Big Mart Sales Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing the important packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('dataset/dataset_for_ML.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Unnamed: 0</th>\n",
       "      <th>Item_MRP_a</th>\n",
       "      <th>Item_MRP_b</th>\n",
       "      <th>Item_MRP_c</th>\n",
       "      <th>Item_MRP_d</th>\n",
       "      <th>Outlet_Type_Grocery Store</th>\n",
       "      <th>Outlet_Type_Supermarket Type1</th>\n",
       "      <th>Outlet_Type_Supermarket Type2</th>\n",
       "      <th>Outlet_Type_Supermarket Type3</th>\n",
       "      <th>Outlet_Location_Type_Tier 1</th>\n",
       "      <th>...</th>\n",
       "      <th>Outlet_Identifier_OUT013</th>\n",
       "      <th>Outlet_Identifier_OUT017</th>\n",
       "      <th>Outlet_Identifier_OUT018</th>\n",
       "      <th>Outlet_Identifier_OUT019</th>\n",
       "      <th>Outlet_Identifier_OUT027</th>\n",
       "      <th>Outlet_Identifier_OUT035</th>\n",
       "      <th>Outlet_Identifier_OUT045</th>\n",
       "      <th>Outlet_Identifier_OUT046</th>\n",
       "      <th>Outlet_Identifier_OUT049</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3735.1380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>443.4228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2097.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>732.3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>994.7052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Item_MRP_a  Item_MRP_b  Item_MRP_c  Item_MRP_d  \\\n",
       "0           0           0           0           0           1   \n",
       "1           1           1           0           0           0   \n",
       "2           2           0           0           1           0   \n",
       "3           3           0           0           1           0   \n",
       "4           4           1           0           0           0   \n",
       "\n",
       "   Outlet_Type_Grocery Store  Outlet_Type_Supermarket Type1  \\\n",
       "0                          0                              1   \n",
       "1                          0                              0   \n",
       "2                          0                              1   \n",
       "3                          1                              0   \n",
       "4                          0                              1   \n",
       "\n",
       "   Outlet_Type_Supermarket Type2  Outlet_Type_Supermarket Type3  \\\n",
       "0                              0                              0   \n",
       "1                              1                              0   \n",
       "2                              0                              0   \n",
       "3                              0                              0   \n",
       "4                              0                              0   \n",
       "\n",
       "   Outlet_Location_Type_Tier 1  ...  Outlet_Identifier_OUT013  \\\n",
       "0                            1  ...                         0   \n",
       "1                            0  ...                         0   \n",
       "2                            1  ...                         0   \n",
       "3                            0  ...                         0   \n",
       "4                            0  ...                         1   \n",
       "\n",
       "   Outlet_Identifier_OUT017  Outlet_Identifier_OUT018  \\\n",
       "0                         0                         0   \n",
       "1                         0                         1   \n",
       "2                         0                         0   \n",
       "3                         0                         0   \n",
       "4                         0                         0   \n",
       "\n",
       "   Outlet_Identifier_OUT019  Outlet_Identifier_OUT027  \\\n",
       "0                         0                         0   \n",
       "1                         0                         0   \n",
       "2                         0                         0   \n",
       "3                         0                         0   \n",
       "4                         0                         0   \n",
       "\n",
       "   Outlet_Identifier_OUT035  Outlet_Identifier_OUT045  \\\n",
       "0                         0                         0   \n",
       "1                         0                         0   \n",
       "2                         0                         0   \n",
       "3                         0                         0   \n",
       "4                         0                         0   \n",
       "\n",
       "   Outlet_Identifier_OUT046  Outlet_Identifier_OUT049  Item_Outlet_Sales  \n",
       "0                         0                         1          3735.1380  \n",
       "1                         0                         0           443.4228  \n",
       "2                         0                         1          2097.2700  \n",
       "3                         0                         0           732.3800  \n",
       "4                         0                         0           994.7052  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we can see while importng we got an unnecessary column and we can get rid of that column by deleting that column.\n",
    "\n",
    "del dataset['Unnamed: 0']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting the dataset into training and testing dataset.\n",
    "\n",
    "train, test = train_test_split(dataset, test_size = 0.20, random_state = 2019)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((6816, 34), (1705, 34))"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the shape of training and testing dataset.\n",
    "\n",
    "train.shape , test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating the label dataset for training and testing dataset.\n",
    "\n",
    "train_label = train['Item_Outlet_Sales']\n",
    "test_label = test['Item_Outlet_Sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# removing the label dataset from training and testing dataset.\n",
    "\n",
    "del train['Item_Outlet_Sales']\n",
    "del test['Item_Outlet_Sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((6816, 33), (1705, 33))"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now we can check the shape of training and testing dataset.\n",
    "\n",
    "train.shape , test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now after getting the dataset in accurate from, we can start implementing our ML approaches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Cross validation score and means square error from the SKLearn Library.\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's create a method to evaluate the performance of each model so, we don't have to write the duplicate code.\n",
    "\n",
    "def modelfit(model, train, test, train_label, test_label, model_name, graph_flag ):\n",
    "    \n",
    "    # fitting the model on training dataset.\n",
    "    model.fit( train, train_label)\n",
    "    \n",
    "    # checking the model on testing dataset.\n",
    "    predict_model = model.predict(test)\n",
    "    \n",
    "    # let's find the mean square error\n",
    "    mse = mean_squared_error(test_label, predict_model)\n",
    "    \n",
    "    #Printing Model Report:\n",
    "    print ('\\nModel Report For ', (model_name), '\\n\\n')\n",
    "    print(\"RMSE : %.4g\" % np.sqrt(mse))\n",
    "    # let's find the \n",
    "    c_val_score = score = np.sqrt(-(cross_val_score(model, train, train_label, cv = 10, scoring = 'neg_mean_squared_error')))\n",
    "    print( 'CV Score : Mean - %.4g | Std - %.4g | Min - %.4g | Max - %.4g' % (np.mean(c_val_score), np.std(c_val_score), np.min(c_val_score), np.max(c_val_score)))\n",
    "    if graph_flag:\n",
    "        fig, axes = plt.subplots(1, 1, figsize=(12,8))\n",
    "        coef1 = pd.Series(model.coef_, train.columns).sort_values()\n",
    "        coef1.plot(kind='bar', title='Model Coefficients')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report For  Linear Regression \n",
      "\n",
      "\n",
      "RMSE : 1171\n",
      "CV Score : Mean - 1150 | Std - 31.53 | Min - 1105 | Max - 1216\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Importing the Linear Regression Model.\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "\n",
    "# Initialize the Empty linear Regression Model.\n",
    "\n",
    "linear_regression = LinearRegression()\n",
    "# Checking the performance of the model.\n",
    "modelfit(linear_regression, train, test, train_label, test_label, \"Linear Regression\", True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ridge Model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report For  Ridge Model \n",
      "\n",
      "\n",
      "RMSE : 1171\n",
      "CV Score : Mean - 1148 | Std - 33.21 | Min - 1104 | Max - 1214\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Importing the Ridge Model.\n",
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "# Initializing the Ridge Model\n",
    "ridge_model = Ridge(alpha = 0.05, solver = 'cholesky')\n",
    "\n",
    "# Evaluating the Ridge Model.\n",
    "modelfit(ridge_model, train, test, train_label, test_label, \"Ridge Model\", True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LASSO Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report For  Lasso Model \n",
      "\n",
      "\n",
      "RMSE : 1171\n",
      "CV Score : Mean - 1148 | Std - 33.22 | Min - 1104 | Max - 1214\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Importing the Lasso Model\n",
    "\n",
    "from sklearn.linear_model import Lasso\n",
    "\n",
    "# Initializing the Lasso model\n",
    "lasso_model = Lasso(alpha = 0.01)\n",
    "\n",
    "# Evaluating the Lasso Model\n",
    "modelfit(lasso_model, train, test, train_label, test_label, \"Lasso Model\", True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bagging Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report For  BaggingRegressor Model \n",
      "\n",
      "\n",
      "RMSE : 1151\n",
      "CV Score : Mean - 1157 | Std - 45.06 | Min - 1074 | Max - 1207\n"
     ]
    }
   ],
   "source": [
    "# Importing the Bagging Regressor Model\n",
    "\n",
    "from sklearn.ensemble import BaggingRegressor\n",
    "\n",
    "# Initializing the BaggingRegressor Model\n",
    "br = BaggingRegressor(max_samples = 70)\n",
    "\n",
    "# Evaluating the bagging Regressor Model\n",
    "modelfit(br, train, test, train_label, test_label,\"BaggingRegressor Model\", False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVR Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report For  SVR Model \n",
      "\n",
      "\n",
      "RMSE : 1436\n",
      "CV Score : Mean - 1392 | Std - 45.67 | Min - 1332 | Max - 1489\n"
     ]
    }
   ],
   "source": [
    "# Importing the SVR\n",
    "\n",
    "from sklearn.svm import SVR\n",
    "\n",
    "# Initializing the SVR Model\n",
    "svr = SVR(epsilon = 15, kernel = 'linear')\n",
    "\n",
    "# Evaluating the SVR Model.\n",
    "modelfit(svr, train, test, train_label, test_label, 'SVR Model', False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report For  DTR Model \n",
      "\n",
      "\n",
      "RMSE : 1123\n",
      "CV Score : Mean - 1097 | Std - 32.99 | Min - 1047 | Max - 1155\n"
     ]
    }
   ],
   "source": [
    "# Importing the DecisionTreeRegressor\n",
    "\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "# Initializing the Decision Tree Regressor\n",
    "\n",
    "dtr = DecisionTreeRegressor()\n",
    "\n",
    "# Evaluating the Decision Tree Regressor\n",
    "\n",
    "modelfit(dtr, train, test, train_label, test_label, 'DTR Model', False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report For  RandomForestRegressor \n",
      "\n",
      "\n",
      "RMSE : 1123\n",
      "CV Score : Mean - 1097 | Std - 32.69 | Min - 1047 | Max - 1154\n"
     ]
    }
   ],
   "source": [
    "# Importing the RandomForestRegressor\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "#Initializing the RandomForestRegressor\n",
    "\n",
    "rf = RandomForestRegressor()\n",
    "\n",
    "# Evaluating the RandomForestRegressor\n",
    "\n",
    "modelfit(rf, train, test, train_label, test_label, 'RandomForestRegressor', False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ADA Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report For  Adaptive Boosting Model \n",
      "\n",
      "\n",
      "RMSE : 1195\n",
      "CV Score : Mean - 1181 | Std - 28.9 | Min - 1121 | Max - 1216\n"
     ]
    }
   ],
   "source": [
    "# importing adaptive boosting regressor algorithm\n",
    "\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "\n",
    "# initializing adaboosting algorithm\n",
    "ada = AdaBoostRegressor()\n",
    "# evaluating adaboosting\n",
    "modelfit(ada, train, test, train_label, test_label, 'Adaptive Boosting Model', False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report For  Gradient Boosting Model \n",
      "\n",
      "\n",
      "RMSE : 1123\n",
      "CV Score : Mean - 1097 | Std - 33.25 | Min - 1046 | Max - 1155\n"
     ]
    }
   ],
   "source": [
    "# Importing Gradient Boosting\n",
    "\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "# Initializing Gradient Boosting\n",
    "\n",
    "gbr = GradientBoostingRegressor()\n",
    "\n",
    "# Evaluatin Gradient Boosting Algorithm\n",
    "modelfit(gbr, train, test, train_label, test_label, 'Gradient Boosting Model', False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now after checking the performance of each algorithm lets trained out whole model on complete training set and import testing dataset and preprocess it for getting the predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing the test dataset.\n",
    "\n",
    "df_test = pd.read_csv('dataset/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5681, 11)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# checking the shape of test dataset.\n",
    "df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDW58</td>\n",
       "      <td>20.750</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.007565</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>107.8622</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FDW14</td>\n",
       "      <td>8.300</td>\n",
       "      <td>reg</td>\n",
       "      <td>0.038428</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>87.3198</td>\n",
       "      <td>OUT017</td>\n",
       "      <td>2007</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NCN55</td>\n",
       "      <td>14.600</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.099575</td>\n",
       "      <td>Others</td>\n",
       "      <td>241.7538</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDQ58</td>\n",
       "      <td>7.315</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.015388</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>155.0340</td>\n",
       "      <td>OUT017</td>\n",
       "      <td>2007</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FDY38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.118599</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>234.2300</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1985</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility    Item_Type  \\\n",
       "0           FDW58       20.750          Low Fat         0.007565  Snack Foods   \n",
       "1           FDW14        8.300              reg         0.038428        Dairy   \n",
       "2           NCN55       14.600          Low Fat         0.099575       Others   \n",
       "3           FDQ58        7.315          Low Fat         0.015388  Snack Foods   \n",
       "4           FDY38          NaN          Regular         0.118599        Dairy   \n",
       "\n",
       "   Item_MRP Outlet_Identifier  Outlet_Establishment_Year Outlet_Size  \\\n",
       "0  107.8622            OUT049                       1999      Medium   \n",
       "1   87.3198            OUT017                       2007         NaN   \n",
       "2  241.7538            OUT010                       1998         NaN   \n",
       "3  155.0340            OUT017                       2007         NaN   \n",
       "4  234.2300            OUT027                       1985      Medium   \n",
       "\n",
       "  Outlet_Location_Type        Outlet_Type  \n",
       "0               Tier 1  Supermarket Type1  \n",
       "1               Tier 2  Supermarket Type1  \n",
       "2               Tier 3      Grocery Store  \n",
       "3               Tier 2  Supermarket Type1  \n",
       "4               Tier 3  Supermarket Type3  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the top 5 rows of testing dataset.\n",
    "\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# making a list of the attributes which can effect the sales.\n",
    "\n",
    "attributes = ['Item_MRP',\n",
    " 'Outlet_Type',\n",
    " 'Outlet_Size',\n",
    " 'Outlet_Location_Type',\n",
    " 'Outlet_Establishment_Year',\n",
    " 'Outlet_Identifier',\n",
    " 'Item_Type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# limiting the dataset to only those attributes which can effect the sale of the outlet.\n",
    "\n",
    "df_test = df_test[attributes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5681, 7)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the shape of the sales.\n",
    "\n",
    "df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5681 entries, 0 to 5680\n",
      "Data columns (total 7 columns):\n",
      "Item_MRP                     5681 non-null float64\n",
      "Outlet_Type                  5681 non-null object\n",
      "Outlet_Size                  4075 non-null object\n",
      "Outlet_Location_Type         5681 non-null object\n",
      "Outlet_Establishment_Year    5681 non-null int64\n",
      "Outlet_Identifier            5681 non-null object\n",
      "Item_Type                    5681 non-null object\n",
      "dtypes: float64(1), int64(1), object(5)\n",
      "memory usage: 310.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# checking the info about the test dataset\n",
    "\n",
    "df_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# changing the data type of the attributes to the categorical data type.\n",
    "\n",
    "df_test.Item_MRP = pd.cut(df_test.Item_MRP,bins=[25,75,140,205,270],labels=['a','b','c','d'],right=True)\n",
    "df_test.Item_Type = df_test.Item_Type.astype('category')\n",
    "df_test.Outlet_Size = df_test.Outlet_Size.astype('category')\n",
    "df_test.Outlet_Identifier = df_test.Outlet_Identifier.astype('category')\n",
    "df_test.Outlet_Establishment_Year = df_test.Outlet_Establishment_Year.astype('category')\n",
    "df_test.Outlet_Type = df_test.Outlet_Type.astype('category')\n",
    "df_test.Outlet_Location_Type = df_test.Outlet_Location_Type.astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5681 entries, 0 to 5680\n",
      "Data columns (total 7 columns):\n",
      "Item_MRP                     5681 non-null category\n",
      "Outlet_Type                  5681 non-null category\n",
      "Outlet_Size                  4075 non-null category\n",
      "Outlet_Location_Type         5681 non-null category\n",
      "Outlet_Establishment_Year    5681 non-null category\n",
      "Outlet_Identifier            5681 non-null category\n",
      "Item_Type                    5681 non-null category\n",
      "dtypes: category(7)\n",
      "memory usage: 41.0 KB\n"
     ]
    }
   ],
   "source": [
    "# again chacking the info of the test dataset.\n",
    "\n",
    "df_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# making a function to replace the null value in the outlet_size attribute\n",
    "\n",
    "def function_replacing_null_Values(x):\n",
    "    if x == 'OUT010' :\n",
    "        return 'High'\n",
    "    elif x == 'OUT045' :\n",
    "        return 'Medium'\n",
    "    elif x == 'OUT017' :\n",
    "        return 'Medium'\n",
    "    elif x == 'OUT013' :\n",
    "        return 'High'\n",
    "    elif x == 'OUT046' :\n",
    "        return 'Small'\n",
    "    elif x == 'OUT035' :\n",
    "        return 'Small'\n",
    "    elif x == 'OUT019' :\n",
    "        return 'Small'\n",
    "    elif x == 'OUT027' :\n",
    "        return 'Medium'\n",
    "    elif x == 'OUT049' :\n",
    "        return 'Medium'\n",
    "    elif x == 'OUT018' :\n",
    "        return 'Medium'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using Function to fill null values.\n",
    "\n",
    "df_test['Outlet_Size'] = df_test.Outlet_Identifier.apply(function_replacing_null_Values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# making hot encoded vectors from the categorical variables.\n",
    "\n",
    "test_data = pd.get_dummies(df_test.iloc[:,0:6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5681, 33)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the shape of the test_data\n",
    "\n",
    "test_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now after preprocessing the test dataset, we can get our complete dataset of training and lets train the gradient boosting algorithm on complete."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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_MRP_a</th>\n",
       "      <th>Item_MRP_b</th>\n",
       "      <th>Item_MRP_c</th>\n",
       "      <th>Item_MRP_d</th>\n",
       "      <th>Outlet_Type_Grocery Store</th>\n",
       "      <th>Outlet_Type_Supermarket Type1</th>\n",
       "      <th>Outlet_Type_Supermarket Type2</th>\n",
       "      <th>Outlet_Type_Supermarket Type3</th>\n",
       "      <th>Outlet_Location_Type_Tier 1</th>\n",
       "      <th>Outlet_Location_Type_Tier 2</th>\n",
       "      <th>...</th>\n",
       "      <th>Outlet_Identifier_OUT013</th>\n",
       "      <th>Outlet_Identifier_OUT017</th>\n",
       "      <th>Outlet_Identifier_OUT018</th>\n",
       "      <th>Outlet_Identifier_OUT019</th>\n",
       "      <th>Outlet_Identifier_OUT027</th>\n",
       "      <th>Outlet_Identifier_OUT035</th>\n",
       "      <th>Outlet_Identifier_OUT045</th>\n",
       "      <th>Outlet_Identifier_OUT046</th>\n",
       "      <th>Outlet_Identifier_OUT049</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3735.1380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>443.4228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2097.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>732.3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>994.7052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Item_MRP_a  Item_MRP_b  Item_MRP_c  Item_MRP_d  Outlet_Type_Grocery Store  \\\n",
       "0           0           0           0           1                          0   \n",
       "1           1           0           0           0                          0   \n",
       "2           0           0           1           0                          0   \n",
       "3           0           0           1           0                          1   \n",
       "4           1           0           0           0                          0   \n",
       "\n",
       "   Outlet_Type_Supermarket Type1  Outlet_Type_Supermarket Type2  \\\n",
       "0                              1                              0   \n",
       "1                              0                              1   \n",
       "2                              1                              0   \n",
       "3                              0                              0   \n",
       "4                              1                              0   \n",
       "\n",
       "   Outlet_Type_Supermarket Type3  Outlet_Location_Type_Tier 1  \\\n",
       "0                              0                            1   \n",
       "1                              0                            0   \n",
       "2                              0                            1   \n",
       "3                              0                            0   \n",
       "4                              0                            0   \n",
       "\n",
       "   Outlet_Location_Type_Tier 2  ...  Outlet_Identifier_OUT013  \\\n",
       "0                            0  ...                         0   \n",
       "1                            0  ...                         0   \n",
       "2                            0  ...                         0   \n",
       "3                            0  ...                         0   \n",
       "4                            0  ...                         1   \n",
       "\n",
       "   Outlet_Identifier_OUT017  Outlet_Identifier_OUT018  \\\n",
       "0                         0                         0   \n",
       "1                         0                         1   \n",
       "2                         0                         0   \n",
       "3                         0                         0   \n",
       "4                         0                         0   \n",
       "\n",
       "   Outlet_Identifier_OUT019  Outlet_Identifier_OUT027  \\\n",
       "0                         0                         0   \n",
       "1                         0                         0   \n",
       "2                         0                         0   \n",
       "3                         0                         0   \n",
       "4                         0                         0   \n",
       "\n",
       "   Outlet_Identifier_OUT035  Outlet_Identifier_OUT045  \\\n",
       "0                         0                         0   \n",
       "1                         0                         0   \n",
       "2                         0                         0   \n",
       "3                         0                         0   \n",
       "4                         0                         0   \n",
       "\n",
       "   Outlet_Identifier_OUT046  Outlet_Identifier_OUT049  Item_Outlet_Sales  \n",
       "0                         0                         1          3735.1380  \n",
       "1                         0                         0           443.4228  \n",
       "2                         0                         1          2097.2700  \n",
       "3                         0                         0           732.3800  \n",
       "4                         0                         0           994.7052  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first lets create the labeled dataset from the training set.\n",
    "\n",
    "train_label = dataset['Item_Outlet_Sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's remove the labeled dataset from the training set.\n",
    "\n",
    "del dataset['Item_Outlet_Sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8521, 33)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# after removing the dataset let's check the shape of training set.\n",
    "\n",
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5681, 33)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to verify let's check the shape of test set.\n",
    "\n",
    "test_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8521,)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# and train label shape.\n",
    "\n",
    "train_label.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's train Gradient Boosting Algorithm and take the predictions and add it to the submission file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Gradient Boosting and GradientSearchCV\n",
    "\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Initializing Gradient Boosting\n",
    "\n",
    "gb = GradientBoostingRegressor(max_depth = 7, n_estimators=200, learning_rate = 0.01)\n",
    "\n",
    "# making a list of params for the Gradient Search CV\n",
    "param = [{'min_samples_split': [5, 9, 13], 'max_leaf_nodes': [3, 5, 7, 9], 'max_features': [8, 10, 15, 18]}]\n",
    "\n",
    "# initializing the GradientSearchCV with the params\n",
    "gs = GridSearchCV(gb, param, cv = 5, scoring = 'neg_mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=GradientBoostingRegressor(alpha=0.9, ccp_alpha=0.0,\n",
       "                                                 criterion='friedman_mse',\n",
       "                                                 init=None, learning_rate=0.01,\n",
       "                                                 loss='ls', max_depth=7,\n",
       "                                                 max_features=None,\n",
       "                                                 max_leaf_nodes=None,\n",
       "                                                 min_impurity_decrease=0.0,\n",
       "                                                 min_impurity_split=None,\n",
       "                                                 min_samples_leaf=1,\n",
       "                                                 min_samples_split=2,\n",
       "                                                 min_weight_fraction_leaf=0.0,\n",
       "                                                 n_estimators=200,\n",
       "                                                 n_iter_...ne,\n",
       "                                                 presort='deprecated',\n",
       "                                                 random_state=None,\n",
       "                                                 subsample=1.0, tol=0.0001,\n",
       "                                                 validation_fraction=0.1,\n",
       "                                                 verbose=0, warm_start=False),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid=[{'max_features': [8, 10, 15, 18],\n",
       "                          'max_leaf_nodes': [3, 5, 7, 9],\n",
       "                          'min_samples_split': [5, 9, 13]}],\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='neg_mean_squared_error', verbose=0)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "    \n",
    "# fitting the model on training dataset.\n",
    "gs.fit(dataset, train_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "gb = gs.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# getting the output of testing dataset after training.\n",
    "\n",
    "predict = gb.predict(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5681,)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the shape of the predictions.\n",
    "\n",
    "predict.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing the submission dataset.\n",
    "\n",
    "df_submission = pd.read_csv('dataset/submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5681, 4)"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the shape of the submission dataset.\n",
    "\n",
    "df_submission.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "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>Unnamed: 0</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>0</td>\n",
       "      <td>FDW58</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1636.244023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>FDW14</td>\n",
       "      <td>OUT017</td>\n",
       "      <td>1409.355910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>NCN55</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>710.594286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>FDQ58</td>\n",
       "      <td>OUT017</td>\n",
       "      <td>2355.184955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>FDY38</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>5857.916205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>FDH56</td>\n",
       "      <td>OUT046</td>\n",
       "      <td>1853.228827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>FDL48</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>733.598282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>FDC48</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>2273.425151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>FDN33</td>\n",
       "      <td>OUT045</td>\n",
       "      <td>1499.740111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>FDA36</td>\n",
       "      <td>OUT017</td>\n",
       "      <td>3203.582302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 Item_Identifier Outlet_Identifier  Item_Outlet_Sales\n",
       "0           0           FDW58            OUT049        1636.244023\n",
       "1           1           FDW14            OUT017        1409.355910\n",
       "2           2           NCN55            OUT010         710.594286\n",
       "3           3           FDQ58            OUT017        2355.184955\n",
       "4           4           FDY38            OUT027        5857.916205\n",
       "5           5           FDH56            OUT046        1853.228827\n",
       "6           6           FDL48            OUT018         733.598282\n",
       "7           7           FDC48            OUT027        2273.425151\n",
       "8           8           FDN33            OUT045        1499.740111\n",
       "9           9           FDA36            OUT017        3203.582302"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the top 10 rows of the submission dataset.\n",
    "\n",
    "df_submission.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we can see, we have an unnecessary column Unnamed: 0, we can del that column\n",
    "\n",
    "del df_submission['Unnamed: 0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a new column in the submission dataset, for saving the predicted sales value.\n",
    "\n",
    "df_submission['Item_Outlet_Sales_Predicted'] = predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "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>Outlet_Identifier</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "      <th>Item_Outlet_Sales_Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDW58</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1636.244023</td>\n",
       "      <td>1745.354231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FDW14</td>\n",
       "      <td>OUT017</td>\n",
       "      <td>1409.355910</td>\n",
       "      <td>1745.354231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NCN55</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>710.594286</td>\n",
       "      <td>700.963057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDQ58</td>\n",
       "      <td>OUT017</td>\n",
       "      <td>2355.184955</td>\n",
       "      <td>2679.345907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FDY38</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>5857.916205</td>\n",
       "      <td>5433.756941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FDH56</td>\n",
       "      <td>OUT046</td>\n",
       "      <td>1853.228827</td>\n",
       "      <td>1745.354231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>FDL48</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>733.598282</td>\n",
       "      <td>975.824869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>FDC48</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>2273.425151</td>\n",
       "      <td>2659.351363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>FDN33</td>\n",
       "      <td>OUT045</td>\n",
       "      <td>1499.740111</td>\n",
       "      <td>1745.354231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FDA36</td>\n",
       "      <td>OUT017</td>\n",
       "      <td>3203.582302</td>\n",
       "      <td>2679.345907</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier Outlet_Identifier  Item_Outlet_Sales  \\\n",
       "0           FDW58            OUT049        1636.244023   \n",
       "1           FDW14            OUT017        1409.355910   \n",
       "2           NCN55            OUT010         710.594286   \n",
       "3           FDQ58            OUT017        2355.184955   \n",
       "4           FDY38            OUT027        5857.916205   \n",
       "5           FDH56            OUT046        1853.228827   \n",
       "6           FDL48            OUT018         733.598282   \n",
       "7           FDC48            OUT027        2273.425151   \n",
       "8           FDN33            OUT045        1499.740111   \n",
       "9           FDA36            OUT017        3203.582302   \n",
       "\n",
       "   Item_Outlet_Sales_Predicted  \n",
       "0                  1745.354231  \n",
       "1                  1745.354231  \n",
       "2                   700.963057  \n",
       "3                  2679.345907  \n",
       "4                  5433.756941  \n",
       "5                  1745.354231  \n",
       "6                   975.824869  \n",
       "7                  2659.351363  \n",
       "8                  1745.354231  \n",
       "9                  2679.345907  "
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the top 10 values.\n",
    "\n",
    "df_submission.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_submission.to_csv('predicted_dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}