SegmindHackathon-Copy3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "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": 71,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\basto\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3146: 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 + 'TEST.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(305163, 7)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        122098\n",
       "5674        163\n",
       "5697        142\n",
       "5723        142\n",
       "6214        141\n",
       "          ...  \n",
       "23005         1\n",
       "18521         1\n",
       "16472         1\n",
       "18071         1\n",
       "1118          1\n",
       "Name: Sales, Length: 20727, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train['Sales'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reduce 0's from trainset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        122098\n",
       "5674        163\n",
       "5697        142\n",
       "5723        142\n",
       "6214        141\n",
       "          ...  \n",
       "23005         1\n",
       "18521         1\n",
       "16472         1\n",
       "18071         1\n",
       "1118          1\n",
       "Name: Sales, Length: 20727, dtype: int64"
      ]
     },
     "execution_count": 74,
     "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": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df_train.sample(frac = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df_train[df_train['Sales']!=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "xdf = df_train.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5674     163\n",
       "5723     142\n",
       "5697     142\n",
       "6214     141\n",
       "5818     141\n",
       "        ... \n",
       "22232      1\n",
       "21559      1\n",
       "19579      1\n",
       "30428      1\n",
       "20557      1\n",
       "Name: Sales, Length: 20726, dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xdf['Sales'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "xdf = xdf.sample(frac = 1)\n",
    "xdf = xdf.iloc[:305163]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6439     83\n",
       "5674     82\n",
       "4383     80\n",
       "5619     79\n",
       "5723     78\n",
       "         ..\n",
       "18213     1\n",
       "28448     1\n",
       "16322     1\n",
       "18818     1\n",
       "19761     1\n",
       "Name: Sales, Length: 18453, dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xdf['Sales'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(305163, 9)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xdf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(305163, 7)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Observe Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>518738</th>\n",
       "      <td>1049</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-03-24</td>\n",
       "      <td>3482</td>\n",
       "      <td>611</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52265</th>\n",
       "      <td>976</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-06-15</td>\n",
       "      <td>11796</td>\n",
       "      <td>991</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439423</th>\n",
       "      <td>899</td>\n",
       "      <td>2</td>\n",
       "      <td>2014-06-03</td>\n",
       "      <td>7808</td>\n",
       "      <td>786</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695310</th>\n",
       "      <td>336</td>\n",
       "      <td>3</td>\n",
       "      <td>2013-10-16</td>\n",
       "      <td>11009</td>\n",
       "      <td>1772</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53712</th>\n",
       "      <td>193</td>\n",
       "      <td>6</td>\n",
       "      <td>2015-06-13</td>\n",
       "      <td>4580</td>\n",
       "      <td>718</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Store  DayOfWeek        Date  Sales  Customers  Open  Promo  \\\n",
       "518738   1049          1  2014-03-24   3482        611     1      0   \n",
       "52265     976          1  2015-06-15  11796        991     1      1   \n",
       "439423    899          2  2014-06-03   7808        786     1      1   \n",
       "695310    336          3  2013-10-16  11009       1772     1      0   \n",
       "53712     193          6  2015-06-13   4580        718     1      0   \n",
       "\n",
       "       StateHoliday  SchoolHoliday  \n",
       "518738            0              0  \n",
       "52265             0              0  \n",
       "439423            0              0  \n",
       "695310            0              1  \n",
       "53712             0              0  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract year, month and day from 'Date'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "518738    2014-03-24\n",
       "52265     2015-06-15\n",
       "439423    2014-06-03\n",
       "695310    2013-10-16\n",
       "53712     2015-06-13\n",
       "             ...    \n",
       "335422    2014-09-17\n",
       "704396    2013-10-08\n",
       "275447    2014-11-20\n",
       "89563     2015-05-12\n",
       "702927    2013-10-09\n",
       "Name: Date, Length: 305163, dtype: object"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xdf['Date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "xdf['Date'] = pd.to_datetime(xdf['Date'])\n",
    "df_test['Date'] = pd.to_datetime(df_test['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "518738   2014-03-24\n",
       "52265    2015-06-15\n",
       "439423   2014-06-03\n",
       "695310   2013-10-16\n",
       "53712    2015-06-13\n",
       "            ...    \n",
       "335422   2014-09-17\n",
       "704396   2013-10-08\n",
       "275447   2014-11-20\n",
       "89563    2015-05-12\n",
       "702927   2013-10-09\n",
       "Name: Date, Length: 305163, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xdf['Date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "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": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "xdf.drop('Date', axis = 1, inplace = True)\n",
    "df_test.drop('Date', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(305163, 11)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xdf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 305163 entries, 518738 to 702927\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": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0', '0', '0', ..., '0', '0', '0'], dtype=object)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xdf['StateHoliday'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    246978\n",
       "0     57827\n",
       "a       266\n",
       "b        57\n",
       "c        35\n",
       "Name: StateHoliday, dtype: int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xdf['StateHoliday'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    297219\n",
       "a      5714\n",
       "b      2230\n",
       "Name: StateHoliday, dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test['StateHoliday'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "xdf['StateHoliday'] = xdf['StateHoliday'].replace({'0':'0','a':'1','b':'1','c':'1'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    246978\n",
       "0     57827\n",
       "1       358\n",
       "Name: StateHoliday, dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xdf['StateHoliday'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test['StateHoliday'] = df_test['StateHoliday'].replace({'a':'1','b':'1'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "xdf['StateHoliday'] = pd.to_numeric(xdf['StateHoliday'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test['StateHoliday'] = pd.to_numeric(df_test['StateHoliday'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    304805\n",
       "1       358\n",
       "Name: StateHoliday, dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's recheck \n",
    "\n",
    "xdf['StateHoliday'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    297219\n",
       "1      7944\n",
       "Name: StateHoliday, dtype: int64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test['StateHoliday'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "xdf.drop(['Customers','Open'], axis =1 , inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This are converted to Numeric and fixed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    248860\n",
       "1     56303\n",
       "Name: SchoolHoliday, dtype: int64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# school holiday\n",
    "\n",
    "xdf['SchoolHoliday'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 104,
     "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": {},
   "source": [
    "# Exploratory Data Analaysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate (Target Attribute)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6439     83\n",
       "5674     82\n",
       "4383     80\n",
       "5619     79\n",
       "5723     78\n",
       "         ..\n",
       "18213     1\n",
       "28448     1\n",
       "16322     1\n",
       "18818     1\n",
       "19761     1\n",
       "Name: Sales, Length: 18453, dtype: int64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xdf['Sales'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "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": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness: 1.586689\n",
      "Kurtosis: 4.738181\n"
     ]
    }
   ],
   "source": [
    "# Skew and kurt\n",
    "print(\"Skewness: %f\" % xdf['Sales'].skew())\n",
    "print(\"Kurtosis: %f\" % xdf['Sales'].kurt())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "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": 109,
   "metadata": {},
   "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": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6439     83\n",
       "5674     82\n",
       "4383     80\n",
       "5619     79\n",
       "5723     78\n",
       "         ..\n",
       "18213     1\n",
       "28448     1\n",
       "16322     1\n",
       "18818     1\n",
       "19761     1\n",
       "Name: Sales, Length: 18453, dtype: int64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xdf['Sales'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "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": {},
   "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": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import skew, norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "xdf['Sales'] = np.log1p(xdf['Sales'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " mu = 8.78 and sigma = 0.42\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": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(305163, 9)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xdf.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "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": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(305163, 9)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xdf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Promo</th>\n",
       "      <th>StateHoliday</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>518738</th>\n",
       "      <td>1049</td>\n",
       "      <td>1</td>\n",
       "      <td>8.155649</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52265</th>\n",
       "      <td>976</td>\n",
       "      <td>1</td>\n",
       "      <td>9.375601</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439423</th>\n",
       "      <td>899</td>\n",
       "      <td>2</td>\n",
       "      <td>8.963032</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695310</th>\n",
       "      <td>336</td>\n",
       "      <td>3</td>\n",
       "      <td>9.306559</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53712</th>\n",
       "      <td>193</td>\n",
       "      <td>6</td>\n",
       "      <td>8.429673</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>6</td>\n",
       "      <td>13</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>335422</th>\n",
       "      <td>1035</td>\n",
       "      <td>3</td>\n",
       "      <td>8.410943</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>704396</th>\n",
       "      <td>502</td>\n",
       "      <td>2</td>\n",
       "      <td>9.457747</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275447</th>\n",
       "      <td>876</td>\n",
       "      <td>4</td>\n",
       "      <td>9.138952</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89563</th>\n",
       "      <td>364</td>\n",
       "      <td>2</td>\n",
       "      <td>8.454253</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>702927</th>\n",
       "      <td>148</td>\n",
       "      <td>3</td>\n",
       "      <td>9.016391</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>305163 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Store  DayOfWeek     Sales  Promo  StateHoliday  SchoolHoliday  Year  \\\n",
       "518738   1049          1  8.155649      0             0              0  2014   \n",
       "52265     976          1  9.375601      1             0              0  2015   \n",
       "439423    899          2  8.963032      1             0              0  2014   \n",
       "695310    336          3  9.306559      0             0              1  2013   \n",
       "53712     193          6  8.429673      0             0              0  2015   \n",
       "...       ...        ...       ...    ...           ...            ...   ...   \n",
       "335422   1035          3  8.410943      1             0              0  2014   \n",
       "704396    502          2  9.457747      1             0              0  2013   \n",
       "275447    876          4  9.138952      0             0              0  2014   \n",
       "89563     364          2  8.454253      0             0              0  2015   \n",
       "702927    148          3  9.016391      1             0              1  2013   \n",
       "\n",
       "        Month  Day  \n",
       "518738      3   24  \n",
       "52265       6   15  \n",
       "439423      6    3  \n",
       "695310     10   16  \n",
       "53712       6   13  \n",
       "...       ...  ...  \n",
       "335422      9   17  \n",
       "704396     10    8  \n",
       "275447     11   20  \n",
       "89563       5   12  \n",
       "702927     10    9  \n",
       "\n",
       "[305163 rows x 9 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>Open</th>\n",
       "      <th>Promo</th>\n",
       "      <th>StateHoliday</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305158</th>\n",
       "      <td>412</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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 × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Store  DayOfWeek  Open  Promo  StateHoliday  SchoolHoliday  Year  \\\n",
       "0         767          1     0      0             1              1  2013   \n",
       "1        1111          6     1      0             0              0  2013   \n",
       "2          45          4     1      1             0              1  2013   \n",
       "3         500          4     1      0             0              1  2013   \n",
       "4          82          2     1      0             0              1  2013   \n",
       "...       ...        ...   ...    ...           ...            ...   ...   \n",
       "305158    412          2     1      1             0              1  2013   \n",
       "305159    936          6     1      0             0              0  2013   \n",
       "305160     21          1     1      0             0              0  2013   \n",
       "305161   1077          6     1      0             0              0  2013   \n",
       "305162    321          6     1      0             0              0  2013   \n",
       "\n",
       "        Month  Day  \n",
       "0           4    1  \n",
       "1           3   16  \n",
       "2           8    1  \n",
       "3           5   23  \n",
       "4           8    6  \n",
       "...       ...  ...  \n",
       "305158      5   28  \n",
       "305159      3    2  \n",
       "305160      9   16  \n",
       "305161      6   22  \n",
       "305162      9    7  \n",
       "\n",
       "[305163 rows x 9 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(589948, 9)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the dataset\n",
    "\n",
    "X = xdf.drop(['Sales'], axis =1)\n",
    "y = xdf['Sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "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": 124,
   "metadata": {},
   "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": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((244130, 8), (61033, 8))"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>Promo</th>\n",
       "      <th>StateHoliday</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>421974</th>\n",
       "      <td>175</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107601</th>\n",
       "      <td>562</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501272</th>\n",
       "      <td>308</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338680</th>\n",
       "      <td>459</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489154</th>\n",
       "      <td>455</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>4</td>\n",
       "      <td>19</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>553720</th>\n",
       "      <td>351</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658723</th>\n",
       "      <td>544</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>704215</th>\n",
       "      <td>321</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360991</th>\n",
       "      <td>313</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2014</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623771</th>\n",
       "      <td>157</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61033 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Store  DayOfWeek  Promo  StateHoliday  SchoolHoliday  Year  Month  Day\n",
       "421974    175          3      1             0              0  2014      6   18\n",
       "107601    562          7      0             0              0  2015      4   26\n",
       "501272    308          2      0             0              0  2014      4    8\n",
       "338680    459          6      0             0              0  2014      9   13\n",
       "489154    455          6      0             0              0  2014      4   19\n",
       "...       ...        ...    ...           ...            ...   ...    ...  ...\n",
       "553720    351          4      1             0              0  2014      2   20\n",
       "658723    544          1      1             0              0  2013     11   18\n",
       "704215    321          2      1             0              0  2013     10    8\n",
       "360991    313          3      1             0              1  2014      8   20\n",
       "623771    157          4      1             0              0  2013     12   19\n",
       "\n",
       "[61033 rows x 8 columns]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "ct = ColumnTransformer([\n",
    "                ('scaler', StandardScaler(),['Store','DayOfWeek','Year','Month','Day'])\n",
    "], remainder = 'passthrough')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling and Evaluation Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = pd.DataFrame(columns = ['R2_score','MSE','RMSE','MAE'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression()\n",
    "lr_pipe = Pipeline([\n",
    "            ('column-transformer', ct),\n",
    "            ('LinearRegression', lr)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('column-transformer',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('scaler', StandardScaler(),\n",
       "                                                  ['Store', 'DayOfWeek', 'Year',\n",
       "                                                   'Month', 'Day'])])),\n",
       "                ('LinearRegression', LinearRegression())])"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_pipe.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_pred = lr_pipe.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score: 0.18215417476845996\n",
      "Mean Squarred Error: 0.147\n",
      "RMSE: 0.38330458041628146\n",
      "Mean Absolute Error : 0.2971507900319457\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": {},
   "source": [
    "## GradientBoostingRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbr = GradientBoostingRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbr_pipe = Pipeline([\n",
    "            ('column-transformer', ct),\n",
    "            ('GradientBoostingRegressor', gbr)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('column-transformer',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('scaler', StandardScaler(),\n",
       "                                                  ['Store', 'DayOfWeek', 'Year',\n",
       "                                                   'Month', 'Day'])])),\n",
       "                ('GradientBoostingRegressor', GradientBoostingRegressor())])"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbr_pipe.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbr_pred = gbr_pipe.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score: 0.32114492571711506\n",
      "Mean Squarred Error: 0.12195332496357165\n",
      "RMSE: 0.3492181624193846\n",
      "Mean Absolute Error : 0.270986909116669\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": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "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": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('column-transformer',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('scaler', StandardScaler(),\n",
       "                                                  ['Store', 'DayOfWeek', '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": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_pipe.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_pred = xgb_pipe.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score: 0.3568763998154969\n",
      "Mean Squarred Error: 0.116 \n",
      "RMSE: 0.33990340241670086\n",
      "Mean Absolute Error : 0.2641928556108499\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": 550,
   "metadata": {},
   "outputs": [],
   "source": [
    "real_ytest = np.floor(np.expm1(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 556,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 7740.855918352478\n"
     ]
    }
   ],
   "source": [
    "print(\"RMSE:\", math.sqrt(mean_squared_error(real_ytest, xgb_pred)))"
   ]
  },
  {
   "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": 534,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(305163, 9)"
      ]
     },
     "execution_count": 534,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 535,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(244130, 9)"
      ]
     },
     "execution_count": 535,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 540,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Submission\n",
    "xgb_pred = xgb_pipe.predict(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 541,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = pd.DataFrame(columns = ['Index','Sales'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 542,
   "metadata": {},
   "outputs": [],
   "source": [
    "output['Index'] = df_test.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 545,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 545,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 544,
   "metadata": {},
   "outputs": [],
   "source": [
    "output['Sales'] = np.floor(np.expm1(xgb_pipe.predict(df_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 546,
   "metadata": {},
   "outputs": [],
   "source": [
    "output.to_csv('output.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 547,
   "metadata": {},
   "outputs": [],
   "source": [
    "mysub= pd.read_csv('output.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 548,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
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       "    <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": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mysub"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2,321.8847 << Public Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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