PredictingSaleswithCustomers.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "Cid2PmLiD8Pg"
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_style('darkgrid')\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Mi1a56iwD8Pm",
    "outputId": "e5b881dd-2e3a-439c-d590-a70797d9372d",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\basto\\anaconda3\\envs\\tfgpu\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3145: DtypeWarning: Columns (7) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    }
   ],
   "source": [
    "path = \"E:/Hackathon/SegmindAIChallenge/dataset/\"\n",
    "df_train = pd.read_csv(path + 'TRAIN.csv')\n",
    "df_test = pd.read_csv(path + 'custom_df_test.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "f9GofBu1EZQ1"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 402
    },
    "id": "hLkpm0hBElUZ",
    "outputId": "67bb4208-9ffc-417a-bfb3-22728d696228"
   },
   "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>Store</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>Date</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Customers</th>\n",
       "      <th>Open</th>\n",
       "      <th>Promo</th>\n",
       "      <th>StateHoliday</th>\n",
       "      <th>SchoolHoliday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2015-07-31</td>\n",
       "      <td>5263</td>\n",
       "      <td>555</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2015-07-31</td>\n",
       "      <td>6064</td>\n",
       "      <td>625</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2015-07-31</td>\n",
       "      <td>8314</td>\n",
       "      <td>821</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2015-07-31</td>\n",
       "      <td>13995</td>\n",
       "      <td>1498</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2015-07-31</td>\n",
       "      <td>4822</td>\n",
       "      <td>559</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>712041</th>\n",
       "      <td>342</td>\n",
       "      <td>2</td>\n",
       "      <td>2013-10-01</td>\n",
       "      <td>10182</td>\n",
       "      <td>836</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>712042</th>\n",
       "      <td>343</td>\n",
       "      <td>2</td>\n",
       "      <td>2013-10-01</td>\n",
       "      <td>5352</td>\n",
       "      <td>588</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>712043</th>\n",
       "      <td>344</td>\n",
       "      <td>2</td>\n",
       "      <td>2013-10-01</td>\n",
       "      <td>6652</td>\n",
       "      <td>1043</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>712044</th>\n",
       "      <td>345</td>\n",
       "      <td>2</td>\n",
       "      <td>2013-10-01</td>\n",
       "      <td>3842</td>\n",
       "      <td>475</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>712045</th>\n",
       "      <td>346</td>\n",
       "      <td>2</td>\n",
       "      <td>2013-10-01</td>\n",
       "      <td>6143</td>\n",
       "      <td>599</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>712046 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Store  DayOfWeek        Date  Sales  Customers  Open  Promo  \\\n",
       "0           1          5  2015-07-31   5263        555     1      1   \n",
       "1           2          5  2015-07-31   6064        625     1      1   \n",
       "2           3          5  2015-07-31   8314        821     1      1   \n",
       "3           4          5  2015-07-31  13995       1498     1      1   \n",
       "4           5          5  2015-07-31   4822        559     1      1   \n",
       "...       ...        ...         ...    ...        ...   ...    ...   \n",
       "712041    342          2  2013-10-01  10182        836     1      0   \n",
       "712042    343          2  2013-10-01   5352        588     1      0   \n",
       "712043    344          2  2013-10-01   6652       1043     1      0   \n",
       "712044    345          2  2013-10-01   3842        475     1      0   \n",
       "712045    346          2  2013-10-01   6143        599     1      0   \n",
       "\n",
       "       StateHoliday  SchoolHoliday  \n",
       "0                 0              1  \n",
       "1                 0              1  \n",
       "2                 0              1  \n",
       "3                 0              1  \n",
       "4                 0              1  \n",
       "...             ...            ...  \n",
       "712041            0              0  \n",
       "712042            0              0  \n",
       "712043            0              1  \n",
       "712044            0              0  \n",
       "712045            0              0  \n",
       "\n",
       "[712046 rows x 9 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 402
    },
    "id": "A3TGEAVdD8Pn",
    "outputId": "8e234740-fa26-453e-cf87-8adc96c4db42"
   },
   "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>Store</th>\n",
       "      <th>Open</th>\n",
       "      <th>Promo</th>\n",
       "      <th>SchoolHoliday</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Customers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>767</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1111</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>484.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>729.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>662.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>619.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305158</th>\n",
       "      <td>412</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>5</td>\n",
       "      <td>28</td>\n",
       "      <td>775.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305159</th>\n",
       "      <td>936</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>644.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305160</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>581.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305161</th>\n",
       "      <td>1077</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>627.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305162</th>\n",
       "      <td>321</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>588.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>305163 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Store  Open  Promo  SchoolHoliday  Year  Month  Day  Customers\n",
       "0         767     0      0              1  2013      4    1        0.0\n",
       "1        1111     1      0              0  2013      3   16      484.0\n",
       "2          45     1      1              1  2013      8    1      729.0\n",
       "3         500     1      0              1  2013      5   23      662.0\n",
       "4          82     1      0              1  2013      8    6      619.0\n",
       "...       ...   ...    ...            ...   ...    ...  ...        ...\n",
       "305158    412     1      1              1  2013      5   28      775.0\n",
       "305159    936     1      0              0  2013      3    2      644.0\n",
       "305160     21     1      0              0  2013      9   16      581.0\n",
       "305161   1077     1      0              0  2013      6   22      627.0\n",
       "305162    321     1      0              0  2013      9    7      588.0\n",
       "\n",
       "[305163 rows x 8 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "tWeAyJHLD8Po"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BwKjQK41D8Pp"
   },
   "source": [
    "## Reduce 0's from trainset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UjS8cjynD8Pp",
    "outputId": "46658af3-1d85-45e4-bb47-3cb20cd0d61a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        122098\n",
       "5674        163\n",
       "5697        142\n",
       "5723        142\n",
       "6214        141\n",
       "          ...  \n",
       "770           1\n",
       "19912         1\n",
       "20623         1\n",
       "18699         1\n",
       "19737         1\n",
       "Name: Sales, Length: 20727, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reduce huge number of 0's in df_train\n",
    "\n",
    "df_train['Sales'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "slmFag7HD8Pq"
   },
   "outputs": [],
   "source": [
    "df_train = df_train.sample(frac = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "4kKyG-WWD8Pq"
   },
   "outputs": [],
   "source": [
    "#filterzeros = df_train[df_train['Sales'] == 0][:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "cepYPWfAD8Pr"
   },
   "outputs": [],
   "source": [
    "#filterzeros['Sales'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "Pp-f04gQD8Pr"
   },
   "outputs": [],
   "source": [
    "#df_train = df_train[df_train['Sales']!=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "y8SeFgyBD8Ps"
   },
   "outputs": [],
   "source": [
    "#xdf = pd.concat([df_train, filterzeros])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "d9HgQGgqv_uq"
   },
   "outputs": [],
   "source": [
    "xdf = df_train.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vUJ-NoQ_D8Pt",
    "outputId": "ee15047b-95a5-41b1-eb40-fe8113d79beb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        122098\n",
       "5674        163\n",
       "5697        142\n",
       "5723        142\n",
       "6214        141\n",
       "          ...  \n",
       "770           1\n",
       "19912         1\n",
       "20623         1\n",
       "18699         1\n",
       "19737         1\n",
       "Name: Sales, Length: 20727, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xdf['Sales'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "Dcxpa1ZTD8Pt"
   },
   "outputs": [],
   "source": [
    "xdf = xdf.sample(frac = 1)\n",
    "xdf = xdf.iloc[:305163]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ook4cVurD8Pt",
    "outputId": "88c09ad7-7004-448e-b705-a5d85c2292a4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        52232\n",
       "6823        72\n",
       "5697        70\n",
       "5313        66\n",
       "6217        66\n",
       "         ...  \n",
       "17729        1\n",
       "1353         1\n",
       "17985        1\n",
       "17353        1\n",
       "2047         1\n",
       "Name: Sales, Length: 17824, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xdf['Sales'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Q9LS4Y1hD8Pu",
    "outputId": "46dc0493-95a7-4a48-9bed-277f5c609099"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(305163, 9)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xdf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hD4_6EbDD8Pv",
    "outputId": "c7fe2028-ed89-4f90-b908-543a0ba542dd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(305163, 8)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 136
    },
    "id": "kYS8IU8zD8Pv",
    "outputId": "18e49354-43eb-46bc-e12c-5d1237547f6b"
   },
   "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>Store</th>\n",
       "      <th>Open</th>\n",
       "      <th>Promo</th>\n",
       "      <th>SchoolHoliday</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Customers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>767</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1111</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>484.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>729.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store  Open  Promo  SchoolHoliday  Year  Month  Day  Customers\n",
       "0    767     0      0              1  2013      4    1        0.0\n",
       "1   1111     1      0              0  2013      3   16      484.0\n",
       "2     45     1      1              1  2013      8    1      729.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uxqBiy8dD8Pw"
   },
   "source": [
    "# Observe Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "id": "Rtfxjcx1D8Pw",
    "outputId": "16d95029-a915-463e-cd22-b3d54fec16b4"
   },
   "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>Store</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>Date</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Customers</th>\n",
       "      <th>Open</th>\n",
       "      <th>Promo</th>\n",
       "      <th>StateHoliday</th>\n",
       "      <th>SchoolHoliday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>692675</th>\n",
       "      <td>1046</td>\n",
       "      <td>6</td>\n",
       "      <td>2013-10-19</td>\n",
       "      <td>5596</td>\n",
       "      <td>514</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>602741</th>\n",
       "      <td>312</td>\n",
       "      <td>2</td>\n",
       "      <td>2014-01-07</td>\n",
       "      <td>7406</td>\n",
       "      <td>625</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354419</th>\n",
       "      <td>280</td>\n",
       "      <td>3</td>\n",
       "      <td>2014-08-27</td>\n",
       "      <td>4964</td>\n",
       "      <td>418</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156752</th>\n",
       "      <td>653</td>\n",
       "      <td>5</td>\n",
       "      <td>2015-03-13</td>\n",
       "      <td>5601</td>\n",
       "      <td>503</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245417</th>\n",
       "      <td>751</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-12-22</td>\n",
       "      <td>5469</td>\n",
       "      <td>651</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Store  DayOfWeek        Date  Sales  Customers  Open  Promo  \\\n",
       "692675   1046          6  2013-10-19   5596        514     1      0   \n",
       "602741    312          2  2014-01-07   7406        625     1      1   \n",
       "354419    280          3  2014-08-27   4964        418     1      0   \n",
       "156752    653          5  2015-03-13   5601        503     1      0   \n",
       "245417    751          1  2014-12-22   5469        651     1      0   \n",
       "\n",
       "       StateHoliday  SchoolHoliday  \n",
       "692675            0              0  \n",
       "602741            0              1  \n",
       "354419            0              1  \n",
       "156752            0              0  \n",
       "245417            0              1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "nMc7rS0xD8Px"
   },
   "outputs": [],
   "source": [
    "# Extract year, month and day from 'Date'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0DEZxu3zD8Px",
    "outputId": "e4bab0fa-75a7-4d6c-b381-f5f1b1d5c562"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "692675    2013-10-19\n",
       "602741    2014-01-07\n",
       "354419    2014-08-27\n",
       "156752    2015-03-13\n",
       "245417    2014-12-22\n",
       "             ...    \n",
       "22318     2015-07-11\n",
       "277366    2014-11-18\n",
       "616798    2013-12-26\n",
       "12784     2015-07-20\n",
       "278404    2014-11-17\n",
       "Name: Date, Length: 305163, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xdf['Date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "Hb4B6CYJD8Py"
   },
   "outputs": [],
   "source": [
    "xdf['Date'] = pd.to_datetime(xdf['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "l9Ix8TD6D8Py",
    "outputId": "bd12d089-b817-4f3a-cee2-6040dbf79041"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "692675   2013-10-19\n",
       "602741   2014-01-07\n",
       "354419   2014-08-27\n",
       "156752   2015-03-13\n",
       "245417   2014-12-22\n",
       "            ...    \n",
       "22318    2015-07-11\n",
       "277366   2014-11-18\n",
       "616798   2013-12-26\n",
       "12784    2015-07-20\n",
       "278404   2014-11-17\n",
       "Name: Date, Length: 305163, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xdf['Date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "CP0Dbma0D8Pz"
   },
   "outputs": [],
   "source": [
    "xdf['Year'] = xdf['Date'].dt.year\n",
    "xdf['Month'] = xdf['Date'].dt.month\n",
    "xdf['Day'] = xdf['Date'].dt.day\n",
    "\n",
    "\n",
    "##df_test['Year'] = df_test['Date'].dt.year\n",
    "#df_test['Month'] = df_test['Date'].dt.month\n",
    "#df_test['Day'] = df_test['Date'].dt.day\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "3puS__5HD8P0"
   },
   "outputs": [],
   "source": [
    "xdf.drop('Date', axis = 1, inplace = True)\n",
    "#df_test.drop('Date', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HP5eWvWyD8P0",
    "outputId": "21a6ee8e-e41d-423a-88c8-4e0bf1b71405"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(305163, 11)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xdf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "x2uUoCBND8P1",
    "outputId": "1a2e4753-9331-487e-94fe-dc30bd318171"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 305163 entries, 692675 to 278404\n",
      "Data columns (total 11 columns):\n",
      " #   Column         Non-Null Count   Dtype \n",
      "---  ------         --------------   ----- \n",
      " 0   Store          305163 non-null  int64 \n",
      " 1   DayOfWeek      305163 non-null  int64 \n",
      " 2   Sales          305163 non-null  int64 \n",
      " 3   Customers      305163 non-null  int64 \n",
      " 4   Open           305163 non-null  int64 \n",
      " 5   Promo          305163 non-null  int64 \n",
      " 6   StateHoliday   305163 non-null  object\n",
      " 7   SchoolHoliday  305163 non-null  int64 \n",
      " 8   Year           305163 non-null  int64 \n",
      " 9   Month          305163 non-null  int64 \n",
      " 10  Day            305163 non-null  int64 \n",
      "dtypes: int64(10), object(1)\n",
      "memory usage: 27.9+ MB\n"
     ]
    }
   ],
   "source": [
    "xdf.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e0_oh_-ID8P1",
    "outputId": "ada6e24a-64e2-4ef0-cf20-119f20ae5262"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0', '0', 0, ..., 'c', '0', '0'], dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xdf['StateHoliday'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HzG4n9S7D8P2",
    "outputId": "e7034976-4584-4429-9688-9c3ba7b3955d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    239284\n",
       "0     56025\n",
       "a      6242\n",
       "b      1888\n",
       "c      1724\n",
       "Name: StateHoliday, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xdf['StateHoliday'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "uxZQUJ-xD8P2"
   },
   "outputs": [],
   "source": [
    "#df_test['StateHoliday'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "qGH1MRxVD8P3"
   },
   "outputs": [],
   "source": [
    "xdf['StateHoliday'] = xdf['StateHoliday'].replace({'0':'0','a':'1','b':'1','c':'1'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DrslucCrD8P4",
    "outputId": "8946b775-d379-4624-ead1-d71e650b5496"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    239284\n",
       "0     56025\n",
       "1      9854\n",
       "Name: StateHoliday, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xdf['StateHoliday'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "BvJZ14vgD8P4"
   },
   "outputs": [],
   "source": [
    "#df_test['StateHoliday'] = df_test['StateHoliday'].replace({'a':'1','b':'1'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "uE1Luy3eD8P4"
   },
   "outputs": [],
   "source": [
    "xdf['StateHoliday'] = pd.to_numeric(xdf['StateHoliday'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "gNGq2ebVD8P5"
   },
   "outputs": [],
   "source": [
    "#df_test['StateHoliday'] = pd.to_numeric(df_test['StateHoliday'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OMMFBDnHD8P5",
    "outputId": "0c039f0b-b093-4077-9cf3-ea00f190eed7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    295309\n",
       "1      9854\n",
       "Name: StateHoliday, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's recheck \n",
    "\n",
    "xdf['StateHoliday'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "wERDHElOD8P6"
   },
   "outputs": [],
   "source": [
    "#df_test['StateHoliday'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SIMv2xrBD8P6"
   },
   "source": [
    "This need to be converted to Numeric and fixed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_f8YY5IiD8P7",
    "outputId": "e01f12e9-08ee-4706-d8a7-a2f8037dd4f2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    252831\n",
       "1     52332\n",
       "Name: SchoolHoliday, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# school holiday\n",
    "\n",
    "xdf['SchoolHoliday'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wiqKiVL-GjdF",
    "outputId": "70167c77-7cdb-40c2-8d9b-689a18d456fa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store            0\n",
       "DayOfWeek        0\n",
       "Sales            0\n",
       "Customers        0\n",
       "Open             0\n",
       "Promo            0\n",
       "StateHoliday     0\n",
       "SchoolHoliday    0\n",
       "Year             0\n",
       "Month            0\n",
       "Day              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xdf.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 564
    },
    "id": "ULNYh_KTD8P7",
    "outputId": "ca99555b-8683-49b9-a929-f6cb793c1100"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = xdf.corr()\n",
    "plt.figure(figsize = (10,8))\n",
    "sns.heatmap(corr, vmax = .8, annot = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "utzipsEFD8P7"
   },
   "source": [
    "# Exploratory Data Analaysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UQ1WK3ROD8P8",
    "outputId": "7a5d1254-f953-42e3-bdee-eeea6ec5e794"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    252947\n",
       "0     52216\n",
       "Name: Open, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xdf['Open'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eEDBEbmUD8P8"
   },
   "source": [
    "## Univariate (Target Attribute)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "muRvpCAqD8P8",
    "outputId": "d2f2e12a-97c5-4f71-c900-a9c97f812122"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        52232\n",
       "6823        72\n",
       "5697        70\n",
       "5313        66\n",
       "6217        66\n",
       "         ...  \n",
       "17729        1\n",
       "1353         1\n",
       "17985        1\n",
       "17353        1\n",
       "2047         1\n",
       "Name: Sales, Length: 17824, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xdf['Sales'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 441
    },
    "id": "oaVLbPgxD8P8",
    "outputId": "6e28707e-fba4-4552-edf8-5ab414fe1ed4",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Lets check its distribution\n",
    "\n",
    "sns.displot(x = 'Sales', data = xdf, aspect = 2, height = 6, kde = True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JV4Pq57RD8P9",
    "outputId": "dfd1d53f-424c-4d5c-fc5f-c708506cc4c9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness: 0.638425\n",
      "Kurtosis: 1.757683\n"
     ]
    }
   ],
   "source": [
    "# Skew and kurt\n",
    "print(\"Skewness: %f\" % xdf['Sales'].skew())\n",
    "print(\"Kurtosis: %f\" % xdf['Sales'].kurt())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "id": "BM63pHiSD8P9",
    "outputId": "608f7758-0ae5-4cb4-ef4c-a0ef84209739"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's plot how Sales relates to some of the features in the dataset\n",
    "\n",
    "data = pd.concat([xdf['Sales'], xdf['StateHoliday']], axis=1)\n",
    "f, ax = plt.subplots(figsize=(8, 6))\n",
    "fig = sns.boxplot(x=xdf['StateHoliday'], y=\"Sales\", data=data)\n",
    "fig.axis(ymin=0, ymax=800000);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "id": "_-sy1ZeBD8P9",
    "outputId": "291882b0-f8c5-4038-a3ae-8b35faa68d8b"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's plot how Sales relates to some of the features in the dataset\n",
    "\n",
    "data = pd.concat([xdf['Sales'], xdf['Promo']], axis=1)\n",
    "f, ax = plt.subplots(figsize=(8, 6))\n",
    "fig = sns.boxplot(x=xdf['Promo'], y=\"Sales\", data=data)\n",
    "fig.axis(ymin=0, ymax=800000);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-1ubXFVJD8P-",
    "outputId": "63dbc1ed-c2ae-4d71-d342-0c72c70e9dd7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        52232\n",
       "6823        72\n",
       "5697        70\n",
       "5313        66\n",
       "6217        66\n",
       "         ...  \n",
       "17729        1\n",
       "1353         1\n",
       "17985        1\n",
       "17353        1\n",
       "2047         1\n",
       "Name: Sales, Length: 17824, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xdf['Sales'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "37GqSdlED8P-"
   },
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 458
    },
    "id": "HOThMTjeD8P-",
    "outputId": "ecabedd8-e3f7-4810-843b-132837b72e55",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"white\")\n",
    "sns.set_color_codes(palette='deep')\n",
    "f, ax = plt.subplots(figsize=(8, 7))\n",
    "#Check the new distribution \n",
    "sns.distplot(xdf['Sales'], color=\"b\");\n",
    "ax.xaxis.grid(False)\n",
    "ax.set(ylabel=\"Frequency\")\n",
    "ax.set(xlabel=\"Sales\")\n",
    "ax.set(title=\"SalePrice distribution\")\n",
    "sns.despine(trim=True, left=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_29aMrNhD8P-"
   },
   "source": [
    "The Sales is skewed to the right. This is a problem because most ML models don't do well with non-normally distributed data. We can apply a log(1+x) transform to fix the skew."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "U5m0ELWKD8P_"
   },
   "outputs": [],
   "source": [
    "from scipy.stats import skew, norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "id": "zaUBS6_cD8P_"
   },
   "outputs": [],
   "source": [
    "xdf['Sales'] = np.log1p(xdf['Sales'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 509
    },
    "id": "KgfYPtKoD8P_",
    "outputId": "4af708c0-3131-411e-a689-c96713ef7b02"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " mu = 7.28 and sigma = 3.33\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"white\")\n",
    "sns.set_color_codes(palette='deep')\n",
    "f, ax = plt.subplots(figsize=(8, 7))\n",
    "#Check the new distribution \n",
    "sns.distplot(xdf['Sales'] , fit=norm, color=\"b\");\n",
    "\n",
    "# Get the fitted parameters used by the function\n",
    "(mu, sigma) = norm.fit(xdf['Sales'])\n",
    "print( '\\n mu = {:.2f} and sigma = {:.2f}\\n'.format(mu, sigma))\n",
    "\n",
    "#Now plot the distribution\n",
    "plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, sigma)],\n",
    "            loc='best')\n",
    "ax.xaxis.grid(False)\n",
    "ax.set(ylabel=\"Frequency\")\n",
    "ax.set(xlabel=\"SalePrice\")\n",
    "ax.set(title=\"SalePrice distribution\")\n",
    "sns.despine(trim=True, left=True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "E6wDsI8iD8QA",
    "outputId": "a911e9ca-8157-41fa-9976-3351e1d9d787"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store            0\n",
       "DayOfWeek        0\n",
       "Sales            0\n",
       "Customers        0\n",
       "Open             0\n",
       "Promo            0\n",
       "StateHoliday     0\n",
       "SchoolHoliday    0\n",
       "Year             0\n",
       "Month            0\n",
       "Day              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xdf.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rM1I6xHoD8QA"
   },
   "source": [
    "## Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 547
    },
    "id": "pLq1L4LLD8QA",
    "outputId": "c57bf572-a20b-410a-a344-f6ae2e3aaf6c",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's check the correlation of the feature with target\n",
    "\n",
    "corr = xdf.corr()\n",
    "\n",
    "plt.figure(figsize = (10,8))\n",
    "sns.heatmap(corr, vmax = .8, annot = True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "id": "e1rPIAoED8QA"
   },
   "outputs": [],
   "source": [
    "xdf.drop(['DayOfWeek','StateHoliday'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "id": "qqtGiseoD8QB"
   },
   "outputs": [],
   "source": [
    "#df_test.drop(['DayOfWeek'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BnO4-U4-D8QD"
   },
   "source": [
    "# Dataset Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "id": "wfAK-bmBD8QE"
   },
   "outputs": [],
   "source": [
    "# Splitting the dataset\n",
    "\n",
    "X = xdf.drop(['Sales'], axis =1)\n",
    "y = xdf['Sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "id": "lvIm5ceoD8QF"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "id": "e1x3Ii-rD8QG"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "id": "PpjchGXeD8QG"
   },
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YymHqzVTD8QH",
    "outputId": "30575c88-3ba3-4b9e-e7e3-561e72dafb09"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((244130, 8), (61033, 8))"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 402
    },
    "id": "YMCpDtX6D8QI",
    "outputId": "b003e779-d9d4-4c93-88e3-266817bdb688",
    "scrolled": true
   },
   "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>Store</th>\n",
       "      <th>Customers</th>\n",
       "      <th>Open</th>\n",
       "      <th>Promo</th>\n",
       "      <th>SchoolHoliday</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>138059</th>\n",
       "      <td>915</td>\n",
       "      <td>610</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440262</th>\n",
       "      <td>623</td>\n",
       "      <td>1028</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593552</th>\n",
       "      <td>43</td>\n",
       "      <td>523</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555347</th>\n",
       "      <td>863</td>\n",
       "      <td>846</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191890</th>\n",
       "      <td>111</td>\n",
       "      <td>517</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>9</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344216</th>\n",
       "      <td>373</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144046</th>\n",
       "      <td>212</td>\n",
       "      <td>351</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311620</th>\n",
       "      <td>526</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132249</th>\n",
       "      <td>680</td>\n",
       "      <td>1240</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417406</th>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61033 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Store  Customers  Open  Promo  SchoolHoliday  Year  Month  Day\n",
       "138059    915        610     1      1              1  2015      3   30\n",
       "440262    623       1028     1      1              0  2014      6    2\n",
       "593552     43        523     1      0              0  2014      1   15\n",
       "555347    863        846     1      1              0  2014      2   19\n",
       "191890    111        517     1      0              0  2015      2    9\n",
       "...       ...        ...   ...    ...            ...   ...    ...  ...\n",
       "344216    373          0     0      0              0  2014      9    7\n",
       "144046    212        351     1      0              0  2015      3   24\n",
       "311620    526          0     0      0              0  2014     10   12\n",
       "132249    680       1240     1      0              0  2015      4    4\n",
       "417406     67          0     0      0              0  2014      6   22\n",
       "\n",
       "[61033 rows x 8 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 402
    },
    "id": "Wmq9hi6Xsj-F",
    "outputId": "54dd97b5-e4f3-4d41-ef45-c90f256e8401"
   },
   "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>Store</th>\n",
       "      <th>Open</th>\n",
       "      <th>Promo</th>\n",
       "      <th>SchoolHoliday</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Customers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>767</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1111</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>484.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>729.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>662.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>619.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305158</th>\n",
       "      <td>412</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>5</td>\n",
       "      <td>28</td>\n",
       "      <td>775.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305159</th>\n",
       "      <td>936</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>644.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305160</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>581.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305161</th>\n",
       "      <td>1077</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>627.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305162</th>\n",
       "      <td>321</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>588.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>305163 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Store  Open  Promo  SchoolHoliday  Year  Month  Day  Customers\n",
       "0         767     0      0              1  2013      4    1        0.0\n",
       "1        1111     1      0              0  2013      3   16      484.0\n",
       "2          45     1      1              1  2013      8    1      729.0\n",
       "3         500     1      0              1  2013      5   23      662.0\n",
       "4          82     1      0              1  2013      8    6      619.0\n",
       "...       ...   ...    ...            ...   ...    ...  ...        ...\n",
       "305158    412     1      1              1  2013      5   28      775.0\n",
       "305159    936     1      0              0  2013      3    2      644.0\n",
       "305160     21     1      0              0  2013      9   16      581.0\n",
       "305161   1077     1      0              0  2013      6   22      627.0\n",
       "305162    321     1      0              0  2013      9    7      588.0\n",
       "\n",
       "[305163 rows x 8 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "id": "46-ISljBIb7k"
   },
   "outputs": [],
   "source": [
    "first_col = df_test.pop(\"Customers\")\n",
    "\n",
    "df_test.insert(1, \"Customers\", first_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 402
    },
    "id": "I1R_J743JRRF",
    "outputId": "a7ec4d2f-78bd-4d58-e6f7-5c5708840385"
   },
   "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>Store</th>\n",
       "      <th>Customers</th>\n",
       "      <th>Open</th>\n",
       "      <th>Promo</th>\n",
       "      <th>SchoolHoliday</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>767</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1111</td>\n",
       "      <td>484.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45</td>\n",
       "      <td>729.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>500</td>\n",
       "      <td>662.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>82</td>\n",
       "      <td>619.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>8</td>\n",
       "      <td>6</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305158</th>\n",
       "      <td>412</td>\n",
       "      <td>775.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>5</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305159</th>\n",
       "      <td>936</td>\n",
       "      <td>644.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305160</th>\n",
       "      <td>21</td>\n",
       "      <td>581.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305161</th>\n",
       "      <td>1077</td>\n",
       "      <td>627.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305162</th>\n",
       "      <td>321</td>\n",
       "      <td>588.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>305163 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Store  Customers  Open  Promo  SchoolHoliday  Year  Month  Day\n",
       "0         767        0.0     0      0              1  2013      4    1\n",
       "1        1111      484.0     1      0              0  2013      3   16\n",
       "2          45      729.0     1      1              1  2013      8    1\n",
       "3         500      662.0     1      0              1  2013      5   23\n",
       "4          82      619.0     1      0              1  2013      8    6\n",
       "...       ...        ...   ...    ...            ...   ...    ...  ...\n",
       "305158    412      775.0     1      1              1  2013      5   28\n",
       "305159    936      644.0     1      0              0  2013      3    2\n",
       "305160     21      581.0     1      0              0  2013      9   16\n",
       "305161   1077      627.0     1      0              0  2013      6   22\n",
       "305162    321      588.0     1      0              0  2013      9    7\n",
       "\n",
       "[305163 rows x 8 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5_pFIqteD8QI"
   },
   "source": [
    "### Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "id": "nxpQo09fD8QJ"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "id": "mOUJT9jhD8QJ"
   },
   "outputs": [],
   "source": [
    "ct = ColumnTransformer([\n",
    "                ('scaler', StandardScaler(),['Store','Customers','Year','Month','Day'])\n",
    "], remainder = 'passthrough')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "225WVpOvD8QJ"
   },
   "source": [
    "# Modeling and Evaluation Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "id": "YQNgIwFCD8QJ"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n",
    "import math"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DHF-tG1oD8QJ"
   },
   "source": [
    "## Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "id": "-Ept5n7eD8QK"
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "id": "2oiSYSKbD8QK"
   },
   "outputs": [],
   "source": [
    "lr = LinearRegression()\n",
    "lr_pipe = Pipeline([\n",
    "            ('column-transformer', ct),\n",
    "            ('LinearRegression', lr)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ffQOuF_tD8QK",
    "outputId": "1b1f752f-2b37-408c-a714-012070efd501"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('column-transformer',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('scaler', StandardScaler(),\n",
       "                                                  ['Store', 'Customers', 'Year',\n",
       "                                                   'Month', 'Day'])])),\n",
       "                ('LinearRegression', LinearRegression())])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_pipe.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "id": "7nXjX-SvD8QK"
   },
   "outputs": [],
   "source": [
    "lr_pred = lr_pipe.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0XbzwhiaD8QL",
    "outputId": "2c2c4025-c6ee-4fa9-ea77-9b4db0a94ac5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score: 0.9950112863089915\n",
      "Mean Squarred Error: 0.055\n",
      "RMSE: 0.23467358677965622\n",
      "Mean Absolute Error : 0.16261948356183645\n"
     ]
    }
   ],
   "source": [
    "print(\"R2 Score:\", r2_score(y_test, lr_pred))\n",
    "print(\"Mean Squarred Error: %.3f\" % mean_squared_error(y_test, lr_pred))\n",
    "print(\"RMSE:\", math.sqrt(mean_squared_error(y_test, lr_pred)))\n",
    "print(\"Mean Absolute Error : \" + str(mean_absolute_error(y_test,lr_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ohCQgOKHD8QL"
   },
   "source": [
    "## GradientBoostingRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "id": "mrKXANYVD8QL"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "id": "1lmIkUbBD8QL"
   },
   "outputs": [],
   "source": [
    "gbr = GradientBoostingRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "id": "HzTq9z_ID8QM"
   },
   "outputs": [],
   "source": [
    "gbr_pipe = Pipeline([\n",
    "            ('column-transformer', ct),\n",
    "            ('GradientBoostingRegressor', gbr)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XQS-M-2RD8QM",
    "outputId": "4225ebd3-35a1-43c4-8ca5-ed1765539bcf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('column-transformer',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('scaler', StandardScaler(),\n",
       "                                                  ['Store', 'Customers', 'Year',\n",
       "                                                   'Month', 'Day'])])),\n",
       "                ('GradientBoostingRegressor', GradientBoostingRegressor())])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbr_pipe.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "id": "2jtYDZ-sD8QM"
   },
   "outputs": [],
   "source": [
    "gbr_pred = gbr_pipe.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PK89fjsRD8QM",
    "outputId": "08b204ca-e3bc-4588-ce88-52bf31817ad1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score: 0.9972546648214737\n",
      "Mean Squarred Error: 0.030306460475491622\n",
      "RMSE: 0.17408750809719697\n",
      "Mean Absolute Error : 0.1283326853363149\n"
     ]
    }
   ],
   "source": [
    "print(\"R2 Score:\", r2_score(y_test, gbr_pred))\n",
    "print(\"Mean Squarred Error:\", mean_squared_error(y_test, gbr_pred))\n",
    "print(\"RMSE:\", math.sqrt(mean_squared_error(y_test, gbr_pred)))\n",
    "print(\"Mean Absolute Error : \" + str(mean_absolute_error(y_test,gbr_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "id": "2evlfSP8xcEf"
   },
   "outputs": [],
   "source": [
    "## Hyperparamter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "nm9i8KE1xecd"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "zLlqw_nQxewO"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "DWBqfkQSxe7J"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xmKSskVTD8QN"
   },
   "source": [
    "## XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "id": "YI2ScHlBD8QN"
   },
   "outputs": [],
   "source": [
    "from xgboost import XGBRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "id": "pqOZWRlXD8QN"
   },
   "outputs": [],
   "source": [
    "xgb = XGBRegressor(n_estimators = 100, learning_rate = 0.05)\n",
    "xgb_pipe = Pipeline([\n",
    "            ('column-transformer', ct),\n",
    "            ('XGBRegression', xgb)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KFkxQ-dYD8QN",
    "outputId": "7ac2001e-f1e1-4bb1-84b8-e3a8db724420"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('column-transformer',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('scaler', StandardScaler(),\n",
       "                                                  ['Store', 'Customers', 'Year',\n",
       "                                                   'Month', 'Day'])])),\n",
       "                ('XGBRegression',\n",
       "                 XGBRegressor(base_score=0.5, booster='gbtree',\n",
       "                              colsample_bylevel=1, colsample_bynode=1,\n",
       "                              colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "                              importance_type='gain',\n",
       "                              interaction_constraints='', learning_rate=0.05,\n",
       "                              max_delta_step=0, max_depth=6, min_child_weight=1,\n",
       "                              missing=nan, monotone_constraints='()',\n",
       "                              n_estimators=100, n_jobs=8, num_parallel_tree=1,\n",
       "                              random_state=0, reg_alpha=0, reg_lambda=1,\n",
       "                              scale_pos_weight=1, subsample=1,\n",
       "                              tree_method='exact', validate_parameters=1,\n",
       "                              verbosity=None))])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_pipe.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "id": "9XaJPmK1D8QN"
   },
   "outputs": [],
   "source": [
    "xgb_pred = xgb_pipe.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gJqhwl-BD8QO",
    "outputId": "ce481661-095f-4afa-940c-e09ee367dc2a",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score: 0.9972034018965031\n",
      "Mean Squarred Error: 0.031 \n",
      "RMSE: 0.17570533593699877\n",
      "Mean Absolute Error : 0.12868552917979895\n"
     ]
    }
   ],
   "source": [
    "print(\"R2 Score:\", r2_score(y_test, xgb_pred))\n",
    "print(\"Mean Squarred Error: %.3f \" % mean_squared_error(y_test, xgb_pred))\n",
    "print(\"RMSE:\", math.sqrt(mean_squared_error(y_test, xgb_pred)))\n",
    "print(\"Mean Absolute Error : \" + str(mean_absolute_error(y_test,xgb_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "id": "Lr3d-Rz5y3W3"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bdV7qGe7D8QO",
    "outputId": "90cef7ad-2a3f-4eb6-a440-d5d7ed04fad6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((305163, 8), (305163, 9))"
      ]
     },
     "execution_count": 218,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.shape, xdf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "id": "-bkExOWTD8QO"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "id": "Ib9cJoHuD8QP"
   },
   "outputs": [],
   "source": [
    "output = pd.DataFrame(columns = ['Index','Sales'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "id": "cg7dVoojD8QP"
   },
   "outputs": [],
   "source": [
    "output['Index'] = df_test.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 402
    },
    "id": "u5zYY4z5D8QP",
    "outputId": "e5bd982c-db5d-4ea6-8116-a5846cd7dca0"
   },
   "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>Index</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305158</th>\n",
       "      <td>305158</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305159</th>\n",
       "      <td>305159</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305160</th>\n",
       "      <td>305160</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305161</th>\n",
       "      <td>305161</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305162</th>\n",
       "      <td>305162</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>305163 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Index Sales\n",
       "0            0   NaN\n",
       "1            1   NaN\n",
       "2            2   NaN\n",
       "3            3   NaN\n",
       "4            4   NaN\n",
       "...        ...   ...\n",
       "305158  305158   NaN\n",
       "305159  305159   NaN\n",
       "305160  305160   NaN\n",
       "305161  305161   NaN\n",
       "305162  305162   NaN\n",
       "\n",
       "[305163 rows x 2 columns]"
      ]
     },
     "execution_count": 221,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ydQSCuknQn9g",
    "outputId": "59210fd8-9a3b-4608-d5f1-10523c110636"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "416334    4280\n",
       "161432    3793\n",
       "479295    6013\n",
       "539444    6644\n",
       "515776    4276\n",
       "          ... \n",
       "148616    5002\n",
       "640478    8901\n",
       "11440     6722\n",
       "544227    5773\n",
       "261850    9899\n",
       "Name: Sales, Length: 589948, dtype: int64"
      ]
     },
     "execution_count": 222,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train['Sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {
    "id": "uJJuUCzBD8QQ"
   },
   "outputs": [],
   "source": [
    "output['Sales'] = np.floor(np.expm1(gbr_pipe.predict(df_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZTwSnxz8Ja-B",
    "outputId": "16076771-7368-47fb-893b-ac08b21baed5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0        50754\n",
       "7634.0      1280\n",
       "5351.0      1235\n",
       "5504.0      1047\n",
       "5525.0       987\n",
       "           ...  \n",
       "9355.0         1\n",
       "9306.0         1\n",
       "4296.0         1\n",
       "11915.0        1\n",
       "9874.0         1\n",
       "Name: Sales, Length: 5107, dtype: int64"
      ]
     },
     "execution_count": 246,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output['Sales'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 402
    },
    "id": "3ke2tRLKvfXC",
    "outputId": "d1818f76-9f74-4a35-a78f-7aa635167d29"
   },
   "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>Index</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4832.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>7568.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>5720.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305158</th>\n",
       "      <td>305158</td>\n",
       "      <td>8460.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305159</th>\n",
       "      <td>305159</td>\n",
       "      <td>5824.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305160</th>\n",
       "      <td>305160</td>\n",
       "      <td>5258.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305161</th>\n",
       "      <td>305161</td>\n",
       "      <td>5415.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305162</th>\n",
       "      <td>305162</td>\n",
       "      <td>5232.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>305163 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Index   Sales\n",
       "0            0     0.0\n",
       "1            1  4832.0\n",
       "2            2  7568.0\n",
       "3            3  5720.0\n",
       "4            4  5725.0\n",
       "...        ...     ...\n",
       "305158  305158  8460.0\n",
       "305159  305159  5824.0\n",
       "305160  305160  5258.0\n",
       "305161  305161  5415.0\n",
       "305162  305162  5232.0\n",
       "\n",
       "[305163 rows x 2 columns]"
      ]
     },
     "execution_count": 247,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {
    "id": "3t1iSRuzD8QQ"
   },
   "outputs": [],
   "source": [
    "output.to_csv('output6.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "nsGyFjZXD8QQ"
   },
   "outputs": [],
   "source": [
    "mysub= pd.read_csv('output.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Vmk7MFipD8QQ",
    "outputId": "14e67591-4f05-4dd7-a703-27ab66ea7bae"
   },
   "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>Index</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3523.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>7205.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>5211.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5219.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305158</th>\n",
       "      <td>305158</td>\n",
       "      <td>7929.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305159</th>\n",
       "      <td>305159</td>\n",
       "      <td>5246.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305160</th>\n",
       "      <td>305160</td>\n",
       "      <td>5194.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305161</th>\n",
       "      <td>305161</td>\n",
       "      <td>4781.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305162</th>\n",
       "      <td>305162</td>\n",
       "      <td>4873.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>305163 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Index   Sales\n",
       "0            0     0.0\n",
       "1            1  3523.0\n",
       "2            2  7205.0\n",
       "3            3  5211.0\n",
       "4            4  5219.0\n",
       "...        ...     ...\n",
       "305158  305158  7929.0\n",
       "305159  305159  5246.0\n",
       "305160  305160  5194.0\n",
       "305161  305161  4781.0\n",
       "305162  305162  4873.0\n",
       "\n",
       "[305163 rows x 2 columns]"
      ]
     },
     "execution_count": 548,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mysub"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yUKyu5RMD8QR"
   },
   "source": [
    "## 2,321.8847 << Public Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Dxr9cEajD8QR"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [
    "yUKyu5RMD8QR"
   ],
   "name": "PredictingSaleswithCustomers.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "tfgpu",
   "language": "python",
   "name": "tfgpu"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}