REFER.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "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>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>9.30</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>3735.1380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRC01</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>443.4228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FDN15</td>\n",
       "      <td>17.50</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>Meat</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>2097.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDX07</td>\n",
       "      <td>19.20</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>732.3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCD19</td>\n",
       "      <td>8.93</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Household</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>1987</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>994.7052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "0           FDA15         9.30          Low Fat         0.016047   \n",
       "1           DRC01         5.92          Regular         0.019278   \n",
       "2           FDN15        17.50          Low Fat         0.016760   \n",
       "3           FDX07        19.20          Regular         0.000000   \n",
       "4           NCD19         8.93          Low Fat         0.000000   \n",
       "\n",
       "               Item_Type  Item_MRP Outlet_Identifier  \\\n",
       "0                  Dairy  249.8092            OUT049   \n",
       "1            Soft Drinks   48.2692            OUT018   \n",
       "2                   Meat  141.6180            OUT049   \n",
       "3  Fruits and Vegetables  182.0950            OUT010   \n",
       "4              Household   53.8614            OUT013   \n",
       "\n",
       "   Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
       "0                       1999      Medium               Tier 1   \n",
       "1                       2009      Medium               Tier 3   \n",
       "2                       1999      Medium               Tier 1   \n",
       "3                       1998         NaN               Tier 3   \n",
       "4                       1987        High               Tier 3   \n",
       "\n",
       "         Outlet_Type  Item_Outlet_Sales  \n",
       "0  Supermarket Type1          3735.1380  \n",
       "1  Supermarket Type2           443.4228  \n",
       "2  Supermarket Type1          2097.2700  \n",
       "3      Grocery Store           732.3800  \n",
       "4  Supermarket Type1           994.7052  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(r\"C:\\Users\\sanjay\\Desktop\\Analytics Vidya\\ALL THE PYNB FILES OF THE COMPLETE COURSE\\train (1).csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8523, 12)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier               object\n",
       "Item_Weight                  float64\n",
       "Item_Fat_Content              object\n",
       "Item_Visibility              float64\n",
       "Item_Type                     object\n",
       "Item_MRP                     float64\n",
       "Outlet_Identifier             object\n",
       "Outlet_Establishment_Year      int64\n",
       "Outlet_Size                   object\n",
       "Outlet_Location_Type          object\n",
       "Outlet_Type                   object\n",
       "Item_Outlet_Sales            float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data.drop(['Item_Identifier'], axis = 1)\n",
    "# fig, ax = plt.subplots(figsize = (12,12))\n",
    "# ax = sns.heatmap(data.corr(),annot = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis of Continuous variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "conti_feature = [feature for feature in data.columns if data[feature].dtypes != 'O']\n",
    "print(len(conti_feature))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# for feature in conti_feature:  #analyzed the conti variable by creating histograms to understand there distribution\n",
    "#     data1 = data.copy()\n",
    "#     data[feature].hist(bins=25)\n",
    "#     plt.xlabel(feature)\n",
    "#     plt.ylabel('count')\n",
    "#     plt.title(feature)\n",
    "#     plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for feature in conti_feature:  #analyzed presence of outlier in conti by creating boxplot to understand there distribution\n",
    "#     data1 = data.copy()\n",
    "#     data[feature].plot.box()\n",
    "#     plt.title(feature)\n",
    "#     plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis of Categorical Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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>Item_Identifier</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRC01</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FDN15</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>Meat</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDX07</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCD19</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>Household</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier Item_Fat_Content              Item_Type Outlet_Identifier  \\\n",
       "0           FDA15          Low Fat                  Dairy            OUT049   \n",
       "1           DRC01          Regular            Soft Drinks            OUT018   \n",
       "2           FDN15          Low Fat                   Meat            OUT049   \n",
       "3           FDX07          Regular  Fruits and Vegetables            OUT010   \n",
       "4           NCD19          Low Fat              Household            OUT013   \n",
       "\n",
       "  Outlet_Size Outlet_Location_Type        Outlet_Type  \n",
       "0      Medium               Tier 1  Supermarket Type1  \n",
       "1      Medium               Tier 3  Supermarket Type2  \n",
       "2      Medium               Tier 1  Supermarket Type1  \n",
       "3         NaN               Tier 3      Grocery Store  \n",
       "4        High               Tier 3  Supermarket Type1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cate_feature = [feature for feature in data.columns if (data[feature].dtypes == 'O')]\n",
    "print(len(cate_feature))\n",
    "data[cate_feature].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for feature in cate_feature:\n",
    "    data1 = data.copy()\n",
    "    if feature in 'Item_Identifier':\n",
    "        pass\n",
    "    else:\n",
    "        data1.groupby(feature)['Item_Outlet_Sales'].median().plot.bar()\n",
    "        plt.xlabel(feature)\n",
    "        plt.ylabel('Sales')\n",
    "        plt.title(feature)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jImJ0ebI4IqJwCYKIiMIlCCIiCpcgiIgoXIIgIqJwCYKIiMIlCCIiCpcgiIgoXIIgIqJwCYKIiMIlCCIiCpcgiIgoXIIgIqJwCYKIiMIlCCIiCpcgiIgoXIIgIqJwCYKIiMIlCCIiCpcgiIgoXIIgIqJwCYKIiMIlCCIiCpcgiIgoXIIgIqJwCYKIiMI1GgSSZklaJmm5pGN7rD9A0q8krZV0eJO1REREb40FgaRJwKnAy4CZwJGSZnY1uw44CvhmU3VERMTItmxw2/sBy22vAJB0FnAosHSoge1r6nUPNlhHRESMoMlTQ7sB13fMr6yXrTdJcyQtkrRo1apVm6S4iIioNBkE6rHMG7Ih26fbHrQ9ODAwsJFlRUREpyaDYCWwe8f8FOCGBj8vIiI2QJNBsBCYIWm6pMnAEcC8Bj8vIiI2QGNBYHstMBdYAFwNnG17iaQTJc0GkPQsSSuBVwFflLSkqXoiIqK3Ju8awvZ8YH7XsuM7phdSnTKKiIiW5MniiIjCJQgiIgqXIIiIKFyCICKicAmCiIjCJQgiIgqXIIiIKFyCICKicAmCiIjCJQgiIgqXIIiIKFyCICKicAmCiIjCJQgiIgqXIIiIKFyCICKicI0OTBMxkUw79oK2S+Cakw5pu4QoUI4IIiIKlyCIiChcgiAionAJgoiIwiUIIiIKlyCIiChcgiAionB5jiAiHiHPVKxTwr7IEUFEROEaDQJJsyQtk7Rc0rE91m8t6dv1+l9ImtZkPRER8UiNBYGkScCpwMuAmcCRkmZ2NXszcJvtJwGnAJ9oqp6IiOitySOC/YDltlfYvg84Czi0q82hwFfr6XOBF0tSgzVFREQX2W5mw9LhwCzbb6nnXw882/bcjjZX1W1W1vO/r9us7trWHGBOPftkYFkjRa+fXYDVo7YqQ/ZFJfthneyLdcbLvtjD9kCvFU3eNdTrl3136vTTBtunA6dviqI2FUmLbA+2Xcd4kH1RyX5YJ/tinYmwL5o8NbQS2L1jfgpww3BtJG0J7ATc2mBNERHRpckgWAjMkDRd0mTgCGBeV5t5wBvq6cOBi9zUuaqIiOipsVNDttdKmgssACYBX7G9RNKJwCLb84B/B74uaTnVkcARTdXTgHF1qqpl2ReV7Id1si/WGff7orGLxRERMTHkyeKIiMIlCCIiCpcg6JOk5/WzLCLKJuld/SwbTxIE/ft8n8siomxv6LHsqLEuYn2kG+pRSHousD8wIOmYjlU7Ut0NVZy6H6kFtg9qu5a2SPqY7ffX0wfb/q+2a2qbpPN45AOhdwCLgC/avmfsqxo7ko4E/g6YLqnzVvkdgFvaqao/CYLRTQa2p9pXO3Qsv5Pq2Yfi2H5A0t2SdrJ9R9v1tGQW8P56+hNA8UEArAAGgG/V868BbgL2Ar4EvL6lusbKpcCNVF1K/GvH8jXAFa1U1KfcPtonSXvYvrbtOsYLSWcDz6H6ArxraLntd7ZW1BiS9Cvbf9U9XTJJP7V9QK9lkpbYflpbtcXIckTQv7slfRJ4GrDN0ELbf91eSa26oH6V6rH1qUJ1TD/E9qfbKatVA5Km2r4OQNJUql/HAPe1V9bYkvQcquuHT6U6ozAJuMv2jq0WNoIEQf++AXwbeDnwdqoLQqtarahFtr86eqvN2pdYd6qwcxp6dJxYiH8GLql7ERYwHfhHSY9iXXfzJfgCVS8J5wCDwN8DT2q1olHk1FCfJC22va+kK2zvXS+72PYL266tDZJmAB+nGnSo8wjpia0VNU5Ierftz7RdRxskbQ08hSoI/v/mfoG4l6HeRru+Ky61vX/btQ0nt4/27/763xslHSJpH6oeVUt1BvBvwFrgRcDXgK+3WtH4cczoTTY/krYD3gPMtX05sLukl7dcVhvurjvavFzSyZKOBh7VdlEjSRD07yOSdqI6/P3fwJeBo9stqVXb2r6Q6qjyWtsnAKVeL+lW6ih7Z1BdC3huPb8S+Eh75bTm9VTfrXOpbqTYHXhlqxWNItcI+mT7/HryDqpfwKW7R9IWwO/qXmb/CDy25ZrGi1LPt+5p+zX1/fTY/ktJQ89KutD2i4F/tP0+4B7gQy2X1ZccEYxC0g87po9rs5Zx5t3AdsA7gX2pfgX1eqJysyRpjaQ7e7zWALu2XV9L7pO0LXUQStoTuLfdksbUEyS9EJgtaR9Jf9X5aru4keRi8Sgk/dr2PvV07heP6KH+5f964M1UNxD8EHgecJTtn7RY2pipx2l/M/B8qoG5Oo+GPJ5vNU8QjCIPDj3cMN0IPMT27DEsJ8YRSYuBl1A9aCjgMtvjYdD2MSXpg7Y/3HYd6yNBMApJtwM/pfoP+wX19ENK++KrD32HZfvisaolxhdJpwJn2l7Ydi2xfhIEo8gXX0R/JC2l6lfoWqq7ZUR1SmTvVguLUSUIYoNI+gM9ThHlgbJySdqj1/L00TX+5fbR2FCDHdPbAK8CHtNSLTEO5Au/IulE4GfApbbvGq39eJAjgthkJF1i+/lt1xHRJklvorpz6LlUXVD/DPip7e+3WtgIEgR9kvRE2yvarmO86LoveguqI4R/sP2MlkqKGFckPR54NVVPBI+2vcMob2lNgqBPkn4K7EZ1f/BPgZ/ZvrLdqtoj6ccds2uBa4BP2V7WTkUR44OkL1M9S3ET1dHAJcCvbK9ttbAR5BpBn+rBNSYDzwIOBC6QtL3tIs+L2043GxG9/Q+qMQhuB24FVo/nEIAEQd8kPZ/qOYIXADsD51OlfZG6B2Kp3QEsrnuejCiS7b8BkPRU4KXAjyVNsj1ueytOEPTvYqpBuD8OzLddzIhLwxisX+fV84dQnTZ7u6RzbJ/cWmURLaq73n4BcADwaOAixvmPxlwj6JOknan6TjmA6vTQg8DPbX+w1cJaImkB8Erbf67ntwfOBf6G6qhgZpv1RbSlfsJ66DriDW3X048cEfTJ9u2SVlD1LT4F2B/Yqt2qWjWVh49Dez+wR931cEk9TkY8jO1/kvQ44Fn13XW/tH1z23WNJEHQp3oc1mVUdwCcBryx8NND3wQukzR0b/QrgG/V49Muba+siHZJehXwKeAnVN1sfF7Se2yf22phI8ipoT5J2sL2g23XMZ5I2pfqwRkBl9he1HJJEa2T9Bvg4KGjAEkDwI/G8zM2GZimf7tK+q6kmyXdJOk7ksbtXQBjZFvgznqg9mslTW+7oIhxYIuuU0G3MM6/a8d1cePMGcA8qtGndqO6W+aMVitqkaT/A7wPGBq1bSvgP9qrKGLc+IGkBZKOknQUcAEwv+WaRpRTQ32SdLntZ462rBSSLgf2oXpicmgEtyvS5XAESHol1V2Goupn6LstlzSiXCzu32pJrwO+Vc8fSXXIV6r7bFvS0Pi0j2q7oIjxwvZ3gO+0XUe/EgT9exPwBeAUqn74LwXe2GpF7Tpb0heBnSW9lWr/fLnlmiJaI2kNvYdxHRqgZ8cxLqlvOTW0ESS9u75QWiRJB1ONUStgge3/armkiNgACYKNIOk621PbrmM8kDQJOML2N9quJSLWT+4a2jhqu4CxJmlHScdJ+oKkl6gyF1hB1fd6REwwOSLYCCUeEdRPEt8G/Bx4MVWnWpOBd6XX0YiJKUEwilEuAG1ru6gL7pKutP0/6+lJwGpgqu017VYWERuqqC+xDTGeh5dryf1DE7YfkPSHhEDExJYjglgvkh4A7hqapepm4m4mwC1yEdFbgiAionC5aygionAJgoiIwiUIYtyTNDQc5jRJf9fwZ50g6Y+SLq9fJ43S/ihJu47SZitJJ0n6naSrJP1S0ss2sL7DJG3wMKBjsQ9j4kkQxEQyDRiLL7FTbD+zfh07StujqLomH8mHgScAT7f9dKrR3Db0brTDgI0ZD3oaY7MPYwJJEMREchLwgvqX+tGSJkn6pKSFkq6Q9DYASQdKuljS2ZJ+W/8af239S/xKSXuu7wdLOr7+nKsknV4/UX04MAh8o65p2x7v2w54K/AO2/cC2L7J9tn1+iPrmq6S9ImO9/1Z0kcl/UbSZZIeJ2l/YDbwyfrz9qxfP5C0WNLPJD2lfv+Zkj4n6VJJK+paH7EP13c/xGbKdl55jesX8Of63wOB8zuWzwE+UE9vDSwCptftbqf6Fb418EfgQ3W7dwGfGeGzTqjbX16/Xlovf0xHm68Dr6infwIMjrC9vYFfD7NuV+A6YIDqmZ6LgMPqde74jJM7/s4zgcM7tnEhMKOefjZwUUe7c6h+7M0Elvfah3nlZTsPlMWE9hJg745fuzsBM4D7gIW2bwSQ9Hvgh3WbK4EXjbLdU2x/qmvZiyS9F9gOeAywhGqUuo3xLOAntlfVdX4DOAD4Xv03nF+3Wwwc3P1mSdsD+wPnSA91e7V1R5PvuRpne6mkx21krbEZSxDERCaqUy4LHrZQOhC4t2PRgx3zD7Ke/91L2gb4v1S//K+XdAKwTZ9vXw5MlbSDH/kE9kidFt5ve+ghnweGqXkL4HYPP0pe5z4oroPE6F+uEcREsoaHX2RdAPyDpK0AJO3V0EhpQ1/6q+tf4Yd3rOuu6WFs3w38O/A5SZPrOp9Qj3b3C+CFknap+206Erh4lFoe+jzbdwJ/kPSqeruS9Ix+3x8xJEEQE8kVwNr6AurRVCOiLQV+Jekq4Is0cJRr+3bgS1Snlb4HLOxYfSZw2nAXi2sfAFZRnaK5qt7GqvrU1XHAj4HfUI3//P1RyjkLeI+kX9cXvV8LvFnSb6hOVx06yvu792FEupiIiChdjggiIgqXi8VRJEn/Aryqa/E5tj+6Edv8LtXtq53e130xO2K8yamhiIjC5dRQREThEgQREYVLEEREFC5BEBFRuARBRETh/hvq/zBLIvS4SgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for feature in cate_feature:\n",
    "    data1 = data.copy()\n",
    "    if feature in 'Item_Identifier':\n",
    "        pass\n",
    "    else:\n",
    "        (data1[feature].value_counts()/len(data1)).plot.bar()\n",
    "        plt.xlabel(feature)\n",
    "        plt.ylabel('count')\n",
    "        plt.title(feature)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis of missing value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Outlet_Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.30</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.92</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.50</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19.20</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.93</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Item_Weight Outlet_Size\n",
       "0         9.30      Medium\n",
       "1         5.92      Medium\n",
       "2        17.50      Medium\n",
       "3        19.20         NaN\n",
       "4         8.93        High"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_nan = [feature for feature in data.columns if data[feature].isnull().sum()>1]\n",
    "print(len(feature_nan)) #seeing missing value of both conti+categ features (complete dataset)\n",
    "data[feature_nan].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for feature in feature_nan: #since there are many missing value, so lets c if they have any relation with item outlet sales\n",
    "#     data1 = data.copy()\n",
    "#     data1[feature] = np.where(data[feature].isnull(),1,0)\n",
    "#     data1.groupby(feature)['Item_Outlet_Sales'].median().plot.bar()\n",
    "#     plt.title(feature)\n",
    "#     plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DATA MANIPULATION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check for Duplicate values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated().any()  #are there any DUPLICATE values in the complete dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check for missing value - of continuous and categorical variable separetely & filling it"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Working with missing value in continuous variable and imputing it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Item_Weight</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.30</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>1999</td>\n",
       "      <td>3735.1380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.92</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>2009</td>\n",
       "      <td>443.4228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.50</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>1999</td>\n",
       "      <td>2097.2700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Item_Weight  Item_Visibility  Item_MRP  Outlet_Establishment_Year  \\\n",
       "0         9.30         0.016047  249.8092                       1999   \n",
       "1         5.92         0.019278   48.2692                       2009   \n",
       "2        17.50         0.016760  141.6180                       1999   \n",
       "\n",
       "   Item_Outlet_Sales  \n",
       "0          3735.1380  \n",
       "1           443.4228  \n",
       "2          2097.2700  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[conti_feature].head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no. of continuous feature with missing value 1\n",
      "no of missing value in that continuous feature is Item_Weight    1463\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "conti_feature_nan = [feature for feature in conti_feature if data[feature].isnull().sum()>0]\n",
    "print('no. of continuous feature with missing value',len(conti_feature_nan))\n",
    "print('no of missing value in that continuous feature is {}'.format(data[conti_feature_nan].isnull().sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Item_Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Item_Weight\n",
       "0         9.30\n",
       "1         5.92"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[conti_feature_nan].head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Item_Weight\n",
      "0         9.30\n",
      "1         5.92\n",
      "Item_Weight    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "for feature in conti_feature_nan: #filled the missing value with mean and printed the result, n again chcked for missing vlu\n",
    "    data[feature] = data[feature].fillna(data[feature].mean())\n",
    "print(data[conti_feature_nan].head(2))\n",
    "print(data[conti_feature_nan].isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Working with missing value in categorical variable and imputing it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Item_Identifier</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRC01</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FDN15</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>Meat</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDX07</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCD19</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>Household</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier Item_Fat_Content              Item_Type Outlet_Identifier  \\\n",
       "0           FDA15          Low Fat                  Dairy            OUT049   \n",
       "1           DRC01          Regular            Soft Drinks            OUT018   \n",
       "2           FDN15          Low Fat                   Meat            OUT049   \n",
       "3           FDX07          Regular  Fruits and Vegetables            OUT010   \n",
       "4           NCD19          Low Fat              Household            OUT013   \n",
       "\n",
       "  Outlet_Size Outlet_Location_Type        Outlet_Type  \n",
       "0      Medium               Tier 1  Supermarket Type1  \n",
       "1      Medium               Tier 3  Supermarket Type2  \n",
       "2      Medium               Tier 1  Supermarket Type1  \n",
       "3         NaN               Tier 3      Grocery Store  \n",
       "4        High               Tier 3  Supermarket Type1  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[cate_feature].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no of categorical feature with missing value 1\n",
      "no of missing value in that categorical feature is Outlet_Size    2410\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "cate_feature_nan = [feature for feature in cate_feature if data[feature].isnull().sum()>0]\n",
    "print('no of categorical feature with missing value',len(cate_feature_nan))\n",
    "print('no of missing value in that categorical feature is {}'.format(data[cate_feature_nan].isnull().sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Outlet_Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Outlet_Size\n",
       "0      Medium\n",
       "1      Medium"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[cate_feature_nan].head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Outlet_Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Missing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Outlet_Size\n",
       "0      Medium\n",
       "1      Medium\n",
       "2      Medium\n",
       "3     Missing\n",
       "4        High"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for feature in cate_feature_nan: #just filled all missing cate. variable with term as 'Missing'\n",
    "    data[feature] = data[feature].fillna('Missing')\n",
    "data[cate_feature_nan].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Outlet_Size    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[cate_feature_nan].isnull().sum()  #hence now no missing value in this column "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with continuous variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "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>Item_Weight</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.30</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>1999</td>\n",
       "      <td>3735.1380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.92</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>2009</td>\n",
       "      <td>443.4228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.50</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>1999</td>\n",
       "      <td>2097.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19.20</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>1998</td>\n",
       "      <td>732.3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.93</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>1987</td>\n",
       "      <td>994.7052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Item_Weight  Item_Visibility  Item_MRP  Outlet_Establishment_Year  \\\n",
       "0         9.30         0.016047  249.8092                       1999   \n",
       "1         5.92         0.019278   48.2692                       2009   \n",
       "2        17.50         0.016760  141.6180                       1999   \n",
       "3        19.20         0.000000  182.0950                       1998   \n",
       "4         8.93         0.000000   53.8614                       1987   \n",
       "\n",
       "   Item_Outlet_Sales  \n",
       "0          3735.1380  \n",
       "1           443.4228  \n",
       "2          2097.2700  \n",
       "3           732.3800  \n",
       "4           994.7052  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[conti_feature].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. skewness to symmetrical using log, square root and cube root transformation (variable transformation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for feature in conti_feature:        #using logarithmeic transformation on conti feature \n",
    "#     data1 = data.copy()\n",
    "#     if 0 in data1[feature].unique():\n",
    "#         pass\n",
    "#     else:\n",
    "#         data1[feature] = np.log(data1[feature]).hist()\n",
    "#         plt.xlabel(feature)\n",
    "#         plt.ylabel('Item_Outlet_Sales')\n",
    "#         plt.title(feature)\n",
    "#         plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for feature in conti_feature:                             # variable transformation - square root\n",
    "#     data1 = data.copy()                                     \n",
    "#     data1[feature] = np.sqrt(data1[feature]).hist()\n",
    "#     plt.xlabel(feature)\n",
    "#     plt.ylabel('Item_Outlet_Sales')\n",
    "#     plt.title(feature)\n",
    "#     plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for feature in conti_feature:                        #variable transformation - cube root\n",
    "#     data1 = data.copy()\n",
    "#     data1[feature] = np.power(data1[feature],1/3).hist()\n",
    "#     plt.xlabel(feature)\n",
    "#     plt.ylabel('Item_outlet_sales')\n",
    "#     plt.title(feature)\n",
    "#     plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for feature in conti_feature:        #we have selected sqrt root transformation as our variable transformation \n",
    "#     data1 = data.copy()                  #since it gives more symmetric results\n",
    "#     data1[feature] = np.sqrt(data1[feature])\n",
    "#     data1['Item_Outlet_Sales'] = np.sqrt(data1['Item_Outlet_Sales'])\n",
    "#     plt.scatter(data1[feature],data1['Item_Outlet_Sales'])\n",
    "#     plt.xlabel(feature)\n",
    "#     plt.ylabel('Item_Outelt_Sales')\n",
    "#     plt.title(feature)\n",
    "#     plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Item_Weight</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.30</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>1999</td>\n",
       "      <td>3735.1380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.92</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>2009</td>\n",
       "      <td>443.4228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.50</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>1999</td>\n",
       "      <td>2097.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19.20</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>1998</td>\n",
       "      <td>732.3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.93</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>1987</td>\n",
       "      <td>994.7052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Item_Weight  Item_Visibility  Item_MRP  Outlet_Establishment_Year  \\\n",
       "0         9.30         0.016047  249.8092                       1999   \n",
       "1         5.92         0.019278   48.2692                       2009   \n",
       "2        17.50         0.016760  141.6180                       1999   \n",
       "3        19.20         0.000000  182.0950                       1998   \n",
       "4         8.93         0.000000   53.8614                       1987   \n",
       "\n",
       "   Item_Outlet_Sales  \n",
       "0          3735.1380  \n",
       "1           443.4228  \n",
       "2          2097.2700  \n",
       "3           732.3800  \n",
       "4           994.7052  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[conti_feature].head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.30</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>1999</td>\n",
       "      <td>3735.1380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.92</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>2009</td>\n",
       "      <td>443.4228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.50</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>1999</td>\n",
       "      <td>2097.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19.20</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>1998</td>\n",
       "      <td>732.3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.93</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>1987</td>\n",
       "      <td>994.7052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Item_Weight  Item_Visibility  Item_MRP  Outlet_Establishment_Year  \\\n",
       "0         9.30         0.016047  249.8092                       1999   \n",
       "1         5.92         0.019278   48.2692                       2009   \n",
       "2        17.50         0.016760  141.6180                       1999   \n",
       "3        19.20         0.000000  182.0950                       1998   \n",
       "4         8.93         0.000000   53.8614                       1987   \n",
       "\n",
       "   Item_Outlet_Sales  \n",
       "0          3735.1380  \n",
       "1           443.4228  \n",
       "2          2097.2700  \n",
       "3           732.3800  \n",
       "4           994.7052  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[conti_feature].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12d11fcaac8>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Item_Visibility'].plot.box()  #just for checking purpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12d11f24c88>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.loc[data['Item_Visibility']>0.17,'Item_Visibility'] = np.mean(data['Item_Visibility'])\n",
    "data['Item_Visibility'].plot.box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12d121bb248>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.loc[data['Item_Outlet_Sales']>5800,'Item_Outlet_Sales'] = np.mean(data['Item_Outlet_Sales'])\n",
    "data['Item_Outlet_Sales'].plot.box()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with categorical variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>Item_Identifier</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRC01</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FDN15</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>Meat</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDX07</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>Missing</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCD19</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>Household</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier Item_Fat_Content              Item_Type Outlet_Identifier  \\\n",
       "0           FDA15          Low Fat                  Dairy            OUT049   \n",
       "1           DRC01          Regular            Soft Drinks            OUT018   \n",
       "2           FDN15          Low Fat                   Meat            OUT049   \n",
       "3           FDX07          Regular  Fruits and Vegetables            OUT010   \n",
       "4           NCD19          Low Fat              Household            OUT013   \n",
       "\n",
       "  Outlet_Size Outlet_Location_Type        Outlet_Type  \n",
       "0      Medium               Tier 1  Supermarket Type1  \n",
       "1      Medium               Tier 3  Supermarket Type2  \n",
       "2      Medium               Tier 1  Supermarket Type1  \n",
       "3     Missing               Tier 3      Grocery Store  \n",
       "4        High               Tier 3  Supermarket Type1  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[cate_feature].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Item_Identifier : 1559 labels\n",
      "Item_Fat_Content : 5 labels\n",
      "Item_Type : 16 labels\n",
      "Outlet_Identifier : 10 labels\n",
      "Outlet_Size : 4 labels\n",
      "Outlet_Location_Type : 3 labels\n",
      "Outlet_Type : 4 labels\n"
     ]
    }
   ],
   "source": [
    "for col in data[cate_feature].columns:              #in order to check that how many labels each categorical feature has \n",
    "    print(col ,':' ,len(data[col].unique()),'labels')        #data[col] give us the values inside the dataset col. by col."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### use count/frequency encoding in 'Item Identifier' col since it has large unique values which couldn't be handled that well by label encoding(as it is more useful for those categories which has lesser no. of labels). Dont use get_dummies cz it will increse our column nos. alot and if the column no. (feature) gets increases then it will disturb the dimensionality of the model leading to a bad accuracy. We could also use COMBINE SPARSE CLASS TECHNIQUE which I be writiing that after this codes as a comment(combine sparse class does the same thing by counting the label no. in a feature but it also removes those labels from a column which contributes 0.01% less to the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Count/Frequency encoding technique - Technique 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'FDW13': 10,\n",
       " 'FDG33': 10,\n",
       " 'NCY18': 9,\n",
       " 'NCB18': 9,\n",
       " 'FDW26': 9,\n",
       " 'FDX04': 9,\n",
       " 'NCI54': 9,\n",
       " 'FDF56': 9,\n",
       " 'FDW49': 9,\n",
       " 'FDV38': 9,\n",
       " 'NCJ30': 9,\n",
       " 'FDO19': 9,\n",
       " 'DRN47': 9,\n",
       " 'FDD38': 9,\n",
       " 'FDG09': 9,\n",
       " 'FDQ40': 9,\n",
       " 'DRE49': 9,\n",
       " 'NCF42': 9,\n",
       " 'FDF52': 9,\n",
       " 'FDU12': 9,\n",
       " 'NCL31': 9,\n",
       " 'FDP25': 9,\n",
       " 'FDX31': 9,\n",
       " 'FDV60': 9,\n",
       " 'NCQ06': 9,\n",
       " 'FDT07': 9,\n",
       " 'FDX20': 9,\n",
       " 'FDA50': 8,\n",
       " 'FDT32': 8,\n",
       " 'DRA59': 8,\n",
       " 'NCC18': 8,\n",
       " 'NCK53': 8,\n",
       " 'NCP05': 8,\n",
       " 'FDL58': 8,\n",
       " 'FDR44': 8,\n",
       " 'FDK58': 8,\n",
       " 'DRI03': 8,\n",
       " 'NCM07': 8,\n",
       " 'FDS52': 8,\n",
       " 'FDT24': 8,\n",
       " 'FDA39': 8,\n",
       " 'FDV26': 8,\n",
       " 'FDW24': 8,\n",
       " 'DRJ24': 8,\n",
       " 'FDT49': 8,\n",
       " 'NCX05': 8,\n",
       " 'FDZ20': 8,\n",
       " 'FDS55': 8,\n",
       " 'FDX45': 8,\n",
       " 'FDR52': 8,\n",
       " 'FDY03': 8,\n",
       " 'FDA44': 8,\n",
       " 'NCX29': 8,\n",
       " 'NCQ43': 8,\n",
       " 'FDO10': 8,\n",
       " 'DRK12': 8,\n",
       " 'FDT40': 8,\n",
       " 'NCE31': 8,\n",
       " 'NCS53': 8,\n",
       " 'FDI22': 8,\n",
       " 'NCV06': 8,\n",
       " 'FDX50': 8,\n",
       " 'FDR46': 8,\n",
       " 'NCO54': 8,\n",
       " 'FDG57': 8,\n",
       " 'FDH33': 8,\n",
       " 'FDL34': 8,\n",
       " 'NCK29': 8,\n",
       " 'FDB17': 8,\n",
       " 'FDF04': 8,\n",
       " 'FDR59': 8,\n",
       " 'FDA04': 8,\n",
       " 'DRF03': 8,\n",
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       " 'FDL20': 8,\n",
       " 'FDF16': 8,\n",
       " 'FDO32': 8,\n",
       " 'DRP35': 8,\n",
       " 'FDU19': 8,\n",
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       " 'DRF01': 8,\n",
       " 'FDJ58': 8,\n",
       " 'DRD25': 8,\n",
       " 'FDJ55': 8,\n",
       " 'FDZ21': 8,\n",
       " 'FDW48': 8,\n",
       " 'FDR48': 8,\n",
       " 'FDZ26': 8,\n",
       " 'FDO52': 8,\n",
       " 'NCQ05': 8,\n",
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       " 'FDZ33': 8,\n",
       " 'FDX58': 8,\n",
       " 'NCE54': 8,\n",
       " 'FDG38': 8,\n",
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       " 'DRF23': 8,\n",
       " 'NCI29': 8,\n",
       " 'FDK20': 8,\n",
       " 'DRK35': 8,\n",
       " 'FDI41': 8,\n",
       " 'FDU13': 8,\n",
       " 'FDY47': 8,\n",
       " 'NCK05': 8,\n",
       " 'FDR43': 8,\n",
       " 'NCV41': 8,\n",
       " 'FDG24': 8,\n",
       " 'NCB31': 8,\n",
       " 'FDL10': 8,\n",
       " 'FDY55': 8,\n",
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       " 'NCY29': 8,\n",
       " 'FDR23': 8,\n",
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       " 'FDA15': 8,\n",
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       " 'FDD05': 8,\n",
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       " 'FDT21': 8,\n",
       " 'FDT55': 8,\n",
       " 'FDA13': 8,\n",
       " 'FDU23': 8,\n",
       " 'FDR04': 8,\n",
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       " 'FDP28': 8,\n",
       " 'FDJ44': 8,\n",
       " 'FDV20': 8,\n",
       " 'FDC14': 8,\n",
       " 'FDS33': 8,\n",
       " 'FDV09': 8,\n",
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       " 'FDP11': 8,\n",
       " 'FDT25': 7,\n",
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       " 'FDN04': 7,\n",
       " 'FDT46': 7,\n",
       " 'FDY02': 7,\n",
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       " 'FDI07': 7,\n",
       " 'FDT34': 7,\n",
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       " 'FDZ08': 7,\n",
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       " 'FDD57': 7,\n",
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       " 'DRL01': 7,\n",
       " 'NCS54': 7,\n",
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       " 'FDA01': 7,\n",
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       " 'DRD15': 7,\n",
       " 'FDQ56': 7,\n",
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       " 'FDW50': 7,\n",
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       " 'FDV45': 7,\n",
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       " 'NCM30': 7,\n",
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       " 'FDX19': 7,\n",
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       " 'FDR01': 7,\n",
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       " 'FDW09': 7,\n",
       " 'NCO17': 7,\n",
       " 'FDP10': 7,\n",
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       " 'NCP06': 7,\n",
       " 'FDA02': 7,\n",
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       " 'NCR05': 7,\n",
       " 'FDN15': 7,\n",
       " 'FDI21': 7,\n",
       " 'NCM29': 7,\n",
       " 'FDR02': 7,\n",
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       " 'FDL36': 7,\n",
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       " 'FDV28': 7,\n",
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       " 'FDO34': 7,\n",
       " 'FDG50': 7,\n",
       " 'FDZ23': 7,\n",
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       " 'FDE51': 7,\n",
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       " 'FDR09': 7,\n",
       " 'FDS04': 7,\n",
       " 'FDL13': 7,\n",
       " 'FDW32': 7,\n",
       " 'DRI11': 7,\n",
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       " 'FDA07': 7,\n",
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       " 'FDS32': 7,\n",
       " 'FDI45': 7,\n",
       " 'DRG37': 7,\n",
       " 'FDB36': 7,\n",
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       " 'FDU21': 7,\n",
       " 'FDE17': 7,\n",
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       " 'DRL60': 7,\n",
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       " 'FDZ35': 7,\n",
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       " 'FDR55': 7,\n",
       " 'FDI57': 7,\n",
       " 'FDO57': 7,\n",
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       " 'FDG34': 7,\n",
       " 'DRD24': 7,\n",
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       " 'FDU34': 7,\n",
       " 'FDX26': 7,\n",
       " 'FDW19': 7,\n",
       " 'NCL05': 7,\n",
       " 'FDJ45': 7,\n",
       " 'NCP50': 7,\n",
       " 'FDK03': 7,\n",
       " 'FDI20': 7,\n",
       " 'FDY33': 7,\n",
       " 'NCQ38': 7,\n",
       " 'FDQ10': 7,\n",
       " 'NCI31': 7,\n",
       " 'FDW38': 7,\n",
       " 'FDW34': 7,\n",
       " 'FDD10': 7,\n",
       " 'FDB56': 7,\n",
       " 'FDU25': 7,\n",
       " 'DRM37': 7,\n",
       " 'FDS59': 7,\n",
       " 'NCV54': 7,\n",
       " 'FDB34': 7,\n",
       " 'FDB15': 7,\n",
       " 'FDF57': 7,\n",
       " 'NCO05': 7,\n",
       " 'NCJ18': 7,\n",
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       " 'FDD50': 7,\n",
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       " 'NCM43': 7,\n",
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       " 'FDL46': 7,\n",
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       " 'FDN60': 7,\n",
       " 'FDE33': 7,\n",
       " 'NCD54': 7,\n",
       " 'FDP24': 7,\n",
       " 'FDL40': 7,\n",
       " 'FDN57': 7,\n",
       " 'FDU26': 7,\n",
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       " 'FDO23': 7,\n",
       " 'NCY41': 7,\n",
       " 'DRB48': 7,\n",
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       " 'FDI14': 7,\n",
       " 'FDA32': 7,\n",
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       " 'FDI32': 7,\n",
       " 'DRC25': 7,\n",
       " 'FDL52': 7,\n",
       " 'FDS31': 7,\n",
       " 'FDX40': 7,\n",
       " 'FDR22': 7,\n",
       " 'FDD21': 6,\n",
       " 'FDF08': 6,\n",
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       " 'FDT20': 6,\n",
       " 'FDU33': 6,\n",
       " 'NCX30': 6,\n",
       " 'FDC02': 6,\n",
       " 'NCH18': 6,\n",
       " 'FDZ55': 6,\n",
       " 'FDD51': 6,\n",
       " 'DRF36': 6,\n",
       " 'FDP44': 6,\n",
       " 'FDD04': 6,\n",
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       " 'FDL56': 6,\n",
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       " 'FDD52': 6,\n",
       " 'FDU39': 6,\n",
       " 'FDN39': 6,\n",
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       " 'FDY28': 6,\n",
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       " 'FDV22': 6,\n",
       " 'FDO20': 6,\n",
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       " 'NCN05': 6,\n",
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       " 'FDM51': 6,\n",
       " 'NCU42': 6,\n",
       " 'FDE29': 6,\n",
       " 'NCR17': 6,\n",
       " 'NCC07': 6,\n",
       " 'FDS37': 6,\n",
       " 'FDW35': 6,\n",
       " 'FDK15': 6,\n",
       " 'FDS12': 6,\n",
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       " 'FDS24': 6,\n",
       " 'FDY21': 6,\n",
       " 'FDI34': 6,\n",
       " 'FDR60': 6,\n",
       " 'FDO40': 6,\n",
       " 'FDI52': 6,\n",
       " 'DRC01': 6,\n",
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       " 'FDH44': 6,\n",
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       " 'FDO25': 6,\n",
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       " 'FDZ12': 6,\n",
       " 'FDR32': 6,\n",
       " 'FDB32': 6,\n",
       " 'FDZ45': 6,\n",
       " 'DRL49': 6,\n",
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       " 'FDE24': 6,\n",
       " 'FDD11': 6,\n",
       " 'FDT08': 6,\n",
       " 'DRK59': 6,\n",
       " 'FDT59': 6,\n",
       " 'FDZ10': 6,\n",
       " 'FDS36': 6,\n",
       " 'FDP22': 6,\n",
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       " 'FDF26': 6,\n",
       " 'FDJ53': 6,\n",
       " 'FDG47': 6,\n",
       " 'FDA47': 6,\n",
       " 'FDZ39': 6,\n",
       " 'FDX16': 6,\n",
       " 'FDX57': 6,\n",
       " 'FDD44': 6,\n",
       " 'FDK14': 6,\n",
       " 'FDV48': 6,\n",
       " 'FDV15': 6,\n",
       " 'FDJ34': 6,\n",
       " 'FDW46': 6,\n",
       " 'DRJ39': 6,\n",
       " 'FDL09': 6,\n",
       " 'FDC47': 6,\n",
       " 'FDD41': 6,\n",
       " 'NCK07': 6,\n",
       " 'DRF13': 6,\n",
       " 'FDM28': 6,\n",
       " 'FDL04': 6,\n",
       " 'FDI16': 6,\n",
       " 'FDY48': 6,\n",
       " 'FDT28': 6,\n",
       " 'NCP30': 6,\n",
       " 'DRA12': 6,\n",
       " 'DRJ13': 6,\n",
       " 'FDU51': 6,\n",
       " 'FDG56': 6,\n",
       " 'FDA21': 6,\n",
       " 'NCB42': 6,\n",
       " 'FDW51': 6,\n",
       " 'FDQ09': 6,\n",
       " 'NCB19': 6,\n",
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       " 'FDW39': 6,\n",
       " 'FDI28': 6,\n",
       " 'FDI56': 6,\n",
       " 'FDZ25': 6,\n",
       " 'FDH45': 6,\n",
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       " 'FDX15': 6,\n",
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       " 'FDH41': 6,\n",
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       " 'FDZ09': 6,\n",
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       " 'FDZ44': 6,\n",
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       " 'FDU32': 6,\n",
       " 'FDV37': 6,\n",
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       " 'DRN37': 6,\n",
       " 'FDA46': 6,\n",
       " 'NCI42': 6,\n",
       " 'FDU07': 6,\n",
       " 'FDU15': 6,\n",
       " 'FDT10': 6,\n",
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       " 'DRG15': 6,\n",
       " 'FDO08': 6,\n",
       " 'NCP29': 6,\n",
       " 'FDE36': 6,\n",
       " 'FDW47': 6,\n",
       " 'NCO42': 6,\n",
       " 'FDM04': 6,\n",
       " 'FDK45': 6,\n",
       " 'FDA10': 6,\n",
       " 'DRD60': 6,\n",
       " 'FDU24': 6,\n",
       " 'FDG59': 6,\n",
       " 'FDW23': 6,\n",
       " 'FDB29': 6,\n",
       " 'FDX60': 6,\n",
       " 'FDX01': 6,\n",
       " 'FDJ38': 6,\n",
       " 'FDP33': 6,\n",
       " 'FDN23': 6,\n",
       " 'DRO47': 6,\n",
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       " 'NCD19': 6,\n",
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       " 'FDT36': 6,\n",
       " 'NCR30': 6,\n",
       " 'NCQ42': 6,\n",
       " 'FDQ15': 6,\n",
       " 'FDD08': 6,\n",
       " 'NCU54': 6,\n",
       " 'FDR20': 6,\n",
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       " 'FDN38': 6,\n",
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       " 'FDL57': 6,\n",
       " 'FDC53': 6,\n",
       " 'FDB49': 6,\n",
       " 'FDG46': 6,\n",
       " 'NCB43': 6,\n",
       " 'DRH25': 6,\n",
       " 'DRH36': 6,\n",
       " 'NCN43': 6,\n",
       " 'FDX23': 6,\n",
       " 'NCH54': 6,\n",
       " 'FDC33': 6,\n",
       " 'FDH35': 6,\n",
       " 'FDW27': 6,\n",
       " 'FDJ60': 6,\n",
       " 'DRZ11': 6,\n",
       " 'FDF41': 6,\n",
       " 'NCO02': 6,\n",
       " 'FDL51': 6,\n",
       " 'NCX41': 6,\n",
       " 'NCW29': 6,\n",
       " 'NCF31': 6,\n",
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       " 'FDG16': 6,\n",
       " 'NCO06': 6,\n",
       " 'FDT31': 6,\n",
       " 'FDW56': 6,\n",
       " 'FDG26': 6,\n",
       " 'NCL41': 6,\n",
       " 'FDB60': 6,\n",
       " 'FDT57': 6,\n",
       " 'FDG45': 6,\n",
       " 'FDX46': 6,\n",
       " 'FDA49': 6,\n",
       " 'FDD40': 6,\n",
       " 'FDK09': 6,\n",
       " 'FDE32': 6,\n",
       " 'FDW59': 6,\n",
       " 'FDG21': 6,\n",
       " 'FDU11': 6,\n",
       " 'FDZ59': 6,\n",
       " 'FDT09': 6,\n",
       " 'FDG31': 6,\n",
       " 'FDF24': 6,\n",
       " 'FDD14': 6,\n",
       " 'FDO39': 6,\n",
       " 'FDQ13': 6,\n",
       " 'FDB23': 6,\n",
       " 'FDC08': 6,\n",
       " 'FDS56': 6,\n",
       " 'FDO09': 6,\n",
       " 'DRB25': 6,\n",
       " 'FDD56': 6,\n",
       " 'FDI50': 6,\n",
       " 'FDD09': 6,\n",
       " 'FDB08': 6,\n",
       " 'FDY40': 6,\n",
       " 'NCA05': 6,\n",
       " 'FDN46': 6,\n",
       " 'FDN02': 6,\n",
       " 'NCM05': 6,\n",
       " 'FDE34': 6,\n",
       " 'FDK24': 6,\n",
       " 'FDT26': 6,\n",
       " 'FDX43': 6,\n",
       " 'DRY23': 6,\n",
       " 'NCF30': 6,\n",
       " 'FDB51': 6,\n",
       " 'FDF09': 6,\n",
       " 'FDO50': 6,\n",
       " 'FDE05': 6,\n",
       " 'FDZ16': 6,\n",
       " 'FDQ48': 6,\n",
       " 'DRH03': 6,\n",
       " 'FDG12': 6,\n",
       " 'FDA23': 6,\n",
       " 'FDX56': 6,\n",
       " 'FDH60': 6,\n",
       " 'NCN19': 6,\n",
       " 'FDZ04': 6,\n",
       " 'FDG53': 6,\n",
       " 'FDA20': 6,\n",
       " 'FDX33': 6,\n",
       " 'FDF34': 6,\n",
       " 'DRI23': 6,\n",
       " 'FDK34': 6,\n",
       " 'NCR38': 6,\n",
       " 'FDE46': 6,\n",
       " 'FDM01': 6,\n",
       " 'FDJ41': 6,\n",
       " 'NCO30': 6,\n",
       " 'FDO12': 6,\n",
       " 'FDV51': 6,\n",
       " 'FDI19': 6,\n",
       " 'FDS48': 6,\n",
       " 'FDQ46': 6,\n",
       " 'DRK11': 6,\n",
       " 'FDH58': 6,\n",
       " 'NCI43': 6,\n",
       " 'NCC54': 6,\n",
       " 'NCP55': 6,\n",
       " 'FDT48': 6,\n",
       " 'FDQ58': 6,\n",
       " 'FDI08': 6,\n",
       " 'NCR41': 6,\n",
       " 'FDV47': 6,\n",
       " 'FDK33': 6,\n",
       " 'DRI47': 6,\n",
       " 'FDD58': 6,\n",
       " 'FDE10': 6,\n",
       " 'NCE30': 6,\n",
       " 'FDK52': 6,\n",
       " 'FDO58': 6,\n",
       " 'FDH53': 6,\n",
       " 'FDK25': 6,\n",
       " 'NCK54': 6,\n",
       " 'NCI18': 6,\n",
       " 'NCK17': 6,\n",
       " 'FDY04': 6,\n",
       " 'FDL43': 6,\n",
       " 'FDN13': 6,\n",
       " 'FDQ21': 6,\n",
       " 'FDI15': 6,\n",
       " 'FDX44': 6,\n",
       " 'FDK02': 6,\n",
       " 'FDH20': 6,\n",
       " 'FDH47': 6,\n",
       " 'FDK43': 6,\n",
       " 'FDV49': 6,\n",
       " 'FDB40': 6,\n",
       " 'NCT30': 6,\n",
       " 'FDY50': 6,\n",
       " 'FDY10': 6,\n",
       " 'FDI60': 6,\n",
       " 'DRJ01': 6,\n",
       " 'FDE04': 6,\n",
       " 'FDR12': 6,\n",
       " 'FDB27': 6,\n",
       " 'DRJ59': 6,\n",
       " 'FDX34': 6,\n",
       " 'NCK19': 6,\n",
       " 'FDS35': 6,\n",
       " 'FDF12': 6,\n",
       " 'NCT42': 6,\n",
       " 'FDN09': 6,\n",
       " 'FDE59': 6,\n",
       " 'FDZ49': 6,\n",
       " 'FDY52': 6,\n",
       " 'FDA03': 6,\n",
       " 'FDD26': 6,\n",
       " 'FDT11': 6,\n",
       " 'FDZ56': 6,\n",
       " 'NCN14': 6,\n",
       " 'FDL39': 6,\n",
       " 'DRE27': 6,\n",
       " 'NCJ29': 6,\n",
       " 'FDQ32': 6,\n",
       " 'FDR26': 6,\n",
       " 'DRE15': 6,\n",
       " 'NCQ50': 6,\n",
       " 'FDX11': 6,\n",
       " 'FDV44': 6,\n",
       " 'NCD18': 6,\n",
       " 'DRH37': 6,\n",
       " 'NCI30': 6,\n",
       " 'FDV32': 6,\n",
       " 'NCA53': 6,\n",
       " 'FDC45': 6,\n",
       " 'NCS29': 6,\n",
       " 'NCN30': 6,\n",
       " 'NCU05': 6,\n",
       " 'FDJ10': 6,\n",
       " 'DRC27': 6,\n",
       " 'FDO15': 6,\n",
       " 'FDW44': 6,\n",
       " 'FDE41': 6,\n",
       " 'NCU18': 6,\n",
       " 'NCJ42': 6,\n",
       " 'FDS15': 6,\n",
       " 'FDU40': 6,\n",
       " 'FDC34': 6,\n",
       " 'FDV46': 6,\n",
       " 'FDT56': 5,\n",
       " 'NCA42': 5,\n",
       " 'DRE13': 5,\n",
       " 'FDY25': 5,\n",
       " 'FDR36': 5,\n",
       " 'DRI39': 5,\n",
       " 'FDB33': 5,\n",
       " 'NCH06': 5,\n",
       " 'DRE37': 5,\n",
       " 'FDB02': 5,\n",
       " 'FDY11': 5,\n",
       " 'FDK10': 5,\n",
       " 'FDZ07': 5,\n",
       " 'NCB06': 5,\n",
       " 'NCG18': 5,\n",
       " 'NCN55': 5,\n",
       " 'NCK30': 5,\n",
       " 'FDV02': 5,\n",
       " 'FDA27': 5,\n",
       " 'FDU28': 5,\n",
       " 'FDX59': 5,\n",
       " 'FDE02': 5,\n",
       " 'NCQ41': 5,\n",
       " 'FDM10': 5,\n",
       " 'FDA52': 5,\n",
       " 'FDB52': 5,\n",
       " 'FDB03': 5,\n",
       " 'FDC58': 5,\n",
       " 'FDY20': 5,\n",
       " 'FDU57': 5,\n",
       " 'FDX51': 5,\n",
       " 'FDA25': 5,\n",
       " 'DRG13': 5,\n",
       " 'FDM13': 5,\n",
       " 'FDD02': 5,\n",
       " 'FDU27': 5,\n",
       " 'FDQ47': 5,\n",
       " 'FDN16': 5,\n",
       " 'FDQ24': 5,\n",
       " 'FDL32': 5,\n",
       " 'NCT41': 5,\n",
       " 'FDE08': 5,\n",
       " 'FDY26': 5,\n",
       " 'DRG49': 5,\n",
       " 'NCM17': 5,\n",
       " 'FDS58': 5,\n",
       " 'FDB50': 5,\n",
       " 'FDX38': 5,\n",
       " 'FDZ47': 5,\n",
       " 'FDE22': 5,\n",
       " 'FDM02': 5,\n",
       " 'FDO36': 5,\n",
       " 'FDS25': 5,\n",
       " 'FDQ03': 5,\n",
       " 'FDS43': 5,\n",
       " 'FDV40': 5,\n",
       " 'NCO55': 5,\n",
       " 'FDT16': 5,\n",
       " 'NCN41': 5,\n",
       " 'DRK01': 5,\n",
       " 'FDK36': 5,\n",
       " 'DRH01': 5,\n",
       " 'FDB38': 5,\n",
       " 'NCV30': 5,\n",
       " 'FDE23': 5,\n",
       " 'FDS34': 5,\n",
       " 'DRZ24': 5,\n",
       " 'FDB44': 5,\n",
       " 'FDP23': 5,\n",
       " 'FDQ14': 5,\n",
       " 'FDZ03': 5,\n",
       " 'FDQ49': 5,\n",
       " 'FDQ44': 5,\n",
       " 'NCV53': 5,\n",
       " 'FDU22': 5,\n",
       " 'FDN48': 5,\n",
       " 'NCM26': 5,\n",
       " 'NCJ31': 5,\n",
       " 'FDB37': 5,\n",
       " 'FDR31': 5,\n",
       " 'FDX35': 5,\n",
       " 'FDS02': 5,\n",
       " 'FDB59': 5,\n",
       " 'FDK48': 5,\n",
       " 'FDR34': 5,\n",
       " 'DRK39': 5,\n",
       " 'NCL55': 5,\n",
       " 'NCN26': 5,\n",
       " 'FDN10': 5,\n",
       " 'FDB53': 5,\n",
       " 'FDN32': 5,\n",
       " 'FDP59': 5,\n",
       " 'NCG42': 5,\n",
       " 'FDG52': 5,\n",
       " 'NCZ53': 5,\n",
       " 'FDU04': 5,\n",
       " 'FDC10': 5,\n",
       " 'NCM54': 5,\n",
       " 'FDR25': 5,\n",
       " 'FDB12': 5,\n",
       " 'FDO38': 5,\n",
       " 'NCE42': 5,\n",
       " 'DRG11': 5,\n",
       " 'NCE18': 5,\n",
       " 'FDN24': 5,\n",
       " 'FDQ11': 5,\n",
       " 'FDV23': 5,\n",
       " 'DRH51': 5,\n",
       " 'FDP26': 5,\n",
       " 'FDZ43': 5,\n",
       " 'FDI38': 5,\n",
       " 'NCJ19': 5,\n",
       " 'NCR54': 5,\n",
       " 'NCD42': 5,\n",
       " 'FDH31': 5,\n",
       " 'FDT37': 5,\n",
       " 'FDC04': 5,\n",
       " 'NCQ30': 5,\n",
       " 'FDN25': 5,\n",
       " 'FDP48': 5,\n",
       " 'FDX49': 5,\n",
       " 'FDO31': 5,\n",
       " 'FDO01': 5,\n",
       " 'FDD35': 5,\n",
       " 'NCT06': 5,\n",
       " 'FDF46': 5,\n",
       " 'FDC51': 5,\n",
       " 'FDS26': 5,\n",
       " 'DRB13': 5,\n",
       " 'FDS49': 5,\n",
       " 'FDY60': 5,\n",
       " 'FDK22': 5,\n",
       " 'FDD53': 5,\n",
       " 'NCN18': 5,\n",
       " 'FDX13': 5,\n",
       " 'FDT04': 5,\n",
       " 'NCA30': 5,\n",
       " 'FDM50': 5,\n",
       " 'FDR24': 5,\n",
       " 'FDH02': 5,\n",
       " 'FDK44': 5,\n",
       " 'NCF43': 5,\n",
       " 'FDA40': 5,\n",
       " 'FDE28': 5,\n",
       " 'FDR49': 5,\n",
       " 'FDG35': 5,\n",
       " 'FDX24': 5,\n",
       " 'FDL03': 5,\n",
       " 'FDA56': 5,\n",
       " 'FDT39': 5,\n",
       " 'NCI55': 5,\n",
       " 'FDT13': 5,\n",
       " 'FDX27': 5,\n",
       " 'FDR14': 5,\n",
       " 'FDQ01': 5,\n",
       " 'FDZ22': 5,\n",
       " 'FDU50': 5,\n",
       " 'NCO14': 5,\n",
       " 'FDF32': 5,\n",
       " 'NCS18': 5,\n",
       " 'FDB22': 5,\n",
       " 'NCS17': 5,\n",
       " 'DRM49': 5,\n",
       " 'FDD03': 5,\n",
       " 'NCP42': 5,\n",
       " 'FDP31': 5,\n",
       " 'FDG60': 5,\n",
       " 'FDB26': 5,\n",
       " 'NCF19': 5,\n",
       " 'FDZ28': 5,\n",
       " 'NCF18': 5,\n",
       " 'FDE53': 5,\n",
       " 'NCD31': 5,\n",
       " 'FDP37': 5,\n",
       " 'FDQ20': 5,\n",
       " 'NCY06': 5,\n",
       " 'FDP40': 5,\n",
       " 'NCU30': 5,\n",
       " 'NCY05': 5,\n",
       " 'FDI33': 5,\n",
       " 'FDO56': 5,\n",
       " 'FDY34': 5,\n",
       " 'DRG27': 5,\n",
       " 'NCO43': 5,\n",
       " 'FDV25': 5,\n",
       " 'FDB09': 5,\n",
       " 'FDO28': 5,\n",
       " 'FDY51': 5,\n",
       " 'NCN29': 5,\n",
       " 'FDZ01': 5,\n",
       " 'FDW57': 5,\n",
       " 'FDB20': 5,\n",
       " 'DRG03': 5,\n",
       " 'FDZ27': 5,\n",
       " 'FDF44': 5,\n",
       " 'NCZ42': 5,\n",
       " 'FDP04': 5,\n",
       " 'FDU58': 5,\n",
       " 'FDB57': 5,\n",
       " 'NCD06': 5,\n",
       " 'NCI06': 5,\n",
       " 'FDE26': 5,\n",
       " 'NCW41': 5,\n",
       " 'FDB39': 5,\n",
       " 'FDA45': 5,\n",
       " 'NCN54': 5,\n",
       " 'FDP08': 5,\n",
       " 'NCV05': 5,\n",
       " 'FDQ07': 5,\n",
       " 'DRD27': 5,\n",
       " 'FDJ15': 5,\n",
       " 'NCK06': 5,\n",
       " 'FDG02': 5,\n",
       " 'FDP45': 5,\n",
       " 'FDE20': 5,\n",
       " 'NCP43': 5,\n",
       " 'FDP38': 5,\n",
       " 'FDV07': 5,\n",
       " 'FDT52': 5,\n",
       " 'FDI24': 5,\n",
       " 'FDO16': 5,\n",
       " 'FDN08': 5,\n",
       " 'FDE14': 5,\n",
       " 'FDM60': 5,\n",
       " 'FDP57': 5,\n",
       " 'FDX55': 5,\n",
       " 'FDX08': 5,\n",
       " ...}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's obtain the counts for each one of the labels in Item_Identifier(since it really has lots of unique values)\n",
    "# let's capture this in a dictionary that we can use to re-map the labels\n",
    "data['Item_Identifier'].value_counts().to_dict()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# And now let's replace each label in X2 by its count\n",
    "\n",
    "# first we make a dictionary that maps each label to the counts\n",
    "data_frequency_map = data['Item_Identifier'].value_counts().to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>9.30</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>3735.1380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRC01</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>443.4228</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility    Item_Type  \\\n",
       "0           FDA15         9.30          Low Fat         0.016047        Dairy   \n",
       "1           DRC01         5.92          Regular         0.019278  Soft Drinks   \n",
       "\n",
       "   Item_MRP Outlet_Identifier  Outlet_Establishment_Year Outlet_Size  \\\n",
       "0  249.8092            OUT049                       1999      Medium   \n",
       "1   48.2692            OUT018                       2009      Medium   \n",
       "\n",
       "  Outlet_Location_Type        Outlet_Type  Item_Outlet_Sales  \n",
       "0               Tier 1  Supermarket Type1          3735.1380  \n",
       "1               Tier 3  Supermarket Type2           443.4228  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# and now we replace 'Item_Identifier' feature in the dataset data\n",
    "data['Item_Identifier'] = data['Item_Identifier'].map(data_frequency_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    8\n",
       "1    6\n",
       "2    7\n",
       "3    6\n",
       "4    6\n",
       "Name: Item_Identifier, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Item_Identifier'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>9.30</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>3735.1380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>443.4228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>17.50</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>Meat</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>2097.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>19.20</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>Missing</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>732.3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>8.93</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Household</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>1987</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>994.7052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "0                8         9.30          Low Fat         0.016047   \n",
       "1                6         5.92          Regular         0.019278   \n",
       "2                7        17.50          Low Fat         0.016760   \n",
       "3                6        19.20          Regular         0.000000   \n",
       "4                6         8.93          Low Fat         0.000000   \n",
       "\n",
       "               Item_Type  Item_MRP Outlet_Identifier  \\\n",
       "0                  Dairy  249.8092            OUT049   \n",
       "1            Soft Drinks   48.2692            OUT018   \n",
       "2                   Meat  141.6180            OUT049   \n",
       "3  Fruits and Vegetables  182.0950            OUT010   \n",
       "4              Household   53.8614            OUT013   \n",
       "\n",
       "   Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
       "0                       1999      Medium               Tier 1   \n",
       "1                       2009      Medium               Tier 3   \n",
       "2                       1999      Medium               Tier 1   \n",
       "3                       1998     Missing               Tier 3   \n",
       "4                       1987        High               Tier 3   \n",
       "\n",
       "         Outlet_Type  Item_Outlet_Sales  \n",
       "0  Supermarket Type1          3735.1380  \n",
       "1  Supermarket Type2           443.4228  \n",
       "2  Supermarket Type1          2097.2700  \n",
       "3      Grocery Store           732.3800  \n",
       "4  Supermarket Type1           994.7052  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Also we can use combine sparse class whereever the nunique is very large - Technique 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data['Item_Identifier'].nunique()  #has many unique values(high cardinality index),\n",
    "# #so use combine sparse class method for this feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# temp= data['Item_Identifier'].value_counts()\n",
    "# temp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data['Item_Identifier_count'] = data['Item_Identifier'].apply(lambda x: temp[x]) # a new col. will be created too\n",
    "# data[['Item_Identifier','Item_Identifier_count']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #every item identifier which has less than 4 count of item identifer will be label as 'other', these are the sparse classes,\n",
    "# # as they have lesser counts\n",
    "# for i in range(0, len(data)):         \n",
    "#     if data['Item_Identifier_count'][i] < 4:\n",
    "#         data['Item_Identifier'][i] = 'other'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data['Item_Identifier'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data = data.drop(['Item_Identifier'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### apply labelencoding to rest all the categorical feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>Item_Identifier</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>Meat</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>Missing</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>Household</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Item_Identifier Item_Fat_Content              Item_Type Outlet_Identifier  \\\n",
       "0                8          Low Fat                  Dairy            OUT049   \n",
       "1                6          Regular            Soft Drinks            OUT018   \n",
       "2                7          Low Fat                   Meat            OUT049   \n",
       "3                6          Regular  Fruits and Vegetables            OUT010   \n",
       "4                6          Low Fat              Household            OUT013   \n",
       "\n",
       "  Outlet_Size Outlet_Location_Type        Outlet_Type  \n",
       "0      Medium               Tier 1  Supermarket Type1  \n",
       "1      Medium               Tier 3  Supermarket Type2  \n",
       "2      Medium               Tier 1  Supermarket Type1  \n",
       "3     Missing               Tier 3      Grocery Store  \n",
       "4        High               Tier 3  Supermarket Type1  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[cate_feature].head(5)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tier 3    3350\n",
       "Tier 2    2785\n",
       "Tier 1    2388\n",
       "Name: Outlet_Location_Type, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Outlet_Location_Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2], dtype=int64)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le.fit_transform(['Tier 1','Tier 2','Tier 3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Outlet_Location_Type'] = data['Outlet_Location_Type'].map({'Tier 1':1, 'Tier 2':2, 'Tier 3':3})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    3\n",
       "2    1\n",
       "3    3\n",
       "4    3\n",
       "Name: Outlet_Location_Type, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Outlet_Location_Type'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>Item_Identifier</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>Medium</td>\n",
       "      <td>3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>Meat</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>Missing</td>\n",
       "      <td>3</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>Household</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>High</td>\n",
       "      <td>3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Item_Identifier Item_Fat_Content              Item_Type Outlet_Identifier  \\\n",
       "0                8          Low Fat                  Dairy            OUT049   \n",
       "1                6          Regular            Soft Drinks            OUT018   \n",
       "2                7          Low Fat                   Meat            OUT049   \n",
       "3                6          Regular  Fruits and Vegetables            OUT010   \n",
       "4                6          Low Fat              Household            OUT013   \n",
       "\n",
       "  Outlet_Size  Outlet_Location_Type        Outlet_Type  \n",
       "0      Medium                     1  Supermarket Type1  \n",
       "1      Medium                     3  Supermarket Type2  \n",
       "2      Medium                     1  Supermarket Type1  \n",
       "3     Missing                     3      Grocery Store  \n",
       "4        High                     3  Supermarket Type1  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[cate_feature].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Item_Identifier'] = le.fit_transform(data['Item_Identifier'])\n",
    "data['Item_Fat_Content'] = le.fit_transform(data['Item_Fat_Content'])\n",
    "data['Item_Type'] = le.fit_transform(data['Item_Type'])\n",
    "data['Outlet_Identifier'] = le.fit_transform(data['Outlet_Identifier'])\n",
    "data['Outlet_Size'] = le.fit_transform(data['Outlet_Size'])\n",
    "data['Outlet_Type'] = le.fit_transform(data['Outlet_Location_Type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>Item_Identifier</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Item_Identifier  Item_Fat_Content  Item_Type  Outlet_Identifier  \\\n",
       "0                7                 1          4                  9   \n",
       "1                5                 2         14                  3   \n",
       "2                6                 1         10                  9   \n",
       "3                5                 2          6                  0   \n",
       "4                5                 1          9                  1   \n",
       "\n",
       "   Outlet_Size  Outlet_Location_Type  Outlet_Type  \n",
       "0            1                     1            0  \n",
       "1            1                     3            2  \n",
       "2            1                     1            0  \n",
       "3            2                     3            2  \n",
       "4            0                     3            2  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[cate_feature].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Handling rare categorical variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for feature in cate_feature: - BUT WE WONT HANDLE RARE VARIAVLE BCZ ITENDIFIIER WILL ALL GET RARE, SO AVAOID\n",
    "#     temp = data.groupby(feature)['Item_Outlet_Sales'].count()/len(data)\n",
    "#     temp_df = temp[temp>0.01].index\n",
    "#     data[feature] = np.where(data[feature].isin(temp_df),data[feature],'Rare_var')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data[cate_feature].head() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.282525</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.094427</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.927507</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.642181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.081274</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.113439</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.072068</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.071148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.770765</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.098622</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.468288</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.358050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.871986</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.640093</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.541667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.121275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.260494</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.095805</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.166782</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Item_Identifier  Item_Weight  Item_Fat_Content  Item_Visibility  Item_Type  \\\n",
       "0         0.777778     0.282525              0.25         0.094427   0.266667   \n",
       "1         0.555556     0.081274              0.50         0.113439   0.933333   \n",
       "2         0.666667     0.770765              0.25         0.098622   0.666667   \n",
       "3         0.555556     0.871986              0.50         0.000000   0.400000   \n",
       "4         0.555556     0.260494              0.25         0.000000   0.600000   \n",
       "\n",
       "   Item_MRP  Outlet_Identifier  Outlet_Establishment_Year  Outlet_Size  \\\n",
       "0  0.927507           1.000000                   0.583333     0.333333   \n",
       "1  0.072068           0.333333                   1.000000     0.333333   \n",
       "2  0.468288           1.000000                   0.583333     0.333333   \n",
       "3  0.640093           0.000000                   0.541667     0.666667   \n",
       "4  0.095805           0.111111                   0.083333     0.000000   \n",
       "\n",
       "   Outlet_Location_Type  Outlet_Type  Item_Outlet_Sales  \n",
       "0                   0.0          0.0           0.642181  \n",
       "1                   1.0          1.0           0.071148  \n",
       "2                   0.0          0.0           0.358050  \n",
       "3                   1.0          1.0           0.121275  \n",
       "4                   1.0          1.0           0.166782  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = MinMaxScaler()\n",
    "scaled_data = scaler.fit_transform(data)\n",
    "scaled_data = pd.DataFrame(scaled_data, columns = data.columns)\n",
    "scaled_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply various models.. knn, linear regression, decision tree,random forest and ensemble "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Segregate variables independent and dependent variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((8523, 11), (8523,))"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data.drop(['Item_Outlet_Sales'],axis=1)\n",
    "y = data['Item_Outlet_Sales']\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Import train test split function "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_X,test_X,train_y,test_y = train_test_split(X,y,random_state = 56)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Implement KNN Regresson(since it is a regression problem) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Applying mse evaluation metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor as KNN #for importing the regressor\n",
    "from sklearn.metrics import mean_squared_error as mse #for importing the evaluation metrics to look for errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test MSE  1081425.1934569157\n"
     ]
    }
   ],
   "source": [
    "reg = KNN(n_neighbors = 5) #creating an instance/object of KNN\n",
    "reg.fit(train_X,train_y) #fitting the model on just the TRAIN data\n",
    "test_predict = reg.predict(test_X) #predicting over the test set\n",
    "k = mse(test_predict,test_y)\n",
    "print('Test MSE ',k)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. We have to check for various values of k(n_neighbors), so let's automate it by using an ELBOW for the regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Elbow(k):\n",
    "    test_mse = [] #initiating an empty list first\n",
    "    for i in k:  #training model for every value of k\n",
    "        reg = KNN(n_neighbors = i) #again creating an instance of KNN\n",
    "        reg.fit(train_X,train_y) #fitting over the TRAIN data\n",
    "        tmp = reg.predict(test_X) #predicting over the test set using a variable tmp\n",
    "        tmp = mse(tmp,test_y)\n",
    "        test_mse.append(tmp)  #appending the values of mse to empty list test_mse\n",
    "    return test_mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "k = range(1,30) #defining k range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = Elbow(k) #calling above defined function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'elbow curve for test')"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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b9iskPQU8HT6fJOn7g21nZg8AzVn240JgiZl1mNlaoAGYH14NZrbGzDqBJcCFkgS8lv0Toi4GLkqpa3FYvgM4J5TP1MaI8kdzO+dcJJtTcN8EziOahBQzexw46yW0+XFJfw+n6KpDbBqwIaVMY4hlik8EWsysu1/8gLrC+p2hfKa6DiLpckkrJK3YunXr0PYyA5+OxznnIlnNam1mG/qFhnra6gbgKOBkYDPwXyGudM0OIT6Uug4Omt1kZvPMbF4ymUxXZMj8FJxzzkWySUAbJL0SMEklkj5NOB13qMxsi5n1mFkv8EP2nwJrBGakFJ1ONPghU3wbMEFSUb/4AXWF9eOJTgVmqmtEJasStOzpoqN7xC49OefcYSmbBPRh4GNEp6saiY5ePjaUxiRNSfl4MdA3Qm4psCCMYKsHZhM9hfURYHYY8VZCNIhgqZkZcB/Q92TWhcDdKXUtDMuXAH8M5TO1MaL6hmJvb+sc6aadc+6wks0ouG3Auw61Ykm3AmcDtZIagauBsyWdTHTqax3wodDGKkm3A08B3cDH+kanSfo4cC9QCCwys1WhiSuBJZK+AjwG3BziNwM/ltRAdOSzYLA2RtKklHuBpk4oG+nmnXPusJHNjahDYmaXpgnfnCbWV/464Lo08d+S5gF4ZraGNKPYzKwdeNuhtDGS/GZU55yL+KO1R9i+BOQDEZxzec4T0AibWOFHQM45BwOcgpP0yYE29LnghqakqIDq8mJPQM65vDfQNaC++d6OBU4nGkUG8GbggVx2aqzz2RCcc26ABJQyB9zvgFPNrDV8vgb4+Yj0bozym1Gdcy67a0AzgdSbVjqBupz0Jk/4dDzOOZfdMOwfA8sl3Ul0/87FHDzDtTsEyaoETa3tmBnRPKnOOZd/srkR9TpJ9wCvDqH3mdljue3W2JasStDe1UtbRzdVpcVxd8c552KR7TDscmCXmX0LaAxT2bgh8ptRnXMuu+cBXU007c1VIVQM/CSXnRrrkpWlgCcg51x+y+YI6GLgLcBuADPbRBaP5HaZ+WwIzjmXXQLqDLNJG0SP5s5tl8Y+PwXnnHPZJaDbJd1I9PydDwK/J3qWjxuiCWXFFBXIE5BzLq9lMwru65JeD+wimhXhi2a2LOc9G8MKCkSt3wvknMtzAyYgSYXAvWb2OsCTzjDy2RCcc/luwFNw4YFteySNH6H+5A2fD845l++ymQmhHXhC0jLCSDgAM7siZ73KA5OqEjy5cWfc3XDOudhkk4B+E15uGCWrEmzf3UlPr1FY4NPxOOfyTzaDEBaPREfyTbIqQU+vsWNPJ7WVibi745xzIy6bmRBmS7pD0lOS1vS9RqJzY1my0u8Fcs7lt2zuA/of4AagG3gN0UzYP85lp/KB34zqnMt32SSgMjP7AyAzW29m1wCvHWwjSYskNUl6Ms26T0sySbXhsyR9W1KDpL9LOjWl7EJJq8NrYUr8NElPhG2+rfBcA0k1kpaF8sskVQ/WRhw8ATnn8l02CahdUgGwWtLHJV0MTMpiu1uA8/sHJc0AXg+8kBJ+AzA7vC4nOuJCUg1wNXAGMB+4ui+hhDKXp2zX19ZngT+Y2WzgD+Fzxjbi0nfdx+8Fcs7lq2wS0CeIHsdwBXAa8E/AwgG3AMzsAaA5zarrgX8hzC0XXAj8yCJ/JZr2ZwpwHrDMzJrNbAfRzbDnh3XjzOwvYZ66HwEXpdTVN3Bicb94ujZiUZEooqKkkKZdnoCcc/kpm1Fwj4TFNuB9L6UxSW8BNprZ4/2eBDoN2JDyuTHEBoo3pokDTDazzaHvmyX1Ha1lqmtzmn5eTnSUxMyZMw9hDw+Nz4bgnMtngyYgSfdx4NEKAGY26HWgfvWUA58Hzk23Ok3MhhAfsAvZbmNmNwE3AcybN2+weocsmg2hPVfVO+fcYS2bG1E/nbJcCvwj0Yi4Q3UUUA/0Hf1MB/4maT7R0ciMlLLTgU0hfna/+J9CfHqa8gBbJE0JRz9TgKYQz9RGbJJVCZ59sTXOLjjnXGwGvQZkZo+mvB4ys08SDQo4JGb2hJlNMrM6M6sjSginmtmLwFLgPWGk2suBneE02r3AuZKqw+CDc4kmR90MtEp6eRj99h7g7tDUUvZfo1rYL56ujdgkfUZs51wey+YUXE3KxwKigQhHZLHdrURHL7WSGoGrzezmDMV/C1wANAB7CNeazKxZ0peBvutQ15pZ38CGjxCNtCsD7gkvgK8SPcPoMqKRdm8bqI04JasS7Grvpr2rh9Liwri745xzIyqbU3CPsv+6SzewFrhssI3M7NJB1telLBvwsQzlFgGL0sRXACekiW8HzkkTz9hGXPruBdrW1sH06vKYe+OccyMrm1Fw9SPRkXyUejOqJyDnXL7J5hTcWwdab2a/HL7u5JdkZSngsyE45/JTNqfgLgNeCfwxfH4N0Ui0nUSn5jwBDdG+IyC/F8g5l4eySUAGzOkbMRaGNn/PzGK/iD/aTawsQfIjIOdcfspmKp66fsOVtwDH5Kg/eaW4sICa8hJPQM65vJTNEdCfJN0L3Ep0NHQpcF9Oe5VHotkQPAE55/JPNqPg+mbAPiuEbjSzO3Pbrfzh88E55/JVNqPgKoClZnanpGOBYyUVm1lX7rs39iUrE6zdtjvubjjn3IjL5hrQA0BC0jTg90QzCNySy07lk75TcNF9ss45lz+ySUAysz3AW4HvmNnFwJzcdit/JKsSdHT30toxlPldnXNu9MoqAUl6BfAu4Dchls3gBZcFfzS3cy5fZZOA/hm4CrjTzFZJOhIfBTdskuHR3P5kVOdcvslmFNwDRNeB+j6vIXo8txsGPhuCcy5fZXME5HLIT8E55/KVJ6CYjS8rprhQnoCcc3nHE1DMJPmTUZ1zeSmbG1GTwAeButTyZvb+3HUrv/hsCM65fJTNcOq7gf8jugm1J7fdyU/JqgQbW9rj7oZzzo2obBJQuZldmfOe5LFkVYKVG3bG3Q3nnBtR2VwD+rWkC3LekzyWrEywfXcH3T29cXfFOedGTLY3ov5a0l5JuyS1StqV647lk6MmVWIGz7zYGndXnHNuxAyagMysyswKzKzMzMaFz+MG207SIklNkp5MiX1Z0t8lrZT0O0lTQ1ySvi2pIaw/NWWbhZJWh9fClPhpkp4I23xbkkK8RtKyUH6ZpOrB2ojb/PoaAB5e2xxzT5xzbuRkNQxbUrWk+ZLO6ntlsdktwPn9Yv9pZiea2cnAr4EvhvgbgNnhdTlwQ2i3BrgaOAOYD1zdl1BCmctTtutr67PAH8xsNvCH8DljG4eDKePLmFlTzvK12+PuinPOjZhBE5CkDxBNxXMv8KXwfs1g24UpfJr7xVJP3VUQPWEV4ELgRxb5KzBB0hTgPGCZmTWb2Q5gGXB+WDfOzP5i0XMMfgRclFLX4rC8uF88XRuHhfn1NSxf2+yPZXDO5Y1srwGdDqw3s9cApwBbh9qgpOskbSCaXbvvCGgasCGlWGOIDRRvTBMHmGxmmwHC+6RB2kjXx8slrZC0YuvWIe/qIZlfX8OOPV00NLWNSHvOORe3bBJQu5m1A0hKmNkzwLFDbdDMPm9mM4CfAh8PYaUrOoT4QLLexsxuMrN5ZjYvmUwOUu3wOMOvAznn8kw2CahR0gTgLmCZpLuBTcPQ9s+Af+xrA5iRsm56aGOg+PQ0cYAtfafWwnvTIG0cFmbWlDN5XILlnoCcc3kim1FwF5tZi5ldA/wrcDP7r6scEkmzUz6+BXgmLC8F3hNGqr0c2BlOn90LnBsGQVQD5wL3hnWtkl4eRr+9h2jGhr66+kbLLewXT9fGYUES8+sn+nUg51zeyOrJppLOBGab2f+EueGmAWsH2eZW4GygVlIj0Wi2CyQdC/QC64EPh+K/BS4AGoA9wPsAzKxZ0peBR0K5a82s7xDhI0Qj7cqAe8IL4KvA7ZIuA14A3jZQG4eT+fU1/OrxTWxo3svMieVxd8c553Iqm8lIrwbmEV33+R+gGPgJ8KqBtjOzS9OEb85Q1oCPZVi3CFiUJr4COCFNfDtwzqG0cbjYfx1ouycg59yYl801oIuJTpftBjCzTUBVLjuVr45OVlJdXuzXgZxzeSGbBNQZjh4MQFJFbruUvwoKxOl1NSxf5wnIOTf2ZZOAbpd0I9GNmx8keizDD3Pbrfw1v76G9dv38OJOfzyDc25sy2YU3NeBO4BfEF0H+qKZfSfXHctXZ9RPBPCjIOfcmJfVKDgzWybp4b7ykmpSRqO5YXT8lCoqE0UsX7udt5w0Ne7uOOdczmQzCu5DwLXAXqLh0yK6HnRkbruWn4oKCzhtVrUPRHDOjXnZXAP6NDDXzOrM7EgzqzczTz45NL++hue2tNG8uzPurjjnXM5kk4CeJ7px042QvvuBHvHrQM65MSyba0BXAX8O14A6+oJmdkXOepXnXjZ9PImiApavbea8uUfE3R3nnMuJbBLQjcAfgSeIrgG5HEsUFXLKzAl+Hcg5N6Zlk4C6zeyTOe+JO8D8+ol894+raW3voqq0OO7uOOfcsMvmGtB94QFtUyTV9L1y3rM8d0Z9Db0Gj67fEXdXnHMuJ7I5AnpneL8qJebDsHPslJkTKCoQy9c2c/axkwbfwDnnRplBE5CZ1Y9ER9yBykuKeNn08X4dyDk3ZmVzCs7FZH59DY83ttDe1RN3V5xzbth5AjqMnVFfQ1eP8dgLLXF3xTnnhp0noMPYabNqkPDTcM65MWnQBCTpD9nE3PAbX1bM8UeMY/m67XF3xTnnhl3GBCSpNAy3rpVUnTIEuw7waZpHyPz6Gh5dv4PObr8H2Dk3tgx0BPQh4FHguPDe97ob+F7uu+Ygug7U3tXLk5t2xt0V55wbVhkTkJl9KwzB/nTKLNj1ZnaSmX13BPuY104PE5P6dSDn3FiTzSCEFyVVAUj6gqRfSjp1sI0kLZLUJOnJlNh/SnpG0t8l3SlpQsq6qyQ1SHpW0nkp8fNDrEHSZ1Pi9ZIelrRa0m2SSkI8ET43hPV1g7VxOKutTHBUssITkHNuzMkmAf2rmbVKOhM4D1gM3JDFdrcA5/eLLQNOMLMTgecIsytImgMsAOaGbb4vqVBSIdHpvjcAc4BLQ1mArwHXm9lsYAdwWYhfBuwws6OB60O5jG1ksR+xm18/kUfWNdPTa3F3xTnnhk02CajvLsg3AjeY2d1AyWAbmdkDQHO/2O/MrDt8/CswPSxfCCwxsw4zWws0APPDq8HM1phZJ7AEuFCSgNcCd4TtFwMXpdS1OCzfAZwTymdq47B3Rn0Nre3dPPPirri74pxzwyabBLRR0o3A24HfSkpkud1g3g/cE5anARtS1jWGWKb4RKAlJZn1xQ+oK6zfGcpnqusgYfLVFZJWbN26dUg7N5zm+3Ug59wYlE0ieTtwL3C+mbUANcBnXkqjkj4PdAM/7QulKWZDiA+lroODZjeZ2Twzm5dMJtMVGVFTJ5QxvbrME5BzbkwZNAGZ2R6gCTgzhLqB1UNtUNJC4E3Au8ysLwE0AjNSik0HNg0Q3wZMkFTUL35AXWH9eKJTgZnqGhXm19ewfG0z+//JnHNudMtmJoSrgSvZ/ziGYuAnQ2lM0vmhrreExNZnKbAgjGCrB2YDy4FHgNlhxFsJ0SCCpSFx3QdcErZfSHR/Ul9dC8PyJcAfQ/lMbYwKZ9TXsH13J89v3R13V5xzblhk8zygi4FTgL8BmNmmvmHZA5F0K3A20UwKjcDVREksASyLxgXwVzP7sJmtknQ78BTREdbHzKwn1PNxolOAhcAiM1sVmrgSWCLpK8BjwM0hfjPwY0kNREc+C0K/M7YxGsyvnwhE14GOnlQZc2+cc+6l02CndCQtN7P5kv5mZqdKqgD+EoZSj3nz5s2zFStWxN0NzIz5//YHXnXURL654JS4u+OccwOS9KiZzRuoTDaDEG4Po+AmSPog8Hvgv4ejgy57kphfX8PDfh3IOTdGZDMI4etE99P8AjgW+KKZfTvXHXMHO6O+hs0722ncsTfurjjn3Es26DUgSV8zsyuJZjHoH3MjKPV+oBk15TH3xjnnXppsTsG9Pk3sDcPdETe4YyZVMb6s2O8Hcs6NCRmPgCR9BPgocKSkv6esqgIeynXH3MEKCsTpdTU82J56MGMAABQhSURBVLCNrp5eigv9gbbOudFroF+wnwFvJrp/5s0pr9PM7N0j0DeXxjvPmMHGlr0senBt3F1xzrmXZKDnAe00s3VmdqmZrU95+fmfGL32uMm8fs5kvvn71Wxq8cEIzrnRy8/hjEJXv3kOhnHtr56KuyvOOTdknoBGoenV5Vxxzmz+d9WL3PdMU9zdcc65IfEENEp94MwjOSpZwReXPkl716iZUcg55/bxBDRKlRQV8OWLTmBD816+f19D3N1xzrlD5gloFHvlUbVcfMo0fnD/GtZsbYu7O845d0g8AY1yV11wHIniAr549yqfI845N6p4AhrlJlWV8pnzjuXBhm38+u+b4+6Oc24U6+7p5cmNO/nxX9fzydtWcvfKjTltL5vnAbnD3LvOmMXPVzTy5V8/xdnHJqkqLY67S865UaCptZ3HXmjhby/s4LEXWniicSd7w6Cm2soS5kwdl9P2PQGNAYUF4isXncBF33+Ibyx7jqvfPDfuLjnnDgMd3T20tneza29X9N7exeotbfsSzsZwM3txoZgzdTzvOH0Gp8ycwKkzq5leXUZ4cGjOeAIaI06aMYF3nTGTxX9exyWnTWfu1PFxd8k5lyOd3b2sbmrl6c2tPL15F5ta9tLa3k1r+/5Es6u9m87u3rTbTx1fyimzqnnfq+o4ZWY1c6eOo7S4cIT3whPQmPKZc4/jnide5At3PckvPvxKCgpy+9eLcy73trd17Es0T2/exVObd9HQ1EZ3bzToKFFUwPTqMsaVFTO+vITpNeWMKy1mXGkRVaVFVJUWM66siKpEMVWlRcyaWMER40tj3quIJ6AxZHx5MZ+74Hg+9fPHuX3FBhbMnxl3l5xzh2Dn3i5Wbmjh0fU7eHxDC09v3kVTa8e+9ZPHJTh+yjhec9wkjp8yjjlTqqibWEHRKJ0Z3xPQGPPWU6dx24oNfPV/n+HcuUdQU1ESd5ecc2mYGeu37+HR9TtYsX4Hf1u/g+eaWjGDAsExk6s48+ha5kwdx/FTotdY+//ZE9AYI0UDEi741v/x1Xue5j8uOSnuLjnngF3tXTz3YusBCWf77k4AqkqLOHVmNW88cQqnzarmpBkTqEyM/Z/nnO2hpEXAm4AmMzshxN4GXAMcD8w3sxUp5a8CLgN6gCvM7N4QPx/4FlAI/LeZfTXE64ElQA3wN+CfzKxTUgL4EXAasB14h5mtG6iNseaYyVVc9up6brx/DW+fN4N5dTVxd8m5vLC7o5t123ezbtse1m5rY+22PeHz7n3JBqC+toKzj53EabOqOW1WNbMnVeblNVvl6u55SWcBbcCPUhLQ8UAvcCPw6b4EJGkOcCswH5gK/B44JlT1HNFjwRuBR4BLzewpSbcDvzSzJZJ+ADxuZjdI+ihwopl9WNIC4GIze0emNsxswJk8582bZytWrBioyGFpd0c3r//G/XT29HL1m+fyphOn5HxIpXOjVW+vsWnnXlY3tdGwpY2123fT0xP9Nvb9bxO968AY0Yi09c17WLdt9wHXayC6ZlM3sYL62grqais4KlnJKTMnUFuZGJkdi5GkR81s3kBlcnYEZGYPSKrrF3s6dKx/8QuBJWbWAayV1ECUKAAazGxN2G4JcKGkp4HXAu8MZRYTHVndEOq6JsTvAL6rqMFMbfxlGHb3sFORKOKW98/n0z9/nP9362Pc+dhGrr1wLtOry+PumnOx6ek1GnfsYfWWNlY3tbG6qZWGpjYamtrY07n/b9EJ5cWUFhViREmo7+90S1kmrCssEDOqyznrmGSUaCZWUFdbTt3ECiry4DTaS3G4/OtMA/6a8rkxxAA29IufAUwEWsysO035aX3bmFm3pJ2h/EBtHEDS5cDlADNnjt6RZMdMruLOj76KW/68jv/63bOce/0DfOrcY3nvK+sozMPDfTe2mBkbmveyaede2tq7ae3oCvfCdB9wT0xrexdtHd3s3NvF+u176Ei5N2byuASzJ1Xx9nkzmD25ktmTqjh6UuWYu9h/uDpcElC6X0Mj/Vx1NkD5geoaaJsDg2Y3ATdBdAouXZnRorBAXHZmPefNncy/3vUkX/71U9z12Eb+/a0v44RpfrOqG1127O7koee38eDqbfzf6m377uTvr6SwgKrSIir77oVJFFM3sYKzZieZPbmSo0OiGV/m01bF6XBJQI3AjJTP04FNYTldfBswQVJROApKLd9XV6OkImA80DxIG2Pe9OpyFr33dH7zxGauWfoUF37vIT5wZj2feN0xlJWM/B3QzmWjo7uHR9fv4MHV23iwYRtPbNyJGVQlinjFURP58D8cyZHJyn03XFaVFlGZKIrlrn536A6XBLQU+JmkbxANEJgNLCc6apkdRrxtBBYA7zQzk3QfcAnRSLiFwN0pdS0kurZzCfDHUD5TG3lDEm86cSqvPjrJv9/zNDc+sIbfPrmZ6y56GWcdk4y7e86xt7OH1U2tLF/bzIMN23h4TTN7u3ooLBCnzpzAJ845hjNn13LS9PGj9uZLt18uR8HdCpwN1AJbgKuJjkS+AySBFmClmZ0Xyn8eeD/QDXzCzO4J8QuAbxINw15kZteF+JHsH4b9GPBuM+uQVAr8GDgltLcgZRBD2jYGMlpHwWXjr2u287lfPsGabbu56OSpXHHObOprK3y0nMu59q4ent/axnNbWnluSxurw/uGHXv2XeQ/MlnBq4+u5czZSV5+ZI3P8j7KZDMKLmcJaKwYywkIoh+C79/XwA33P09Xj1FdXsxJMyZw0vQJnDwzevcLsm4ounp6eXFnOxtb9rJxx17WbtvNc1taWd3UxvrtuwlTmVFUII5MVjB7chXHTKrimMmVnDhjAtMmlMW7A+4l8QQ0DMZ6AuqzoXkPDzZsY+ULLazc0LJvShCAWRPLo4Q0I0pKc6bEM3OuO7zs7uhm8869NO7Yuy/J9L1vatnLi7va9yUZiAbE1E0s55jJVVGymVzJsZOrqKutoNhPp405noCGQb4koP7aOrp5onEnjze27EtKL+5qB6Jnh1x8yjSuPP84JubBDXX5pqunl6bWDrbsamfLzna27GrnxV0dNO1q58Vd0ectuzpo6+g+YLvCAjFlfCnTJpQxrbqM6eF92oRyplWXMXVCKYki/8MlX3gCGgb5moDSeXFnOys3tPBQwzZuXf4CFYkirjz/OBacPiMvpxEZKza27GXFumZWrNvBI+uaeW5L6wFHLhD90TGpqpTJ4xJMHle673XE+ATTq8uZNqGMyeNK/f4yt48noGHgCSi91Vta+cJdT/Lw2mZOmjGBr1x4Ai+b7vcVHe56eo1nX2xlxfpmHlm3g0fXNbNpZ3RkW1FSyKmzqjlp+gSmVZcdkGxqykv8jwx3SDwBDQNPQJmZGXev3MRXfvM0zbs7ePfLZ/Gpc4/1m/tGSMueTnbt7aaju4eO7l7au9K/d3T1sGNPF49taOGx9TtoDafOJo9LcHpdDfNmVTOvrobjjqjyoc1u2MQ6F5wb+yRx0SnTeM1xk/jG757lx39dz2+f2Mzn33g8F508zYdzDzMz4+nNrSx7agu/f3oLT2zcmfW2Ehw7uYq3nDyV0+tqOG1WNdOry/w7crHyI6BB+BFQ9p7cuJPP3/Ukj29o4Yz6Gr580QkcM7kq7m6Nap3dvSxf28yyp17k9083sbFlLxKcMmMC5xw/mcnjSkkUFVBaXJj2PVFcQGlRIWUlhT5y0Y0oPwU3DDwBHZreXoueyHrPM+zu6OZ9r6rjFUdNpKy4iLKSQspLCikrjn4Qy4qjl19bONDOvV386dkmlj21hfuf3UprRzelxQWceXSSc+dM5jXHTSJZ5aMP3eHNE9Aw8AQ0NM27O/naPc9w24oNg5ZNFBVQXlJIeUkRNRUl1FaWUFuZYGJlYt9ybWWC2qoSJlYkqKkoOexHW5kZ29o69z2MbOfern3XYzq6e8Orh/au6L2jK4q1dnSzauNOunuN2soSzjluMq+bM5kzj671OfvcqOIJaBh4AnppGnfsYVtbJ3s6u2nv6mFPZw97O3vY2xW97+ns2Rff3dFN855OtrV1sK21k+27O+jqOfi/TwmSlQmOmzKOuVP7XuOZVVM+okdTZkbz7ijJrN0WPZBs7fbdrA9PxOx/n0yf0uICEkUHniJLhFhpcQEvmzaB18+ZzMkzJhz2ida5THwQgovd9OryIT8Ez8zYtbebrW0dbG/rYFtblJy2t3WwsaWdpzbv4ocPrKE73LRSmSji+ClVzJ06njkhMc2eVEVJUfqRXd090VFHZ3cvnT29dHT1sqerm517utjVHj0/ZufeLnb1f2+P3jfvbKe1fX+SKSwQ06vLqJtYwbxZNdRNLGdWbQX1EyuoqSwhUVRASWGBX/h3LvAE5A5bkhhfXsz48mKOnlSZtkxHdw+rt7SxatNOVm3axapNu7h9xYZ9T7csKSxg6oRSunqMzp4o2XR099DZ3XvQzZYDqUoUMa6smHFlxYwvK6K+toIz6idSV1tBfXj65fTq8ozJzjl3ME9AblRLFBVywrTxBzxcr6fXWLd9d0hIO9m4Yy8lhdHprpLCAkqKotNdJUV9y9F7SWEB5SVFjC8rZlxZ9D6+rJjKRJHfH+NcDngCcmNOYYE4KlnJUclK3nLS1Li745zLwP+sc845FwtPQM4552LhCcg551wsPAE555yLhScg55xzsfAE5JxzLhaegJxzzsXCE5BzzrlY+GSkg5C0FVjfL1wLbIuhO7nm+zX6jNV98/0affrv2ywzSw60gSegIZC0YrBZXkcj36/RZ6zum+/X6DOUffNTcM4552LhCcg551wsPAENzU1xdyBHfL9Gn7G6b75fo88h75tfA3LOORcLPwJyzjkXC09AzjnnYuEJ6BBIOl/Ss5IaJH027v4MJ0nrJD0haaWkFXH3Z6gkLZLUJOnJlFiNpGWSVof36jj7OBQZ9usaSRvDd7ZS0gVx9nEoJM2QdJ+kpyWtkvTPIT4WvrNM+zaqvzdJpZKWS3o87NeXQrxe0sPhO7tNUsmgdfk1oOxIKgSeA14PNAKPAJea2VOxdmyYSFoHzDOzUX2TnKSzgDbgR2Z2Qoj9B9BsZl8NfzhUm9mVcfbzUGXYr2uANjP7epx9eykkTQGmmNnfJFUBjwIXAe9l9H9nmfbt7Yzi702SgAoza5NUDDwI/DPwSeCXZrZE0g+Ax83shoHq8iOg7M0HGsxsjZl1AkuAC2Puk+vHzB4AmvuFLwQWh+XFRD8Co0qG/Rr1zGyzmf0tLLcCTwPTGBvfWaZ9G9Us0hY+FoeXAa8F7gjxrL4zT0DZmwZsSPncyBj4jymFAb+T9Kiky+PuzDCbbGabIfpRACbF3J/h9HFJfw+n6EbdaapUkuqAU4CHGWPfWb99g1H+vUkqlLQSaAKWAc8DLWbWHYpk9fvoCSh7ShMbS+cvX2VmpwJvAD4WTvm4w9sNwFHAycBm4L/i7c7QSaoEfgF8wsx2xd2f4ZRm30b992ZmPWZ2MjCd6OzQ8emKDVaPJ6DsNQIzUj5PBzbF1JdhZ2abwnsTcCfRf1RjxZZwPr7vvHxTzP0ZFma2JfwQ9AI/ZJR+Z+E6wi+An5rZL0N4THxn6fZtrHxvAGbWAvwJeDkwQVJRWJXV76MnoOw9AswOIz1KgAXA0pj7NCwkVYSLpEiqAM4Fnhx4q1FlKbAwLC8E7o6xL8Om7wc6uJhR+J2FC9o3A0+b2TdSVo367yzTvo32701SUtKEsFwGvI7o+tZ9wCWhWFbfmY+COwRhuOQ3gUJgkZldF3OXhoWkI4mOegCKgJ+N1n2TdCtwNtHU8FuAq4G7gNuBmcALwNvMbFRd0M+wX2cTncYxYB3wob7rJqOFpDOB/wOeAHpD+HNE10pG+3eWad8uZRR/b5JOJBpkUEh0EHO7mV0bfkeWADXAY8C7zaxjwLo8ATnnnIuDn4JzzjkXC09AzjnnYuEJyDnnXCw8ATnnnIuFJyDnnHOx8ATk3DCQVJc6U/Uw1nutpNcNUuYaSZ8eqT45N1yKBi/inIuLmX0xrrYlFZpZT1ztu7HPj4CcG2aSjpT0mKTT+8XPlvQnSXdIekbST8Pd8kg6TdL9YTLYe1OmoblF0iVh+YKw3YOSvi3p1ynVzwl1r5F0RUq8SNLiMPHlHZLKQ13nhD4+ESbETIT4OklflPQg8DZJV0h6Kmy/JIf/bC4PeQJybhhJOpZo7q/3mdkjaYqcAnwCmAMcCbwqzBf2HeASMzsNWAQcMBOFpFLgRuANZnYmkOxX73HAeUTzil0d6gQ4FrjJzE4EdgEfDXXdArzDzF5GdCbkIyl1tZvZmWa2BPgscErY/sOH/A/i3AA8ATk3fJJE81+928xWZiiz3Mwaw0SUK4E6oiRxArAsTHH/BaLJHFMdB6wxs7Xh86391v/GzDrCAwWbgMkhvsHMHgrLPwHODO2tNbPnQnwxkDr7+W0py38Hfirp3UA3zg0jvwbk3PDZSfTMqFcBqzKUSZ0bq4fo/0EBq8zsFQPUne5xIIPVCwdPiW9Z1LU7ZfmNRMnpLcC/Spqb8swX514SPwJybvh0Ej0F8j2S3nkI2z0LJCW9AqIp/CXN7VfmGeDI8GAzgHdkWffMvnqJJsF8MNRVJ+noEP8n4P7+G0oqAGaY2X3AvwATgMos23VuUH4E5NwwMrPdkt5EdDptt5kNOiW9mXWGgQbfljSe6P/Lb5JyFGVmeyV9FPhfSduA5Vl26WlgoaQbgdXADWbWLul9wM/D81seAX6QZttC4CehTwKuD89/cW5Y+GzYzo0SkirNrC2MnPsesNrMro+7X84NlZ+Cc270+GAYpLAKGE80Ks65UcuPgJxzzsXCj4Ccc87FwhOQc865WHgCcs45FwtPQM4552LhCcg551ws/j8WPjmejhT3oAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(k,test)\n",
    "plt.xlabel('k neighbors')\n",
    "plt.ylabel('test mean squared error')\n",
    "plt.title('elbow curve for test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "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>k</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1.039984e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1.041020e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1.043388e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1.046934e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>1.048615e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>1.049777e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>1.050139e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>1.051639e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>1.060891e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>1.062096e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>1.070545e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>1.073684e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>1.074296e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>1.078695e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1.081425e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>1.084811e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>1.094619e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>1.096714e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>1.102587e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>1.102663e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1.104413e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>1.107854e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>1.116105e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>1.122786e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>1.124949e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>1.132913e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.163858e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.313660e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1.699867e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     k          test\n",
       "9   10  1.039984e+06\n",
       "10  11  1.041020e+06\n",
       "11  12  1.043388e+06\n",
       "8    9  1.046934e+06\n",
       "12  13  1.048615e+06\n",
       "13  14  1.049777e+06\n",
       "7    8  1.050139e+06\n",
       "6    7  1.051639e+06\n",
       "14  15  1.060891e+06\n",
       "15  16  1.062096e+06\n",
       "16  17  1.070545e+06\n",
       "5    6  1.073684e+06\n",
       "17  18  1.074296e+06\n",
       "18  19  1.078695e+06\n",
       "4    5  1.081425e+06\n",
       "19  20  1.084811e+06\n",
       "20  21  1.094619e+06\n",
       "21  22  1.096714e+06\n",
       "23  24  1.102587e+06\n",
       "22  23  1.102663e+06\n",
       "3    4  1.104413e+06\n",
       "24  25  1.107854e+06\n",
       "25  26  1.116105e+06\n",
       "26  27  1.122786e+06\n",
       "27  28  1.124949e+06\n",
       "28  29  1.132913e+06\n",
       "2    3  1.163858e+06\n",
       "1    2  1.313660e+06\n",
       "0    1  1.699867e+06"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {'k': k,'test' : test}\n",
    "d = pd.DataFrame(d)\n",
    "d.sort_values(by=['test'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test MSE  1039983.6155808403\n"
     ]
    }
   ],
   "source": [
    "reg = KNN(n_neighbors = 10) #creating instance of KNN and selecting that k value which results in minimum test error\n",
    "reg.fit(train_X,train_y) #fitting over the TRAIN data\n",
    "test_predict = reg.predict(test_X) #predicting over the test set and calculate mse(over test data)\n",
    "k = mse(test_predict, test_y)\n",
    "print('Test MSE ',k)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross validation - used in for KNN model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### we have seen and used both train and test set, what if we get an unseen data, will our model still be able to perform that well??? In order to check that we create a cross validation, A common practice in data science competitions is to iterate over various models to find a better performing model. However, it becomes difficult to distinguish whether this improvement in score is coming because we are capturing the relationship better, or we are just over-fitting the data(just good on training set and performs poorly on any unseen data). To find the right answer for this question, we use validation techniques. This method helps us in achieving more generalized relationships. Cross Validation is a technique which involves reserving a particular sample of a dataset on which you do not train the model. Later, you test your model on this sample before finalizing it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.39802593, 0.36238842, 0.36786355, 0.34678565, 0.45061277,\n",
       "       0.38596081, 0.43657498, 0.41153143, 0.4109381 , 0.34696347])"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "score = cross_val_score( KNN(n_neighbors = 3), X = train_X, y = train_y, cv = 10)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39.176451134619505, 3.4302746620783773)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Consistency using Mean and standard deviation in percentage -#mean should be high and std. deviation low for a better model\n",
    "score.mean()*100, score.std()*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Val_score(n_neighbors): #just automating the process for different k neighbors(n neighbors value)\n",
    "    '''\n",
    "  takes range of n_neighbors as input\n",
    "  returns Mean and Standard Deviation for each value of n_neighbors\n",
    "  '''\n",
    "    avg = []\n",
    "    std = []\n",
    "  \n",
    "    for i in n_neighbors:\n",
    "    \n",
    "    # 10 fold cross validation for every value of n_neighbor\n",
    "      score = cross_val_score( KNN(n_neighbors = i) , X = train_X, y = train_y, cv = 10)\n",
    "    \n",
    "    # adding mean to avg list\n",
    "      avg.append(score.mean())\n",
    "    \n",
    "    # adding standard deviation to std list\n",
    "      std.append(score.std())\n",
    "    \n",
    "    return avg, std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_neighbors = range(1,50)\n",
    "mean, std = Val_score(n_neighbors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Mean Validation score')"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(n_neighbors, mean, color = 'green', label = 'mean' ) #plotting mean validation score for each k value\n",
    "plt.xlabel('n_neighbors')\n",
    "plt.ylabel('Mean Score')\n",
    "plt.title('Mean Validation score') #in below curve we see that mean score is high when n_neighbors is between(0-10),so lets chck"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Mean Validation score')"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(n_neighbors[0:10], mean[0:10], color = 'green', label = 'mean' )\n",
    "plt.xlabel('n_neighbors')\n",
    "plt.ylabel('Mean Score')\n",
    "plt.title('Mean Validation score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Standard Deviation of Validation score')"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(n_neighbors[0:10], std[0:10], color = 'red', label = 'Standard deviation' )\n",
    "plt.xlabel('n_neighbors')  ##plotting std. deviation validation score for each k value\n",
    "plt.ylabel('magnitude') #above we got 0-10 giving high mean score,and out of it frm the plt above n_neighbor = 8 seemsto b best\n",
    "plt.title('Standard Deviation of Validation score')#n_neighbor = 8, has high mean score and less std dev which is very GOOD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.45382537, 0.4188698 , 0.43072856, 0.41579041, 0.49871631,\n",
       "       0.45290867, 0.47034198, 0.45515255, 0.45324485, 0.44010521])"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = cross_val_score( KNN(n_neighbors = 8), X = train_X, y = train_y, cv = 10)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(44.89683713854647, 2.3335132726647245)"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Consistency using Mean and standard deviation in percentage -#mean should be high and std. deviation low for a better model\n",
    "score.mean()*100, score.std()*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.4353509421327393, 0.5848621352878781)"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = KNN(n_neighbors = 8 ) #now lets check the performance on the test set or trying optimum model over test set\n",
    "reg.fit(train_X, train_y)\n",
    "\n",
    "score1 = reg.score(train_X, train_y)\n",
    "\n",
    "score = reg.score(test_X, test_y)\n",
    "score, score1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#APPLY CROSS VAL ON LINEAR REGREESION AND SEEEEEEE..THEN START WITH DECISION TREE, RANDOM FOREST..ETC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### IMP --> KNN - fit on train data and predict over test data and LINEAR REGRESSION - fit on train data and predict over train and test data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Implement Linear Regression(since it is a regression problem) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression as LR #imported requ. library\n",
    "from sklearn.metrics import mean_absolute_error as mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LR() #creating an instance of it\n",
    "lr.fit(train_X,train_y) #fitting ove the TRAIN data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Mean absolute error 853.6089454725252\n"
     ]
    }
   ],
   "source": [
    "train_predict = lr.predict(train_X)    # predicting over train data first and calculate mae(over train data)\n",
    "k = mae(train_predict,train_y)\n",
    "print('Training Mean absolute error', k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Mean absolute error 853.1575470196949\n"
     ]
    }
   ],
   "source": [
    "test_predict = lr.predict(test_X)  # predicting over test data first and calculate mae(over test data)\n",
    "k = mae(test_predict,test_y)\n",
    "print('Test Mean absolute error', k)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Parameters of Linear Regression - mae, r2,adj_r2,lr.coef_,lr.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(853.1575470196949,\n",
       " 0.3586958061921437,\n",
       " 0.3578669557477909,\n",
       " array([-1.48639052e+01,  6.52770026e-01,  2.92886165e+01, -1.31650603e+03,\n",
       "         3.95835497e+00,  1.17936970e+01,  2.04861619e+02,  7.08236657e+00,\n",
       "        -2.12158398e+02,  2.28419335e+02,  2.28419335e+02]),\n",
       " -15075.319845359265)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from math import sqrt                   #first by MAE\n",
    "mae = mae(test_y,test_predict)    \n",
    "from sklearn.metrics import r2_score\n",
    "r2 = r2_score(test_y, test_predict)\n",
    "\n",
    "adj_r2 = 1 - float(len(y)-1)/(len(y)-len(lr.coef_)-1)*(1 - r2)\n",
    "\n",
    "mae, r2, adj_r2,  lr.coef_, lr.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1092.109430328695,\n",
       " 0.3586958061921437,\n",
       " 0.3578669557477909,\n",
       " array([-1.48639052e+01,  6.52770026e-01,  2.92886165e+01, -1.31650603e+03,\n",
       "         3.95835497e+00,  1.17936970e+01,  2.04861619e+02,  7.08236657e+00,\n",
       "        -2.12158398e+02,  2.28419335e+02,  2.28419335e+02]),\n",
       " -15075.319845359265)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error            #SECOND BY RMSE\n",
    "rmse = sqrt(mean_squared_error(test_y, test_predict))\n",
    "from sklearn.metrics import r2_score\n",
    "r2 = r2_score(test_y, test_predict)\n",
    "\n",
    "adj_r2 = 1 - float(len(y)-1)/(len(y)-len(lr.coef_)-1)*(1 - r2)\n",
    "\n",
    "rmse, r2, adj_r2, lr.coef_, lr.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sns.scatterplot(test_y,test_predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Checking assumptions of Linear Models (optional steps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### a). HOMOSCEDASTICITY - checking by plotting a residual/error curve(there should be a constant variance of the residuals/error, THERE\n",
    "##### SHOULD BE NO TREND(INCREASING/DECREASING ETC..) - use scatter plot for it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "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>actual value</th>\n",
       "      <th>predicted value</th>\n",
       "      <th>residuals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2172</th>\n",
       "      <td>697.7584</td>\n",
       "      <td>1734.230311</td>\n",
       "      <td>-1036.471911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6228</th>\n",
       "      <td>1414.1592</td>\n",
       "      <td>1644.482127</td>\n",
       "      <td>-230.322927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2404</th>\n",
       "      <td>3078.6592</td>\n",
       "      <td>1763.978316</td>\n",
       "      <td>1314.680884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>1314.2892</td>\n",
       "      <td>2415.123827</td>\n",
       "      <td>-1100.834627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4945</th>\n",
       "      <td>1403.5064</td>\n",
       "      <td>1328.583363</td>\n",
       "      <td>74.923037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      actual value  predicted value    residuals\n",
       "2172      697.7584      1734.230311 -1036.471911\n",
       "6228     1414.1592      1644.482127  -230.322927\n",
       "2404     3078.6592      1763.978316  1314.680884\n",
       "99       1314.2892      2415.123827 -1100.834627\n",
       "4945     1403.5064      1328.583363    74.923037"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "residuals = pd.DataFrame({'actual value':test_y,'predicted value':test_predict}) #first define the residuals\n",
    "residuals['residuals'] = residuals['actual value'] - residuals['predicted value']\n",
    "residuals.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.figure(figsize = (10,6), dpi = 120, facecolor = 'w', edgecolor = 'b')\n",
    "# f = range(0,2131)\n",
    "# k = [0 for i in range(0,2131)]\n",
    "# plt.scatter(f,residuals.residuals[:],label='residuals')\n",
    "# plt.xlabel('fitted points')\n",
    "# plt.ylabel('residuals')\n",
    "# plt.plot(f,k,color = 'red',label = 'regression line')\n",
    "# plt.title('Residual plot')\n",
    "# plt.ylim(-4000,4000)\n",
    "# plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Conclusion of above plot - There is no constant variance of residuals, no pattern identified, no correlation between residuals --- GOOD SIGN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### b). should follow NORMAL DISTRIBUTION -- Checking distribution of Residuals - use histogram and Q-Q Plot for it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.figure(figsize = (10,6), dpi = 120,facecolor = 'w', edgecolor = 'b')   #plotting a histogram\n",
    "# plt.hist(residuals.residuals,bins =150)\n",
    "# plt.xlabel('Error')\n",
    "# plt.ylabel('frequency')\n",
    "# plt.title('Distribution of Error terms')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Above plot --According to the Histogram, the distribution of error is nearly normal, But there are some outliers on the Higher end of the errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from statsmodels.graphics.gofplots import qqplot  #plotting a q-q plot\n",
    "# fig,ax = plt.subplots(figsize =(5,5),dpi = 120)\n",
    "# qqplot(residuals.residuals,line = 's', ax=ax)     #residuals.residuals = blue line(error) and line ='s'(red line)      \n",
    "# plt.ylabel('Residuals Quantiles')\n",
    "# plt.title('checking distribution of residual error')\n",
    "# plt.xlabel('Ideal scale quantiles')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Above plot -- the error is not deviating much from the red line so it means it follows a normal distribution with less outliers and its  GOOD SIGN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### c). MULTICOLLINEARITY - the degree by which independent variable are dependent on each other. Should be AS LESS AS POSSIBLE. Detected by calculating VIF(variance inflation factor), if VIF >5 or if VIF is high, then multicollinearity is high ( some independent variables re co-related with each other, WHICH ISN'T GOOD as it would be difficult to interpret the outcome of model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor  #just importing VIF func from statsmodel\n",
    "from statsmodels.tools.tools import add_constant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier                   1.004281\n",
       "Item_Weight                       1.003250\n",
       "Item_Fat_Content                  1.016528\n",
       "Item_Visibility                   1.014567\n",
       "Item_Type                         1.016459\n",
       "Item_MRP                          1.444245\n",
       "Outlet_Identifier                 2.401024\n",
       "Outlet_Establishment_Year         1.095218\n",
       "Outlet_Size                       1.713457\n",
       "Outlet_Location_Type         317786.440777\n",
       "Outlet_Type                  117625.686146\n",
       "Item_Outlet_Sales                 1.633309\n",
       "dtype: float64"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VIF = pd.Series([variance_inflation_factor(data.values,i) for i in range(data.shape[1])],index = data.columns)\n",
    "VIF   #if we have vif>5, then simple drop it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier              1.004281\n",
       "Item_Weight                  1.003250\n",
       "Item_Fat_Content             1.016528\n",
       "Item_Visibility              1.014567\n",
       "Item_Type                    1.016459\n",
       "Item_MRP                     1.444245\n",
       "Outlet_Identifier            2.401024\n",
       "Outlet_Establishment_Year    1.095218\n",
       "Outlet_Size                  1.713457\n",
       "Item_Outlet_Sales            1.633309\n",
       "dtype: float64"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VIF = VIF[VIF <= 5]   #kept all the values where vif <=5 and in the process any value of vif>5, got deleted on it own\n",
    "VIF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now if have calculated VIF(by removing two usless feature which has VIF >5) , so lets check if adjusted r2 has varied or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression as LR #imported requ. library\n",
    "from sklearn.metrics import mean_absolute_error as mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(853.1575470196949,\n",
       " 0.3586958061921437,\n",
       " 0.3578669557477909,\n",
       " array([-1.48639052e+01,  6.52770026e-01,  2.92886165e+01, -1.31650603e+03,\n",
       "         3.95835497e+00,  1.17936970e+01,  2.04861619e+02,  7.08236657e+00,\n",
       "        -2.12158398e+02,  2.28419335e+02,  2.28419335e+02]),\n",
       " -15075.319845359265)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from math import sqrt                   #first by MAE\n",
    "mae = mae(test_y,test_predict)    \n",
    "from sklearn.metrics import r2_score\n",
    "r2 = r2_score(test_y, test_predict)\n",
    "\n",
    "adj_r2 = 1 - float(len(y)-1)/(len(y)-len(lr.coef_)-1)*(1 - r2)\n",
    "\n",
    "mae, r2, adj_r2,  lr.coef_, lr.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1092.109430328695,\n",
       " 0.3586958061921437,\n",
       " 0.3578669557477909,\n",
       " array([-1.48639052e+01,  6.52770026e-01,  2.92886165e+01, -1.31650603e+03,\n",
       "         3.95835497e+00,  1.17936970e+01,  2.04861619e+02,  7.08236657e+00,\n",
       "        -2.12158398e+02,  2.28419335e+02,  2.28419335e+02]),\n",
       " -15075.319845359265)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error            #SECOND BY RMSE\n",
    "rmse = sqrt(mean_squared_error(test_y, test_predict))\n",
    "from sklearn.metrics import r2_score\n",
    "r2 = r2_score(test_y, test_predict)\n",
    "\n",
    "adj_r2 = 1 - float(len(y)-1)/(len(y)-len(lr.coef_)-1)*(1 - r2)\n",
    "\n",
    "rmse, r2, adj_r2, lr.coef_, lr.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 4. Model interpretability - NOTEBOOK ANALYTICS VIDYA (OPTIONAL)- FOR NORMALIZING AND REPEATING ALL THE STEPS AGAIN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Selection - can check and see if we get better result because here after feature selection MSE rised"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import seaborn as sns                    \n",
    "# #get correlations of each features in dataset\n",
    "# corrmat = data.corr()\n",
    "# top_corr_features = corrmat.index\n",
    "# plt.figure(figsize=(20,20))\n",
    "# #plot heat map\n",
    "# g=sns.heatmap(data[top_corr_features].corr(),annot=True,cmap=\"RdYlGn\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.linear_model import Lasso\n",
    "# from sklearn.feature_selection import SelectFromModel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1. first working on train_X and train_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sel_ = SelectFromModel(Lasso(alpha=0.005, random_state=0)) \n",
    "# sel_.fit(train_X,train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sel_.get_support()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # let's print the number of total and selected features\n",
    "\n",
    "# # this is how we can make a list of the selected features\n",
    "# selected_feat = train_X.columns[(sel_.get_support())]\n",
    "\n",
    "# # let's print some stats\n",
    "# print('total features: {}'.format((train_X.shape[1])))\n",
    "# print('selected features: {}'.format(len(selected_feat)))\n",
    "# print('features with coefficients shrank to zero: {}'.format(\n",
    "#     np.sum(sel_.estimator_.coef_ == 0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# selected_feat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_X=train_X[selected_feat]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_X.head()   #same apply on the test data too, that is test_X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2. Secondly working on test_y and test_y "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sel_ = SelectFromModel(Lasso(alpha=0.005, random_state=0)) \n",
    "# sel_.fit(test_X,test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sel_.get_support()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # let's print the number of total and selected features\n",
    "\n",
    "# # this is how we can make a list of the selected features\n",
    "# selected_feat = test_X.columns[(sel_.get_support())]\n",
    "\n",
    "# # let's print some stats\n",
    "# print('total features: {}'.format((test_X.shape[1])))\n",
    "# print('selected features: {}'.format(len(selected_feat)))\n",
    "# print('features with coefficients shrank to zero: {}'.format(\n",
    "#     np.sum(sel_.estimator_.coef_ == 0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# selected_feat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test_X=test_X[selected_feat]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test_X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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