supermarket_regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Start by importing all the libraries i will be using"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "#Makes graph display in notebook\n",
    "%matplotlib inline   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import my data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "supermarket_data = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product_Identifier</th>\n",
       "      <th>Supermarket_Identifier</th>\n",
       "      <th>Product_Supermarket_Identifier</th>\n",
       "      <th>Product_Weight</th>\n",
       "      <th>Product_Fat_Content</th>\n",
       "      <th>Product_Shelf_Visibility</th>\n",
       "      <th>Product_Type</th>\n",
       "      <th>Product_Price</th>\n",
       "      <th>Supermarket_Opening_Year</th>\n",
       "      <th>Supermarket _Size</th>\n",
       "      <th>Supermarket_Location_Type</th>\n",
       "      <th>Supermarket_Type</th>\n",
       "      <th>Product_Supermarket_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DRA12</td>\n",
       "      <td>CHUKWUDI010</td>\n",
       "      <td>DRA12_CHUKWUDI010</td>\n",
       "      <td>11.6</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.068535</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>357.54</td>\n",
       "      <td>2005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>709.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRA12</td>\n",
       "      <td>CHUKWUDI013</td>\n",
       "      <td>DRA12_CHUKWUDI013</td>\n",
       "      <td>11.6</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.040912</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>355.79</td>\n",
       "      <td>1994</td>\n",
       "      <td>High</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>6381.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DRA12</td>\n",
       "      <td>CHUKWUDI017</td>\n",
       "      <td>DRA12_CHUKWUDI017</td>\n",
       "      <td>11.6</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.041178</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>350.79</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>6381.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DRA12</td>\n",
       "      <td>CHUKWUDI018</td>\n",
       "      <td>DRA12_CHUKWUDI018</td>\n",
       "      <td>11.6</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.041113</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>355.04</td>\n",
       "      <td>2016</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>2127.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DRA12</td>\n",
       "      <td>CHUKWUDI035</td>\n",
       "      <td>DRA12_CHUKWUDI035</td>\n",
       "      <td>11.6</td>\n",
       "      <td>Ultra Low fat</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>354.79</td>\n",
       "      <td>2011</td>\n",
       "      <td>Small</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>2481.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product_Identifier Supermarket_Identifier Product_Supermarket_Identifier  \\\n",
       "0              DRA12            CHUKWUDI010              DRA12_CHUKWUDI010   \n",
       "1              DRA12            CHUKWUDI013              DRA12_CHUKWUDI013   \n",
       "2              DRA12            CHUKWUDI017              DRA12_CHUKWUDI017   \n",
       "3              DRA12            CHUKWUDI018              DRA12_CHUKWUDI018   \n",
       "4              DRA12            CHUKWUDI035              DRA12_CHUKWUDI035   \n",
       "\n",
       "   Product_Weight Product_Fat_Content  Product_Shelf_Visibility Product_Type  \\\n",
       "0            11.6             Low Fat                  0.068535  Soft Drinks   \n",
       "1            11.6             Low Fat                  0.040912  Soft Drinks   \n",
       "2            11.6             Low Fat                  0.041178  Soft Drinks   \n",
       "3            11.6             Low Fat                  0.041113  Soft Drinks   \n",
       "4            11.6       Ultra Low fat                  0.000000  Soft Drinks   \n",
       "\n",
       "   Product_Price  Supermarket_Opening_Year Supermarket _Size  \\\n",
       "0         357.54                      2005               NaN   \n",
       "1         355.79                      1994              High   \n",
       "2         350.79                      2014               NaN   \n",
       "3         355.04                      2016            Medium   \n",
       "4         354.79                      2011             Small   \n",
       "\n",
       "  Supermarket_Location_Type   Supermarket_Type  Product_Supermarket_Sales  \n",
       "0                 Cluster 3      Grocery Store                     709.08  \n",
       "1                 Cluster 3  Supermarket Type1                    6381.69  \n",
       "2                 Cluster 2  Supermarket Type1                    6381.69  \n",
       "3                 Cluster 3  Supermarket Type2                    2127.23  \n",
       "4                 Cluster 2  Supermarket Type1                    2481.77  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supermarket_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4990, 13)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Displaty the number of rows and columns\n",
    "supermarket_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product_Weight</th>\n",
       "      <th>Product_Shelf_Visibility</th>\n",
       "      <th>Product_Price</th>\n",
       "      <th>Supermarket_Opening_Year</th>\n",
       "      <th>Product_Supermarket_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4188.000000</td>\n",
       "      <td>4990.000000</td>\n",
       "      <td>4990.000000</td>\n",
       "      <td>4990.000000</td>\n",
       "      <td>4990.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.908838</td>\n",
       "      <td>0.066916</td>\n",
       "      <td>391.803796</td>\n",
       "      <td>2004.783567</td>\n",
       "      <td>6103.520164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.703256</td>\n",
       "      <td>0.053058</td>\n",
       "      <td>119.378259</td>\n",
       "      <td>8.283151</td>\n",
       "      <td>4447.333835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.555000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>78.730000</td>\n",
       "      <td>1992.000000</td>\n",
       "      <td>83.230000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.767500</td>\n",
       "      <td>0.027273</td>\n",
       "      <td>307.890000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>2757.660000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12.600000</td>\n",
       "      <td>0.053564</td>\n",
       "      <td>393.860000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>5374.675000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>17.100000</td>\n",
       "      <td>0.095358</td>\n",
       "      <td>465.067500</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>8522.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>21.350000</td>\n",
       "      <td>0.328391</td>\n",
       "      <td>667.220000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>32717.410000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Product_Weight  Product_Shelf_Visibility  Product_Price  \\\n",
       "count     4188.000000               4990.000000    4990.000000   \n",
       "mean        12.908838                  0.066916     391.803796   \n",
       "std          4.703256                  0.053058     119.378259   \n",
       "min          4.555000                  0.000000      78.730000   \n",
       "25%          8.767500                  0.027273     307.890000   \n",
       "50%         12.600000                  0.053564     393.860000   \n",
       "75%         17.100000                  0.095358     465.067500   \n",
       "max         21.350000                  0.328391     667.220000   \n",
       "\n",
       "       Supermarket_Opening_Year  Product_Supermarket_Sales  \n",
       "count               4990.000000                4990.000000  \n",
       "mean                2004.783567                6103.520164  \n",
       "std                    8.283151                4447.333835  \n",
       "min                 1992.000000                  83.230000  \n",
       "25%                 1994.000000                2757.660000  \n",
       "50%                 2006.000000                5374.675000  \n",
       "75%                 2011.000000                8522.240000  \n",
       "max                 2016.000000               32717.410000  "
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supermarket_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product_Identifier                 object\n",
       "Supermarket_Identifier             object\n",
       "Product_Supermarket_Identifier     object\n",
       "Product_Weight                    float64\n",
       "Product_Fat_Content                object\n",
       "Product_Shelf_Visibility          float64\n",
       "Product_Type                       object\n",
       "Product_Price                     float64\n",
       "Supermarket_Opening_Year            int64\n",
       "Supermarket _Size                  object\n",
       "Supermarket_Location_Type          object\n",
       "Supermarket_Type                   object\n",
       "Product_Supermarket_Sales         float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supermarket_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product_Identifier</th>\n",
       "      <th>Supermarket_Identifier</th>\n",
       "      <th>Product_Supermarket_Identifier</th>\n",
       "      <th>Product_Weight</th>\n",
       "      <th>Product_Fat_Content</th>\n",
       "      <th>Product_Shelf_Visibility</th>\n",
       "      <th>Product_Type</th>\n",
       "      <th>Product_Price</th>\n",
       "      <th>Supermarket_Opening_Year</th>\n",
       "      <th>Supermarket _Size</th>\n",
       "      <th>Supermarket_Location_Type</th>\n",
       "      <th>Supermarket_Type</th>\n",
       "      <th>Product_Supermarket_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DRA12</td>\n",
       "      <td>CHUKWUDI010</td>\n",
       "      <td>DRA12_CHUKWUDI010</td>\n",
       "      <td>11.6</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.068535</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>357.54</td>\n",
       "      <td>2005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>709.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRA12</td>\n",
       "      <td>CHUKWUDI013</td>\n",
       "      <td>DRA12_CHUKWUDI013</td>\n",
       "      <td>11.6</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.040912</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>355.79</td>\n",
       "      <td>1994</td>\n",
       "      <td>High</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>6381.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product_Identifier Supermarket_Identifier Product_Supermarket_Identifier  \\\n",
       "0              DRA12            CHUKWUDI010              DRA12_CHUKWUDI010   \n",
       "1              DRA12            CHUKWUDI013              DRA12_CHUKWUDI013   \n",
       "\n",
       "   Product_Weight Product_Fat_Content  Product_Shelf_Visibility Product_Type  \\\n",
       "0            11.6             Low Fat                  0.068535  Soft Drinks   \n",
       "1            11.6             Low Fat                  0.040912  Soft Drinks   \n",
       "\n",
       "   Product_Price  Supermarket_Opening_Year Supermarket _Size  \\\n",
       "0         357.54                      2005               NaN   \n",
       "1         355.79                      1994              High   \n",
       "\n",
       "  Supermarket_Location_Type   Supermarket_Type  Product_Supermarket_Sales  \n",
       "0                 Cluster 3      Grocery Store                     709.08  \n",
       "1                 Cluster 3  Supermarket Type1                    6381.69  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supermarket_data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Remove Id columns\n",
    "cols_2_remove = ['Product_Identifier', 'Supermarket_Identifier', 'Product_Supermarket_Identifier']\n",
    "\n",
    "newdata = supermarket_data.drop(cols_2_remove, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product_Weight</th>\n",
       "      <th>Product_Fat_Content</th>\n",
       "      <th>Product_Shelf_Visibility</th>\n",
       "      <th>Product_Type</th>\n",
       "      <th>Product_Price</th>\n",
       "      <th>Supermarket_Opening_Year</th>\n",
       "      <th>Supermarket _Size</th>\n",
       "      <th>Supermarket_Location_Type</th>\n",
       "      <th>Supermarket_Type</th>\n",
       "      <th>Product_Supermarket_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.6</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.068535</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>357.54</td>\n",
       "      <td>2005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>709.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.6</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.040912</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>355.79</td>\n",
       "      <td>1994</td>\n",
       "      <td>High</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>6381.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11.6</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.041178</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>350.79</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>6381.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.6</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.041113</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>355.04</td>\n",
       "      <td>2016</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>2127.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.6</td>\n",
       "      <td>Ultra Low fat</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>354.79</td>\n",
       "      <td>2011</td>\n",
       "      <td>Small</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>2481.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Product_Weight Product_Fat_Content  Product_Shelf_Visibility Product_Type  \\\n",
       "0            11.6             Low Fat                  0.068535  Soft Drinks   \n",
       "1            11.6             Low Fat                  0.040912  Soft Drinks   \n",
       "2            11.6             Low Fat                  0.041178  Soft Drinks   \n",
       "3            11.6             Low Fat                  0.041113  Soft Drinks   \n",
       "4            11.6       Ultra Low fat                  0.000000  Soft Drinks   \n",
       "\n",
       "   Product_Price  Supermarket_Opening_Year Supermarket _Size  \\\n",
       "0         357.54                      2005               NaN   \n",
       "1         355.79                      1994              High   \n",
       "2         350.79                      2014               NaN   \n",
       "3         355.04                      2016            Medium   \n",
       "4         354.79                      2011             Small   \n",
       "\n",
       "  Supermarket_Location_Type   Supermarket_Type  Product_Supermarket_Sales  \n",
       "0                 Cluster 3      Grocery Store                     709.08  \n",
       "1                 Cluster 3  Supermarket Type1                    6381.69  \n",
       "2                 Cluster 2  Supermarket Type1                    6381.69  \n",
       "3                 Cluster 3  Supermarket Type2                    2127.23  \n",
       "4                 Cluster 2  Supermarket Type1                    2481.77  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4990, 10)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_cols = ['Product_Fat_Content','Product_Type',\n",
    "            'Supermarket _Size', 'Supermarket_Location_Type',\n",
    "           'Supermarket_Type' ]\n",
    "\n",
    "num_cols = ['Product_Weight', 'Product_Shelf_Visibility',\n",
    "            'Product_Price', 'Supermarket_Opening_Year', 'Product_Supermarket_Sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value Count for Product_Fat_Content\n",
      "Low Fat          3039\n",
      "Normal Fat       1773\n",
      "Ultra Low fat     178\n",
      "Name: Product_Fat_Content, dtype: int64\n",
      "---------------------------\n",
      "Value Count for Product_Type\n",
      "Snack Foods              758\n",
      "Fruits and Vegetables    747\n",
      "Household                567\n",
      "Frozen Foods             457\n",
      "Canned                   376\n",
      "Dairy                    350\n",
      "Baking Goods             344\n",
      "Health and Hygiene       307\n",
      "Meat                     264\n",
      "Soft Drinks              261\n",
      "Breads                   137\n",
      "Hard Drinks              134\n",
      "Others                   100\n",
      "Starchy Foods             81\n",
      "Breakfast                 66\n",
      "Seafood                   41\n",
      "Name: Product_Type, dtype: int64\n",
      "---------------------------\n",
      "Value Count for Supermarket _Size\n",
      "Medium    1582\n",
      "Small     1364\n",
      "High       594\n",
      "Name: Supermarket _Size, dtype: int64\n",
      "---------------------------\n",
      "Value Count for Supermarket_Location_Type\n",
      "Cluster 3    1940\n",
      "Cluster 2    1581\n",
      "Cluster 1    1469\n",
      "Name: Supermarket_Location_Type, dtype: int64\n",
      "---------------------------\n",
      "Value Count for Supermarket_Type\n",
      "Supermarket Type1    3304\n",
      "Grocery Store         724\n",
      "Supermarket Type2     500\n",
      "Supermarket Type3     462\n",
      "Name: Supermarket_Type, dtype: int64\n",
      "---------------------------\n"
     ]
    }
   ],
   "source": [
    "for col in cat_cols:\n",
    "    print('Value Count for', col)\n",
    "    print(newdata[col].value_counts())\n",
    "    print(\"---------------------------\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DATA VISUALIZATION\n",
    "### BAR PLOT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Low Fat          3039\n",
       "Normal Fat       1773\n",
       "Ultra Low fat     178\n",
       "Name: Product_Fat_Content, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdata['Product_Fat_Content'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Db56l/TzgvAVVJ0m6x/hJYEnqlAEgSZ0yACSpUwaAJHXKAJCkThkAktQpA0CSOmUASFKnDABJ6pQBIEmdMgAkqVMGgCR1ygCQpE4ZAJLUKQNAkjplAEhSpwwASeqUASBJnTIAJKlTBoAkdcoAkKROGQCS1CkDQJI6ZQBIUqcMAEnqlAEgSZ0yACSpUwaAJHXKAJCkThkAktSpeQMgyelJbkhy2UjbPknOT/Ll9nfv1p4kb0uyLsklSQ4dWWZV6//lJKvumZsjSdpaW7MHcAbwrBltJwIXVNVBwAVtGuBI4KB2WQ2cAkNgACcBTwQOA07aFBqSpOmYNwCq6lPATTOajwbObNfPBI4ZaT+rBhcCeyV5MPBM4PyquqmqbgbO5+6hIkmaoMUeA3hgVX0doP19QGtfBqwf6behtW2p/W6SrE6yNsnajRs3LrI8SdJ8tvVB4MzSVnO0372x6tSqWllVK5cuXbpNi5Mk3WWxAXB9G9qh/b2htW8Alo/02x+4bo52SdKULDYA1gCbzuRZBbx/pP0l7Wygw4Fb2xDRh4FnJNm7Hfx9RmuTJE3Jkvk6JDkbeDKwX5INDGfzvAU4N8nxwLXA81v384BnA+uA7wAvA6iqm5K8Efhs6/eGqpp5YFmSNEHzBkBVHbeFWUfM0reAE7awntOB0xdUnSTpHuMngSWpUwaAJHXKAJCkThkAktQpA0CSOmUASFKnDABJ6pQBIEmdMgAkqVMGgCR1ygCQpE4ZAJLUKQNAkjplAEhSpwwASeqUASBJnTIAJKlTBoAkdcoAkKROGQCS1CkDQJI6ZQBIUqcMAEnqlAEgSZ0yACSpUwaAJHXKAJCkThkAktQpA0CSOmUASFKnxgqAJL+W5PIklyU5O8m9kxyY5KIkX07y90l2bX13a9Pr2vwV2+IGSJIWZ9EBkGQZ8CpgZVU9GtgFOBZ4K3ByVR0E3Awc3xY5Hri5qh4OnNz6SZKmZNwhoCXAfZIsAXYHvg48FXhPm38mcEy7fnSbps0/IknG3L4kaZEWHQBV9TXgT4BrGV74bwUuBm6pqjtbtw3AsnZ9GbC+LXtn67/vYrcvSRrPOENAezO8qz8QeAhwX+DIWbrWpkXmmDe63tVJ1iZZu3HjxsWWJ0maxzhDQE8DvlJVG6vq/wHvA34M2KsNCQHsD1zXrm8AlgO0+XsCN81caVWdWlUrq2rl0qVLxyhPkjSXcQLgWuDwJLu3sfwjgCuAjwM/2/qsAt7frq9p07T5H6uqu+0BSJImY5xjABcxHMz9HHBpW9epwGuB1yRZxzDGf1pb5DRg39b+GuDEMeqWJI1pyfxdtqyqTgJOmtF8NXDYLH3vAJ4/zvYkSduOnwSWpE4ZAJLUKQNAkjplAEhSpwwASeqUASBJnTIAJKlTBoAkdcoAkKROGQCS1CkDQJI6ZQBIUqcMAEnqlAEgSZ0yACSpUwaAJHXKAJCkThkAktQpA0CSOmUASFKnDABJ6pQBIEmdMgAkqVMGgCR1ygCQpE4ZAJLUKQNAkjplAEhSpwwASeqUASBJnTIAJKlTYwVAkr2SvCfJl5JcmeRJSfZJcn6SL7e/e7e+SfK2JOuSXJLk0G1zEyRJizHuHsCfAx+qqh8BfhS4EjgRuKCqDgIuaNMARwIHtctq4JQxty1JGsOiAyDJ/YGfAk4DqKrvVdUtwNHAma3bmcAx7frRwFk1uBDYK8mDF125JGks4+wBPAzYCPxNks8n+esk9wUeWFVfB2h/H9D6LwPWjyy/obVtJsnqJGuTrN24ceMY5UmS5rJkzGUPBV5ZVRcl+XPuGu6ZTWZpq7s1VJ0KnAqwcuXKu82XtHN6zJmPmXYJ95hLV1067RJmNc4ewAZgQ1Vd1KbfwxAI128a2ml/bxjpv3xk+f2B68bYviRpDIsOgKr6BrA+ySNb0xHAFcAaYFVrWwW8v11fA7yknQ10OHDrpqEiSdLkjTMEBPBK4F1JdgWuBl7GECrnJjkeuBZ4fut7HvBsYB3wndZXkjQlYwVAVX0BWDnLrCNm6VvACeNsT5K07fhJYEnqlAEgSZ0yACSpUwaAJHXKAJCkThkAktQpA0CSOmUASFKnDABJ6pQBIEmdMgAkqVMGgCR1ygCQpE4ZAJLUKQNAkjplAEhSpwwASeqUASBJnTIAJKlTBoAkdcoAkKROGQCS1CkDQJI6ZQBIUqcMAEnqlAEgSZ0yACSpUwaAJHXKAJCkThkAktSpsQMgyS5JPp/kX9r0gUkuSvLlJH+fZNfWvlubXtfmrxh325KkxdsWewC/Clw5Mv1W4OSqOgi4GTi+tR8P3FxVDwdObv0kSVMyVgAk2R94DvDXbTrAU4H3tC5nAse060e3adr8I1p/SdIUjLsH8GfAbwE/aNP7ArdU1Z1tegOwrF1fBqwHaPNvbf03k2R1krVJ1m7cuHHM8iRJW7LoAEjyXOCGqrp4tHmWrrUV8+5qqDq1qlZW1cqlS5cutjxJ0jyWjLHsjwNHJXk2cG/g/gx7BHslWdLe5e8PXNf6bwCWAxuSLAH2BG4aY/uSpDEseg+gql5XVftX1QrgWOBjVfUi4OPAz7Zuq4D3t+tr2jRt/seq6m57AJKkybgnPgfwWuA1SdYxjPGf1tpPA/Zt7a8BTrwHti1J2krjDAH9t6r6BPCJdv1q4LBZ+twBPH9bbE+SND4/CSxJnTIAJKlTBoAkdcoAkKROGQCS1CkDQJI6ZQBIUqcMAEnqlAEgSZ0yACSpUwaAJHXKAJCkThkAktQpA0CSOmUASFKnDABJ6pQBIEmdMgAkqVMGgCR1ygCQpE4ZAJLUKQNAkjplAEhSpwwASeqUASBJnTIAJKlTBoAkdcoAkKROGQCS1CkDQJI6tegASLI8yceTXJnk8iS/2tr3SXJ+ki+3v3u39iR5W5J1SS5Jcui2uhGSpIUbZw/gTuDXq+pRwOHACUkOBk4ELqiqg4AL2jTAkcBB7bIaOGWMbUuSxrToAKiqr1fV59r1bwNXAsuAo4EzW7czgWPa9aOBs2pwIbBXkgcvunJJ0li2yTGAJCuAxwMXAQ+sqq/DEBLAA1q3ZcD6kcU2tDZJ0hSMHQBJ9gDeC7y6qr41V9dZ2mqW9a1OsjbJ2o0bN45bniRpC8YKgCQ/xPDi/66qel9rvn7T0E77e0Nr3wAsH1l8f+C6meusqlOramVVrVy6dOk45UmS5jDOWUABTgOurKo/HZm1BljVrq8C3j/S/pJ2NtDhwK2bhookSZO3ZIxlfxz4n8ClSb7Q2n4beAtwbpLjgWuB57d55wHPBtYB3wFeNsa2JUljWnQAVNW/Mvu4PsARs/Qv4ITFbk+StG35SWBJ6pQBIEmdMgAkqVMGgCR1ygCQpE4ZAJLUKQNAkjplAEhSpwwASeqUASBJnTIAJKlTBoAkdcoAkKROGQCS1CkDQJI6ZQBIUqcMAEnqlAEgSZ0yACSpUwaAJHXKAJCkThkAktQpA0CSOmUASFKnDABJ6pQBIEmdMgAkqVMGgCR1ygCQpE4ZAJLUqYkHQJJnJbkqybokJ056+5KkwUQDIMkuwNuBI4GDgeOSHDzJGiRJg0nvARwGrKuqq6vqe8A5wNETrkGSxOQDYBmwfmR6Q2uTJE3YkglvL7O01WYdktXA6jZ5W5Kr7vGqpmc/4MZJbSxvndSWujHRx4/fn+3fR4s02f+9l078sXvo1nSadABsAJaPTO8PXDfaoapOBU6dZFHTkmRtVa2cdh1aHB+/HZeP3WDSQ0CfBQ5KcmCSXYFjgTUTrkGSxIT3AKrqziSvAD4M7AKcXlWXT7IGSdJg0kNAVNV5wHmT3u52qouhrp2Yj9+Oy8cOSFXN30uStNPxqyAkqVMGgCR1ygCQpE5N/CCwIMneDJ+H+O/7v6o+N72KpD4kWcbwIanR/71PTa+i6TIAJizJG4GXAv/JXZ+CLuCp06pJc0tyKTM+sb5pFlBV9dgJl6RFSPJW4AXAFcD3W3MB3QaAZwFNWPtqi8e0L8PTDiDJnB+rr6qvTqoWLV7733tsVX132rVsL9wDmLzLgL2AG6ZdiLaOL/A7jauBHwIMgMY9gAlLshJ4P0MQ/PcTsaqOmlpRmlOSbzP3END9J1ySFiDJXzA8fsuAHwUuYPP/vVdNqbSpcw9g8s4E3gpcCvxgyrVoK1TV/aZdg8aytv29GL97bDPuAUxYkk9W1U9Puw4tXpIHAPfeNF1V106xHGnRDIAJS/KnDLufa9h8N9TTQLdzSY4C/jfwEIZjOA8FrqyqQ6ZamLbKFs7mupVhD+FNVfXNyVc1XQ4BTd7j29/DR9o8DXTH8EaGx+2jVfX4JE8BjptyTdp6H2Q4/fPdbfpYhuM4twJnAM+bTlnT4x6AtJU2/YhIki8Cj6+qHyT5TFUdNu3aNL8k/1ZVPz5bW5JLq+ox06ptWtwDmLAkvzdbe1W9YdK1aMFuSbIHwweH3pXkBuDOKdekrbdHkidW1UUASQ4D9mjzunwcDYDJu33k+r2B5wJXTqkWLczRwB3ArwEvAvYEDO4dxy8Ap7cQD/At4BeS3Bf4w6lWNiUOAU1Zkt2ANVX1zGnXoq2T5P5s/l0yN02xHC1Qkj0ZXvtumXYt0+YewPTtDjxs2kVofkl+ieEd/38xfIYjDAfwffy2Y0leXFV/l+Q1M9oBqKo/nUph2wEDYMJmnIq2C7AUhxF2FL8BHFJVN067EC3IfdtfP9A3g0NAEzbji8XuBK6vqi4PQO1oknwI+B9V9Z1p1yJtCwbAhCS5f1V9K8k+s813HHn7l+TxwN8AF+F3yewwkrxtrvk9P34OAU3OuxnO+LmYYQgoI/McR94x/BXwMfwepx3NxSPXfx84aVqFbG/cA5C2UpJPV9WPTbsOLV6Sz1fV4+fv2Qf3ACYkyaFzzfe7gHYIH0+yGvhnNh8Ccvhux+E73hHuAUxIko+3q/cGVgJfZBgGeixwUVX9xLRq09ZJ8pV2dbN/mqpy+G4HkeRzVTXnm7GeuAcwIVX1FIAk5wCrq+rSNv1ohtMLtZ1K8gRgfVUd2KZXAT8DXAO8fnqVaWvM+EGf3ZN8a9MsOv9BH/cAJizJF6rqcfO1afuR5HPA06rqpiQ/BZwDvBJ4HPCoqvrZqRYoLZJ7AJN3ZZK/Bv6O4V3Ji/G7gLZ3u4yM878AOLWq3gu8N8kXpliXNJZ7TbuADr0MuBz4VeDVwBWtTduvXZJserN0BMOpoJv4Jko7LJ+8E1ZVdyR5B3BeVV017Xq0Vc4GPpnkRobvAfq/AEkezvBjItIOyWMAE9Z+VvCPgV2r6sAkjwPeUFVHTbk0zSHJ4cCDgY9U1e2t7RHAHp7Cqx2VATBhSS5m+PnHT2z6QEqSS6rqsdOtTFJvPAYweXdWlcMGkqbOYwCTd1mSFzIcWDwIeBXw6SnXJKlD7gFM3iuBQxi+SuBshp+le/VUK5LUJY8BSFKnHAKakCRr5prvWUCSJs0AmJwnAesZhn0uYvPfA5CkiXMIaEKS7AI8HTiO4RtAPwCcXVWXT7UwSd3yIPCEVNX3q+pDVbUKOBxYB3wiySunXJqkTjkENEFJdgOew7AXsAJ4G/C+adYkqV8OAU1IkjOBRwMfBM6pqsumXJKkzhkAE5LkB8DtbXL0Tu/+RykkTYcBIEmd8iCwJHXKAJCkThkAktQpA0CSOmUASFKn/j9wTco+dIsEtgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in cat_cols:\n",
    "    fig = plt.figure(figsize=(6,6)) # define plot area\n",
    "    ax = fig.gca() # define axis  \n",
    "    \n",
    "    counts = newdata[col].value_counts() # find the counts for each unique category\n",
    "    counts.plot.bar(ax = ax) # Use the plot.bar method on the counts data frame\n",
    "    ax.set_title('Bar plot for ' + col)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scatter plot for Numerical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>Product_Weight</th>\n",
       "      <th>Product_Fat_Content</th>\n",
       "      <th>Product_Shelf_Visibility</th>\n",
       "      <th>Product_Type</th>\n",
       "      <th>Product_Price</th>\n",
       "      <th>Supermarket_Opening_Year</th>\n",
       "      <th>Supermarket _Size</th>\n",
       "      <th>Supermarket_Location_Type</th>\n",
       "      <th>Supermarket_Type</th>\n",
       "      <th>Product_Supermarket_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.6</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.068535</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>357.54</td>\n",
       "      <td>2005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>709.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.6</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.040912</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>355.79</td>\n",
       "      <td>1994</td>\n",
       "      <td>High</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>6381.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11.6</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.041178</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>350.79</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>6381.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Product_Weight Product_Fat_Content  Product_Shelf_Visibility Product_Type  \\\n",
       "0            11.6             Low Fat                  0.068535  Soft Drinks   \n",
       "1            11.6             Low Fat                  0.040912  Soft Drinks   \n",
       "2            11.6             Low Fat                  0.041178  Soft Drinks   \n",
       "\n",
       "   Product_Price  Supermarket_Opening_Year Supermarket _Size  \\\n",
       "0         357.54                      2005               NaN   \n",
       "1         355.79                      1994              High   \n",
       "2         350.79                      2014               NaN   \n",
       "\n",
       "  Supermarket_Location_Type   Supermarket_Type  Product_Supermarket_Sales  \n",
       "0                 Cluster 3      Grocery Store                     709.08  \n",
       "1                 Cluster 3  Supermarket Type1                    6381.69  \n",
       "2                 Cluster 2  Supermarket Type1                    6381.69  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in num_cols:\n",
    "    fig = plt.figure(figsize=(6,6)) # define plot area\n",
    "    ax = fig.gca() # define axis  \n",
    "\n",
    "    newdata.plot.scatter(x = col, y = 'Product_Supermarket_Sales', ax = ax)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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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dwH8A/5DO/zjwOWB1eny2t9Me+CTwvbTPn3AnvdWg/qt2rl69ukKR2Ei1trayZs0aWltbKx1KxRUb/fUKSW8mG/k1obcuIi4rtk9E/I7BZzM+bIDtg0H6byLiUrIp+PvXtwNvLhaHmVk5dXV1sWzZMiKCZcuW0dLSwuTJkysdVsXkmablfOCi9FgAfBXwmMcqM23atD7l3XbbrUKRmDWW1tbWV2aw2LJlS8O3VvK0VN4P7A3cHhEnStqV7JKTjcDixYvp6Ogo+XH734X98MMPl3Shrjlz5njhL7MBtLW10d3dDUB3dzcrVqzgrLPOqnBUlZOno/6FNLS4R9JOZKO18vSp2CjaZZddipates2cObNo2arbwoULaWpqAqCpqelVMyQ0mjwtlXZJO5N1nt8KPAvcUtao6li5vu13dXVx/PHHExGMHz+e733vew19XbeWnHnmmZx99tmvlBv5W24tamlp4ZprrgGy2cFbWlqG2KO+5Rn99Q8R8WREfJdstFZLRJxY/tBsOJqbm5k0Kbt96KijjnJCqSFXXnlln/JPf/rTCkViI9Hc3MzUqVOBbIbpRv+/l3f0117A7N7tJc0puJnRqsTUqVN58cUXG/6bUq3xqp21rauri4ceegiAhx56iMcee6yhE0ue0V+9w3mPB45Jj6PLHJeNQFNTE3Pnzm3of9Bmo23JkiVs2bIFyEZ/LVmypMIRVVaelsqBETGv7JGYmdWga6+9tk+5ra2N8847r0LRVF6e0V83pnVQzMysn95WymDlRpOnpdJKllgeBl4iu0s+ImKvskZmZlYDJPVZvrvRF6DNk1QuJZvDay3Q2CnYrAzGjh3L5s2b+5StdixcuJDly5e/UvZ9KkN7ICK8apAZ5ZkRYaDLJ54NoXaccsopfZLKKaecUsFoKi9PUvm9pB8BvyC7/AWAhxSblcakSZP6TJnee7+R1Y7eS2CNfukL8iWViWTJpLBNF4CTijWccnzj7+rq4n3vex8A48aN82wINaa1tZWxY8fS09PD2LFjaW1tbehZEYqO/pI0FlgTESf2e3xilOIzq3vNzc2vJJGjjz7aCaXGtLW10dPTA0BPT0/DLwc91Br1m/E092ZlN3XqVHbYYQfPhlCDFi5cyLhx2UWfcePGNXxHfZ77VG6Q9E1JB0nat/dR9sjMGohnQ6hdLS0tr4ze27x5c8N/McjTp/L29POzBXUBHFr6cMzMak/vfSqF96s0qjyzFC8Y4OGEYmZGNsy8WLnR5JlQcldJl0halsrzJJ1U/tDMzKrfqlWr+pRXrlxZmUCqRJ4+lR8Ay4HeRc//CHyqXAGZmVntypNUmiPiCtIULRHRA2wuvks2Zb6kRyXdVVB3gaSHJN2RHkcVvHaupA5Jf5C0qKD+iFTXIemcgvrdJd0saZ2kn0gan/M9m5lZmeRJKs9JmkzWOY+kA4Gncuz3A+CIAeq/ERH7pMc16ZjzgA8Bb0r7fFvS2HSfzLeAI4F5wIcLZkz+SjrWXOAJwJfkzMwqLE9SOQtYCuwh6X+Ay4DTh9opIq4HHs8Zx7HA5RHxUkTcD3QA+6dHR0TcFxEvA5cDxyqbC+FQ4L/S/q3AcTnPZWZWMttvv33RcqMZckhxRNwm6WDgDWTT3v8hIrq34ZynSToBaAc+ExFPANOBmwq26Ux1AA/2qz8AmAw8mS7F9d/ezGzUeD2VvvKM/poAnAF8DrgQODXVjcR3gD2AfYCNwNd7TzPAtjGC+gFJOllSu6T2TZs2DS9iM7MiFi1a1Kd8xBEDXfVvHHlufrwMeAa4KJU/DPwn8IHhniwiHul9Luk/gKtTsROYWbDpDGBDej5QfRews6RxqbVSuP1A570YuBhg/vz5vjvJrAGVY9kCgO7uvhdu1q1b19BLF+RJKm+IiL0Lyisl3TmSk0maFhEbU/G9QO/IsKXAjyT9K9nQ5bnALWQtkrmSdgceIuvM/0hEhKSVwPvJ+llagKtGEpOZ2bZoampi3Lhx9PT0MHnyZJqamiodUkXlSSq3SzowIm4CkHQA8D9D7STpx8AhQLOkTuB84BBJ+5BdqloPnAIQEXdLugK4B+gBTk2TWSLpNLL7ZMYCl0bE3ekU/whcLunzwO3AJbnesZk1pHJ+2//kJz/J+vXrvWwB+ZLKAcAJkh5I5VnAvZLWUmSt+oj48ADVg/7hj4gvAF8YoP4a4JoB6u8jGx1mZlZRnhB0qzxJpbF7nczMLLc8SWXAju2IeGCgejMza1x5ksov2TqMdwKwO/AHsrvfzczMXpHn5se/KiynBbpOKVtEZmZWs/JM09JHRNwGvLUMsZiZWY0bsqUi6ayC4hhgX8C3pZuZ2avk6VN5TcHzHrI+livLE46ZmdWyPH0qF/Y+l7QL2USOnurEzMxeZdA+FUn/LOkv0/PtJP0a+BPwiKR3j1aAZmZWO4p11H+QbOgwZHNrjQGmAAcDXyxzXGZmVoOKJZWXCy5zLQJ+HBGbI+Je8vXFmJlZgymWVF6S9GZJU4AFwIqC1xp7aTMzMxtQsRbHmWTL9U4hWwv+fgBJR5HNCmxmZtbHoEklIm4G/nKA+gFnDTYzMxv2HfVmZmaDcVIxM7OSGTKpSNouT52ZmVmelsqNOevMzKzBDdpRL2kqMB2YKOktZOupAOyEhxSbmdkAig0pXgR8HJgBfJ2tSeVp4LzyhmVmZrWo2JDiVqBV0vER4VmJzcxsSHn6VPaTtHNvQdIukj4/1E6SLpX0qKS7CuomSWqTtC793CXVS9JiSR2S1qTVJXv3aUnbr5PUUlC/n6S1aZ/FkoSZmVVUnqRyZEQ82VuIiCeAo3Ls9wPgiH515wDXRcRc4LpUBjgSmJseJwPfgSwJAecDBwD7A+f3JqK0zckF+/U/l5mZjbI8SWVs4RBiSROBIYcUR8T1wOP9qo8FWtPzVuC4gvrLInMTsLOkaWT9Om0R8XhKZm3AEem1nSLixjTp5WUFxzIzswrJM9vw/wOuk/R9IIBPsDUxDNeuEbERICI2Snpdqp8OPFiwXWeqK1bfOUC9mZlVUJ6VH78qaS1wGNkIsM9FxPISxzFQf0iMoH7gg0snk10qY9asWSOJz2rI4sWL6ejoqHQYw7Ju3ToAzjjjjApHMjxz5sypuZitvHKtixIRy4BlJTjfI5KmpVbKNODRVN8JzCzYbgawIdUf0q9+VaqfMcD2A4qIi4GLAebPn++lkOtcR0cHf7zrNmbtuLnSoeQ2vju7Ev3i+tUVjiS/B54dW+kQrAoNmVQkPcPWVsB4oAl4LiJ2GsH5lpKtIvnl9POqgvrTJF1O1in/VEo8y4EvFnTOHw6cGxGPS3pG0oHAzcAJwEUjiMfq1KwdN/NP85+tdBh17fPtO1Y6BKtCeS5/vaawLOk4spFYRUn6MVkro1lSJ9kori8DV0g6CXgA+EDa/BqyEWUdwPPAiencj0v6HND79e2zEdHb+f9JshFmE8laUaVoSZmZ2TYY9rLAEfFzSefk2O7Dg7x02ADbBnDqIMe5FLh0gPp24M1DxWFmZqMnz+Wv9xUUxwDzKdIpbmZmjStPS+WYguc9wHqy+0rMzMz6yNOncuJoBGJmZrWv2NT3F1HkMldE1PXgdN/rMDp8n4NZfSnWUmlPP98BzAN+ksofAG4tZ1DVoKOjg9vX3sOW7SdVOpTc9HL2HeDWPz1c4UjyGfN8/1l8zKzWDTX1PZI+DiyIiO5U/i6wYlSiq7At20/ixXlHVzqMujXhnqsrHYKZlVieCSV3AwrvVdkx1ZmZmfWRZ/TXl4HbJa1M5YOBC8oWkZmZ1aw8o7++L2kZ2fQpAZwTEbVx0d7MzEZV3jvq9wcOSs8D+EV5wjEzs1o2ZJ+KpC8DZwL3pMcZkr5U7sDMzKz25GmpHAXsExFbACS1ArcD55YzMDNrTL5HbHSU6x6xvJe/dmbr0sCvLXkUZiXU2dnJc8+M9dTsZfbnZ8ayQ2fn0BsOU0dHB7fffXv2V6dWbMl+3P7Q7ZWNI68ny3foPEnlS2wd/SXgXbiVYmbltDNsOWRLpaOoW2NW5bmbZGSKJhVJAn4HHAi8lSyp/KNHf1k1mzFjBi/2bPQiXWX2+fYdmTBjxtAbWkMpmlQiIiT9PCL2I1ud0czMbFB52kA3SXpr2SMxM7Oal6dPZQHw95LWA8+RXQKLiNirnIGZmVntyZNUjix7FGZmVheKracyAfh7YA6wFrgkInpGKzAzM6s9xfpUWsnWo19L1lr5+qhEZGZmNatYUpkXEX8bEUuA97N17q9tJmm9pLWS7pDUnuomSWqTtC793CXVS9JiSR2S1kjat+A4LWn7dZJaShWfmZmNTLGk0t37pEyXvRZExD4RMT+VzwGui4i5wHWpDFkraW56nAx8B7IkBJxPNnvy/sD5vYnIzMwqo1hS2VvS0+nxDLBX73NJT5chlmPJLrmRfh5XUH9ZZG4CdpY0DVgEtEXE4xHxBNAGHFGGuMzMLKdiywmPLeN5A1ghKYAlEXExsGtEbEzn3ijpdWnb6cCDBft2prrB6s3MrELyTihZau+IiA0pcbRJ+n2RbTVAXRSpf/UBpJPJLp0xa9as4cZqNeiBZ2trQslHns8uGuy6fe3Md/XAs2PZs9JBWNWpSFKJiA3p56OSfkbWJ/KIpGmplTINeDRt3gnMLNh9BrAh1R/Sr37VIOe7GLgYYP78+QMmHqsfc+bMqXQIw/Zymjp9wuy5FY4kvz2pzd+1ldeoJxVJOwBjIuKZ9Pxw4LNkc4u1AF9OP69KuywFTpN0OVmn/FMp8SwHvljQOX84nj3ZqK01LXr1xrx48eIKR2K2bSrRUtkV+Fk2ATLjgB9FxK8krQaukHQS8ADwgbT9NWQLhXUAzwMnAkTE45I+B6xO2302Ih6nRDo7Oxnz/FNMuOfqUh3S+hnz/GN0dvp+Wuurs7MTnirv9OwN70nojNKvhQMVSCoRcR+w9wD1jwGHDVAfwKmDHOtS4NJSx2hmZiNTqY76qjdjxgweeWkcL847utKh1K0J91zNjBlTKx2GVZkZM2awSZu8SFcZjVk1hhnTy7MWjtuXZmZWMk4qZmZWMk4qZmZWMk4qZmZWMk4qZmZWMk4qZmZWMk4qZmZWMk4qZmZWMk4qZmZWMk4qZmZWMk4qZmZWMk4qZmZWMp5Q0syqz5M1NvX9s+lnrSw2+iRlW3zdScXMqkotria5Lq3cOXd6jazcOb18v2cnlSLGPP94TS3SpRefBiAm7FThSPIZ8/zjgKe+t768cmdtc1IZRG1+W3oGgLl71Mof6qk1+Xs2s8E5qQzC35bMzIavhnrCzMys2jmpmJlZyTipmJlZybhPxWwYFi9eTEdHR8mP2zsktRx9eXPmzKnJPkKrTTXfUpF0hKQ/SOqQdE6l4zEbiYkTJzJx4sRKh2G2zWq6pSJpLPAtYCHQCayWtDQi7qlsZFav/I2/dpWrlQluaRaq6aQC7A90RMR9AJIuB44Fqjap+B+2Wf1xK3OrWk8q04EHC8qdwAEViqXi/A/bbHD+UjQ6aj2paIC6eNVG0snAyQCzZs0qd0xF+R+2mdWzWu+o7wRmFpRnABv6bxQRF0fE/IiYP2XKlFELzsys0dR6UlkNzJW0u6TxwIeApRWOycysYdX05a+I6JF0GrAcGAtcGhF3VzgsM7OGVdNJBSAirgGuqXQcZmZW+5e/zMysijipmJlZyTipmJlZyTipmJlZySjiVfcK1jVJm4A/VzqOMmoGuiodhI2IP7vaVu+f319ExJA3+jVcUql3ktojYn6l47Dh82dX2/z5ZXz5y8zMSsZJxczMSsZJpf5cXOkAbMT82dU2f364T8XMzErILRUzMysZJ5UKkTRV0uWS/iTpHknXSNpT0mxJd43wmB+XtNs2xnWspDWS7pDULumd23K8elXFn99H0+e3RtINkvbeluPVoyr+7P5S0o2SXpJ09rYcq5KcVCpAkoCfAasiYo+ImAecB+y6jYf+ODCsf9iS+k8qeh2wd0TsA3wC+N42xlR3qvzzux84OCL2Aj6Hr/P3UeWf3ePAGcC/bGMsFeWkUhkLgO6I+G5vRUTcERG/Ldwoffv5ZkH5akmHSBor6QeS7pK0VtKnJb0fmA/8MLUyJkraT9JvJN04CjiXAAAHlElEQVQqabmkaek4qyR9UdJvgDMLzxkRz8bWjrYdGGAlTavqz++GiHgiFW8iW7jOtqrmz+7RiFgNdJfx/ZddzU99X6PeDNy6DfvvA0yPiDcDSNo5Ip5Ma8ucHRHtkpqAi4BjI2KTpA8CXyBrfQDsHBEHD3RwSe8FvgS8DvjrbYizXlX151fgJGDZNsRZj2rls6tZTiq16T7g9ZIuAn4JrBhgmzeQ/Qdqy1r8jAU2Frz+k8EOHhE/A34m6V1kl1DeXaK4LVPWzw9A0gKypOI+sdIq+2dX65xUKuNu4P05tuuh7yXKCQAR8UTqgF0EnAr8DVu/BfUScHdEvG2QYz831Mkj4npJe0hqjoh6ntNouKr685O0F1lf2JER8ViOOBtJVX929cB9KpXxa2A7SX/XWyHprZL6N4nXA/tIGiNpJrB/2rYZGBMRVwL/F9g3bf8M8Jr0/A/AFElvS/s0SXrTUIFJmpM6M5G0LzAe8B+mvqr585sF/DfwsYj440jfYB2r2s+uXrilUgEREanf4t8knQO8SPaP+FP9Nv0fstE8a4G7gNtS/XTg+5J6vxScm37+APiupBeAt5F9I1ss6bVkn/W/kX1TK+Z44ARJ3cALwAcLOu6Nqv/8/hmYDHw7fTfo8SSHW1XzZydpKtAO7ARskfQpYF5EPD2yd1sZvqPezMxKxpe/zMysZJxUzMysZJxUzMysZJxUzMysZJxUzMysZJxUzMysZJxUrCIk/R9Jd2vrNPsHVDqmvDTMKdLT9h8ZYptDJF297dENfX5J8yUtLtGxJ6fP7w5JD0t6qKA8vhTnsNrimx9t1KU7jY8G9o2Il9JdymX5A5RmB1BEbCnR8caOYLfZwEeAH5Uihm09f0S0k91kt83SNDD7AEi6AHg2Imp66nbbNm6pWCVMA7oi4iWAiOiKiA2S1qcE0/ttelV6foGk/5T0a0nr+k2x8b8krU4tngtT3WxJ90r6Ntmd0DMlPSvpK8qmIr9W0v7KpiG/T9J7Cvb7raTb0uPtqf4QSSsl/YjsDmsKzv96SbenqT7GSvpaQTynpM2+DByUvr1/eji/KEmHpeOvlXSppO1S/VuVLcJ1p6RbJL1msPj7n7+wVSRpkqSfp3hvUjZvWO/v/NKC39EZw4k7HeNLkk4tKH9F0j9Ienf6ff5c2SJZ30rJH0lHKluo6jZJP5G0w3DPaxUWEX74MaoPYEfgDuCPwLfJFpWCbLqM5vR8PtlCSgAXAHcCE4Fm4EGyBZEOJ1uESmRfkK4G3kX2zXwLcGDBOYNsgkXIFmlaATQBewN3pPrtgQnp+VygPT0/hGwSwN1TeTbZ1B1vAG4H9kn1JwP/lJ5vR9Ya2D3tf/UQv5NXbUM2ieGDwJ6pfBnZdCLjyWbLfWuq34nsqkOx+K8e6FxkU7Sfn54fWvC7uAC4Ib2PZrL535qGeA8XkE3/3lveA1idno9NMe9CNuv18+n3OJZsPq7jyJZa+A2wfdrn/wDnVfrfqx/De/jyl426iHhW0n7AQWSLJv0kzcNUzFUR8QLwgqSVZBP8vZMssdyettmR7I/pA8CfI+Kmgv1fBn6Vnq8FXoqIbklryf64QZZkvilpH2AzsGfB/rdExP0F5SnAVcDxEdE7p9PhwF7KFm0CeG2K5+Uh3ttg3gDcH1snhmwlmxn3OmBjZAs6EWluqPStfrD4B/NOsvneiIhfpz6S16bXfhlZa/IlSY+SrY7YmTf4iPiTpGck/RXwF2S/wydSo+SmiFif4r6crVP0zwNuSNuMB36X93xWHZxUrCIiYjOwCliV/rC30He68Qn9dxmgLOBLEbGk8AVJs3n19OLdkb7+krViei+9bdHWZV0/DTxC1noZQzbZYK/+x3uKrBXxDrZOFCjg9IhY3i+eQxgZFakfaNK+YvEP5xy9x36poG4zI/t7cQnZUruzgcLPabDP81cR8bERnMeqhPtUbNRJeoOkuQVV+wB/Jrv8tV+qO77fbsdKmiBpMtnlm9XAcuATknZMx50u6XXbENpryVoAW4CPkV2aGczLZJdsTtDWkVXLgU8qW/kPSXum1kPhtOjD8XtgtqQ5qfwxsstDvwd2k/TWdJ7XpMQ4WPzFzn898NF0nEPI+rpKOSvulcAxZJ/xtQX1B0qalQY+/A1Zi+QG4GBJr0/x7NDv34nVALdUrBJ2BC6StDNZ66SDrD/ijcAlks4Dbu63zy1kK+3NAj4XERuADZLeCNyYLpc8C/wt2bfqkfg2cKWkDwArGWIxpYh4TtLRZCv8PUe2MNZs4LbU8byJLPGsAXok3Qn8ICK+McghD5NUeHnpA8CJwE9T0lgNfDciXla2RO1FkiaSLVHw7iLx9zk/Wy8XQtYP8n1Ja8j6OVqKvefhiogXJV0PPBx9R+DdAHwdeBNZi3VpRISkk8guh/aOBjwPWFfKmKy8PPW9VT15qGrNUrbuyB3AcRFxX6p7N3BaRBxX0eCsLHz5y8zKInXQ/4msn+S+Ssdjo8MtFbNRJGkR8JV+1fdHxHsrEc9wpP6s6wZ46bDIboI0c1IxM7PS8eUvMzMrGScVMzMrGScVMzMrGScVMzMrGScVMzMrmf8P3jKooKDlxYUAAAAASUVORK5CYII=\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 col in cat_cols:\n",
    "    sns.boxplot(col, 'Product_Supermarket_Sales', data=newdata)\n",
    "    plt.xlabel(col) # Set text for the x axis\n",
    "    plt.ylabel('Product Supermarket Sales')# Set text for y axis\n",
    "    plt.show()\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FEATURE ENGINEERING\n",
    "\n",
    "Transform categorical features into numerical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#save the target value\n",
    "y_target = newdata['Product_Supermarket_Sales']\n",
    "newdata.drop(['Product_Supermarket_Sales'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>Product_Weight</th>\n",
       "      <th>Product_Fat_Content</th>\n",
       "      <th>Product_Shelf_Visibility</th>\n",
       "      <th>Product_Type</th>\n",
       "      <th>Product_Price</th>\n",
       "      <th>Supermarket_Opening_Year</th>\n",
       "      <th>Supermarket _Size</th>\n",
       "      <th>Supermarket_Location_Type</th>\n",
       "      <th>Supermarket_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.6</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.068535</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>357.54</td>\n",
       "      <td>2005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.6</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.040912</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>355.79</td>\n",
       "      <td>1994</td>\n",
       "      <td>High</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Product_Weight Product_Fat_Content  Product_Shelf_Visibility Product_Type  \\\n",
       "0            11.6             Low Fat                  0.068535  Soft Drinks   \n",
       "1            11.6             Low Fat                  0.040912  Soft Drinks   \n",
       "\n",
       "   Product_Price  Supermarket_Opening_Year Supermarket _Size  \\\n",
       "0         357.54                      2005               NaN   \n",
       "1         355.79                      1994              High   \n",
       "\n",
       "  Supermarket_Location_Type   Supermarket_Type  \n",
       "0                 Cluster 3      Grocery Store  \n",
       "1                 Cluster 3  Supermarket Type1  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdata.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Option 1: You can use the pandas get_dummies function when working smaller categories__"
   ]
  },
  {
   "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>Product_Weight</th>\n",
       "      <th>Product_Shelf_Visibility</th>\n",
       "      <th>Product_Price</th>\n",
       "      <th>Supermarket_Opening_Year</th>\n",
       "      <th>Product_Fat_Content_Low Fat</th>\n",
       "      <th>Product_Fat_Content_Normal Fat</th>\n",
       "      <th>Product_Fat_Content_Ultra Low fat</th>\n",
       "      <th>Product_Type_Baking Goods</th>\n",
       "      <th>Product_Type_Breads</th>\n",
       "      <th>Product_Type_Breakfast</th>\n",
       "      <th>...</th>\n",
       "      <th>Supermarket _Size_High</th>\n",
       "      <th>Supermarket _Size_Medium</th>\n",
       "      <th>Supermarket _Size_Small</th>\n",
       "      <th>Supermarket_Location_Type_Cluster 1</th>\n",
       "      <th>Supermarket_Location_Type_Cluster 2</th>\n",
       "      <th>Supermarket_Location_Type_Cluster 3</th>\n",
       "      <th>Supermarket_Type_Grocery Store</th>\n",
       "      <th>Supermarket_Type_Supermarket Type1</th>\n",
       "      <th>Supermarket_Type_Supermarket Type2</th>\n",
       "      <th>Supermarket_Type_Supermarket Type3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.6</td>\n",
       "      <td>0.068535</td>\n",
       "      <td>357.54</td>\n",
       "      <td>2005</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.6</td>\n",
       "      <td>0.040912</td>\n",
       "      <td>355.79</td>\n",
       "      <td>1994</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11.6</td>\n",
       "      <td>0.041178</td>\n",
       "      <td>350.79</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.6</td>\n",
       "      <td>0.041113</td>\n",
       "      <td>355.04</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>354.79</td>\n",
       "      <td>2011</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Product_Weight  Product_Shelf_Visibility  Product_Price  \\\n",
       "0            11.6                  0.068535         357.54   \n",
       "1            11.6                  0.040912         355.79   \n",
       "2            11.6                  0.041178         350.79   \n",
       "3            11.6                  0.041113         355.04   \n",
       "4            11.6                  0.000000         354.79   \n",
       "\n",
       "   Supermarket_Opening_Year  Product_Fat_Content_Low Fat  \\\n",
       "0                      2005                            1   \n",
       "1                      1994                            1   \n",
       "2                      2014                            1   \n",
       "3                      2016                            1   \n",
       "4                      2011                            0   \n",
       "\n",
       "   Product_Fat_Content_Normal Fat  Product_Fat_Content_Ultra Low fat  \\\n",
       "0                               0                                  0   \n",
       "1                               0                                  0   \n",
       "2                               0                                  0   \n",
       "3                               0                                  0   \n",
       "4                               0                                  1   \n",
       "\n",
       "   Product_Type_Baking Goods  Product_Type_Breads  Product_Type_Breakfast  \\\n",
       "0                          0                    0                       0   \n",
       "1                          0                    0                       0   \n",
       "2                          0                    0                       0   \n",
       "3                          0                    0                       0   \n",
       "4                          0                    0                       0   \n",
       "\n",
       "                  ...                  Supermarket _Size_High  \\\n",
       "0                 ...                                       0   \n",
       "1                 ...                                       1   \n",
       "2                 ...                                       0   \n",
       "3                 ...                                       0   \n",
       "4                 ...                                       0   \n",
       "\n",
       "   Supermarket _Size_Medium  Supermarket _Size_Small  \\\n",
       "0                         0                        0   \n",
       "1                         0                        0   \n",
       "2                         0                        0   \n",
       "3                         1                        0   \n",
       "4                         0                        1   \n",
       "\n",
       "   Supermarket_Location_Type_Cluster 1  Supermarket_Location_Type_Cluster 2  \\\n",
       "0                                    0                                    0   \n",
       "1                                    0                                    0   \n",
       "2                                    0                                    1   \n",
       "3                                    0                                    0   \n",
       "4                                    0                                    1   \n",
       "\n",
       "   Supermarket_Location_Type_Cluster 3  Supermarket_Type_Grocery Store  \\\n",
       "0                                    1                               1   \n",
       "1                                    1                               0   \n",
       "2                                    0                               0   \n",
       "3                                    1                               0   \n",
       "4                                    0                               0   \n",
       "\n",
       "   Supermarket_Type_Supermarket Type1  Supermarket_Type_Supermarket Type2  \\\n",
       "0                                   0                                   0   \n",
       "1                                   1                                   0   \n",
       "2                                   1                                   0   \n",
       "3                                   0                                   1   \n",
       "4                                   1                                   0   \n",
       "\n",
       "   Supermarket_Type_Supermarket Type3  \n",
       "0                                   0  \n",
       "1                                   0  \n",
       "2                                   0  \n",
       "3                                   0  \n",
       "4                                   0  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummified_data = pd.get_dummies(newdata)\n",
    "dummified_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "for cat in cat_cols:\n",
    "    lb = LabelEncoder()\n",
    "    lb.fit(list(newdata[cat].values))\n",
    "    newdata[cat] = lb.transform(list(newdata[cat].values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product_Weight</th>\n",
       "      <th>Product_Fat_Content</th>\n",
       "      <th>Product_Shelf_Visibility</th>\n",
       "      <th>Product_Type</th>\n",
       "      <th>Product_Price</th>\n",
       "      <th>Supermarket_Opening_Year</th>\n",
       "      <th>Supermarket _Size</th>\n",
       "      <th>Supermarket_Location_Type</th>\n",
       "      <th>Supermarket_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.068535</td>\n",
       "      <td>14</td>\n",
       "      <td>357.54</td>\n",
       "      <td>2005</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.040912</td>\n",
       "      <td>14</td>\n",
       "      <td>355.79</td>\n",
       "      <td>1994</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.041178</td>\n",
       "      <td>14</td>\n",
       "      <td>350.79</td>\n",
       "      <td>2014</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.041113</td>\n",
       "      <td>14</td>\n",
       "      <td>355.04</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14</td>\n",
       "      <td>354.79</td>\n",
       "      <td>2011</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Product_Weight  Product_Fat_Content  Product_Shelf_Visibility  \\\n",
       "0            11.6                    0                  0.068535   \n",
       "1            11.6                    0                  0.040912   \n",
       "2            11.6                    0                  0.041178   \n",
       "3            11.6                    0                  0.041113   \n",
       "4            11.6                    2                  0.000000   \n",
       "\n",
       "   Product_Type  Product_Price  Supermarket_Opening_Year  Supermarket _Size  \\\n",
       "0            14         357.54                      2005                  3   \n",
       "1            14         355.79                      1994                  0   \n",
       "2            14         350.79                      2014                  3   \n",
       "3            14         355.04                      2016                  1   \n",
       "4            14         354.79                      2011                  2   \n",
       "\n",
       "   Supermarket_Location_Type  Supermarket_Type  \n",
       "0                          2                 0  \n",
       "1                          2                 1  \n",
       "2                          1                 1  \n",
       "3                          2                 2  \n",
       "4                          1                 1  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdata.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fill in Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product_Weight               802\n",
       "Product_Fat_Content            0\n",
       "Product_Shelf_Visibility       0\n",
       "Product_Type                   0\n",
       "Product_Price                  0\n",
       "Supermarket_Opening_Year       0\n",
       "Supermarket _Size              0\n",
       "Supermarket_Location_Type      0\n",
       "Supermarket_Type               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdata.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.908838347659676"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_pw = newdata['Product_Weight'].mean()\n",
    "mean_pw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "newdata['Product_Weight'].fillna(mean_pw, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product_Weight               0\n",
       "Product_Fat_Content          0\n",
       "Product_Shelf_Visibility     0\n",
       "Product_Type                 0\n",
       "Product_Price                0\n",
       "Supermarket_Opening_Year     0\n",
       "Supermarket _Size            0\n",
       "Supermarket_Location_Type    0\n",
       "Supermarket_Type             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdata.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product_Weight</th>\n",
       "      <th>Product_Fat_Content</th>\n",
       "      <th>Product_Shelf_Visibility</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.6</td>\n",
       "      <td>0</td>\n",
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       "      <td>357.54</td>\n",
       "      <td>2005</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.6</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.041178</td>\n",
       "      <td>14</td>\n",
       "      <td>350.79</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.041113</td>\n",
       "      <td>14</td>\n",
       "      <td>355.04</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14</td>\n",
       "      <td>354.79</td>\n",
       "      <td>2011</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Product_Weight  Product_Fat_Content  Product_Shelf_Visibility  \\\n",
       "0            11.6                    0                  0.068535   \n",
       "1            11.6                    0                  0.040912   \n",
       "2            11.6                    0                  0.041178   \n",
       "3            11.6                    0                  0.041113   \n",
       "4            11.6                    2                  0.000000   \n",
       "\n",
       "   Product_Type  Product_Price  Supermarket_Opening_Year  Supermarket _Size  \\\n",
       "0            14         357.54                      2005                  3   \n",
       "1            14         355.79                      1994                  0   \n",
       "2            14         350.79                      2014                  3   \n",
       "3            14         355.04                      2016                  1   \n",
       "4            14         354.79                      2011                  2   \n",
       "\n",
       "   Supermarket_Location_Type  Supermarket_Type  \n",
       "0                          2                 0  \n",
       "1                          2                 1  \n",
       "2                          1                 1  \n",
       "3                          2                 2  \n",
       "4                          1                 1  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdata.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PERFORM NORMALIZATION AND SCALING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:625: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "scaler.fit(newdata)\n",
    "\n",
    "scaled_data = scaler.transform(newdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.30379891, -0.75902667,  0.03051274,  1.57502366, -0.28704749,\n",
       "        0.02613191,  1.2558981 ,  1.10288196, -1.47708624])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_data  # train data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### splitting our data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(scaled_data, y_target, test_size = 0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data is (3493, 9)\n",
      "Training target is (3493,)\n",
      "test data is (1497, 9)\n",
      "test target is (1497,)\n"
     ]
    }
   ],
   "source": [
    "print(\"Training data is\", X_train.shape)\n",
    "print(\"Training target is\", y_train.shape)\n",
    "print(\"test data is\", X_test.shape)\n",
    "print(\"test target is\", y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TRAINING MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_absolute_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### USING A LINEAR REGRESSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_model = LinearRegression() #create an object\n",
    "lr_model.fit(X_train, y_train) # traing part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = lr_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2442.2788941144063"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print error\n",
    "lr_error = mean_absolute_error(predictions, y_test)\n",
    "lr_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### USING A DECISION TREE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=6, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=2,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_model = DecisionTreeRegressor(max_depth=6, min_samples_leaf=2)\n",
    "dt_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = dt_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2186.8327545559973"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_error = mean_absolute_error(pred, y_test)\n",
    "dt_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### USING KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "          metric_params=None, n_jobs=None, n_neighbors=60, p=2,\n",
       "          weights='uniform')"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_model = KNeighborsRegressor(n_neighbors=60)\n",
    "knn_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_prediction = knn_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2253.7537972611885"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_error = mean_absolute_error(knn_prediction, y_test)\n",
    "knn_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### USING RANDOM FOREST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=5,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=500, n_jobs=None,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_model = RandomForestRegressor(n_estimators=500, max_depth=5)\n",
    "rf_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_pred = rf_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2133.2360777343424"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_error = mean_absolute_error(rf_pred, y_test)\n",
    "rf_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### USING BOOSTING TECHNIQUES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import AdaBoostRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostRegressor(base_estimator=None, learning_rate=1.0, loss='linear',\n",
       "         n_estimators=150, random_state=None)"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ada_model = AdaBoostRegressor(n_estimators=150)\n",
    "ada_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "ada_pred = ada_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2312.2203715516694"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ada_error = mean_absolute_error(ada_pred, y_test)\n",
    "ada_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "             learning_rate=0.01, loss='ls', max_depth=3, max_features=None,\n",
       "             max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "             min_impurity_split=None, min_samples_leaf=1,\n",
       "             min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "             n_estimators=1000, n_iter_no_change=None, presort='auto',\n",
       "             random_state=None, subsample=1.0, tol=0.0001,\n",
       "             validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb_model = GradientBoostingRegressor(n_estimators=1000,\n",
    "                                     learning_rate=0.01)\n",
    "gb_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "gb_pred = gb_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2175.7680585720555"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb_error = mean_absolute_error(gb_pred, y_test)\n",
    "gb_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AVERAGING PREDICTIONS OF THREE MODELS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7101.5514576 ,  9792.07982122,  3656.38456661, ...,\n",
       "       10519.93099984,  6076.41864001,  8967.0666938 ])"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_pred = (rf_pred + gb_pred + pred) / 3\n",
    "avg_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2146.779761528258"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_error = mean_absolute_error(avg_pred, y_test)\n",
    "avg_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### WEIGHTED AVERAGE OF THREE MODELS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "weighted_avg_pred = (0.9 * rf_pred + 0.1 * gb_pred + 0.1 * pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2230.798004016773"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weighted_avg_error = mean_absolute_error(weighted_avg_pred, y_test)\n",
    "weighted_avg_error"
   ]
  }
 ],
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