Cash_Demand_Forecasting_of_ATMs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b68ab9da",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "51c2c698",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>atm_name</th>\n",
       "      <th>weekday</th>\n",
       "      <th>festival_religion</th>\n",
       "      <th>working_day</th>\n",
       "      <th>holiday_sequence</th>\n",
       "      <th>trans_date_set</th>\n",
       "      <th>trans_month</th>\n",
       "      <th>trans_year</th>\n",
       "      <th>prevweek_mean</th>\n",
       "      <th>total_amount_withdrawn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "      <td>Mount Road ATM</td>\n",
       "      <td>MONDAY</td>\n",
       "      <td>NH</td>\n",
       "      <td>W</td>\n",
       "      <td>WWW</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>648600.0</td>\n",
       "      <td>897100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "      <td>Mount Road ATM</td>\n",
       "      <td>TUESDAY</td>\n",
       "      <td>NH</td>\n",
       "      <td>W</td>\n",
       "      <td>WWW</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>648600.0</td>\n",
       "      <td>826000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21</td>\n",
       "      <td>Mount Road ATM</td>\n",
       "      <td>WEDNESDAY</td>\n",
       "      <td>NH</td>\n",
       "      <td>W</td>\n",
       "      <td>WWW</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>648600.0</td>\n",
       "      <td>754400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26</td>\n",
       "      <td>Mount Road ATM</td>\n",
       "      <td>THURSDAY</td>\n",
       "      <td>NH</td>\n",
       "      <td>W</td>\n",
       "      <td>WWW</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>648600.0</td>\n",
       "      <td>834200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31</td>\n",
       "      <td>Mount Road ATM</td>\n",
       "      <td>FRIDAY</td>\n",
       "      <td>NH</td>\n",
       "      <td>W</td>\n",
       "      <td>WWW</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>648600.0</td>\n",
       "      <td>575300</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>2239</th>\n",
       "      <td>11565</td>\n",
       "      <td>Mount Road ATM</td>\n",
       "      <td>MONDAY</td>\n",
       "      <td>NH</td>\n",
       "      <td>W</td>\n",
       "      <td>HWW</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>2017</td>\n",
       "      <td>276058.0</td>\n",
       "      <td>447400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2240</th>\n",
       "      <td>11570</td>\n",
       "      <td>Mount Road ATM</td>\n",
       "      <td>TUESDAY</td>\n",
       "      <td>NH</td>\n",
       "      <td>W</td>\n",
       "      <td>WWW</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>2017</td>\n",
       "      <td>276058.0</td>\n",
       "      <td>153800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2241</th>\n",
       "      <td>11575</td>\n",
       "      <td>Mount Road ATM</td>\n",
       "      <td>WEDNESDAY</td>\n",
       "      <td>NH</td>\n",
       "      <td>W</td>\n",
       "      <td>WWW</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>2017</td>\n",
       "      <td>276058.0</td>\n",
       "      <td>167100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2242</th>\n",
       "      <td>11580</td>\n",
       "      <td>Mount Road ATM</td>\n",
       "      <td>THURSDAY</td>\n",
       "      <td>NH</td>\n",
       "      <td>W</td>\n",
       "      <td>WWH</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>2017</td>\n",
       "      <td>276058.0</td>\n",
       "      <td>317400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2243</th>\n",
       "      <td>11585</td>\n",
       "      <td>Mount Road ATM</td>\n",
       "      <td>FRIDAY</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>WHH</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>2017</td>\n",
       "      <td>276058.0</td>\n",
       "      <td>305100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2244 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0        atm_name    weekday festival_religion working_day  \\\n",
       "0             11  Mount Road ATM     MONDAY                NH           W   \n",
       "1             16  Mount Road ATM    TUESDAY                NH           W   \n",
       "2             21  Mount Road ATM  WEDNESDAY                NH           W   \n",
       "3             26  Mount Road ATM   THURSDAY                NH           W   \n",
       "4             31  Mount Road ATM     FRIDAY                NH           W   \n",
       "...          ...             ...        ...               ...         ...   \n",
       "2239       11565  Mount Road ATM     MONDAY                NH           W   \n",
       "2240       11570  Mount Road ATM    TUESDAY                NH           W   \n",
       "2241       11575  Mount Road ATM  WEDNESDAY                NH           W   \n",
       "2242       11580  Mount Road ATM   THURSDAY                NH           W   \n",
       "2243       11585  Mount Road ATM     FRIDAY                 H           H   \n",
       "\n",
       "     holiday_sequence  trans_date_set  trans_month  trans_year  prevweek_mean  \\\n",
       "0                 WWW               1            1        2011       648600.0   \n",
       "1                 WWW               1            1        2011       648600.0   \n",
       "2                 WWW               1            1        2011       648600.0   \n",
       "3                 WWW               2            1        2011       648600.0   \n",
       "4                 WWW               2            1        2011       648600.0   \n",
       "...               ...             ...          ...         ...            ...   \n",
       "2239              HWW               5            9        2017       276058.0   \n",
       "2240              WWW               6            9        2017       276058.0   \n",
       "2241              WWW               6            9        2017       276058.0   \n",
       "2242              WWH               6            9        2017       276058.0   \n",
       "2243              WHH               6            9        2017       276058.0   \n",
       "\n",
       "      total_amount_withdrawn  \n",
       "0                     897100  \n",
       "1                     826000  \n",
       "2                     754400  \n",
       "3                     834200  \n",
       "4                     575300  \n",
       "...                      ...  \n",
       "2239                  447400  \n",
       "2240                  153800  \n",
       "2241                  167100  \n",
       "2242                  317400  \n",
       "2243                  305100  \n",
       "\n",
       "[2244 rows x 11 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('atm_data_m2.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4407c6b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9e560627",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       2244.0\n",
       "mean      515717.0\n",
       "std       255179.0\n",
       "min          100.0\n",
       "25%       349475.0\n",
       "50%       514100.0\n",
       "75%       684925.0\n",
       "max      1410700.0\n",
       "Name: total_amount_withdrawn, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,6))\n",
    "plt.subplot(1,2,1)\n",
    "data['total_amount_withdrawn'].hist(bins=50)\n",
    "plt.title('Total amount withdrawan')\n",
    "plt.subplot(1,2,2)\n",
    "stats.probplot(data['total_amount_withdrawn'], plot=plt);\n",
    "data['total_amount_withdrawn'].describe().T.round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a311a8e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trans_date_set</th>\n",
       "      <th>trans_month</th>\n",
       "      <th>trans_year</th>\n",
       "      <th>prevweek_mean</th>\n",
       "      <th>total_amount_withdrawn</th>\n",
       "      <th>weekday_FRIDAY</th>\n",
       "      <th>weekday_MONDAY</th>\n",
       "      <th>weekday_SATURDAY</th>\n",
       "      <th>weekday_SUNDAY</th>\n",
       "      <th>weekday_THURSDAY</th>\n",
       "      <th>...</th>\n",
       "      <th>working_day_H</th>\n",
       "      <th>working_day_W</th>\n",
       "      <th>holiday_sequence_HHH</th>\n",
       "      <th>holiday_sequence_HHW</th>\n",
       "      <th>holiday_sequence_HWH</th>\n",
       "      <th>holiday_sequence_HWW</th>\n",
       "      <th>holiday_sequence_WHH</th>\n",
       "      <th>holiday_sequence_WHW</th>\n",
       "      <th>holiday_sequence_WWH</th>\n",
       "      <th>holiday_sequence_WWW</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>648600.0</td>\n",
       "      <td>897100</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>648600.0</td>\n",
       "      <td>826000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>648600.0</td>\n",
       "      <td>754400</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>648600.0</td>\n",
       "      <td>834200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>648600.0</td>\n",
       "      <td>575300</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2239</th>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>2017</td>\n",
       "      <td>276058.0</td>\n",
       "      <td>447400</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2240</th>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>2017</td>\n",
       "      <td>276058.0</td>\n",
       "      <td>153800</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2241</th>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>2017</td>\n",
       "      <td>276058.0</td>\n",
       "      <td>167100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2242</th>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>2017</td>\n",
       "      <td>276058.0</td>\n",
       "      <td>317400</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2243</th>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>2017</td>\n",
       "      <td>276058.0</td>\n",
       "      <td>305100</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2244 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      trans_date_set  trans_month  trans_year  prevweek_mean  \\\n",
       "0                  1            1        2011       648600.0   \n",
       "1                  1            1        2011       648600.0   \n",
       "2                  1            1        2011       648600.0   \n",
       "3                  2            1        2011       648600.0   \n",
       "4                  2            1        2011       648600.0   \n",
       "...              ...          ...         ...            ...   \n",
       "2239               5            9        2017       276058.0   \n",
       "2240               6            9        2017       276058.0   \n",
       "2241               6            9        2017       276058.0   \n",
       "2242               6            9        2017       276058.0   \n",
       "2243               6            9        2017       276058.0   \n",
       "\n",
       "      total_amount_withdrawn  weekday_FRIDAY  weekday_MONDAY  \\\n",
       "0                     897100               0               1   \n",
       "1                     826000               0               0   \n",
       "2                     754400               0               0   \n",
       "3                     834200               0               0   \n",
       "4                     575300               1               0   \n",
       "...                      ...             ...             ...   \n",
       "2239                  447400               0               1   \n",
       "2240                  153800               0               0   \n",
       "2241                  167100               0               0   \n",
       "2242                  317400               0               0   \n",
       "2243                  305100               1               0   \n",
       "\n",
       "      weekday_SATURDAY  weekday_SUNDAY  weekday_THURSDAY  ...  working_day_H  \\\n",
       "0                    0               0                 0  ...              0   \n",
       "1                    0               0                 0  ...              0   \n",
       "2                    0               0                 0  ...              0   \n",
       "3                    0               0                 1  ...              0   \n",
       "4                    0               0                 0  ...              0   \n",
       "...                ...             ...               ...  ...            ...   \n",
       "2239                 0               0                 0  ...              0   \n",
       "2240                 0               0                 0  ...              0   \n",
       "2241                 0               0                 0  ...              0   \n",
       "2242                 0               0                 1  ...              0   \n",
       "2243                 0               0                 0  ...              1   \n",
       "\n",
       "      working_day_W  holiday_sequence_HHH  holiday_sequence_HHW  \\\n",
       "0                 1                     0                     0   \n",
       "1                 1                     0                     0   \n",
       "2                 1                     0                     0   \n",
       "3                 1                     0                     0   \n",
       "4                 1                     0                     0   \n",
       "...             ...                   ...                   ...   \n",
       "2239              1                     0                     0   \n",
       "2240              1                     0                     0   \n",
       "2241              1                     0                     0   \n",
       "2242              1                     0                     0   \n",
       "2243              0                     0                     0   \n",
       "\n",
       "      holiday_sequence_HWH  holiday_sequence_HWW  holiday_sequence_WHH  \\\n",
       "0                        0                     0                     0   \n",
       "1                        0                     0                     0   \n",
       "2                        0                     0                     0   \n",
       "3                        0                     0                     0   \n",
       "4                        0                     0                     0   \n",
       "...                    ...                   ...                   ...   \n",
       "2239                     0                     1                     0   \n",
       "2240                     0                     0                     0   \n",
       "2241                     0                     0                     0   \n",
       "2242                     0                     0                     0   \n",
       "2243                     0                     0                     1   \n",
       "\n",
       "      holiday_sequence_WHW  holiday_sequence_WWH  holiday_sequence_WWW  \n",
       "0                        0                     0                     1  \n",
       "1                        0                     0                     1  \n",
       "2                        0                     0                     1  \n",
       "3                        0                     0                     1  \n",
       "4                        0                     0                     1  \n",
       "...                    ...                   ...                   ...  \n",
       "2239                     0                     0                     0  \n",
       "2240                     0                     0                     1  \n",
       "2241                     0                     0                     1  \n",
       "2242                     0                     1                     0  \n",
       "2243                     0                     0                     0  \n",
       "\n",
       "[2244 rows x 27 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Copy the original data\n",
    "features = data.copy()\n",
    "# Select the numeric columns\n",
    "numeric_subset = data[['trans_date_set','trans_month','trans_year','prevweek_mean', 'total_amount_withdrawn']]\n",
    "# Select the categorical columns\n",
    "# dropped atm_name\n",
    "categorical_subset = data[['weekday','festival_religion', 'working_day',  'holiday_sequence']]\n",
    "# One hot encoding\n",
    "categorical_subset = pd.get_dummies(categorical_subset)\n",
    "# Join the two dataframes using concat\n",
    "features = pd.concat([numeric_subset, categorical_subset], axis = 1)\n",
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "062cd777",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "length of Xtrain and Xtest:  1795 449\n",
      "length of ytrain and ytest:  1795 449\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X = features.copy().drop(columns = ['total_amount_withdrawn', 'trans_date_set', 'trans_month','trans_year', 'working_day_H', 'working_day_W'])\n",
    "y = features['total_amount_withdrawn'].copy()\n",
    "\n",
    "Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, shuffle= False, test_size = 0.2, random_state = 42)\n",
    "\n",
    "print('length of Xtrain and Xtest: ', len(Xtrain), len(Xtest))\n",
    "print('length of ytrain and ytest: ', len(ytrain), len(ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b2f96ce4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "linear = LinearRegression()\n",
    "linear.fit(Xtrain, ytrain)\n",
    "\n",
    "# make predictions\n",
    "lin_pred = linear.predict(Xtest)\n",
    "\n",
    "# Prediction vs Actual\n",
    "linpred = pd.DataFrame(lin_pred[-10:]) # predicting last 10 values\n",
    "linpred.rename(columns = {0: 'lin_predicted'}, inplace=True) # renaming the column\n",
    "linpred = linpred.round(decimals=0) # rounding the decimal values\n",
    "\n",
    "d = pd.DataFrame(data['total_amount_withdrawn']).tail(10) # calling last 10 values of original amt wothdrawn\n",
    "\n",
    "linpred.index = d.index # mapping the index of both dataframe\n",
    "linok = pd.concat([linpred, d], axis=1)\n",
    "\n",
    "linok['accuracy'] = round(linok.apply(lambda row: row.lin_predicted /row.total_amount_withdrawn *100, axis = 1),2)\n",
    "linok['accuracy'] = pd.Series([\"{0:.2f}%\".format(val) for val in linok['accuracy']],index = linok.index)\n",
    "linok = linok.reset_index()\n",
    "\n",
    "linok['index'] = pd.to_datetime(linok['index']) # convert to datetime type\n",
    "linok['day_of_week'] = linok['index'].dt.day_name() # get the day of the week\n",
    "linok = linok.drop(columns=['index']) # drop the original index column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "80d9375c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lin_predicted</th>\n",
       "      <th>total_amount_withdrawn</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>day_of_week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>508440.0</td>\n",
       "      <td>308400</td>\n",
       "      <td>164.86%</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>488103.0</td>\n",
       "      <td>312600</td>\n",
       "      <td>156.14%</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>461633.0</td>\n",
       "      <td>337100</td>\n",
       "      <td>136.94%</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>572903.0</td>\n",
       "      <td>251100</td>\n",
       "      <td>228.16%</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>374079.0</td>\n",
       "      <td>182700</td>\n",
       "      <td>204.75%</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>530596.0</td>\n",
       "      <td>447400</td>\n",
       "      <td>118.60%</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>506257.0</td>\n",
       "      <td>153800</td>\n",
       "      <td>329.17%</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>518418.0</td>\n",
       "      <td>167100</td>\n",
       "      <td>310.24%</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>524031.0</td>\n",
       "      <td>317400</td>\n",
       "      <td>165.10%</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>209299.0</td>\n",
       "      <td>305100</td>\n",
       "      <td>68.60%</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   lin_predicted  total_amount_withdrawn accuracy day_of_week\n",
       "0       508440.0                  308400  164.86%    Thursday\n",
       "1       488103.0                  312600  156.14%    Thursday\n",
       "2       461633.0                  337100  136.94%    Thursday\n",
       "3       572903.0                  251100  228.16%    Thursday\n",
       "4       374079.0                  182700  204.75%    Thursday\n",
       "5       530596.0                  447400  118.60%    Thursday\n",
       "6       506257.0                  153800  329.17%    Thursday\n",
       "7       518418.0                  167100  310.24%    Thursday\n",
       "8       524031.0                  317400  165.10%    Thursday\n",
       "9       209299.0                  305100   68.60%    Thursday"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linok"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "32838bef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "246600.89086859688 480655.8517569125\n",
      "Excess prediction: %\n",
      " 194.91\n"
     ]
    }
   ],
   "source": [
    "mean_lin_test = ytest.mean()\n",
    "mean_lin_pred = lin_pred.mean()\n",
    "print(mean_lin_test, mean_lin_pred)\n",
    "\n",
    "accuracy = (mean_lin_pred/mean_lin_test)*100\n",
    "\n",
    "print(\"Excess prediction: %\\n\", format(round(accuracy,2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5f6a66d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(linok.index, linok.total_amount_withdrawn, label='Actual')\n",
    "plt.plot(linok.index, linok.lin_predicted, label='Linear Regression Predicted')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Amount Withdrawn')\n",
    "plt.title('Linear Regression Predicted vs Actual')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d7b33236",
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBRegressor\n",
    "xgb = XGBRegressor(objective = 'reg:squarederror' )\n",
    "xgb.fit(Xtrain, ytrain)\n",
    "\n",
    "# make predictions\n",
    "\n",
    "xgb_pred = xgb.predict(Xtest)\n",
    "\n",
    "xgbpred = pd.DataFrame(xgb_pred[-10:])\n",
    "\n",
    "xgbpred.rename(columns = {0: 'xgb_predicted'}, inplace=True)\n",
    "\n",
    "xgbpred = xgbpred.round(decimals=0)\n",
    "\n",
    "xgbpred.index = d.index\n",
    "\n",
    "xgbok = pd.concat([xgbpred, d], axis=1)\n",
    "\n",
    "# convert index to DatetimeIndex\n",
    "xgbok.index = pd.to_datetime(xgbok.index)\n",
    "\n",
    "# compute accuracy\n",
    "xgbok['accuracy'] = round(xgbok.apply(lambda row: row.xgb_predicted/row.total_amount_withdrawn *100, axis=1),2)\n",
    "\n",
    "# format accuracy as percentage\n",
    "xgbok['accuracy'] = pd.Series([\"{0:.2f}%\".format(val) for val in xgbok['accuracy']], index= xgbok.index)\n",
    "\n",
    "# add day_of_week column\n",
    "xgbok = xgbok.assign(day_of_week = lambda x: x.index.day_name())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e3b2f857",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>xgb_predicted</th>\n",
       "      <th>total_amount_withdrawn</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>day_of_week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000002234</th>\n",
       "      <td>29213.0</td>\n",
       "      <td>308400</td>\n",
       "      <td>9.47%</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000002235</th>\n",
       "      <td>356450.0</td>\n",
       "      <td>312600</td>\n",
       "      <td>114.03%</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000002236</th>\n",
       "      <td>197966.0</td>\n",
       "      <td>337100</td>\n",
       "      <td>58.73%</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000002237</th>\n",
       "      <td>90477.0</td>\n",
       "      <td>251100</td>\n",
       "      <td>36.03%</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000002238</th>\n",
       "      <td>267407.0</td>\n",
       "      <td>182700</td>\n",
       "      <td>146.36%</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000002239</th>\n",
       "      <td>199052.0</td>\n",
       "      <td>447400</td>\n",
       "      <td>44.49%</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000002240</th>\n",
       "      <td>591902.0</td>\n",
       "      <td>153800</td>\n",
       "      <td>384.85%</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000002241</th>\n",
       "      <td>127836.0</td>\n",
       "      <td>167100</td>\n",
       "      <td>76.50%</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000002242</th>\n",
       "      <td>250156.0</td>\n",
       "      <td>317400</td>\n",
       "      <td>78.81%</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000002243</th>\n",
       "      <td>94842.0</td>\n",
       "      <td>305100</td>\n",
       "      <td>31.09%</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               xgb_predicted  total_amount_withdrawn accuracy  \\\n",
       "1970-01-01 00:00:00.000002234        29213.0                  308400    9.47%   \n",
       "1970-01-01 00:00:00.000002235       356450.0                  312600  114.03%   \n",
       "1970-01-01 00:00:00.000002236       197966.0                  337100   58.73%   \n",
       "1970-01-01 00:00:00.000002237        90477.0                  251100   36.03%   \n",
       "1970-01-01 00:00:00.000002238       267407.0                  182700  146.36%   \n",
       "1970-01-01 00:00:00.000002239       199052.0                  447400   44.49%   \n",
       "1970-01-01 00:00:00.000002240       591902.0                  153800  384.85%   \n",
       "1970-01-01 00:00:00.000002241       127836.0                  167100   76.50%   \n",
       "1970-01-01 00:00:00.000002242       250156.0                  317400   78.81%   \n",
       "1970-01-01 00:00:00.000002243        94842.0                  305100   31.09%   \n",
       "\n",
       "                              day_of_week  \n",
       "1970-01-01 00:00:00.000002234    Thursday  \n",
       "1970-01-01 00:00:00.000002235    Thursday  \n",
       "1970-01-01 00:00:00.000002236    Thursday  \n",
       "1970-01-01 00:00:00.000002237    Thursday  \n",
       "1970-01-01 00:00:00.000002238    Thursday  \n",
       "1970-01-01 00:00:00.000002239    Thursday  \n",
       "1970-01-01 00:00:00.000002240    Thursday  \n",
       "1970-01-01 00:00:00.000002241    Thursday  \n",
       "1970-01-01 00:00:00.000002242    Thursday  \n",
       "1970-01-01 00:00:00.000002243    Thursday  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgbok"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3a9d0c49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(xgbok.index, xgbok.total_amount_withdrawn, label='Actual')\n",
    "plt.plot(xgbok.index, xgbok.xgb_predicted, label='XGBoost Predicted')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Amount Withdrawn')\n",
    "plt.title('XGBoost Predicted vs Actual')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b5152c6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "246600.89086859688 286123.56\n",
      "accuracy: 86.19\n"
     ]
    }
   ],
   "source": [
    "mean_xgb_test = ytest.mean()\n",
    "mean_xgb_pred = xgb_pred.mean()\n",
    "print(mean_xgb_test, mean_xgb_pred)\n",
    "\n",
    "accuracy = (mean_xgb_test / mean_xgb_pred)*100\n",
    "print('accuracy:', round(accuracy, 2) )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7d009ae",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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