y_test':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": 51,
   "id": "ba2bf8ed",
   "metadata": {
    "id": "ba2bf8ed"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.displot(data=y_train, color='green', bins=5)\n",
    "plt.show()"
   ]
  },
  {
   "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": 52,
   "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>11078 us</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17135 associates</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>99 CE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>99 CE co</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>99 CE corporation</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3051</th>\n",
       "      <td>YEN BROS</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3052</th>\n",
       "      <td>YEN BROS co</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3053</th>\n",
       "      <td>YEN BROS corp</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3054</th>\n",
       "      <td>YEN BROS corporation</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3055</th>\n",
       "      <td>YEN BROS llc</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3056 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             name_customer  doc_id\n",
       "0                 11078 us       1\n",
       "1         17135 associates       1\n",
       "2                   99 CE        1\n",
       "3                 99 CE co       1\n",
       "4        99 CE corporation       2\n",
       "...                    ...     ...\n",
       "3051             YEN BROS        1\n",
       "3052           YEN BROS co       1\n",
       "3053         YEN BROS corp       1\n",
       "3054  YEN BROS corporation       1\n",
       "3055          YEN BROS llc       1\n",
       "\n",
       "[3056 rows x 2 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_X_train = X_train.groupby(by=[\"name_customer\"], as_index=False)[\"doc_id\"].count()\n",
    "temp_X_train"
   ]
  },
  {
   "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": 53,
   "id": "9576bf33",
   "metadata": {
    "id": "9576bf33"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=temp_X_train, x=\"doc_id\", kde=False, color='red', bins=5)\n",
    "plt.show()"
   ]
  },
  {
   "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": 54,
   "id": "4fecec77",
   "metadata": {
    "id": "4fecec77"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=X_train, x=\"buisness_year\", color='red', bins=5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "qr1jGhfOKjnw",
   "metadata": {
    "id": "qr1jGhfOKjnw"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=X_train, x=\"buisness_year\", y=\"doc_id\", bins=5)\n",
    "plt.show()"
   ]
  },
  {
   "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": 56,
   "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>46683</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200769623</td>\n",
       "      <td>WAL-MAR foundation</td>\n",
       "      <td>2019</td>\n",
       "      <td>1.930007e+09</td>\n",
       "      <td>2019-10-13</td>\n",
       "      <td>2019-10-28</td>\n",
       "      <td>2019-10-13</td>\n",
       "      <td>NAH4</td>\n",
       "      <td>38439.450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200772595</td>\n",
       "      <td>SAFEW corporation</td>\n",
       "      <td>2019</td>\n",
       "      <td>1.928838e+09</td>\n",
       "      <td>2019-02-21</td>\n",
       "      <td>2019-04-27</td>\n",
       "      <td>2019-02-21</td>\n",
       "      <td>NAGD</td>\n",
       "      <td>2588.650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13099</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200494102</td>\n",
       "      <td>MCCA llc</td>\n",
       "      <td>2019</td>\n",
       "      <td>1.929297e+09</td>\n",
       "      <td>2019-05-10</td>\n",
       "      <td>2019-05-26</td>\n",
       "      <td>2019-05-10</td>\n",
       "      <td>C106</td>\n",
       "      <td>48174.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6451</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200726979</td>\n",
       "      <td>BJ'S  corporation</td>\n",
       "      <td>2019</td>\n",
       "      <td>1.930248e+09</td>\n",
       "      <td>2019-12-08</td>\n",
       "      <td>2019-12-23</td>\n",
       "      <td>2019-12-08</td>\n",
       "      <td>NAA8</td>\n",
       "      <td>785.990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20492</th>\n",
       "      <td>U001</td>\n",
       "      <td>200759878</td>\n",
       "      <td>SA us</td>\n",
       "      <td>2019</td>\n",
       "      <td>1.929093e+09</td>\n",
       "      <td>2019-04-06</td>\n",
       "      <td>2019-04-21</td>\n",
       "      <td>2019-04-06</td>\n",
       "      <td>NAH4</td>\n",
       "      <td>55825.020</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9723</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200769623</td>\n",
       "      <td>WAL-MAR co</td>\n",
       "      <td>2019</td>\n",
       "      <td>1.928993e+09</td>\n",
       "      <td>2019-03-20</td>\n",
       "      <td>2019-04-04</td>\n",
       "      <td>2019-03-20</td>\n",
       "      <td>NAH4</td>\n",
       "      <td>56565.370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41318</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200773364</td>\n",
       "      <td>U R M</td>\n",
       "      <td>2019</td>\n",
       "      <td>1.929021e+09</td>\n",
       "      <td>2019-03-26</td>\n",
       "      <td>2019-04-10</td>\n",
       "      <td>2019-03-26</td>\n",
       "      <td>NAA8</td>\n",
       "      <td>8710.130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6410</th>\n",
       "      <td>CA02</td>\n",
       "      <td>0140106293</td>\n",
       "      <td>ATLANT corporation</td>\n",
       "      <td>2019</td>\n",
       "      <td>2.960528e+09</td>\n",
       "      <td>2019-02-06</td>\n",
       "      <td>2019-02-25</td>\n",
       "      <td>2019-02-15</td>\n",
       "      <td>CA10</td>\n",
       "      <td>9392.642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15221</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200769623</td>\n",
       "      <td>WAL-MAR co</td>\n",
       "      <td>2019</td>\n",
       "      <td>1.929908e+09</td>\n",
       "      <td>2019-09-21</td>\n",
       "      <td>2019-10-06</td>\n",
       "      <td>2019-09-21</td>\n",
       "      <td>NAH4</td>\n",
       "      <td>14498.860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41947</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200900909</td>\n",
       "      <td>SYSCO llc</td>\n",
       "      <td>2019</td>\n",
       "      <td>1.929350e+09</td>\n",
       "      <td>2019-05-20</td>\n",
       "      <td>2019-06-04</td>\n",
       "      <td>2019-05-20</td>\n",
       "      <td>NAA8</td>\n",
       "      <td>1578.540</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23494 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      business_code cust_number       name_customer  buisness_year  \\\n",
       "46683          U001  0200769623  WAL-MAR foundation           2019   \n",
       "119            U001  0200772595   SAFEW corporation           2019   \n",
       "13099          U001  0200494102            MCCA llc           2019   \n",
       "6451           U001  0200726979   BJ'S  corporation           2019   \n",
       "20492          U001   200759878               SA us           2019   \n",
       "...             ...         ...                 ...            ...   \n",
       "9723           U001  0200769623          WAL-MAR co           2019   \n",
       "41318          U001  0200773364             U R M             2019   \n",
       "6410           CA02  0140106293  ATLANT corporation           2019   \n",
       "15221          U001  0200769623          WAL-MAR co           2019   \n",
       "41947          U001  0200900909           SYSCO llc           2019   \n",
       "\n",
       "             doc_id posting_date due_in_date baseline_create_date  \\\n",
       "46683  1.930007e+09   2019-10-13  2019-10-28           2019-10-13   \n",
       "119    1.928838e+09   2019-02-21  2019-04-27           2019-02-21   \n",
       "13099  1.929297e+09   2019-05-10  2019-05-26           2019-05-10   \n",
       "6451   1.930248e+09   2019-12-08  2019-12-23           2019-12-08   \n",
       "20492  1.929093e+09   2019-04-06  2019-04-21           2019-04-06   \n",
       "...             ...          ...         ...                  ...   \n",
       "9723   1.928993e+09   2019-03-20  2019-04-04           2019-03-20   \n",
       "41318  1.929021e+09   2019-03-26  2019-04-10           2019-03-26   \n",
       "6410   2.960528e+09   2019-02-06  2019-02-25           2019-02-15   \n",
       "15221  1.929908e+09   2019-09-21  2019-10-06           2019-09-21   \n",
       "41947  1.929350e+09   2019-05-20  2019-06-04           2019-05-20   \n",
       "\n",
       "      cust_payment_terms  converted_usd  \n",
       "46683               NAH4      38439.450  \n",
       "119                 NAGD       2588.650  \n",
       "13099               C106      48174.000  \n",
       "6451                NAA8        785.990  \n",
       "20492               NAH4      55825.020  \n",
       "...                  ...            ...  \n",
       "9723                NAH4      56565.370  \n",
       "41318               NAA8       8710.130  \n",
       "6410                CA10       9392.642  \n",
       "15221               NAH4      14498.860  \n",
       "41947               NAA8       1578.540  \n",
       "\n",
       "[23494 rows x 10 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "08ccc819",
   "metadata": {
    "id": "08ccc819"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>23494.00000</td>\n",
       "      <td>2.349400e+04</td>\n",
       "      <td>23494.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2019.13429</td>\n",
       "      <td>2.013695e+09</td>\n",
       "      <td>30658.212426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.34097</td>\n",
       "      <td>2.841794e+08</td>\n",
       "      <td>36383.634560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2019.00000</td>\n",
       "      <td>1.928511e+09</td>\n",
       "      <td>0.790000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2019.00000</td>\n",
       "      <td>1.929182e+09</td>\n",
       "      <td>4537.035000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2019.00000</td>\n",
       "      <td>1.929733e+09</td>\n",
       "      <td>16944.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2019.00000</td>\n",
       "      <td>1.930212e+09</td>\n",
       "      <td>45446.867500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2020.00000</td>\n",
       "      <td>9.500000e+09</td>\n",
       "      <td>668593.360000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       buisness_year        doc_id  converted_usd\n",
       "count    23494.00000  2.349400e+04   23494.000000\n",
       "mean      2019.13429  2.013695e+09   30658.212426\n",
       "std          0.34097  2.841794e+08   36383.634560\n",
       "min       2019.00000  1.928511e+09       0.790000\n",
       "25%       2019.00000  1.929182e+09    4537.035000\n",
       "50%       2019.00000  1.929733e+09   16944.880000\n",
       "75%       2019.00000  1.930212e+09   45446.867500\n",
       "max       2020.00000  9.500000e+09  668593.360000"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abd7ac8b",
   "metadata": {
    "id": "abd7ac8b"
   },
   "source": [
    "#### The \"business_code\" column inside X_train, is a categorical column, so you need to perform Labelencoder on that particular column\n",
    "\n",
    "- Note - call the Label Encoder from sklearn library and use the fit() function on \"business_code\" column\n",
    "- Note - Please fill in the blanks (two) to complete this code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "7c223545",
   "metadata": {
    "id": "7c223545"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LabelEncoder()"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "business_coder = LabelEncoder()\n",
    "business_coder.fit(X_train['business_code'])"
   ]
  },
  {
   "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": 59,
   "id": "4269c307",
   "metadata": {
    "id": "4269c307"
   },
   "outputs": [],
   "source": [
    "X_train['business_code_enc'] = business_coder.fit_transform(X_train['business_code'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "70a53712",
   "metadata": {
    "id": "70a53712"
   },
   "outputs": [],
   "source": [
    "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": 61,
   "id": "1196a002",
   "metadata": {
    "id": "1196a002",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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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>business_code_enc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46683</th>\n",
       "      <td>U001</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>U001</td>\n",
       "      <td>1</td>\n",
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       "      <th>13099</th>\n",
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       "      <th>6451</th>\n",
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       "      <th>20492</th>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>9723</th>\n",
       "      <td>U001</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>41318</th>\n",
       "      <td>U001</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6410</th>\n",
       "      <td>CA02</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15221</th>\n",
       "      <td>U001</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>41947</th>\n",
       "      <td>U001</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>23494 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      business_code  business_code_enc\n",
       "46683          U001                  1\n",
       "119            U001                  1\n",
       "13099          U001                  1\n",
       "6451           U001                  1\n",
       "20492          U001                  1\n",
       "...             ...                ...\n",
       "9723           U001                  1\n",
       "41318          U001                  1\n",
       "6410           CA02                  0\n",
       "15221          U001                  1\n",
       "41947          U001                  1\n",
       "\n",
       "[23494 rows x 2 columns]"
      ]
     },
     "execution_count": 61,
     "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": 62,
   "id": "1052868a",
   "metadata": {
    "id": "1052868a"
   },
   "outputs": [],
   "source": [
    "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": 63,
   "id": "1a0f955c",
   "metadata": {
    "id": "1a0f955c"
   },
   "outputs": [],
   "source": [
    "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": 64,
   "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": 65,
   "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": 66,
   "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": 67,
   "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": 68,
   "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": 69,
   "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": 70,
   "id": "bc79a316",
   "metadata": {
    "id": "bc79a316"
   },
   "outputs": [
    {
     "data": {
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       "      <td>float64</td>\n",
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       "      <th>posting_date</th>\n",
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       "      <td>datetime64[ns]</td>\n",
       "    </tr>\n",
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       "      <td>datetime64[ns]</td>\n",
       "    </tr>\n",
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       "    </tr>\n",
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       "      <td>int32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cust_payment_terms_enc</th>\n",
       "      <td>int32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     0\n",
       "cust_number                      int32\n",
       "buisness_year                    int16\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"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(X_train.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b33242d8",
   "metadata": {
    "id": "b33242d8"
   },
   "outputs": [
    {
     "data": {
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       "      <td>int16</td>\n",
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       "    </tr>\n",
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       "      <td>datetime64[ns]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baseline_create_date</th>\n",
       "      <td>datetime64[ns]</td>\n",
       "    </tr>\n",
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       "      <th>converted_usd</th>\n",
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       "      <td>int32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>int32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cust_payment_terms_enc</th>\n",
       "      <td>int32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     0\n",
       "cust_number                      int32\n",
       "buisness_year                    int16\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"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(X_test.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "6bd4da71",
   "metadata": {
    "id": "6bd4da71"
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <td>float64</td>\n",
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       "      <td>datetime64[ns]</td>\n",
       "    </tr>\n",
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       "      <td>datetime64[ns]</td>\n",
       "    </tr>\n",
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       "      <td>datetime64[ns]</td>\n",
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       "      <th>name_customer_enc</th>\n",
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       "    <tr>\n",
       "      <th>cust_payment_terms_enc</th>\n",
       "      <td>int32</td>\n",
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      "text/plain": [
       "                                     0\n",
       "cust_number                      int32\n",
       "buisness_year                    int16\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"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(X_val.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": 73,
   "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"
   ]
  },
  {
   "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": 74,
   "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": 75,
   "id": "32b240e1",
   "metadata": {
    "id": "32b240e1"
   },
   "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",
    "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"
   ]
  },
  {
   "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": 76,
   "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": 77,
   "id": "5c745547",
   "metadata": {
    "id": "5c745547"
   },
   "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",
    "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"
   ]
  },
  {
   "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": 78,
   "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": 79,
   "id": "aee9d828",
   "metadata": {
    "id": "aee9d828"
   },
   "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>Train</th>\n",
       "      <th>Test</th>\n",
       "      <th>Validation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cust_number</th>\n",
       "      <td>int32</td>\n",
       "      <td>int32</td>\n",
       "      <td>int32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>buisness_year</th>\n",
       "      <td>int16</td>\n",
       "      <td>int16</td>\n",
       "      <td>int16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>doc_id</th>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>converted_usd</th>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>business_code_enc</th>\n",
       "      <td>int32</td>\n",
       "      <td>int32</td>\n",
       "      <td>int32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name_customer_enc</th>\n",
       "      <td>int32</td>\n",
       "      <td>int32</td>\n",
       "      <td>int32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cust_payment_terms_enc</th>\n",
       "      <td>int32</td>\n",
       "      <td>int32</td>\n",
       "      <td>int32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day_of_postingdate</th>\n",
       "      <td>int64</td>\n",
       "      <td>int64</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month_of_postingdate</th>\n",
       "      <td>int64</td>\n",
       "      <td>int64</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year_of_postingdate</th>\n",
       "      <td>int64</td>\n",
       "      <td>int64</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day_of_createdate</th>\n",
       "      <td>int64</td>\n",
       "      <td>int64</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month_of_createdate</th>\n",
       "      <td>int64</td>\n",
       "      <td>int64</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year_of_createdate</th>\n",
       "      <td>int64</td>\n",
       "      <td>int64</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day_of_due</th>\n",
       "      <td>int64</td>\n",
       "      <td>int64</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month_of_due</th>\n",
       "      <td>int64</td>\n",
       "      <td>int64</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year_of_due</th>\n",
       "      <td>int64</td>\n",
       "      <td>int64</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Train     Test Validation\n",
       "cust_number               int32    int32      int32\n",
       "buisness_year             int16    int16      int16\n",
       "doc_id                  float64  float64    float64\n",
       "converted_usd           float64  float64    float64\n",
       "business_code_enc         int32    int32      int32\n",
       "name_customer_enc         int32    int32      int32\n",
       "cust_payment_terms_enc    int32    int32      int32\n",
       "day_of_postingdate        int64    int64      int64\n",
       "month_of_postingdate      int64    int64      int64\n",
       "year_of_postingdate       int64    int64      int64\n",
       "day_of_createdate         int64    int64      int64\n",
       "month_of_createdate       int64    int64      int64\n",
       "year_of_createdate        int64    int64      int64\n",
       "day_of_due                int64    int64      int64\n",
       "month_of_due              int64    int64      int64\n",
       "year_of_due               int64    int64      int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\"Train\":X_train.dtypes,\"Test\":X_test.dtypes,\"Validation\":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": 80,
   "id": "e882509f",
   "metadata": {
    "id": "e882509f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 80,
     "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": 81,
   "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": 82,
   "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": 83,
   "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": 84,
   "id": "dd91d1a2",
   "metadata": {
    "id": "dd91d1a2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['year_of_createdate',\n",
       " 'month_of_due',\n",
       " 'year_of_postingdate',\n",
       " 'month_of_createdate',\n",
       " 'day_of_createdate',\n",
       " 'year_of_due']"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train=X_train.copy()\n",
    "handling_correlation(train.copy(), threshold=0.85)"
   ]
  },
  {
   "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": 85,
   "id": "2e8f2fe4",
   "metadata": {
    "id": "2e8f2fe4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "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)\n",
    "plt.show()"
   ]
  },
  {
   "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": 86,
   "id": "a9b2080f",
   "metadata": {
    "id": "a9b2080f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VarianceThreshold(threshold=0.8)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import VarianceThreshold\n",
    "sel = VarianceThreshold(0.8)\n",
    "sel.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "6cb8c3dc",
   "metadata": {
    "id": "6cb8c3dc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.77833834e+15, 1.16255908e-01, 8.07544916e+16, 1.32371252e+09,\n",
       "       2.95552036e-01, 1.05154705e+06, 1.42931761e+02, 7.57900109e+01,\n",
       "       1.22385606e+01, 1.17117448e-01, 7.72866281e+01, 1.22464552e+01,\n",
       "       1.17244688e-01, 7.62405693e+01, 1.20839720e+01, 1.19220664e-01])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel.variances_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62633a84",
   "metadata": {
    "id": "62633a84"
   },
   "source": [
    "### 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": 88,
   "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": 89,
   "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)"
   ]
  },
  {
   "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": 90,
   "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": 91,
   "id": "fe653295",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fe653295",
    "outputId": "0c7429ca-50d0-42a2-96a1-effaa92f549e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "526512.9000099458"
      ]
     },
     "execution_count": 91,
     "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": 92,
   "id": "9c405bd3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9c405bd3",
    "outputId": "9d78f4a9-33fc-48d1-edc8-c997eca38de0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['LinearRegression'],[286294186803.3687],[0.33468812423819017],"
     ]
    }
   ],
   "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": 93,
   "id": "ccb5de08",
   "metadata": {
    "id": "ccb5de08"
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR\n",
    "\n",
    "Algorithm.append('SVR')\n",
    "regressor = SVR()\n",
    "regressor.fit(X_train, y_train)\n",
    "predicted = regressor.predict(X_test)"
   ]
  },
  {
   "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": 94,
   "id": "5bb9db76",
   "metadata": {
    "id": "5bb9db76"
   },
   "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": 95,
   "id": "d6ee71b1",
   "metadata": {
    "id": "d6ee71b1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "657894.4212013086"
      ]
     },
     "execution_count": 95,
     "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": 96,
   "id": "aa72c1ec",
   "metadata": {
    "id": "aa72c1ec"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['LinearRegression', 'SVR'],[286294186803.3687, 433174826811.74567],[0.33468812423819017, -0.006644108903466117],"
     ]
    }
   ],
   "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": 97,
   "id": "1b6a51eb",
   "metadata": {
    "id": "1b6a51eb"
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "Algorithm.append('DecisionTreRegressor')\n",
    "regressor = DecisionTreeRegressor()\n",
    "regressor.fit(X_train, y_train)\n",
    "predicted = regressor.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": 98,
   "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": 99,
   "id": "155fb55c",
   "metadata": {
    "id": "155fb55c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "475974.256740603"
      ]
     },
     "execution_count": 99,
     "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": "sbGXvBLQZw5E",
   "metadata": {
    "id": "sbGXvBLQZw5E"
   },
   "source": [
    "### Display The Comparison Lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "1d74d515",
   "metadata": {
    "id": "1d74d515"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['LinearRegression', 'SVR', 'DecisionTreRegressor'],[286294186803.3687, 433174826811.74567, 206746570877.28873],[0.33468812423819017, -0.006644108903466117, 0.5195468325308226],"
     ]
    }
   ],
   "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": 101,
   "id": "a69e476a",
   "metadata": {
    "id": "a69e476a"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "Algorithm.append('RandomForestRegressor')\n",
    "regressor = RandomForestRegressor()\n",
    "regressor.fit(X_train, y_train)\n",
    "predicted = regressor.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": 102,
   "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": 103,
   "id": "55b9fb54",
   "metadata": {
    "id": "55b9fb54"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "333839.7542739442"
      ]
     },
     "execution_count": 103,
     "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": "tiBawcCsaw_Z",
   "metadata": {
    "id": "tiBawcCsaw_Z"
   },
   "source": [
    "### Display The Comparison Lists\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "8277c13e",
   "metadata": {
    "id": "8277c13e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['LinearRegression', 'SVR', 'DecisionTreRegressor', 'RandomForestRegressor'],[286294186803.3687, 433174826811.74567, 206746570877.28873, 115478585370.61313],[0.33468812423819017, -0.006644108903466117, 0.5195468325308226, 0.731642213552836],"
     ]
    }
   ],
   "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": 105,
   "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": 106,
   "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": 107,
   "id": "e78ac250",
   "metadata": {
    "id": "e78ac250"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "310690.89057949465"
      ]
     },
     "execution_count": 107,
     "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": 108,
   "id": "f765ba35",
   "metadata": {
    "id": "f765ba35"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['LinearRegression', 'SVR', 'DecisionTreRegressor', 'RandomForestRegressor', 'XGB Regressor'],[286294186803.3687, 433174826811.74567, 206746570877.28873, 115478585370.61313, 102555529401.07504],[0.33468812423819017, -0.006644108903466117, 0.5195468325308226, 0.731642213552836, 0.7616737789983943],"
     ]
    }
   ],
   "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": 109,
   "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>2.862942e+11</td>\n",
       "      <td>0.334688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SVR</td>\n",
       "      <td>4.331748e+11</td>\n",
       "      <td>-0.006644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DecisionTreRegressor</td>\n",
       "      <td>2.067466e+11</td>\n",
       "      <td>0.519547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RandomForestRegressor</td>\n",
       "      <td>1.154786e+11</td>\n",
       "      <td>0.731642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>XGB Regressor</td>\n",
       "      <td>1.025555e+11</td>\n",
       "      <td>0.761674</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Algorithm     MSE_Score  R2_Score\n",
       "0       LinearRegression  2.862942e+11  0.334688\n",
       "1                    SVR  4.331748e+11 -0.006644\n",
       "2   DecisionTreRegressor  2.067466e+11  0.519547\n",
       "3  RandomForestRegressor  1.154786e+11  0.731642\n",
       "4          XGB Regressor  1.025555e+11  0.761674"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp_df = pd.DataFrame(data = {\"Algorithm\": Algorithm, \"MSE_Score\": MSE_Score, \"R2_Score\": R2_Score})\n",
    "comp_df"
   ]
  },
  {
   "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": 110,
   "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": 111,
   "id": "5fb466d0",
   "metadata": {
    "id": "5fb466d0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "320242.9224839716"
      ]
     },
     "execution_count": 111,
     "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": 112,
   "id": "b47978ea",
   "metadata": {
    "id": "b47978ea"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "310690.89057949465"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_val, predict_testfinal, squared=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30014dbd",
   "metadata": {
    "id": "30014dbd"
   },
   "source": [
    "### Calculate the R2 score for test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "8a162737",
   "metadata": {
    "id": "8a162737"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7616737789983943"
      ]
     },
     "execution_count": 113,
     "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": 114,
   "id": "1a6dc77c",
   "metadata": {
    "id": "1a6dc77c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7756129672738008"
      ]
     },
     "execution_count": 114,
     "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": 115,
   "id": "7a4f1ce8",
   "metadata": {
    "id": "7a4f1ce8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9534812962840034"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12a1c921",
   "metadata": {
    "id": "12a1c921"
   },
   "source": [
    "### Calculate the accuracy for validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "d2579b4f",
   "metadata": {
    "id": "d2579b4f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7756129672738008"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.score(X_val, y_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79b82e84",
   "metadata": {
    "id": "79b82e84"
   },
   "source": [
    "### Calculate the accuracy for test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "f09e6431",
   "metadata": {
    "id": "f09e6431"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7616737789983943"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.score(X_test, y_test)"
   ]
  },
  {
   "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": "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": 118,
   "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>3</th>\n",
       "      <td>CA02</td>\n",
       "      <td>0140105686</td>\n",
       "      <td>SYSC llc</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020</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>7</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200744019</td>\n",
       "      <td>TARG us</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020</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>10</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200418007</td>\n",
       "      <td>AM</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020</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>14</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200739534</td>\n",
       "      <td>OK systems</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020</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>15</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200353024</td>\n",
       "      <td>DECA corporation</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020</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>49975</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200769623</td>\n",
       "      <td>WAL-MAR in</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020</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>49980</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200769623</td>\n",
       "      <td>WAL-MAR corporation</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020</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>49982</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200148860</td>\n",
       "      <td>DOLLA co</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020</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>49992</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200900909</td>\n",
       "      <td>SYSCO co</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020</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>49995</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200561861</td>\n",
       "      <td>CO corporation</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020</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  \\\n",
       "3              CA02  0140105686             SYSC llc        NaT   \n",
       "7              U001  0200744019              TARG us        NaT   \n",
       "10             U001  0200418007                  AM         NaT   \n",
       "14             U001  0200739534           OK systems        NaT   \n",
       "15             U001  0200353024     DECA corporation        NaT   \n",
       "...             ...         ...                  ...        ...   \n",
       "49975          U001  0200769623           WAL-MAR in        NaT   \n",
       "49980          U001  0200769623  WAL-MAR corporation        NaT   \n",
       "49982          U001  0200148860             DOLLA co        NaT   \n",
       "49992          U001  0200900909             SYSCO co        NaT   \n",
       "49995          U001  0200561861       CO corporation        NaT   \n",
       "\n",
       "       buisness_year        doc_id posting_date due_in_date  \\\n",
       "3               2020  2.960623e+09   2020-03-30  2020-04-10   \n",
       "7               2020  1.930659e+09   2020-03-19  2020-04-03   \n",
       "10              2020  1.930611e+09   2020-03-11  2020-03-26   \n",
       "14              2020  1.930788e+09   2020-04-15  2020-04-30   \n",
       "15              2020  1.930817e+09   2020-04-23  2020-04-26   \n",
       "...              ...           ...          ...         ...   \n",
       "49975           2020  1.930625e+09   2020-03-10  2020-03-25   \n",
       "49980           2020  1.930851e+09   2020-05-03  2020-05-18   \n",
       "49982           2020  1.930638e+09   2020-03-11  2020-03-26   \n",
       "49992           2020  1.930702e+09   2020-03-25  2020-04-09   \n",
       "49995           2020  1.930797e+09   2020-04-21  2020-05-06   \n",
       "\n",
       "      baseline_create_date cust_payment_terms  converted_usd  \n",
       "3               2020-03-31               CA10        2309.79  \n",
       "7               2020-03-19               NAA8       11173.02  \n",
       "10              2020-03-11               NAA8        3525.59  \n",
       "14              2020-04-15               NAA8      121105.65  \n",
       "15              2020-04-16               NAM2        3726.06  \n",
       "...                    ...                ...            ...  \n",
       "49975           2020-03-10               NAH4       13114.99  \n",
       "49980           2020-05-03               NAH4        8899.71  \n",
       "49982           2020-03-11               NAA8        4967.06  \n",
       "49992           2020-03-25               NAA8        1998.64  \n",
       "49995           2020-04-21               NAA8        3187.86  \n",
       "\n",
       "[9681 rows x 11 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nulldata"
   ]
  },
  {
   "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": 119,
   "id": "59de1092",
   "metadata": {
    "id": "59de1092"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9681, 11)"
      ]
     },
     "execution_count": 119,
     "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": 120,
   "id": "a6294d29",
   "metadata": {
    "id": "a6294d29"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Info:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 9681 entries, 3 to 49995\n",
      "Data columns (total 11 columns):\n",
      " #   Column                Non-Null Count  Dtype         \n",
      "---  ------                --------------  -----         \n",
      " 0   business_code         9681 non-null   object        \n",
      " 1   cust_number           9681 non-null   object        \n",
      " 2   name_customer         9681 non-null   object        \n",
      " 3   clear_date            0 non-null      datetime64[ns]\n",
      " 4   buisness_year         9681 non-null   int16         \n",
      " 5   doc_id                9681 non-null   float64       \n",
      " 6   posting_date          9681 non-null   datetime64[ns]\n",
      " 7   due_in_date           9681 non-null   datetime64[ns]\n",
      " 8   baseline_create_date  9681 non-null   datetime64[ns]\n",
      " 9   cust_payment_terms    9681 non-null   object        \n",
      " 10  converted_usd         9681 non-null   float64       \n",
      "dtypes: datetime64[ns](4), float64(2), int16(1), object(4)\n",
      "memory usage: 850.9+ KB\n"
     ]
    },
    {
     "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": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Info:\")\n",
    "nulldata.info()\n",
    "nulldata.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe860d94",
   "metadata": {
    "id": "fe860d94"
   },
   "source": [
    "### Storing the Nulldata into a different dataset \n",
    "# for BACKUP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "16352034",
   "metadata": {
    "id": "16352034"
   },
   "outputs": [],
   "source": [
    "nulldata1 = nulldata.copy()"
   ]
  },
  {
   "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": 122,
   "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": 123,
   "id": "c64924be",
   "metadata": {
    "id": "c64924be"
   },
   "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": 124,
   "id": "4166fbe4",
   "metadata": {
    "id": "4166fbe4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['business_code', 'cust_number', 'name_customer', 'clear_date',\n",
       "       'buisness_year', 'doc_id', 'posting_date', 'due_in_date',\n",
       "       'baseline_create_date', 'cust_payment_terms', 'converted_usd',\n",
       "       'business_code_enc', 'day_of_cleardate', 'month_of_cleardate',\n",
       "       'year_of_cleardate', '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",
       "      dtype='object')"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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\n",
    "\n",
    "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\n",
    "\n",
    "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\n",
    "\n",
    "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\n",
    "\n",
    "nulldata.columns"
   ]
  },
  {
   "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": 125,
   "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": 126,
   "id": "d4f72517",
   "metadata": {
    "id": "d4f72517"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>business_code</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cust_number</th>\n",
       "      <td>int32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name_customer</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clear_date</th>\n",
       "      <td>datetime64[ns]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>buisness_year</th>\n",
       "      <td>int16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>doc_id</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>posting_date</th>\n",
       "      <td>datetime64[ns]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>due_in_date</th>\n",
       "      <td>datetime64[ns]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baseline_create_date</th>\n",
       "      <td>datetime64[ns]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cust_payment_terms</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>converted_usd</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>business_code_enc</th>\n",
       "      <td>int32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day_of_cleardate</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month_of_cleardate</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year_of_cleardate</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day_of_postingdate</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month_of_postingdate</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year_of_postingdate</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day_of_due</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month_of_due</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year_of_due</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day_of_createdate</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month_of_createdate</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year_of_createdate</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cust_payment_terms_enc</th>\n",
       "      <td>int32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name_customer_enc</th>\n",
       "      <td>int32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     0\n",
       "business_code                   object\n",
       "cust_number                      int32\n",
       "name_customer                   object\n",
       "clear_date              datetime64[ns]\n",
       "buisness_year                    int16\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"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(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": 127,
   "id": "d7c82076",
   "metadata": {
    "id": "d7c82076"
   },
   "outputs": [],
   "source": [
    "nulldata.drop(['business_code',\"baseline_create_date\",\"due_in_date\",\"posting_date\",\"name_customer\",\"clear_date\",\"cust_payment_terms\"\n",
    ",'day_of_cleardate',\"month_of_cleardate\",\"year_of_cleardate\"], axis=1, 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": 128,
   "id": "4e7ffee0",
   "metadata": {
    "id": "4e7ffee0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 9681 entries, 3 to 49995\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   int16  \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(2), int16(1), int32(4), int64(9)\n",
      "memory usage: 1.1 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": 129,
   "id": "02f4b62d",
   "metadata": {
    "id": "02f4b62d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 9681 entries, 3 to 49995\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   int16  \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(2), int16(1), int32(4), int64(9)\n",
      "memory usage: 1.1 MB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 7832 entries, 7942 to 37093\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   int16  \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(2), int16(1), int32(4), int64(9)\n",
      "memory usage: 871.9 KB\n"
     ]
    }
   ],
   "source": [
    "nulldata.info()\n",
    "X_test.info()"
   ]
  },
  {
   "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": 130,
   "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": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "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": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nulldata.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "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']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dc8b021",
   "metadata": {
    "id": "1dc8b021"
   },
   "source": [
    "### Display the Final Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "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>3</th>\n",
       "      <td>140105686</td>\n",
       "      <td>2020</td>\n",
       "      <td>2.960623e+09</td>\n",
       "      <td>2309.79</td>\n",
       "      <td>67</td>\n",
       "      <td>2688</td>\n",
       "      <td>4</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>7</th>\n",
       "      <td>200744019</td>\n",
       "      <td>2020</td>\n",
       "      <td>1.930659e+09</td>\n",
       "      <td>11173.02</td>\n",
       "      <td>67</td>\n",
       "      <td>2771</td>\n",
       "      <td>20</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>10</th>\n",
       "      <td>200418007</td>\n",
       "      <td>2020</td>\n",
       "      <td>1.930611e+09</td>\n",
       "      <td>3525.59</td>\n",
       "      <td>67</td>\n",
       "      <td>90</td>\n",
       "      <td>20</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>14</th>\n",
       "      <td>200739534</td>\n",
       "      <td>2020</td>\n",
       "      <td>1.930788e+09</td>\n",
       "      <td>121105.65</td>\n",
       "      <td>67</td>\n",
       "      <td>2012</td>\n",
       "      <td>20</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>15</th>\n",
       "      <td>200353024</td>\n",
       "      <td>2020</td>\n",
       "      <td>1.930817e+09</td>\n",
       "      <td>3726.06</td>\n",
       "      <td>67</td>\n",
       "      <td>718</td>\n",
       "      <td>36</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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49975</th>\n",
       "      <td>200769623</td>\n",
       "      <td>2020</td>\n",
       "      <td>1.930625e+09</td>\n",
       "      <td>13114.99</td>\n",
       "      <td>67</td>\n",
       "      <td>2965</td>\n",
       "      <td>34</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49980</th>\n",
       "      <td>200769623</td>\n",
       "      <td>2020</td>\n",
       "      <td>1.930851e+09</td>\n",
       "      <td>8899.71</td>\n",
       "      <td>67</td>\n",
       "      <td>2963</td>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49982</th>\n",
       "      <td>200148860</td>\n",
       "      <td>2020</td>\n",
       "      <td>1.930638e+09</td>\n",
       "      <td>4967.06</td>\n",
       "      <td>67</td>\n",
       "      <td>793</td>\n",
       "      <td>20</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>49992</th>\n",
       "      <td>200900909</td>\n",
       "      <td>2020</td>\n",
       "      <td>1.930702e+09</td>\n",
       "      <td>1998.64</td>\n",
       "      <td>67</td>\n",
       "      <td>2737</td>\n",
       "      <td>20</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49995</th>\n",
       "      <td>200561861</td>\n",
       "      <td>2020</td>\n",
       "      <td>1.930797e+09</td>\n",
       "      <td>3187.86</td>\n",
       "      <td>67</td>\n",
       "      <td>533</td>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>2020</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>2020</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9681 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       cust_number  buisness_year        doc_id  converted_usd  \\\n",
       "3        140105686           2020  2.960623e+09        2309.79   \n",
       "7        200744019           2020  1.930659e+09       11173.02   \n",
       "10       200418007           2020  1.930611e+09        3525.59   \n",
       "14       200739534           2020  1.930788e+09      121105.65   \n",
       "15       200353024           2020  1.930817e+09        3726.06   \n",
       "...            ...            ...           ...            ...   \n",
       "49975    200769623           2020  1.930625e+09       13114.99   \n",
       "49980    200769623           2020  1.930851e+09        8899.71   \n",
       "49982    200148860           2020  1.930638e+09        4967.06   \n",
       "49992    200900909           2020  1.930702e+09        1998.64   \n",
       "49995    200561861           2020  1.930797e+09        3187.86   \n",
       "\n",
       "       business_code_enc  name_customer_enc  cust_payment_terms_enc  \\\n",
       "3                     67               2688                       4   \n",
       "7                     67               2771                      20   \n",
       "10                    67                 90                      20   \n",
       "14                    67               2012                      20   \n",
       "15                    67                718                      36   \n",
       "...                  ...                ...                     ...   \n",
       "49975                 67               2965                      34   \n",
       "49980                 67               2963                      34   \n",
       "49982                 67                793                      20   \n",
       "49992                 67               2737                      20   \n",
       "49995                 67                533                      20   \n",
       "\n",
       "       day_of_postingdate  month_of_postingdate  year_of_postingdate  \\\n",
       "3                      30                     3                 2020   \n",
       "7                      19                     3                 2020   \n",
       "10                     11                     3                 2020   \n",
       "14                     15                     4                 2020   \n",
       "15                     23                     4                 2020   \n",
       "...                   ...                   ...                  ...   \n",
       "49975                  10                     3                 2020   \n",
       "49980                   3                     5                 2020   \n",
       "49982                  11                     3                 2020   \n",
       "49992                  25                     3                 2020   \n",
       "49995                  21                     4                 2020   \n",
       "\n",
       "       day_of_createdate  month_of_createdate  year_of_createdate  day_of_due  \\\n",
       "3                     31                    3                2020          10   \n",
       "7                     19                    3                2020           3   \n",
       "10                    11                    3                2020          26   \n",
       "14                    15                    4                2020          30   \n",
       "15                    16                    4                2020          26   \n",
       "...                  ...                  ...                 ...         ...   \n",
       "49975                 10                    3                2020          25   \n",
       "49980                  3                    5                2020          18   \n",
       "49982                 11                    3                2020          26   \n",
       "49992                 25                    3                2020           9   \n",
       "49995                 21                    4                2020           6   \n",
       "\n",
       "       month_of_due  year_of_due  \n",
       "3                 4         2020  \n",
       "7                 4         2020  \n",
       "10                3         2020  \n",
       "14                4         2020  \n",
       "15                4         2020  \n",
       "...             ...          ...  \n",
       "49975             3         2020  \n",
       "49980             5         2020  \n",
       "49982             3         2020  \n",
       "49992             4         2020  \n",
       "49995             5         2020  \n",
       "\n",
       "[9681 rows x 16 columns]"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nulldata2"
   ]
  },
  {
   "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": 134,
   "id": "9e0b6388",
   "metadata": {
    "id": "9e0b6388"
   },
   "outputs": [],
   "source": [
    "final_result = regressorfinal.predict(nulldata2)"
   ]
  },
  {
   "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": 135,
   "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": 136,
   "id": "4fd46406",
   "metadata": {
    "id": "4fd46406"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\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>avg_delay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.486564e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.374096e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.082230e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.755133e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-3.003025e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9676</th>\n",
       "      <td>7.701648e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9677</th>\n",
       "      <td>7.836192e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9678</th>\n",
       "      <td>-1.291379e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9679</th>\n",
       "      <td>2.953922e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9680</th>\n",
       "      <td>2.763925e+04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9681 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         avg_delay\n",
       "0     6.486564e+05\n",
       "1     3.374096e+05\n",
       "2     1.082230e+06\n",
       "3     4.755133e+05\n",
       "4    -3.003025e+05\n",
       "...            ...\n",
       "9676  7.701648e+04\n",
       "9677  7.836192e+04\n",
       "9678 -1.291379e+05\n",
       "9679  2.953922e+05\n",
       "9680  2.763925e+04\n",
       "\n",
       "[9681 rows x 1 columns]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(final_result)"
   ]
  },
  {
   "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": 137,
   "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": 138,
   "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>3</td>\n",
       "      <td>CA02</td>\n",
       "      <td>0140105686</td>\n",
       "      <td>SYSC llc</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020</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>6.486564e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>U001</td>\n",
       "      <td>0200744019</td>\n",
       "      <td>TARG us</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020</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>3.374096e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>U001</td>\n",
       "      <td>0200418007</td>\n",
       "      <td>AM</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020</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>1.082230e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>U001</td>\n",
       "      <td>0200739534</td>\n",
       "      <td>OK systems</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020</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>4.755133e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15</td>\n",
       "      <td>U001</td>\n",
       "      <td>0200353024</td>\n",
       "      <td>DECA corporation</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020</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>-3.003025e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9676</th>\n",
       "      <td>49975</td>\n",
       "      <td>U001</td>\n",
       "      <td>0200769623</td>\n",
       "      <td>WAL-MAR in</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020</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",
       "      <td>7.701648e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9677</th>\n",
       "      <td>49980</td>\n",
       "      <td>U001</td>\n",
       "      <td>0200769623</td>\n",
       "      <td>WAL-MAR corporation</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020</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",
       "      <td>7.836192e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9678</th>\n",
       "      <td>49982</td>\n",
       "      <td>U001</td>\n",
       "      <td>0200148860</td>\n",
       "      <td>DOLLA co</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020</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",
       "      <td>-1.291379e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9679</th>\n",
       "      <td>49992</td>\n",
       "      <td>U001</td>\n",
       "      <td>0200900909</td>\n",
       "      <td>SYSCO co</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020</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",
       "      <td>2.953922e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9680</th>\n",
       "      <td>49995</td>\n",
       "      <td>U001</td>\n",
       "      <td>0200561861</td>\n",
       "      <td>CO corporation</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020</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",
       "      <td>2.763925e+04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9681 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      key_0 business_code cust_number        name_customer clear_date  \\\n",
       "0         3          CA02  0140105686             SYSC llc        NaT   \n",
       "1         7          U001  0200744019              TARG us        NaT   \n",
       "2        10          U001  0200418007                  AM         NaT   \n",
       "3        14          U001  0200739534           OK systems        NaT   \n",
       "4        15          U001  0200353024     DECA corporation        NaT   \n",
       "...     ...           ...         ...                  ...        ...   \n",
       "9676  49975          U001  0200769623           WAL-MAR in        NaT   \n",
       "9677  49980          U001  0200769623  WAL-MAR corporation        NaT   \n",
       "9678  49982          U001  0200148860             DOLLA co        NaT   \n",
       "9679  49992          U001  0200900909             SYSCO co        NaT   \n",
       "9680  49995          U001  0200561861       CO corporation        NaT   \n",
       "\n",
       "      buisness_year        doc_id posting_date due_in_date  \\\n",
       "0              2020  2.960623e+09   2020-03-30  2020-04-10   \n",
       "1              2020  1.930659e+09   2020-03-19  2020-04-03   \n",
       "2              2020  1.930611e+09   2020-03-11  2020-03-26   \n",
       "3              2020  1.930788e+09   2020-04-15  2020-04-30   \n",
       "4              2020  1.930817e+09   2020-04-23  2020-04-26   \n",
       "...             ...           ...          ...         ...   \n",
       "9676           2020  1.930625e+09   2020-03-10  2020-03-25   \n",
       "9677           2020  1.930851e+09   2020-05-03  2020-05-18   \n",
       "9678           2020  1.930638e+09   2020-03-11  2020-03-26   \n",
       "9679           2020  1.930702e+09   2020-03-25  2020-04-09   \n",
       "9680           2020  1.930797e+09   2020-04-21  2020-05-06   \n",
       "\n",
       "     baseline_create_date cust_payment_terms  converted_usd     avg_delay  \n",
       "0              2020-03-31               CA10        2309.79  6.486564e+05  \n",
       "1              2020-03-19               NAA8       11173.02  3.374096e+05  \n",
       "2              2020-03-11               NAA8        3525.59  1.082230e+06  \n",
       "3              2020-04-15               NAA8      121105.65  4.755133e+05  \n",
       "4              2020-04-16               NAM2        3726.06 -3.003025e+05  \n",
       "...                   ...                ...            ...           ...  \n",
       "9676           2020-03-10               NAH4       13114.99  7.701648e+04  \n",
       "9677           2020-05-03               NAH4        8899.71  7.836192e+04  \n",
       "9678           2020-03-11               NAA8        4967.06 -1.291379e+05  \n",
       "9679           2020-03-25               NAA8        1998.64  2.953922e+05  \n",
       "9680           2020-04-21               NAA8        3187.86  2.763925e+04  \n",
       "\n",
       "[9681 rows x 13 columns]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Final"
   ]
  },
  {
   "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": 139,
   "id": "5iUXOIhzy_HR",
   "metadata": {
    "id": "5iUXOIhzy_HR"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9681, 13)"
      ]
     },
     "execution_count": 139,
     "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": 140,
   "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": 141,
   "id": "740e1486",
   "metadata": {
    "id": "740e1486"
   },
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-04-17 12:10:56.375000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-04-06 21:43:29.593750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-04-07 12:37:10.500000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-05-05 12:05:13.281250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-04-22 12:34:57.531250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9676</th>\n",
       "      <td>2020-03-25 21:23:36.476562500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9677</th>\n",
       "      <td>2020-05-18 21:46:01.921875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9678</th>\n",
       "      <td>2020-03-24 12:07:42.125000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9679</th>\n",
       "      <td>2020-04-12 10:03:12.250000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9680</th>\n",
       "      <td>2020-05-06 07:40:39.251953125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9681 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        clear_date\n",
       "0    2020-04-17 12:10:56.375000000\n",
       "1    2020-04-06 21:43:29.593750000\n",
       "2    2020-04-07 12:37:10.500000000\n",
       "3    2020-05-05 12:05:13.281250000\n",
       "4    2020-04-22 12:34:57.531250000\n",
       "...                            ...\n",
       "9676 2020-03-25 21:23:36.476562500\n",
       "9677 2020-05-18 21:46:01.921875000\n",
       "9678 2020-03-24 12:07:42.125000000\n",
       "9679 2020-04-12 10:03:12.250000000\n",
       "9680 2020-05-06 07:40:39.251953125\n",
       "\n",
       "[9681 rows x 1 columns]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(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": 142,
   "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": 143,
   "id": "a494982f",
   "metadata": {
    "id": "a494982f",
    "scrolled": false
   },
   "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>avg_delay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9676</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9677</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9678</th>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9679</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9680</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9681 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      avg_delay\n",
       "0           7.0\n",
       "1           3.0\n",
       "2          12.0\n",
       "3           5.0\n",
       "4          -4.0\n",
       "...         ...\n",
       "9676        0.0\n",
       "9677        0.0\n",
       "9678       -2.0\n",
       "9679        3.0\n",
       "9680        0.0\n",
       "\n",
       "[9681 rows x 1 columns]"
      ]
     },
     "execution_count": 143,
     "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": 144,
   "id": "c797e4b5",
   "metadata": {
    "id": "c797e4b5"
   },
   "outputs": [],
   "source": [
    "\n",
    "bins = [0,15,30,45,60,100]\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": 145,
   "id": "b31bc6a3",
   "metadata": {
    "id": "b31bc6a3"
   },
   "outputs": [],
   "source": [
    "Final.drop(['key_0',\"avg_delay\"], axis=1, 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": 146,
   "id": "a6e16218",
   "metadata": {
    "id": "a6e16218"
   },
   "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>Aging Bucket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0-15</th>\n",
       "      <td>8237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16-30</th>\n",
       "      <td>205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31-45</th>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46-60</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greatar than 60</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Aging Bucket\n",
       "0-15                     8237\n",
       "16-30                     205\n",
       "31-45                      93\n",
       "46-60                       9\n",
       "Greatar than 60             3"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(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": 147,
   "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>2020-04-17 12:10:56.375000000</td>\n",
       "      <td>2020</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>2020-04-06 21:43:29.593750000</td>\n",
       "      <td>2020</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>2020-04-07 12:37:10.500000000</td>\n",
       "      <td>2020</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>2020-05-05 12:05:13.281250000</td>\n",
       "      <td>2020</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>0-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200353024</td>\n",
       "      <td>DECA corporation</td>\n",
       "      <td>2020-04-22 12:34:57.531250000</td>\n",
       "      <td>2020</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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9676</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200769623</td>\n",
       "      <td>WAL-MAR in</td>\n",
       "      <td>2020-03-25 21:23:36.476562500</td>\n",
       "      <td>2020</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",
       "      <td>0-15</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>2020-05-18 21:46:01.921875000</td>\n",
       "      <td>2020</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",
       "      <td>0-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9678</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200148860</td>\n",
       "      <td>DOLLA co</td>\n",
       "      <td>2020-03-24 12:07:42.125000000</td>\n",
       "      <td>2020</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9679</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200900909</td>\n",
       "      <td>SYSCO co</td>\n",
       "      <td>2020-04-12 10:03:12.250000000</td>\n",
       "      <td>2020</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",
       "      <td>0-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9680</th>\n",
       "      <td>U001</td>\n",
       "      <td>0200561861</td>\n",
       "      <td>CO corporation</td>\n",
       "      <td>2020-05-06 07:40:39.251953125</td>\n",
       "      <td>2020</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",
       "      <td>0-15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9681 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     business_code cust_number        name_customer  \\\n",
       "0             CA02  0140105686             SYSC llc   \n",
       "1             U001  0200744019              TARG us   \n",
       "2             U001  0200418007                  AM    \n",
       "3             U001  0200739534           OK systems   \n",
       "4             U001  0200353024     DECA corporation   \n",
       "...            ...         ...                  ...   \n",
       "9676          U001  0200769623           WAL-MAR in   \n",
       "9677          U001  0200769623  WAL-MAR corporation   \n",
       "9678          U001  0200148860             DOLLA co   \n",
       "9679          U001  0200900909             SYSCO co   \n",
       "9680          U001  0200561861       CO corporation   \n",
       "\n",
       "                        clear_date  buisness_year        doc_id posting_date  \\\n",
       "0    2020-04-17 12:10:56.375000000           2020  2.960623e+09   2020-03-30   \n",
       "1    2020-04-06 21:43:29.593750000           2020  1.930659e+09   2020-03-19   \n",
       "2    2020-04-07 12:37:10.500000000           2020  1.930611e+09   2020-03-11   \n",
       "3    2020-05-05 12:05:13.281250000           2020  1.930788e+09   2020-04-15   \n",
       "4    2020-04-22 12:34:57.531250000           2020  1.930817e+09   2020-04-23   \n",
       "...                            ...            ...           ...          ...   \n",
       "9676 2020-03-25 21:23:36.476562500           2020  1.930625e+09   2020-03-10   \n",
       "9677 2020-05-18 21:46:01.921875000           2020  1.930851e+09   2020-05-03   \n",
       "9678 2020-03-24 12:07:42.125000000           2020  1.930638e+09   2020-03-11   \n",
       "9679 2020-04-12 10:03:12.250000000           2020  1.930702e+09   2020-03-25   \n",
       "9680 2020-05-06 07:40:39.251953125           2020  1.930797e+09   2020-04-21   \n",
       "\n",
       "     due_in_date baseline_create_date cust_payment_terms  converted_usd  \\\n",
       "0     2020-04-10           2020-03-31               CA10        2309.79   \n",
       "1     2020-04-03           2020-03-19               NAA8       11173.02   \n",
       "2     2020-03-26           2020-03-11               NAA8        3525.59   \n",
       "3     2020-04-30           2020-04-15               NAA8      121105.65   \n",
       "4     2020-04-26           2020-04-16               NAM2        3726.06   \n",
       "...          ...                  ...                ...            ...   \n",
       "9676  2020-03-25           2020-03-10               NAH4       13114.99   \n",
       "9677  2020-05-18           2020-05-03               NAH4        8899.71   \n",
       "9678  2020-03-26           2020-03-11               NAA8        4967.06   \n",
       "9679  2020-04-09           2020-03-25               NAA8        1998.64   \n",
       "9680  2020-05-06           2020-04-21               NAA8        3187.86   \n",
       "\n",
       "     Aging Bucket  \n",
       "0            0-15  \n",
       "1            0-15  \n",
       "2            0-15  \n",
       "3            0-15  \n",
       "4             NaN  \n",
       "...           ...  \n",
       "9676         0-15  \n",
       "9677         0-15  \n",
       "9678          NaN  \n",
       "9679         0-15  \n",
       "9680         0-15  \n",
       "\n",
       "[9681 rows x 12 columns]"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Final"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Ji7AoDCB1L_x",
   "metadata": {
    "id": "Ji7AoDCB1L_x"
   },
   "source": [
    "### Store this dataframe into the .csv format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "727d0b8d",
   "metadata": {
    "id": "727d0b8d"
   },
   "outputs": [],
   "source": [
    "Final.to_csv(\"HRC62283WK_RUDRASHIS_DUTTA.csv\")"
   ]
  },
  {
   "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}