Bigmart_lgbm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sales Prediction for Big Mart Outlets\n",
    "\n",
    "The data scientists at BigMart have collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The aim is to build a predictive model and predict the sales of each product at a particular outlet.\n",
    "\n",
    "Using this model, BigMart will try to understand the properties of products and outlets which play a key role in increasing sales.\n",
    "\n",
    "Please note that the data may have missing values as some stores might not report all the data due to technical glitches. Hence, it will be required to treat them accordingly. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy  as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split,KFold\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from pandas_profiling import ProfileReport\n",
    "# Comment this if the data visualisations doesn't work on your side\n",
    "%matplotlib inline\n",
    "plt.style.use('bmh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Train and Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.read_csv('train_BM.csv')\n",
    "df_test = pd.read_csv('test_BM.csv')\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "model_id": "33c42b36df674d1f829236c16266ff17",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Summarize dataset', max=26.0, style=ProgressStyle(descrip…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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       "HBox(children=(FloatProgress(value=0.0, description='Generate report structure', max=1.0, style=ProgressStyle(…"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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       "HBox(children=(FloatProgress(value=0.0, description='Render widgets', max=1.0, style=ProgressStyle(description…"
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       "VBox(children=(Tab(children=(Tab(children=(GridBox(children=(VBox(children=(GridspecLayout(children=(HTML(valu…"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "profile = ProfileReport(df_train,title='Profile Report',explorative=True)\n",
    "profile.to_widgets()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count     8523.000000\n",
      "mean      2181.288914\n",
      "std       1706.499616\n",
      "min         33.290000\n",
      "25%        834.247400\n",
      "50%       1794.331000\n",
      "75%       3101.296400\n",
      "max      13086.964800\n",
      "Name: Item_Outlet_Sales, dtype: float64\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 648x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(df_train['Item_Outlet_Sales'].describe())\n",
    "plt.figure(figsize=(9, 8))\n",
    "sns.distplot(df_train['Item_Outlet_Sales'], color='g', bins=100, hist_kws={'alpha': 0.4});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Pre-Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8523, 13) (5681, 12) (14204, 13)\n"
     ]
    }
   ],
   "source": [
    "# Merging Train and Test Dataset\n",
    "df_train['source']='train'\n",
    "df_test['source']='test'\n",
    "merge_data = pd.concat([df_train,df_test], ignore_index = True)\n",
    "print(df_train.shape, df_test.shape, merge_data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging Same categories into common names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Low Fat    8485\n",
       "Regular    4824\n",
       "LF          522\n",
       "reg         195\n",
       "low fat     178\n",
       "Name: Item_Fat_Content, dtype: int64"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_data['Item_Fat_Content'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Low Fat', 'Regular'], dtype=object)"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_data['Item_Fat_Content'] = merge_data['Item_Fat_Content'].str.replace('low fat','Low Fat')\n",
    "merge_data['Item_Fat_Content'] = merge_data['Item_Fat_Content'].str.replace('LF','Low Fat')\n",
    "merge_data['Item_Fat_Content'] = merge_data['Item_Fat_Content'].str.replace('reg','Regular')\n",
    "merge_data['Item_Fat_Content'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imputing Missing Values with custom method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Item_Identifier                 0\n",
      "Item_Weight                  2439\n",
      "Item_Fat_Content                0\n",
      "Item_Visibility                 0\n",
      "Item_Type                       0\n",
      "Item_MRP                        0\n",
      "Outlet_Identifier               0\n",
      "Outlet_Establishment_Year       0\n",
      "Outlet_Size                  4016\n",
      "Outlet_Location_Type            0\n",
      "Outlet_Type                     0\n",
      "Item_Outlet_Sales            5681\n",
      "source                          0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Finding the null values.\n",
    "print(merge_data.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14204 entries, 0 to 14203\n",
      "Data columns (total 13 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   Item_Identifier            14204 non-null  object \n",
      " 1   Item_Weight                14204 non-null  float64\n",
      " 2   Item_Fat_Content           14204 non-null  object \n",
      " 3   Item_Visibility            14204 non-null  float64\n",
      " 4   Item_Type                  14204 non-null  object \n",
      " 5   Item_MRP                   14204 non-null  float64\n",
      " 6   Outlet_Identifier          14204 non-null  object \n",
      " 7   Outlet_Establishment_Year  14204 non-null  int64  \n",
      " 8   Outlet_Size                14204 non-null  object \n",
      " 9   Outlet_Location_Type       14204 non-null  object \n",
      " 10  Outlet_Type                14204 non-null  object \n",
      " 11  Item_Outlet_Sales          8523 non-null   float64\n",
      " 12  source                     14204 non-null  object \n",
      "dtypes: float64(4), int64(1), object(8)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "def custom_replace(x):\n",
    "    if math.isnan(x['Item_Weight']): \n",
    "        wt = merge_data[(merge_data['Item_Type'] == x['Item_Type']) & (merge_data['Item_Weight'].isnull() == False )]\n",
    "        return merge_data['Item_Weight'].mean() if wt['Item_Weight'].empty else wt['Item_Weight'].iloc[0]\n",
    "    else:\n",
    "        return x['Item_Weight']\n",
    "    \n",
    "# Replacing item_weight values with custom replace method        \n",
    "merge_data['Item_Weight'] = merge_data.apply(lambda x: custom_replace(x),axis=1)\n",
    "\n",
    "# Imputing outlet_size with mode method\n",
    "outlet_size_mode = merge_data['Outlet_Size'].value_counts().index[0]\n",
    "merge_data['Outlet_Size'].fillna(outlet_size_mode,inplace=True)\n",
    "merge_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imputing 0's in Item_Visibility with Mean values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "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",
       "      <th>source</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",
       "      <td>train</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",
       "      <td>train</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",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDX07</td>\n",
       "      <td>19.20</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.065953</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>732.3800</td>\n",
       "      <td>train</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>0.065953</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",
       "      <td>train</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         0.065953   \n",
       "4           NCD19         8.93          Low Fat         0.065953   \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      Medium               Tier 3   \n",
       "4                       1987        High               Tier 3   \n",
       "\n",
       "         Outlet_Type  Item_Outlet_Sales source  \n",
       "0  Supermarket Type1          3735.1380  train  \n",
       "1  Supermarket Type2           443.4228  train  \n",
       "2  Supermarket Type1          2097.2700  train  \n",
       "3      Grocery Store           732.3800  train  \n",
       "4  Supermarket Type1           994.7052  train  "
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replace all 0's Item_Visibility with mean of Item_Visibility\n",
    "val = merge_data['Item_Visibility'].mean()\n",
    "merge_data['Item_Visibility'] = merge_data['Item_Visibility'].replace(to_replace=0.000000, value=val)\n",
    "merge_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### New Feature generation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "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",
       "      <th>source</th>\n",
       "      <th>Item_mrp_x_Visibility</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",
       "      <td>train</td>\n",
       "      <td>4.008763</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",
       "      <td>train</td>\n",
       "      <td>0.930544</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",
       "      <td>train</td>\n",
       "      <td>2.373528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDX07</td>\n",
       "      <td>19.20</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.065953</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>732.3800</td>\n",
       "      <td>train</td>\n",
       "      <td>12.009671</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>0.065953</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",
       "      <td>train</td>\n",
       "      <td>3.552309</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         0.065953   \n",
       "4           NCD19         8.93          Low Fat         0.065953   \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      Medium               Tier 3   \n",
       "4                       1987        High               Tier 3   \n",
       "\n",
       "         Outlet_Type  Item_Outlet_Sales source  Item_mrp_x_Visibility  \n",
       "0  Supermarket Type1          3735.1380  train               4.008763  \n",
       "1  Supermarket Type2           443.4228  train               0.930544  \n",
       "2  Supermarket Type1          2097.2700  train               2.373528  \n",
       "3      Grocery Store           732.3800  train              12.009671  \n",
       "4  Supermarket Type1           994.7052  train               3.552309  "
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_data['Item_mrp_x_Visibility'] = merge_data['Item_Visibility'] * merge_data['Item_MRP']\n",
    "merge_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting established_date to Age of the outlet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    14204.000000\n",
       "mean        15.169319\n",
       "std          8.371664\n",
       "min          4.000000\n",
       "25%          9.000000\n",
       "50%         14.000000\n",
       "75%         26.000000\n",
       "max         28.000000\n",
       "Name: Outlet_Years, dtype: float64"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_data['Outlet_Years'] = 2013 - merge_data['Outlet_Establishment_Year']\n",
    "merge_data['Outlet_Years'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging item_type categories by mapping into common item type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Food              10201\n",
       "Non-Consumable     2686\n",
       "Drinks             1317\n",
       "Name: Item_Type_Combined, dtype: int64"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get the first two characters of ID:\n",
    "merge_data['Item_Type_Combined'] = merge_data['Item_Identifier'].apply(lambda x: x[0:2])\n",
    "#Rename them to more intuitive categories:\n",
    "merge_data['Item_Type_Combined'] = merge_data['Item_Type_Combined'].map({'FD':'Food',\n",
    "                                                             'NC':'Non-Consumable',\n",
    "                                                             'DR':'Drinks'})\n",
    "merge_data['Item_Type_Combined'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Low Fat       6499\n",
       "Regular       5019\n",
       "Non-Edible    2686\n",
       "Name: Item_Fat_Content, dtype: int64"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Mark non-consumables as separate category in low_fat:\n",
    "merge_data.loc[merge_data['Item_Type_Combined']==\"Non-Consumable\",'Item_Fat_Content'] = \"Non-Edible\"\n",
    "merge_data['Item_Fat_Content'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quantile based binning for Item_weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    14204.000000\n",
       "mean        13.009974\n",
       "std          4.646273\n",
       "min          4.555000\n",
       "25%          8.930000\n",
       "50%         12.800000\n",
       "75%         17.200000\n",
       "max         21.350000\n",
       "Name: Item_Weight, dtype: float64"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_data['Item_Weight'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00     4.555\n",
       "0.25     8.930\n",
       "0.50    12.800\n",
       "0.75    17.200\n",
       "1.00    21.350\n",
       "Name: Item_Weight, dtype: float64"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quantile_list = [0, .25, .5, .75, 1.]\n",
    "quantiles = merge_data['Item_Weight'].quantile(quantile_list)\n",
    "quantiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "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",
       "      <th>source</th>\n",
       "      <th>Item_mrp_x_Visibility</th>\n",
       "      <th>Outlet_Years</th>\n",
       "      <th>Item_Type_Combined</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>25-50Q</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",
       "      <td>train</td>\n",
       "      <td>4.008763</td>\n",
       "      <td>14</td>\n",
       "      <td>Food</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRC01</td>\n",
       "      <td>0-25Q</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",
       "      <td>train</td>\n",
       "      <td>0.930544</td>\n",
       "      <td>4</td>\n",
       "      <td>Drinks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FDN15</td>\n",
       "      <td>75-100Q</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",
       "      <td>train</td>\n",
       "      <td>2.373528</td>\n",
       "      <td>14</td>\n",
       "      <td>Food</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDX07</td>\n",
       "      <td>75-100Q</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.065953</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>732.3800</td>\n",
       "      <td>train</td>\n",
       "      <td>12.009671</td>\n",
       "      <td>15</td>\n",
       "      <td>Food</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCD19</td>\n",
       "      <td>0-25Q</td>\n",
       "      <td>Non-Edible</td>\n",
       "      <td>0.065953</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",
       "      <td>train</td>\n",
       "      <td>3.552309</td>\n",
       "      <td>26</td>\n",
       "      <td>Non-Consumable</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "0           FDA15      25-50Q          Low Fat         0.016047   \n",
       "1           DRC01       0-25Q          Regular         0.019278   \n",
       "2           FDN15     75-100Q          Low Fat         0.016760   \n",
       "3           FDX07     75-100Q          Regular         0.065953   \n",
       "4           NCD19       0-25Q       Non-Edible         0.065953   \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      Medium               Tier 3   \n",
       "4                       1987        High               Tier 3   \n",
       "\n",
       "         Outlet_Type  Item_Outlet_Sales source  Item_mrp_x_Visibility  \\\n",
       "0  Supermarket Type1          3735.1380  train               4.008763   \n",
       "1  Supermarket Type2           443.4228  train               0.930544   \n",
       "2  Supermarket Type1          2097.2700  train               2.373528   \n",
       "3      Grocery Store           732.3800  train              12.009671   \n",
       "4  Supermarket Type1           994.7052  train               3.552309   \n",
       "\n",
       "   Outlet_Years Item_Type_Combined  \n",
       "0            14               Food  \n",
       "1             4             Drinks  \n",
       "2            14               Food  \n",
       "3            15               Food  \n",
       "4            26     Non-Consumable  "
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quantile_labels = ['0-25Q', '25-50Q', '50-75Q', '75-100Q']\n",
    "merge_data['Item_Weight'] = pd.qcut(merge_data['Item_Weight'],q=quantile_list, labels=quantile_labels)\n",
    "merge_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Label Encoding categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "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_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "      <th>source</th>\n",
       "      <th>Item_mrp_x_Visibility</th>\n",
       "      <th>Outlet_Years</th>\n",
       "      <th>Item_Type_Combined</th>\n",
       "      <th>Outlet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3735.1380</td>\n",
       "      <td>train</td>\n",
       "      <td>4.008763</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>443.4228</td>\n",
       "      <td>train</td>\n",
       "      <td>0.930544</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2097.2700</td>\n",
       "      <td>train</td>\n",
       "      <td>2.373528</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0.065953</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>732.3800</td>\n",
       "      <td>train</td>\n",
       "      <td>12.009671</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.065953</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>994.7052</td>\n",
       "      <td>train</td>\n",
       "      <td>3.552309</td>\n",
       "      <td>26</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Item_Weight  Item_Fat_Content  Item_Visibility  Item_MRP  Outlet_Size  \\\n",
       "0            1                 0         0.016047  249.8092            1   \n",
       "1            0                 2         0.019278   48.2692            1   \n",
       "2            3                 0         0.016760  141.6180            1   \n",
       "3            3                 2         0.065953  182.0950            1   \n",
       "4            0                 1         0.065953   53.8614            0   \n",
       "\n",
       "   Outlet_Location_Type  Outlet_Type  Item_Outlet_Sales source  \\\n",
       "0                     0            1          3735.1380  train   \n",
       "1                     2            2           443.4228  train   \n",
       "2                     0            1          2097.2700  train   \n",
       "3                     2            0           732.3800  train   \n",
       "4                     2            1           994.7052  train   \n",
       "\n",
       "   Item_mrp_x_Visibility  Outlet_Years  Item_Type_Combined  Outlet  \n",
       "0               4.008763            14                   1       9  \n",
       "1               0.930544             4                   0       3  \n",
       "2               2.373528            14                   1       9  \n",
       "3              12.009671            15                   1       0  \n",
       "4               3.552309            26                   2       1  "
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def label_encoder(data):\n",
    "    le = preprocessing.LabelEncoder()\n",
    "    data = le.fit_transform(data)\n",
    "    return data\n",
    "\n",
    "merge_data['Outlet'] = label_encoder(merge_data['Outlet_Identifier'])\n",
    "merge_data['Item_Fat_Content'] = label_encoder(merge_data['Item_Fat_Content'])\n",
    "merge_data['Outlet_Size'] = label_encoder(merge_data['Outlet_Size'])\n",
    "merge_data['Outlet_Location_Type'] = label_encoder(merge_data['Outlet_Location_Type'])\n",
    "merge_data['Outlet_Type'] = label_encoder(merge_data['Outlet_Type'])\n",
    "merge_data['Item_Type_Combined'] = label_encoder(merge_data['Item_Type_Combined'])\n",
    "merge_data['Item_Weight'] = label_encoder(merge_data['Item_Weight'])\n",
    "merge_data.drop(['Item_Type','Item_Identifier','Outlet_Identifier','Outlet_Establishment_Year'],axis=1, inplace=True)\n",
    "\n",
    "merge_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting up train and test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of X_train  (8437, 11)\n",
      "Size of X_cv  (86, 11)\n",
      "Size of y_train  (8437,)\n",
      "Size of y_cv  (86,)\n"
     ]
    }
   ],
   "source": [
    "#Divide into test and train:\n",
    "X = merge_data[merge_data['source']==\"train\"]\n",
    "test = merge_data[merge_data['source']==\"test\"]\n",
    "\n",
    "#Drop unnecessary columns:\n",
    "y = X['Item_Outlet_Sales'].values\n",
    "test.drop(['source','Item_Outlet_Sales'],axis=1,inplace=True)\n",
    "X.drop(['source','Item_Outlet_Sales'],axis=1,inplace=True)\n",
    "\n",
    "category_col = ['Item_Weight','Item_Fat_Content','Outlet_Type','Outlet','Outlet_Location_Type']\n",
    "\n",
    "X_train, X_cv, y_train, y_cv = train_test_split(X, y, test_size=0.01,shuffle=True,random_state=99)\n",
    "print(\"Size of X_train \", X_train.shape)\n",
    "print(\"Size of X_cv \", X_cv.shape)\n",
    "print(\"Size of y_train \", y_train.shape)\n",
    "print(\"Size of y_cv \", y_cv.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation : Performance Metrics - RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean_absolute_percentage_error(y_true, y_pred): \n",
    "    y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
    "    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Training , Prediction and Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LGBM Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 0\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 715.3685011255773\n",
      "####################################################################################################\n",
      "Fold 1\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1263.8340557061533\n",
      "####################################################################################################\n",
      "Fold 2\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1239.3154027429916\n",
      "####################################################################################################\n",
      "Fold 3\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1196.5944196859823\n",
      "####################################################################################################\n",
      "Fold 4\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1288.6167877822736\n",
      "####################################################################################################\n",
      "Fold 5\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 671.6552484099245\n",
      "####################################################################################################\n",
      "Fold 6\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 899.9238496525286\n",
      "####################################################################################################\n",
      "Fold 7\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1074.3255905200872\n",
      "####################################################################################################\n",
      "Fold 8\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1267.6183193510897\n",
      "####################################################################################################\n",
      "Fold 9\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1236.609862652134\n",
      "####################################################################################################\n",
      "Fold 10\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1318.0897142440626\n",
      "####################################################################################################\n",
      "Fold 11\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1215.4712573556565\n",
      "####################################################################################################\n",
      "Fold 12\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1294.0858069629796\n",
      "####################################################################################################\n",
      "Fold 13\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1141.0525320845777\n",
      "####################################################################################################\n",
      "Fold 14\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1267.8399113552912\n",
      "####################################################################################################\n",
      "Fold 15\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1187.5994407913472\n",
      "####################################################################################################\n",
      "Fold 16\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 817.1140224909637\n",
      "####################################################################################################\n",
      "Fold 17\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 909.9216471014156\n",
      "####################################################################################################\n",
      "Fold 18\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 823.2305850991739\n",
      "####################################################################################################\n",
      "Fold 19\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1038.4510904517124\n",
      "####################################################################################################\n",
      "Fold 20\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1048.176591722757\n",
      "####################################################################################################\n",
      "Fold 21\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1199.0492131947867\n",
      "####################################################################################################\n",
      "Fold 22\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1081.7588203885934\n",
      "####################################################################################################\n",
      "Fold 23\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1114.9407375971382\n",
      "####################################################################################################\n",
      "Fold 24\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 931.0862382855776\n",
      "####################################################################################################\n",
      "Fold 25\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1358.2294122533299\n",
      "####################################################################################################\n",
      "Fold 26\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1024.8560954725747\n",
      "####################################################################################################\n",
      "Fold 27\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1099.0329083214922\n",
      "####################################################################################################\n",
      "Fold 28\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 897.3481977030433\n",
      "####################################################################################################\n",
      "Fold 29\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 995.4662222537129\n",
      "####################################################################################################\n",
      "Fold 30\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1039.593792384007\n",
      "####################################################################################################\n",
      "Fold 31\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 964.7460764118819\n",
      "####################################################################################################\n",
      "Fold 32\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 914.5171271636506\n",
      "####################################################################################################\n",
      "Fold 33\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1619.0465199781124\n",
      "####################################################################################################\n",
      "Fold 34\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1185.7265273295684\n",
      "####################################################################################################\n",
      "Fold 35\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1221.7548005069395\n",
      "####################################################################################################\n",
      "Fold 36\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1114.0857960217024\n",
      "####################################################################################################\n",
      "Fold 37\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 901.8053775248277\n",
      "####################################################################################################\n",
      "Fold 38\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 926.8827405260979\n",
      "####################################################################################################\n",
      "Fold 39\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1159.2076959501517\n",
      "####################################################################################################\n",
      "Fold 40\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 931.3264226412767\n",
      "####################################################################################################\n",
      "Fold 41\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1054.3540207425262\n",
      "####################################################################################################\n",
      "Fold 42\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 977.7765170373492\n",
      "####################################################################################################\n",
      "Fold 43\n",
      " rows of train = 8493 rows of holdout = 30\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LGBM Val OOF RMSE = 746.2099445365327\n",
      "####################################################################################################\n",
      "Fold 44\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1246.7670950748743\n",
      "####################################################################################################\n",
      "Fold 45\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 995.3659588139088\n",
      "####################################################################################################\n",
      "Fold 46\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 779.5069189036641\n",
      "####################################################################################################\n",
      "Fold 47\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 930.6148092290009\n",
      "####################################################################################################\n",
      "Fold 48\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1553.1789026639917\n",
      "####################################################################################################\n",
      "Fold 49\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1009.0546456605605\n",
      "####################################################################################################\n",
      "Fold 50\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1218.0050452405358\n",
      "####################################################################################################\n",
      "Fold 51\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 777.2022201281093\n",
      "####################################################################################################\n",
      "Fold 52\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1131.6307486093363\n",
      "####################################################################################################\n",
      "Fold 53\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 856.5786710342526\n",
      "####################################################################################################\n",
      "Fold 54\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1061.36344719007\n",
      "####################################################################################################\n",
      "Fold 55\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1396.254150050203\n",
      "####################################################################################################\n",
      "Fold 56\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1513.306628003799\n",
      "####################################################################################################\n",
      "Fold 57\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 920.6991026181778\n",
      "####################################################################################################\n",
      "Fold 58\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 953.1208184894446\n",
      "####################################################################################################\n",
      "Fold 59\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 933.1136525663783\n",
      "####################################################################################################\n",
      "Fold 60\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 867.6201389897974\n",
      "####################################################################################################\n",
      "Fold 61\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1116.896640348042\n",
      "####################################################################################################\n",
      "Fold 62\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1075.1060888983468\n",
      "####################################################################################################\n",
      "Fold 63\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1209.9211305714548\n",
      "####################################################################################################\n",
      "Fold 64\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1314.2524849660383\n",
      "####################################################################################################\n",
      "Fold 65\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1245.7949480905982\n",
      "####################################################################################################\n",
      "Fold 66\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1295.7096990439336\n",
      "####################################################################################################\n",
      "Fold 67\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 718.2367422535024\n",
      "####################################################################################################\n",
      "Fold 68\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1068.3389446969472\n",
      "####################################################################################################\n",
      "Fold 69\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1043.1243908327087\n",
      "####################################################################################################\n",
      "Fold 70\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1234.5138839487151\n",
      "####################################################################################################\n",
      "Fold 71\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 688.9132237651036\n",
      "####################################################################################################\n",
      "Fold 72\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 848.3381816558756\n",
      "####################################################################################################\n",
      "Fold 73\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1175.2349872864606\n",
      "####################################################################################################\n",
      "Fold 74\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1211.2074098605615\n",
      "####################################################################################################\n",
      "Fold 75\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 892.9008834098448\n",
      "####################################################################################################\n",
      "Fold 76\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1317.0424940726564\n",
      "####################################################################################################\n",
      "Fold 77\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 925.4909877475808\n",
      "####################################################################################################\n",
      "Fold 78\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1060.696763169585\n",
      "####################################################################################################\n",
      "Fold 79\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 996.4412072433618\n",
      "####################################################################################################\n",
      "Fold 80\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1119.9269774696768\n",
      "####################################################################################################\n",
      "Fold 81\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 949.2400054698943\n",
      "####################################################################################################\n",
      "Fold 82\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 896.4445098552228\n",
      "####################################################################################################\n",
      "Fold 83\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1251.8832023119257\n",
      "####################################################################################################\n",
      "Fold 84\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1531.2222601418462\n",
      "####################################################################################################\n",
      "Fold 85\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1026.0632234254754\n",
      "####################################################################################################\n",
      "Fold 86\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 841.158582894254\n",
      "####################################################################################################\n",
      "Fold 87\n",
      " rows of train = 8493 rows of holdout = 30\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LGBM Val OOF RMSE = 741.3226394754857\n",
      "####################################################################################################\n",
      "Fold 88\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1200.8439371261102\n",
      "####################################################################################################\n",
      "Fold 89\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1127.9093232413243\n",
      "####################################################################################################\n",
      "Fold 90\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 814.2468203243776\n",
      "####################################################################################################\n",
      "Fold 91\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 892.5889497747166\n",
      "####################################################################################################\n",
      "Fold 92\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1135.5629282399282\n",
      "####################################################################################################\n",
      "Fold 93\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1126.1617518193777\n",
      "####################################################################################################\n",
      "Fold 94\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1372.4888348331344\n",
      "####################################################################################################\n",
      "Fold 95\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 913.5393389070947\n",
      "####################################################################################################\n",
      "Fold 96\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 899.911986341873\n",
      "####################################################################################################\n",
      "Fold 97\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1174.8721778515585\n",
      "####################################################################################################\n",
      "Fold 98\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 594.7171339322612\n",
      "####################################################################################################\n",
      "Fold 99\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1081.3027424944046\n",
      "####################################################################################################\n",
      "Fold 100\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 890.9349890326298\n",
      "####################################################################################################\n",
      "Fold 101\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 823.221764732879\n",
      "####################################################################################################\n",
      "Fold 102\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1506.5200502211565\n",
      "####################################################################################################\n",
      "Fold 103\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1072.8603522033454\n",
      "####################################################################################################\n",
      "Fold 104\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 629.9960578723799\n",
      "####################################################################################################\n",
      "Fold 105\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1042.4644329346581\n",
      "####################################################################################################\n",
      "Fold 106\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1293.2391016621361\n",
      "####################################################################################################\n",
      "Fold 107\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1204.9158657117719\n",
      "####################################################################################################\n",
      "Fold 108\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1281.762569242318\n",
      "####################################################################################################\n",
      "Fold 109\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1084.1746583217746\n",
      "####################################################################################################\n",
      "Fold 110\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1075.6672483714124\n",
      "####################################################################################################\n",
      "Fold 111\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 800.2419486288915\n",
      "####################################################################################################\n",
      "Fold 112\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 763.8810748286206\n",
      "####################################################################################################\n",
      "Fold 113\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1234.6710662951764\n",
      "####################################################################################################\n",
      "Fold 114\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 895.3726662276882\n",
      "####################################################################################################\n",
      "Fold 115\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1122.9195280127392\n",
      "####################################################################################################\n",
      "Fold 116\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1173.566097801238\n",
      "####################################################################################################\n",
      "Fold 117\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1080.705055394147\n",
      "####################################################################################################\n",
      "Fold 118\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1172.630247906323\n",
      "####################################################################################################\n",
      "Fold 119\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1234.1571025330343\n",
      "####################################################################################################\n",
      "Fold 120\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1105.6355683926447\n",
      "####################################################################################################\n",
      "Fold 121\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1094.4339101042053\n",
      "####################################################################################################\n",
      "Fold 122\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 930.3012758615812\n",
      "####################################################################################################\n",
      "Fold 123\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 949.4575281944741\n",
      "####################################################################################################\n",
      "Fold 124\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1389.08469723584\n",
      "####################################################################################################\n",
      "Fold 125\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1268.0158499792715\n",
      "####################################################################################################\n",
      "Fold 126\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 912.2384845285006\n",
      "####################################################################################################\n",
      "Fold 127\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 820.5828947828866\n",
      "####################################################################################################\n",
      "Fold 128\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1188.0679229550992\n",
      "####################################################################################################\n",
      "Fold 129\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1047.7752179185316\n",
      "####################################################################################################\n",
      "Fold 130\n",
      " rows of train = 8493 rows of holdout = 30\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LGBM Val OOF RMSE = 882.8809834524485\n",
      "####################################################################################################\n",
      "Fold 131\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 897.2118857393068\n",
      "####################################################################################################\n",
      "Fold 132\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1222.8820693497114\n",
      "####################################################################################################\n",
      "Fold 133\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1018.5868301406457\n",
      "####################################################################################################\n",
      "Fold 134\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1112.835574056059\n",
      "####################################################################################################\n",
      "Fold 135\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1016.7121724782054\n",
      "####################################################################################################\n",
      "Fold 136\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1350.8201494976274\n",
      "####################################################################################################\n",
      "Fold 137\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 823.4190809426813\n",
      "####################################################################################################\n",
      "Fold 138\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1255.8670509948852\n",
      "####################################################################################################\n",
      "Fold 139\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 873.0207171571753\n",
      "####################################################################################################\n",
      "Fold 140\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1094.6257877796536\n",
      "####################################################################################################\n",
      "Fold 141\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1037.890882984738\n",
      "####################################################################################################\n",
      "Fold 142\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1209.3439240109599\n",
      "####################################################################################################\n",
      "Fold 143\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 918.0477273558162\n",
      "####################################################################################################\n",
      "Fold 144\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1561.5718523038618\n",
      "####################################################################################################\n",
      "Fold 145\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 974.854205734054\n",
      "####################################################################################################\n",
      "Fold 146\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 790.5212189637623\n",
      "####################################################################################################\n",
      "Fold 147\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 880.0525073704175\n",
      "####################################################################################################\n",
      "Fold 148\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 981.8017344923425\n",
      "####################################################################################################\n",
      "Fold 149\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1022.3804239863455\n",
      "####################################################################################################\n",
      "Fold 150\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 844.5560000512017\n",
      "####################################################################################################\n",
      "Fold 151\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 991.2430435462751\n",
      "####################################################################################################\n",
      "Fold 152\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 841.2623551703892\n",
      "####################################################################################################\n",
      "Fold 153\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 942.8657715779152\n",
      "####################################################################################################\n",
      "Fold 154\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1112.3469184376784\n",
      "####################################################################################################\n",
      "Fold 155\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 914.376221461274\n",
      "####################################################################################################\n",
      "Fold 156\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1127.072082338025\n",
      "####################################################################################################\n",
      "Fold 157\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 788.9596586987296\n",
      "####################################################################################################\n",
      "Fold 158\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 740.4664643351372\n",
      "####################################################################################################\n",
      "Fold 159\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1191.543120874923\n",
      "####################################################################################################\n",
      "Fold 160\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1257.250372159997\n",
      "####################################################################################################\n",
      "Fold 161\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 924.8311238766656\n",
      "####################################################################################################\n",
      "Fold 162\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1470.5364699702009\n",
      "####################################################################################################\n",
      "Fold 163\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1360.016644794324\n",
      "####################################################################################################\n",
      "Fold 164\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 949.3605423169379\n",
      "####################################################################################################\n",
      "Fold 165\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1409.0486121226072\n",
      "####################################################################################################\n",
      "Fold 166\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1323.2979117952873\n",
      "####################################################################################################\n",
      "Fold 167\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 798.3652540542872\n",
      "####################################################################################################\n",
      "Fold 168\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1217.3128299149885\n",
      "####################################################################################################\n",
      "Fold 169\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 652.4748682879709\n",
      "####################################################################################################\n",
      "Fold 170\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 965.2823534615251\n",
      "####################################################################################################\n",
      "Fold 171\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 891.0273794940032\n",
      "####################################################################################################\n",
      "Fold 172\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 917.9384226854802\n",
      "####################################################################################################\n",
      "Fold 173\n",
      " rows of train = 8493 rows of holdout = 30\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LGBM Val OOF RMSE = 969.2603499819029\n",
      "####################################################################################################\n",
      "Fold 174\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1466.643233359494\n",
      "####################################################################################################\n",
      "Fold 175\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1203.748462083362\n",
      "####################################################################################################\n",
      "Fold 176\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1232.0401259161401\n",
      "####################################################################################################\n",
      "Fold 177\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1285.1724305853677\n",
      "####################################################################################################\n",
      "Fold 178\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 878.1980386065569\n",
      "####################################################################################################\n",
      "Fold 179\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1110.6323255972459\n",
      "####################################################################################################\n",
      "Fold 180\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 959.8663727255506\n",
      "####################################################################################################\n",
      "Fold 181\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 731.9249407325459\n",
      "####################################################################################################\n",
      "Fold 182\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1071.1986215682705\n",
      "####################################################################################################\n",
      "Fold 183\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1187.4937948275717\n",
      "####################################################################################################\n",
      "Fold 184\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 760.9900531982621\n",
      "####################################################################################################\n",
      "Fold 185\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1254.149030009259\n",
      "####################################################################################################\n",
      "Fold 186\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1117.519484943227\n",
      "####################################################################################################\n",
      "Fold 187\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 993.0555542007098\n",
      "####################################################################################################\n",
      "Fold 188\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1097.4078929865414\n",
      "####################################################################################################\n",
      "Fold 189\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1005.142718011194\n",
      "####################################################################################################\n",
      "Fold 190\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 855.7093565123033\n",
      "####################################################################################################\n",
      "Fold 191\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1193.0181401201792\n",
      "####################################################################################################\n",
      "Fold 192\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 984.6741285669851\n",
      "####################################################################################################\n",
      "Fold 193\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1068.6249335658367\n",
      "####################################################################################################\n",
      "Fold 194\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 863.1234372371567\n",
      "####################################################################################################\n",
      "Fold 195\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1074.1907835130792\n",
      "####################################################################################################\n",
      "Fold 196\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1148.1504425834423\n",
      "####################################################################################################\n",
      "Fold 197\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 791.5705057992355\n",
      "####################################################################################################\n",
      "Fold 198\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1153.8302741824834\n",
      "####################################################################################################\n",
      "Fold 199\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1103.5363622588602\n",
      "####################################################################################################\n",
      "Fold 200\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 690.3767542002579\n",
      "####################################################################################################\n",
      "Fold 201\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1257.2520263006868\n",
      "####################################################################################################\n",
      "Fold 202\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 865.7133196638356\n",
      "####################################################################################################\n",
      "Fold 203\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 998.9315157597931\n",
      "####################################################################################################\n",
      "Fold 204\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1267.5206757684975\n",
      "####################################################################################################\n",
      "Fold 205\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 765.4589596939716\n",
      "####################################################################################################\n",
      "Fold 206\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1136.7116733914759\n",
      "####################################################################################################\n",
      "Fold 207\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1098.9071160527494\n",
      "####################################################################################################\n",
      "Fold 208\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1101.8670000272805\n",
      "####################################################################################################\n",
      "Fold 209\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 824.1177654362848\n",
      "####################################################################################################\n",
      "Fold 210\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 722.5071743209758\n",
      "####################################################################################################\n",
      "Fold 211\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 988.5540577617048\n",
      "####################################################################################################\n",
      "Fold 212\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 761.711443299152\n",
      "####################################################################################################\n",
      "Fold 213\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1572.4979567299526\n",
      "####################################################################################################\n",
      "Fold 214\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 968.364648762844\n",
      "####################################################################################################\n",
      "Fold 215\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 999.8685199697109\n",
      "####################################################################################################\n",
      "Fold 216\n",
      " rows of train = 8493 rows of holdout = 30\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LGBM Val OOF RMSE = 1056.6937588316682\n",
      "####################################################################################################\n",
      "Fold 217\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1340.1662066172764\n",
      "####################################################################################################\n",
      "Fold 218\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1544.0127184599978\n",
      "####################################################################################################\n",
      "Fold 219\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 744.1117839632593\n",
      "####################################################################################################\n",
      "Fold 220\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1070.7598533854703\n",
      "####################################################################################################\n",
      "Fold 221\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1261.0078312858625\n",
      "####################################################################################################\n",
      "Fold 222\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1134.503523998735\n",
      "####################################################################################################\n",
      "Fold 223\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 839.773534247342\n",
      "####################################################################################################\n",
      "Fold 224\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1043.2719878537719\n",
      "####################################################################################################\n",
      "Fold 225\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1089.736622790005\n",
      "####################################################################################################\n",
      "Fold 226\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1127.784775457926\n",
      "####################################################################################################\n",
      "Fold 227\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1059.5116332683788\n",
      "####################################################################################################\n",
      "Fold 228\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1199.4814662930944\n",
      "####################################################################################################\n",
      "Fold 229\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1109.589403750556\n",
      "####################################################################################################\n",
      "Fold 230\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 895.5849517020752\n",
      "####################################################################################################\n",
      "Fold 231\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1138.057868275907\n",
      "####################################################################################################\n",
      "Fold 232\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 757.690710749018\n",
      "####################################################################################################\n",
      "Fold 233\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1245.3532613492725\n",
      "####################################################################################################\n",
      "Fold 234\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1103.3368554258075\n",
      "####################################################################################################\n",
      "Fold 235\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1204.2310161846806\n",
      "####################################################################################################\n",
      "Fold 236\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 986.0838637892423\n",
      "####################################################################################################\n",
      "Fold 237\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 959.6749398924403\n",
      "####################################################################################################\n",
      "Fold 238\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 885.6098904103044\n",
      "####################################################################################################\n",
      "Fold 239\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1783.234724285898\n",
      "####################################################################################################\n",
      "Fold 240\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 936.483750056355\n",
      "####################################################################################################\n",
      "Fold 241\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1455.0363818510107\n",
      "####################################################################################################\n",
      "Fold 242\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 647.0895405373954\n",
      "####################################################################################################\n",
      "Fold 243\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 822.2485249915807\n",
      "####################################################################################################\n",
      "Fold 244\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1565.360634307737\n",
      "####################################################################################################\n",
      "Fold 245\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 879.7306678745591\n",
      "####################################################################################################\n",
      "Fold 246\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 865.7021010266151\n",
      "####################################################################################################\n",
      "Fold 247\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 782.0004613033894\n",
      "####################################################################################################\n",
      "Fold 248\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 968.5581130097479\n",
      "####################################################################################################\n",
      "Fold 249\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1058.6708780810827\n",
      "####################################################################################################\n",
      "Fold 250\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 805.6957053642125\n",
      "####################################################################################################\n",
      "Fold 251\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 965.1187196816076\n",
      "####################################################################################################\n",
      "Fold 252\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1253.7934543475471\n",
      "####################################################################################################\n",
      "Fold 253\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 923.9063137420437\n",
      "####################################################################################################\n",
      "Fold 254\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 738.6238480224083\n",
      "####################################################################################################\n",
      "Fold 255\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1099.2064246101877\n",
      "####################################################################################################\n",
      "Fold 256\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1240.1998327163665\n",
      "####################################################################################################\n",
      "Fold 257\n",
      " rows of train = 8493 rows of holdout = 30\n",
      "LGBM Val OOF RMSE = 1004.8320236996626\n",
      "####################################################################################################\n",
      "Fold 258\n",
      " rows of train = 8494 rows of holdout = 29\n",
      "LGBM Val OOF RMSE = 1021.8044976676471\n",
      "####################################################################################################\n",
      "Fold 259\n",
      " rows of train = 8494 rows of holdout = 29\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LGBM Val OOF RMSE = 1065.4003252800676\n",
      "####################################################################################################\n",
      "Fold 260\n",
      " rows of train = 8494 rows of holdout = 29\n",
      "LGBM Val OOF RMSE = 1346.5850108720006\n",
      "####################################################################################################\n",
      "Fold 261\n",
      " rows of train = 8494 rows of holdout = 29\n",
      "LGBM Val OOF RMSE = 1133.9537582453138\n",
      "####################################################################################################\n",
      "Fold 262\n",
      " rows of train = 8494 rows of holdout = 29\n",
      "LGBM Val OOF RMSE = 1188.3571642060422\n",
      "####################################################################################################\n",
      "Fold 263\n",
      " rows of train = 8494 rows of holdout = 29\n",
      "LGBM Val OOF RMSE = 1022.735589510129\n",
      "####################################################################################################\n",
      "Fold 264\n",
      " rows of train = 8494 rows of holdout = 29\n",
      "LGBM Val OOF RMSE = 1251.7237816224776\n",
      "####################################################################################################\n",
      "Fold 265\n",
      " rows of train = 8494 rows of holdout = 29\n",
      "LGBM Val OOF RMSE = 1368.8150504722548\n",
      "####################################################################################################\n",
      "Fold 266\n",
      " rows of train = 8494 rows of holdout = 29\n",
      "LGBM Val OOF RMSE = 967.3732578060116\n",
      "####################################################################################################\n",
      "Fold 267\n",
      " rows of train = 8494 rows of holdout = 29\n",
      "LGBM Val OOF RMSE = 1192.1911504676398\n",
      "####################################################################################################\n",
      "Fold 268\n",
      " rows of train = 8494 rows of holdout = 29\n",
      "LGBM Val OOF RMSE = 1303.905940201886\n",
      "####################################################################################################\n",
      "Fold 269\n",
      " rows of train = 8494 rows of holdout = 29\n",
      "LGBM Val OOF RMSE = 1120.0096639585956\n",
      "####################################################################################################\n",
      "Fold 270\n",
      " rows of train = 8494 rows of holdout = 29\n",
      "LGBM Val OOF RMSE = 742.0000821157\n",
      "####################################################################################################\n",
      "Fold 271\n",
      " rows of train = 8494 rows of holdout = 29\n",
      "LGBM Val OOF RMSE = 1140.2792016660007\n",
      "####################################################################################################\n",
      "Fold 272\n",
      " rows of train = 8494 rows of holdout = 29\n",
      "LGBM Val OOF RMSE = 1043.904276456413\n",
      "####################################################################################################\n",
      "Fold 273\n",
      " rows of train = 8494 rows of holdout = 29\n",
      "LGBM Val OOF RMSE = 1228.3290941364382\n",
      "####################################################################################################\n",
      "Fold 274\n",
      " rows of train = 8494 rows of holdout = 29\n",
      "LGBM Val OOF RMSE = 841.3835686790403\n",
      "####################################################################################################\n",
      "Fold 275\n",
      " rows of train = 8494 rows of holdout = 29\n",
      "LGBM Val OOF RMSE = 1007.1285400623668\n",
      "####################################################################################################\n",
      "Fold 276\n",
      " rows of train = 8494 rows of holdout = 29\n",
      "LGBM Val OOF RMSE = 822.3408493575039\n",
      "####################################################################################################\n",
      "Fold 277\n",
      " rows of train = 8494 rows of holdout = 29\n",
      "LGBM Val OOF RMSE = 780.0535095512337\n",
      "####################################################################################################\n",
      "Fold 278\n",
      " rows of train = 8494 rows of holdout = 29\n",
      "LGBM Val OOF RMSE = 1068.4226181749186\n",
      "####################################################################################################\n",
      "Fold 279\n",
      " rows of train = 8494 rows of holdout = 29\n",
      "LGBM Val OOF RMSE = 940.0109799304031\n",
      "####################################################################################################\n",
      "Fold 280\n",
      " rows of train = 8494 rows of holdout = 29\n",
      "LGBM Val OOF RMSE = 962.713826513575\n",
      "####################################################################################################\n",
      "Fold 281\n",
      " rows of train = 8494 rows of holdout = 29\n",
      "LGBM Val OOF RMSE = 1376.656773194166\n",
      "####################################################################################################\n",
      "Fold 282\n",
      " rows of train = 8494 rows of holdout = 29\n",
      "LGBM Val OOF RMSE = 1102.0328244530451\n",
      "####################################################################################################\n",
      "Fold 283\n",
      " rows of train = 8494 rows of holdout = 29\n",
      "LGBM Val OOF RMSE = 1210.0110246853894\n",
      "####################################################################################################\n",
      "Fold 284\n",
      " rows of train = 8494 rows of holdout = 29\n",
      "LGBM Val OOF RMSE = 971.5271885383133\n",
      "####################################################################################################\n",
      "RMSE Mean Score : \n",
      "1058.54493385 (205.33559705)\n"
     ]
    }
   ],
   "source": [
    "from tqdm.notebook import tqdm ,tnrange\n",
    "import lightgbm as LGBMRegressor\n",
    "\n",
    "params= { }\n",
    "params['learning_rate'] = 0.01\n",
    "params['n_estimators']= 950\n",
    "params['max_depth']= 3\n",
    "params['lambda_l2']=7\n",
    "params['metric'] = 'rmse'\n",
    "params['seed']= 99\n",
    "params['cat_features'] = category_col\n",
    "params['regression_l1'] = 10\n",
    "params['objective'] = 'regression'\n",
    "params['n_jobs'] = -1\n",
    "params['max_bin'] = 800\n",
    "\n",
    "scores = []\n",
    "splits = KFold(n_splits=285,random_state=99)\n",
    "\n",
    "for i, (idxT, idxV) in enumerate(splits.split(X, y)):\n",
    "\n",
    "        print('Fold',i)\n",
    "        print(' rows of train =',len(idxT),'rows of holdout =',len(idxV))\n",
    "\n",
    "        # Training\n",
    "        d_train = LGBMRegressor.Dataset(X.iloc[idxT], label=y[idxT])\n",
    "        clf =  LGBMRegressor.train(params,d_train,50)\n",
    "        \n",
    "        # Prediction\n",
    "        probs_oof = clf.predict(X.iloc[idxV])\n",
    "        \n",
    "        # Evaluation\n",
    "        rmse = np.sqrt(mean_squared_error(y[idxV],probs_oof))\n",
    "        scores.append(rmse)\n",
    "\n",
    "        print ('LGBM Val OOF RMSE =',rmse)\n",
    "        print('#'*100)\n",
    "print('RMSE Mean Score : ')\n",
    "print('%.8f (%.8f)' % (np.array(scores).mean(), np.array(scores).std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Interpretation and Analysis using SHAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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n.childContextTypes?u(\"107\",this.getName()||\"ReactCompositeComponent\"):void 0;for(var i in e)i in n.childContextTypes?void 0:u(\"108\",this.getName()||\"ReactCompositeComponent\",i);return c({},t,e)}return t},_checkContextTypes:function(t,e,n){},receiveComponent:function(t,e,n){var r=this._currentElement,i=this._context;this._pendingElement=null,this.updateComponent(e,r,t,i,n)},performUpdateIfNecessary:function(t){null!=this._pendingElement?v.receiveComponent(this,this._pendingElement,t,this._context):null!==this._pendingStateQueue||this._pendingForceUpdate?this.updateComponent(t,this._currentElement,this._currentElement,this._context,this._context):this._updateBatchNumber=null},updateComponent:function(t,e,n,r,i){var o=this._instance;null==o?u(\"136\",this.getName()||\"ReactCompositeComponent\"):void 0;var a,c=!1;this._context===i?a=o.context:(a=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,a);var f=this._processPendingState(l,a),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,a):this._compositeType===_.PureClass&&(p=!m(s,l)||!m(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,a,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=a)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=c({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var u=r[a];c(o,\"function\"==typeof u?u.call(n,o,t,e):u)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(y(r,i))v.receiveComponent(n,i,t,this._processChildContext(e));else{var a=v.getHostNode(n);v.unmountComponent(n,!1);var u=d.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==d.EMPTY);this._renderedComponent=c;var s=v.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){l.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t,e=this._instance;return t=e.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==_.StatelessFunctional){f.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{f.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||t===!1||s.isValidElement(t)?void 0:u(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n?u(\"110\"):void 0;var r=e.getPublicInstance(),i=n.refs===g?n.refs={}:n.refs;i[t]=r},detachRef:function(t){var e=this.getPublicInstance().refs;delete e[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===_.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=x},function(t,e,n){\"use strict\";var r=n(4),i=n(358),o=n(163),a=n(24),u=n(11),c=n(371),s=n(387),l=n(167),f=n(395);n(1);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&(G[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML?v(\"137\",t._tag,t._currentElement._owner?\" Check the render method of \"+t._currentElement._owner.getName()+\".\":\"\"):void 0),null!=e.dangerouslySetInnerHTML&&(null!=e.children?v(\"60\"):void 0,\"object\"==typeof e.dangerouslySetInnerHTML&&V in e.dangerouslySetInnerHTML?void 0:v(\"61\")),null!=e.style&&\"object\"!=typeof e.style?v(\"62\",r(t)):void 0)}function o(t,e,n,r){if(!(r instanceof I)){var i=t._hostContainerInfo,o=i._node&&i._node.nodeType===H,u=o?i._node:i._ownerDocument;F(e,u),r.getReactMountReady().enqueue(a,{inst:t,registrationName:e,listener:n})}}function a(){var t=this;C.putListener(t.inst,t.registrationName,t.listener)}function u(){var t=this;S.postMountWrapper(t)}function c(){var t=this;A.postMountWrapper(t)}function s(){var t=this;P.postMountWrapper(t)}function l(){var t=this;t._rootNodeID?void 0:v(\"63\");var e=U(t);switch(e?void 0:v(\"64\"),t._tag){case\"iframe\":case\"object\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"video\":case\"audio\":t._wrapperState.listeners=[];for(var n in q)q.hasOwnProperty(n)&&t._wrapperState.listeners.push(k.trapBubbledEvent(n,q[n],e));break;case\"source\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e),k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topReset\",\"reset\",e),k.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function f(){N.postUpdateWrapper(this)}function p(t){Z.call(X,t)||($.test(t)?void 0:v(\"65\",t),X[t]=!0)}function h(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function d(t){var e=t.type;p(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var v=n(2),g=n(3),m=n(332),y=n(334),_=n(20),b=n(82),x=n(21),w=n(156),C=n(22),M=n(83),k=n(51),E=n(157),T=n(4),S=n(351),P=n(352),N=n(158),A=n(355),O=(n(9),n(364)),I=n(369),D=(n(8),n(54)),R=(n(0),n(94),n(80),n(96),n(1),E),L=C.deleteListener,U=T.getNodeFromInstance,F=k.listenTo,j=M.registrationNameModules,B={string:!0,number:!0},W=\"style\",V=\"__html\",z={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},H=11,q={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},Y={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},K={listing:!0,pre:!0,textarea:!0},G=g({menuitem:!0},Y),$=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,X={},Z={}.hasOwnProperty,Q=1;d.displayName=\"ReactDOMComponent\",d.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=Q++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(l,this);break;case\"input\":S.mountWrapper(this,o,e),o=S.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"option\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o);break;case\"select\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"textarea\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o),t.getReactMountReady().enqueue(l,this)}i(this,o);var a,f;null!=e?(a=e._namespaceURI,f=e._tag):n._tag&&(a=n._namespaceURI,f=n._tag),(null==a||a===b.svg&&\"foreignobject\"===f)&&(a=b.html),a===b.html&&(\"svg\"===this._tag?a=b.svg:\"math\"===this._tag&&(a=b.mathml)),this._namespaceURI=a;var p;if(t.useCreateElement){var h,d=n._ownerDocument;if(a===b.html)if(\"script\"===this._tag){var v=d.createElement(\"div\"),g=this._currentElement.type;v.innerHTML=\"<\"+g+\"></\"+g+\">\",h=v.removeChild(v.firstChild)}else h=o.is?d.createElement(this._currentElement.type,o.is):d.createElement(this._currentElement.type);else h=d.createElementNS(a,this._currentElement.type);T.precacheNode(this,h),this._flags|=R.hasCachedChildNodes,this._hostParent||w.setAttributeForRoot(h),this._updateDOMProperties(null,o,t);var y=_(h);this._createInitialChildren(t,o,r,y),p=y}else{var x=this._createOpenTagMarkupAndPutListeners(t,o),C=this._createContentMarkup(t,o,r);p=!C&&Y[this._tag]?x+\"/>\":x+\">\"+C+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"select\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return p},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(j.hasOwnProperty(r))i&&o(this,r,i,t);else{r===W&&(i&&(i=this._previousStyleCopy=g({},e.style)),i=y.createMarkupForStyles(i,this));var a=null;null!=this._tag&&h(this._tag,e)?z.hasOwnProperty(r)||(a=w.createMarkupForCustomAttribute(r,i)):a=w.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+w.createMarkupForRoot()),n+=\" \"+w.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=D(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return K[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&_.queueHTML(r,i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&_.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)_.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=S.getHostProps(this,o),a=S.getHostProps(this,a);break;case\"option\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"select\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"textarea\":o=A.getHostProps(this,o),a=A.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":S.updateWrapper(this);break;case\"textarea\":A.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(f,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(r===W){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else j.hasOwnProperty(r)?t[r]&&L(this,r):h(this._tag,t)?z.hasOwnProperty(r)||w.deleteValueForAttribute(U(this),r):(x.properties[r]||x.isCustomAttribute(r))&&w.deleteValueForProperty(U(this),r);for(r in e){var c=e[r],s=r===W?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(r===W)if(c?c=this._previousStyleCopy=g({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(j.hasOwnProperty(r))c?o(this,r,c,n):s&&L(this,r);else if(h(this._tag,e))z.hasOwnProperty(r)||w.setValueForAttribute(U(this),r,c);else if(x.properties[r]||x.isCustomAttribute(r)){var l=U(this);null!=c?w.setValueForProperty(l,r,c):w.deleteValueForProperty(l,r)}}a&&y.setValueForStyles(U(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=B[typeof t.children]?t.children:null,o=B[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return U(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"html\":case\"head\":case\"body\":v(\"66\",this._tag)}this.unmountChildren(t),T.uncacheNode(this),C.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return U(this)}},g(d.prototype,d.Mixin,O.Mixin),t.exports=d},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(96),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" \";if(t.useCreateElement){var c=n._ownerDocument,s=c.createComment(u);return o.precacheNode(this,s),i(s)}return t.renderToStaticMarkup?\"\":\"<!--\"+u+\"-->\"},receiveComponent:function(){},getHostNode:function(){return o.getNodeFromInstance(this)},unmountComponent:function(){o.uncacheNode(this)}}),t.exports=a},function(t,e,n){\"use strict\";var r={useCreateElement:!0,useFiber:!1};t.exports=r},function(t,e,n){\"use strict\";var r=n(81),i=n(4),o={dangerouslyProcessChildrenUpdates:function(t,e){var n=i.getNodeFromInstance(t);r.processUpdates(n,e)}};t.exports=o},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&f.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=c.executeOnChange(e,t);l.asap(r,this);var i=e.name;if(\"radio\"===e.type&&null!=i){for(var a=s.getNodeFromInstance(this),u=a;u.parentNode;)u=u.parentNode;for(var f=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<f.length;p++){var h=f[p];if(h!==a&&h.form===a.form){var d=s.getInstanceFromNode(h);d?void 0:o(\"90\"),l.asap(r,d)}}}return n}var o=n(2),a=n(3),u=n(156),c=n(85),s=n(4),l=n(11),f=(n(0),n(1),{getHostProps:function(t,e){var n=c.getValue(e),r=c.getChecked(e),i=a({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange});return i},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&u.setValueForProperty(s.getNodeFromInstance(t),\"checked\",n||!1);var r=s.getNodeFromInstance(t),i=c.getValue(e);if(null!=i){var o=\"\"+i;o!==r.value&&(r.value=o)}else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=s.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=f},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(158),c=(n(1),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){var n=a.getNodeFromInstance(t);n.setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length,a=o+r;return{start:o,end:a}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(392),l=n(168),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(2),i=n(3),o=n(81),a=n(20),u=n(4),c=n(54),s=(n(0),n(96),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \",s=\" /react-text \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var l=n._ownerDocument,f=l.createComment(o),p=l.createComment(s),h=a(l.createDocumentFragment());return a.queueChild(h,a(f)),this._stringText&&a.queueChild(h,a(l.createTextNode(this._stringText))),a.queueChild(h,a(p)),u.precacheNode(this,f),this._closingComment=p,h}var d=c(this._stringText);return t.renderToStaticMarkup?d:\"<!--\"+o+\"-->\"+d+\"<!--\"+s+\"-->\"},receiveComponent:function(t,e){if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n?r(\"67\",this._domID):void 0,8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(2),a=n(3),u=n(85),c=n(4),s=n(11),l=(n(0),n(1),{getHostProps:function(t,e){null!=e.dangerouslySetInnerHTML?o(\"91\"):void 0;var n=a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange});return n},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a?o(\"92\"):void 0,Array.isArray(c)&&(c.length<=1?void 0:o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;\n",
       "n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t?void 0:c(\"33\"),\"_hostNode\"in e?void 0:c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in t?void 0:c(\"35\"),\"_hostNode\"in e?void 0:c(\"35\");for(;e;){if(e===t)return!0;e=e._hostParent}return!1}function o(t){return\"_hostNode\"in t?void 0:c(\"36\"),t._hostParent}function a(t,e,n){for(var r=[];t;)r.push(t),t=t._hostParent;var i;for(i=r.length;i-- >0;)e(r[i],\"captured\",n);for(i=0;i<r.length;i++)e(r[i],\"bubbled\",n)}function u(t,e,n,i,o){for(var a=t&&e?r(t,e):null,u=[];t&&t!==a;)u.push(t),t=t._hostParent;for(var c=[];e&&e!==a;)c.push(e),e=e._hostParent;var s;for(s=0;s<u.length;s++)n(u[s],\"bubbled\",i);for(s=c.length;s-- 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r(){C||(C=!0,y.EventEmitter.injectReactEventListener(m),y.EventPluginHub.injectEventPluginOrder(u),y.EventPluginUtils.injectComponentTree(p),y.EventPluginUtils.injectTreeTraversal(d),y.EventPluginHub.injectEventPluginsByName({SimpleEventPlugin:w,EnterLeaveEventPlugin:c,ChangeEventPlugin:a,SelectEventPlugin:x,BeforeInputEventPlugin:o}),y.HostComponent.injectGenericComponentClass(f),y.HostComponent.injectTextComponentClass(v),y.DOMProperty.injectDOMPropertyConfig(i),y.DOMProperty.injectDOMPropertyConfig(s),y.DOMProperty.injectDOMPropertyConfig(b),y.EmptyComponent.injectEmptyComponentFactory(function(t){return new h(t)}),y.Updates.injectReconcileTransaction(_),y.Updates.injectBatchingStrategy(g),y.Component.injectEnvironment(l))}var i=n(331),o=n(333),a=n(335),u=n(337),c=n(338),s=n(341),l=n(343),f=n(346),p=n(4),h=n(348),d=n(356),v=n(354),g=n(357),m=n(361),y=n(362),_=n(367),b=n(372),x=n(373),w=n(374),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof 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v={_enabled:!0,_handleTopLevel:null,WINDOW_HANDLE:s.canUseDOM?window:null,setHandleTopLevel:function(t){v._handleTopLevel=t},setEnabled:function(t){v._enabled=!!t},isEnabled:function(){return v._enabled},trapBubbledEvent:function(t,e,n){return n?c.listen(n,e,v.dispatchEvent.bind(null,t)):null},trapCapturedEvent:function(t,e,n){return n?c.capture(n,e,v.dispatchEvent.bind(null,t)):null},monitorScrollValue:function(t){var e=a.bind(null,t);c.listen(window,\"scroll\",e)},dispatchEvent:function(t,e){if(v._enabled){var n=i.getPooled(t,e);try{p.batchedUpdates(o,n)}finally{i.release(n)}}}};t.exports=v},function(t,e,n){\"use strict\";var r=n(21),i=n(22),o=n(50),a=n(86),u=n(159),c=n(51),s=n(161),l=n(11),f={Component:a.injection,DOMProperty:r.injection,EmptyComponent:u.injection,EventPluginHub:i.injection,EventPluginUtils:o.injection,EventEmitter:c.injection,HostComponent:s.injection,Updates:l.injection};t.exports=f},function(t,e,n){\"use strict\";var r=n(385),i=/\\/?>/,o=/^<\\!\\-\\-/,a={CHECKSUM_ATTR_NAME:\"data-react-checksum\",addChecksumToMarkup:function(t){var e=r(t);return o.test(t)?t:t.replace(i,\" \"+a.CHECKSUM_ATTR_NAME+'=\"'+e+'\"$&')},canReuseMarkup:function(t,e){var n=e.getAttribute(a.CHECKSUM_ATTR_NAME);n=n&&parseInt(n,10);var i=r(t);return i===n}};t.exports=a},function(t,e,n){\"use strict\";function r(t,e,n){return{type:\"INSERT_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:n,afterNode:e}}function i(t,e,n){return{type:\"MOVE_EXISTING\",content:null,fromIndex:t._mountIndex,fromNode:p.getHostNode(t),toIndex:n,afterNode:e}}function o(t,e){return{type:\"REMOVE_NODE\",content:null,fromIndex:t._mountIndex,fromNode:e,toIndex:null,afterNode:null}}function a(t){return{type:\"SET_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function u(t){return{type:\"TEXT_CONTENT\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function c(t,e){return e&&(t=t||[],t.push(e)),t}function s(t,e){f.processChildrenUpdates(t,e)}var l=n(2),f=n(86),p=(n(40),n(9),n(15),n(24)),h=n(342),d=(n(8),n(388)),v=(n(0),{Mixin:{_reconcilerInstantiateChildren:function(t,e,n){return h.instantiateChildren(t,e,n)},_reconcilerUpdateChildren:function(t,e,n,r,i,o){var a,u=0;return a=d(e,u),h.updateChildren(t,a,n,r,i,this,this._hostContainerInfo,o,u),a},mountChildren:function(t,e,n){var r=this._reconcilerInstantiateChildren(t,e,n);this._renderedChildren=r;var i=[],o=0;for(var a in r)if(r.hasOwnProperty(a)){var u=r[a],c=0,s=p.mountComponent(u,e,this,this._hostContainerInfo,n,c);u._mountIndex=o++,i.push(s)}return i},updateTextContent:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[u(t)];s(this,r)},updateMarkup:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[a(t)];s(this,r)},updateChildren:function(t,e,n){this._updateChildren(t,e,n)},_updateChildren:function(t,e,n){var r=this._renderedChildren,i={},o=[],a=this._reconcilerUpdateChildren(r,t,o,i,e,n);if(a||r){var u,l=null,f=0,h=0,d=0,v=null;for(u in a)if(a.hasOwnProperty(u)){var g=r&&r[u],m=a[u];g===m?(l=c(l,this.moveChild(g,v,f,h)),h=Math.max(g._mountIndex,h),g._mountIndex=f):(g&&(h=Math.max(g._mountIndex,h)),l=c(l,this._mountChildAtIndex(m,o[d],v,f,e,n)),d++),f++,v=p.getHostNode(m)}for(u in i)i.hasOwnProperty(u)&&(l=c(l,this._unmountChild(r[u],i[u])));l&&s(this,l),this._renderedChildren=a}},unmountChildren:function(t){var e=this._renderedChildren;h.unmountChildren(e,t),this._renderedChildren=null},moveChild:function(t,e,n,r){if(t._mountIndex<r)return i(t,e,n)},createChild:function(t,e,n){return r(n,e,t._mountIndex)},removeChild:function(t,e){return o(t,e)},_mountChildAtIndex:function(t,e,n,r,i,o){return t._mountIndex=r,this.createChild(t,n,e)},_unmountChild:function(t,e){var n=this.removeChild(t,e);return t._mountIndex=null,n}}});t.exports=v},function(t,e,n){\"use strict\";function r(t){return!(!t||\"function\"!=typeof t.attachRef||\"function\"!=typeof t.detachRef)}var i=n(2),o=(n(0),{addComponentAsRefTo:function(t,e,n){r(n)?void 0:i(\"119\"),n.attachRef(e,t)},removeComponentAsRefFrom:function(t,e,n){r(n)?void 0:i(\"120\");var o=n.getPublicInstance();o&&o.refs[e]===t.getPublicInstance()&&n.detachRef(e)}});t.exports=o},function(t,e,n){\"use strict\";var r=\"SECRET_DO_NOT_PASS_THIS_OR_YOU_WILL_BE_FIRED\";t.exports=r},function(t,e,n){\"use strict\";function r(t){this.reinitializeTransaction(),this.renderToStaticMarkup=!1,this.reactMountReady=o.getPooled(null),this.useCreateElement=t}var i=n(3),o=n(155),a=n(17),u=n(51),c=n(162),s=(n(9),n(53)),l=n(88),f={initialize:c.getSelectionInformation,close:c.restoreSelection},p={initialize:function(){var t=u.isEnabled();return u.setEnabled(!1),t},close:function(t){u.setEnabled(t)}},h={initialize:function(){this.reactMountReady.reset()},close:function(){this.reactMountReady.notifyAll()}},d=[f,p,h],v={getTransactionWrappers:function(){return d},getReactMountReady:function(){return this.reactMountReady},getUpdateQueue:function(){return l},checkpoint:function(){return this.reactMountReady.checkpoint()},rollback:function(t){this.reactMountReady.rollback(t)},destructor:function(){o.release(this.reactMountReady),this.reactMountReady=null}};i(r.prototype,s,v),a.addPoolingTo(r),t.exports=r},function(t,e,n){\"use strict\";function r(t,e,n){\"function\"==typeof t?t(e.getPublicInstance()):o.addComponentAsRefTo(e,t,n)}function i(t,e,n){\"function\"==typeof t?t(null):o.removeComponentAsRefFrom(e,t,n)}var o=n(365),a={};a.attachRefs=function(t,e){if(null!==e&&\"object\"==typeof e){var n=e.ref;null!=n&&r(n,t,e._owner)}},a.shouldUpdateRefs=function(t,e){var n=null,r=null;null!==t&&\"object\"==typeof 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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import shap\n",
    "# print the JS visualization code to the notebook\n",
    "shap.initjs()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {},
   "outputs": [],
   "source": [
    "# explain the GAM model with SHAP\n",
    "background = shap.maskers.Independent(X_cv, max_samples=1000)\n",
    "explainer_ebm = shap.Explainer(clf.predict, background)\n",
    "shap_values_ebm = explainer_ebm(X_cv.iloc[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 576x504 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the waterfall_plot shows how we get from explainer.expected_value to model.predict(X)[sample_ind]\n",
    "shap.plots.waterfall(shap_values_ebm[3], max_display=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shap Summary Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x424.8 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# explain all the predictions in the test set\n",
    "ex = shap.TreeExplainer(clf)\n",
    "shap_values = ex.shap_values(X_cv)\n",
    "shap.summary_plot(shap_values, X_cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above graph, the features have been stacked in descending order of importance.\n",
    "Red indicates high feature value and Blue indicates low feature value. \n",
    "We know that the more positive the SHAP value, \n",
    "the more is the contribution of that feature as SHAP value calculates the difference between predictions \n",
    "with the feature and without the feature. \n",
    "Thus, RED color corresponds to +SHAP and the feature is positively correlated to the target variable and \n",
    "if BLUE color corresponds to –SHAP, it is negatively correlated to the target variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shap Force Plot for one sample Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id='iTE0LL7BLYTY2FZTOTP85'>\n",
       "<div style='color: #900; text-align: center;'>\n",
       "  <b>Visualization omitted, Javascript library not loaded!</b><br>\n",
       "  Have you run `initjs()` in this notebook? If this notebook was from another\n",
       "  user you must also trust this notebook (File -> Trust notebook). If you are viewing\n",
       "  this notebook on github the Javascript has been stripped for security. If you are using\n",
       "  JupyterLab this error is because a JupyterLab extension has not yet been written.\n",
       "</div></div>\n",
       " <script>\n",
       "   if (window.SHAP) SHAP.ReactDom.render(\n",
       "    SHAP.React.createElement(SHAP.AdditiveForceVisualizer, {\"outNames\": [\"f(x)\"], \"baseValue\": 2180.606159768804, \"outValue\": 2046.5230074162484, \"link\": \"identity\", \"featureNames\": [\"Item_Weight\", \"Item_Fat_Content\", \"Item_Visibility\", \"Item_MRP\", \"Outlet_Size\", \"Outlet_Location_Type\", \"Outlet_Type\", \"Item_mrp_x_Visibility\", \"Outlet_Years\", \"Item_Type_Combined\", \"Outlet\"], \"features\": {\"0\": {\"effect\": -2.2152184015905303, \"value\": 0.0}, \"1\": {\"effect\": 5.185290430414082, \"value\": 1.0}, \"2\": {\"effect\": -41.76236986384346, \"value\": 0.089858447}, \"3\": {\"effect\": -235.1033342791022, \"value\": 84.5882}, \"4\": {\"effect\": 0.03375936425008938, \"value\": 1.0}, \"5\": {\"effect\": 5.92447948478618, \"value\": 2.0}, \"6\": {\"effect\": 94.3735155058149, \"value\": 3.0}, \"7\": {\"effect\": 27.28867939377633, \"value\": 7.600964286525399}, \"8\": {\"effect\": 14.77702745592998, \"value\": 28.0}, \"9\": {\"effect\": -8.255216857030367, \"value\": 2.0}, \"10\": {\"effect\": 5.670235414039456, \"value\": 5.0}}, \"plot_cmap\": \"RdBu\", \"labelMargin\": 20}),\n",
       "    document.getElementById('iTE0LL7BLYTY2FZTOTP85')\n",
       "  );\n",
       "</script>"
      ],
      "text/plain": [
       "<shap.plots._force.AdditiveForceVisualizer at 0x7fe33e92f890>"
      ]
     },
     "execution_count": 424,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ex = shap.TreeExplainer(clf)\n",
    "shap_values = ex.shap_values(X_cv)\n",
    "shap.force_plot(ex.expected_value, \n",
    "                shap_values[1], \n",
    "                X_cv.iloc[3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the graph, the base value is used to depict the output of a baseline model without any features. For a regression model, it is the average of the model output over the training data.\n",
    "\n",
    "Features that push the prediction to the right of base value are shown in red, and those pushing the prediction towards the base value are in blue."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shap Force Plot for all Datapoint in training dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id='iL46DXPKV77IKM2UQQL1K'>\n",
       "<div style='color: #900; text-align: center;'>\n",
       "  <b>Visualization omitted, Javascript library not loaded!</b><br>\n",
       "  Have you run `initjs()` in this notebook? If this notebook was from another\n",
       "  user you must also trust this notebook (File -> Trust notebook). If you are viewing\n",
       "  this notebook on github the Javascript has been stripped for security. If you are using\n",
       "  JupyterLab this error is because a JupyterLab extension has not yet been written.\n",
       "</div></div>\n",
       " <script>\n",
       "   if (window.SHAP) SHAP.ReactDom.render(\n",
       "    SHAP.React.createElement(SHAP.AdditiveForceArrayVisualizer, {\"outNames\": [\"f(x)\"], \"baseValue\": 2180.606159768804, \"link\": \"identity\", \"featureNames\": [\"Item_Weight\", \"Item_Fat_Content\", \"Item_Visibility\", \"Item_MRP\", \"Outlet_Size\", \"Outlet_Location_Type\", \"Outlet_Type\", \"Item_mrp_x_Visibility\", \"Outlet_Years\", \"Item_Type_Combined\", \"Outlet\"], \"explanations\": [{\"outValue\": 1357.8113045210698, \"simIndex\": 60.0, \"features\": {\"0\": {\"effect\": 0.6314022951559893, \"value\": 3.0}, \"1\": {\"effect\": 11.9581903276183, \"value\": 2.0}, \"2\": {\"effect\": 7.982536963671601, \"value\": 0.03075661}, \"3\": {\"effect\": -851.6074434381483, \"value\": 86.25399999999999}, \"4\": {\"effect\": 0.12752208666968765, \"value\": 1.0}, \"5\": {\"effect\": 6.901196299639697, \"value\": 1.0}, \"6\": {\"effect\": 65.36660663372865, \"value\": 1.0}, \"7\": {\"effect\": -39.63878895987567, \"value\": 2.6528806389399997}, \"8\": {\"effect\": -12.521647571931329, \"value\": 11.0}, \"9\": {\"effect\": 0.7470467136243484, \"value\": 1.0}, \"10\": {\"effect\": -12.741476597887168, \"value\": 7.0}}}, {\"outValue\": 2046.5230074162484, \"simIndex\": 44.0, \"features\": {\"0\": {\"effect\": -2.2152184015905303, \"value\": 3.0}, \"1\": {\"effect\": 5.185290430414082, \"value\": 2.0}, \"2\": {\"effect\": -41.76236986384346, \"value\": 0.100330684}, \"3\": {\"effect\": -235.1033342791022, \"value\": 123.4388}, \"4\": {\"effect\": 0.03375936425008938, \"value\": 1.0}, \"5\": {\"effect\": 5.92447948478618, \"value\": 0.0}, \"6\": {\"effect\": 94.3735155058149, \"value\": 1.0}, \"7\": {\"effect\": 27.28867939377633, \"value\": 12.3846992361392}, \"8\": {\"effect\": 14.77702745592998, \"value\": 14.0}, \"9\": {\"effect\": -8.255216857030367, \"value\": 1.0}, \"10\": {\"effect\": 5.670235414039456, \"value\": 9.0}}}, {\"outValue\": 4347.314534772193, \"simIndex\": 17.0, \"features\": {\"0\": {\"effect\": -11.707029251779728, \"value\": 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-2.9310469487868467, \"value\": 1.0}, \"5\": {\"effect\": -5.934606168087291, \"value\": 2.0}, \"6\": {\"effect\": 1677.1189935304562, \"value\": 3.0}, \"7\": {\"effect\": -10.604446843262055, \"value\": 29.345277388171198}, \"8\": {\"effect\": 9.98632169407668, \"value\": 28.0}, \"9\": {\"effect\": -32.89248237425722, \"value\": 2.0}, \"10\": {\"effect\": 30.161429437985742, \"value\": 5.0}}}], \"plot_cmap\": \"RdBu\", \"ordering_keys\": null, \"ordering_keys_time_format\": null}),\n",
       "    document.getElementById('iL46DXPKV77IKM2UQQL1K')\n",
       "  );\n",
       "</script>"
      ],
      "text/plain": [
       "<shap.plots._force.AdditiveForceArrayVisualizer at 0x7fe342c85c50>"
      ]
     },
     "execution_count": 425,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shap.force_plot(ex.expected_value, shap_values, X_cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Force_plot can also be used to explain the entire dataset. It is nothing but the same plot for each sample, rotated 90 degrees and stacked side-by-side to explain the predictions for multiple samples in a single plot.\n",
    "\n",
    "The color codes mean the same as in previous chart, with red colored features increasing the logit & the blue colored feature decreasing the logit for each data point."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection\n",
    "\n",
    "####  1) As per the above summary plot we can observe 'Outlet_Size','Item_Type_Combined' features have less impact on model output.\n",
    "####  2) By removing the less impacted feature 'Outlet_Size','Item_Type_Combined' and training the model gave lowest RMSE score."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting on Test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_mrp_x_Visibility</th>\n",
       "      <th>Outlet_Years</th>\n",
       "      <th>Outlet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8523</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.007565</td>\n",
       "      <td>107.8622</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.815960</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8524</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.038428</td>\n",
       "      <td>87.3198</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.355497</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8525</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.099575</td>\n",
       "      <td>241.7538</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>24.072612</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8526</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.015388</td>\n",
       "      <td>155.0340</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.385724</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8527</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.118599</td>\n",
       "      <td>234.2300</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>27.779517</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Item_Weight  Item_Fat_Content  Item_Visibility  Item_MRP  \\\n",
       "8523            3                 0         0.007565  107.8622   \n",
       "8524            0                 2         0.038428   87.3198   \n",
       "8525            2                 1         0.099575  241.7538   \n",
       "8526            0                 0         0.015388  155.0340   \n",
       "8527            1                 2         0.118599  234.2300   \n",
       "\n",
       "      Outlet_Location_Type  Outlet_Type  Item_mrp_x_Visibility  Outlet_Years  \\\n",
       "8523                     0            1               0.815960            14   \n",
       "8524                     1            1               3.355497             6   \n",
       "8525                     2            0              24.072612            15   \n",
       "8526                     1            1               2.385724             6   \n",
       "8527                     2            3              27.779517            28   \n",
       "\n",
       "      Outlet  \n",
       "8523       9  \n",
       "8524       2  \n",
       "8525       0  \n",
       "8526       2  \n",
       "8527       5  "
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1639.15282751, 1371.66500047,  486.81594068, ..., 1864.60123005,\n",
       "       3570.47301744, 1291.55638507])"
      ]
     },
     "execution_count": 393,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred =  clf.predict(test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "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>Outlet_Identifier</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDW58</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1639.152828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FDW14</td>\n",
       "      <td>OUT017</td>\n",
       "      <td>1371.665000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NCN55</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>486.815941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDQ58</td>\n",
       "      <td>OUT017</td>\n",
       "      <td>2515.166360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FDY38</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>6138.777165</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier Outlet_Identifier  Item_Outlet_Sales\n",
       "0           FDW58            OUT049        1639.152828\n",
       "1           FDW14            OUT017        1371.665000\n",
       "2           NCN55            OUT010         486.815941\n",
       "3           FDQ58            OUT017        2515.166360\n",
       "4           FDY38            OUT027        6138.777165"
      ]
     },
     "execution_count": 394,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df = pd.DataFrame(df_test['Item_Identifier'],columns=['Item_Identifier',])\n",
    "final_df = pd.concat([final_df,df_test['Outlet_Identifier']],axis=1)\n",
    "final_df['Item_Outlet_Sales'] = y_pred\n",
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df.to_csv('bigmart_lgbm_12.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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