Grocery Sales Forecasting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  <h1><center>Grocery Sales Forcesting for Supermarket</center></h1>\n",
    "\n",
    "<img src=\"header.png\">\n",
    "\n",
    "Img Source: Kaggle.com\n",
    "\n",
    "## Table of Contents\n",
    "\n",
    "1. Abstract\n",
    "\n",
    "2. Introduction\n",
    "\n",
    "3. Data Sources\n",
    "\n",
    "   3.a. Data Loading\n",
    "   \n",
    "   3.b. Anamoly Detection\n",
    "   \n",
    "   3.c. Data Preparation and Data Cleaning\n",
    "   \n",
    "4. Analyzing Impact of Oil on the Sales\n",
    "\n",
    "5. Data Blending\n",
    "\n",
    "6. Product Purchase Trend\n",
    "\n",
    "   6.a Fetching N most purchased products\n",
    "\n",
    "7. Exploratory Data Analysis\n",
    "\n",
    "8. Data Transformation\n",
    "\n",
    "   8.a. One Hot Encoding\n",
    "   \n",
    "9. Regression Techniques\n",
    "   9.a Linear Regression\n",
    "   \n",
    "   9.b Decision Tree Regressors\n",
    "   \n",
    "   9.c Extra Tree Regressors\n",
    "   \n",
    "   9.d Random Forest Regressors\n",
    "   \n",
    "   9.e Gradient Boosting Regressors\n",
    "   \n",
    "   9.f XGBoost\n",
    "   \n",
    "10. Light Gradient Boosting Method (LGBM)\n",
    "\n",
    "11. Creating Neural Network\n",
    "\n",
    "12. Conclusion\n",
    "\n",
    "13. References and Attributions\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Abstract\n",
    "\n",
    "<img src=\"Forecast.jpg\">\n",
    "\n",
    "Product sales forecasting is a major aspect of purchasing management. Forecasts are crucial in\n",
    "determining inventory stock levels, and accurately estimating future demand for goods has been an\n",
    "ongoing challenge, especially in the Supermarkets and Grocery Stores industry. If goods are not readily\n",
    "available or goods availability is more than demand overall profit can be compromised. As a result, sales\n",
    "forecasting for goods can be significant to ensure loss is minimized. Additionally, the problem becomes\n",
    "more complex as retailers add new locations with unique needs, new products, ever transitioning\n",
    "seasonal tastes, and unpredictable product marketing. In this analysis, a forecasting model is developed\n",
    "using machine learning algorithms to improve the accurately forecasts product sales. The proposed\n",
    "model is especially targeted to support the future purchase and more accurate forecasts product sales\n",
    "and is not intended to change current subjective forecasting methods. A model based on a real grocery\n",
    "store's data is developed in order to validate the use of the various machine learning algorithms. In the\n",
    "case study, multiple regression methods are compared. The methods impact on forecast product\n",
    "availability in store to ensure they have just enough products at right time.\n",
    "\n",
    "## The aim of this project is to forecast more accurate product sales for the Ecuadorian supermarket chain based on certain features.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Introduction\n",
    "\n",
    "In this project, we are trying to forecasts product sales based on the items, stores, transaction and other\n",
    "dependent variables like holidays and oil prices.\n",
    "\n",
    "This is a Kaggle Competition called \"Corporación Favorita Grocery Sales Forecasting\" where the task is to\n",
    "predict stocking of products to better ensure grocery stores please customers by having just enough of\n",
    "the right products at the right time.\n",
    "\n",
    "For this particular problem, we have analyzed the data as a supervised learning problem. In order to\n",
    "forecasts the sales we have compared different regression models like Linear Regression, Decision Tree,\n",
    "ExtraTreeRegressor, Gradient Boosting, Random Forest and XgBoost. Further to optimize the results we\n",
    "have used multilayer perception (MLP: a class of feed forward artificial neural network) and LightGBM (\n",
    "gradient boosting framework that uses tree based learning algorithms).\n",
    "\n",
    "The data comes in the shape of multiple files. First, the training data (train.csv) essentially contains the sales by date, store, and item. The test data (test.csv) contains the same features without the sales information, which we are tasked to predict. The train vs test split is based on the date. In addition, some test items are not included in the train data.\n",
    "\n",
    "<img src=\"background.jpg\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "#Loading all the relevant libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "from sklearn.preprocessing import LabelEncoder,minmax_scale,PolynomialFeatures,StandardScaler,Normalizer\n",
    "from sklearn.model_selection import KFold,GridSearchCV,train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import itemfreq\n",
    "import seaborn as sns\n",
    "from sklearn import linear_model\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LinearRegression, Ridge, LassoCV, ElasticNetCV\n",
    "from sklearn.metrics import mean_squared_error, make_scorer\n",
    "#from sklearn.model_selection import train_test_split\n",
    "%matplotlib inline\n",
    "import datetime\n",
    "from datetime import date, timedelta\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from xgboost import XGBRegressor\n",
    "import keras\n",
    "import sys\n",
    "from keras.layers.core import Dense, Dropout, Activation\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Dropout, Activation\n",
    "import lightgbm as lgb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Data Sources\n",
    "There are 5 additional data files that provide the following information:\n",
    "\n",
    "-- stores.csv : Details about the stores, such as location and type.\n",
    "\n",
    "-- items.csv: Item metadata, such as class and whether they are perishable. Note, that perishable items have a higher scoring weight than others.\n",
    "\n",
    "-- transactions.csv: Count of sales transactions for the training data\n",
    "\n",
    "-- oil.csv: Daily oil price. This is relevant, because “Ecuador is an oil-dependent country and its economical health is highly vulnerable to shocks in oil prices.” (source)\n",
    "\n",
    "-- holidays_events.csv: Holidays in Ecuador. Some holidays can be transferred to another day (possibly from weekend to weekday).\n",
    "\n",
    "\n",
    "<img src=\"Capture1.jpg\">\n",
    "\n",
    "\n",
    "The text in the document by Source Kaggle is licensed under CC BY 3.0 https://creativecommons.org/licenses/by/3.0/us/\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.a. Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Loading the data\n",
    "dtypes = {'store_nbr': np.dtype('int64'),\n",
    "          'item_nbr': np.dtype('int64'),\n",
    "          'unit_sales': np.dtype('float64'),\n",
    "          'onpromotion': np.dtype('O')}\n",
    "\n",
    "Sales = pd.read_csv('favoritaTrain_Set2.csv', dtype=dtypes)\n",
    "test = pd.read_csv('test.csv', dtype=dtypes)\n",
    "stores = pd.read_csv('stores.csv')\n",
    "items = pd.read_csv('items.csv')\n",
    "trans = pd.read_csv('transactions.csv')\n",
    "#oil = pd.read_csv('../input/oil.csv') #we upload this database later\n",
    "holidays = pd.read_csv('holidays_events.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "336867"
      ]
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sampling the data, since the data is too huge to carry put any operations\n",
    "date_mask = (Sales['date'] >= '2017-07-15') & (Sales['date'] <= '2017-08-15')\n",
    "\n",
    "Salesdf = Sales[date_mask]\n",
    "\n",
    "#Print the size\n",
    "len(Salesdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.b. Anamoly Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Load the data\n",
    "oil = pd.read_csv('oil.csv')\n",
    "\n",
    "#add missing date\n",
    "min_oil_date = min(Salesdf.date)\n",
    "max_oil_date = max(Salesdf.date)\n",
    "\n",
    "calendar = []\n",
    "\n",
    "d1 = datetime.datetime.strptime(min_oil_date, '%Y-%m-%d')  # start date\n",
    "d2 = datetime.datetime.strptime(max_oil_date, '%Y-%m-%d')  # end date\n",
    "\n",
    "delta = d2 - d1         # timedelta\n",
    "\n",
    "for i in range(delta.days + 1):\n",
    "    calendar.append(datetime.date.strftime(d1 + timedelta(days=i), '%Y-%m-%d'))\n",
    "\n",
    "calendar = pd.DataFrame({'date':calendar})\n",
    "\n",
    "oil = calendar.merge(oil, left_on='date', right_on='date', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>dcoilwtico</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-07-15</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-07-16</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-07-17</td>\n",
       "      <td>46.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-07-18</td>\n",
       "      <td>46.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-07-19</td>\n",
       "      <td>47.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  dcoilwtico\n",
       "0  2017-07-15         NaN\n",
       "1  2017-07-16         NaN\n",
       "2  2017-07-17       46.02\n",
       "3  2017-07-18       46.40\n",
       "4  2017-07-19       47.10"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oil.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.c Data Preparation and Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date           0\n",
      "dcoilwtico    10\n",
      "dtype: int64 \n",
      "\n",
      "Type :  \n",
      " date           object\n",
      "dcoilwtico    float64\n",
      "dtype: object\n"
     ]
    },
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>dcoilwtico</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-07-15</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-07-16</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-07-17</td>\n",
       "      <td>46.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-07-18</td>\n",
       "      <td>46.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-07-19</td>\n",
       "      <td>47.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "         date  dcoilwtico\n",
       "0  2017-07-15         NaN\n",
       "1  2017-07-16         NaN\n",
       "2  2017-07-17       46.02\n",
       "3  2017-07-18       46.40\n",
       "4  2017-07-19       47.10"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check how many NA\n",
    "print(oil.isnull().sum(), '\\n')\n",
    "\n",
    "#Type\n",
    "print('Type : ', '\\n', oil.dtypes)\n",
    "\n",
    "#Print the 3 first line\n",
    "oil.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Analyzing Impact of Oil on the sales of other products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Check index to apply the formula\n",
    "na_index_oil = oil[oil['dcoilwtico'].isnull() == True].index.values\n",
    "\n",
    "#Define the index to use to apply the formala\n",
    "na_index_oil_plus = na_index_oil.copy()\n",
    "na_index_oil_minus = np.maximum(0, na_index_oil-1)\n",
    "\n",
    "for i in range(len(na_index_oil)):\n",
    "    k = 1\n",
    "    while (na_index_oil[min(i+k,len(na_index_oil)-1)] == na_index_oil[i]+k):\n",
    "        k += 1\n",
    "    na_index_oil_plus[i] = min(len(oil)-1, na_index_oil_plus[i] + k )\n",
    "\n",
    "#Apply the formula\n",
    "for i in range(len(na_index_oil)):\n",
    "    if (na_index_oil[i] == 0):\n",
    "        oil.loc[na_index_oil[i], 'dcoilwtico'] = oil.loc[na_index_oil_plus[i], 'dcoilwtico']\n",
    "    elif (na_index_oil[i] == len(oil)):\n",
    "        oil.loc[na_index_oil[i], 'dcoilwtico'] = oil.loc[na_index_oil_minus[i], 'dcoilwtico']\n",
    "    else:\n",
    "        oil.loc[na_index_oil[i], 'dcoilwtico'] = (oil.loc[na_index_oil_plus[i], 'dcoilwtico'] + oil.loc[na_index_oil_minus[i], 'dcoilwtico'])/ 2   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date          0\n",
       "dcoilwtico    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oil.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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P093HUsZQVxBJ0aG9QR96sjXvSFKgi0eKapqZnxrn6zJEHCNrXBQvf/l83G5L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\n4v8DNQ5ZIkbx+5YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d7e2132828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the oil values\n",
    "oil_plot = oil['dcoilwtico'].copy()\n",
    "oil_plot.index = oil['date'].copy()\n",
    "oil_plot.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# By the end of the analysis it is evident that the sale of oil as an important commodity has a significant impact on the unit sales of other products."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The text in the document by Source Wikipedia is licensed under CC BY 3.0 https://creativecommons.org/licenses/by/3.0/us/\n",
    "\n",
    "The text in the document by Analytics Vidhya is licensed under CC BY 3.0 https://creativecommons.org/licenses/by/3.0/us/\n",
    "\n",
    "The code in the document by Source Kaggle is licensed under the MIT License https://opensource.org/licenses/MIT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Data Blending"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Merge train\n",
    "Salesdf = Salesdf.drop('id', axis = 1)\n",
    "Salesdf = Salesdf.merge(stores, left_on='store_nbr', right_on='store_nbr', how='left')\n",
    "Salesdf = Salesdf.merge(items, left_on='item_nbr', right_on='item_nbr', how='left')\n",
    "Salesdf = Salesdf.merge(holidays, left_on='date', right_on='date', how='left')\n",
    "Salesdf = Salesdf.merge(oil, left_on='date', right_on='date', how='left')\n",
    "Salesdf = Salesdf.drop(['description', 'state', 'locale_name', 'class'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 336867 entries, 0 to 336866\n",
      "Data columns (total 14 columns):\n",
      "date           336867 non-null object\n",
      "store_nbr      336867 non-null int64\n",
      "item_nbr       336867 non-null int64\n",
      "unit_sales     336867 non-null float64\n",
      "onpromotion    336867 non-null object\n",
      "city           336867 non-null object\n",
      "type_x         336867 non-null object\n",
      "cluster        336867 non-null int64\n",
      "family         336867 non-null object\n",
      "perishable     336867 non-null int64\n",
      "type_y         73283 non-null object\n",
      "locale         73283 non-null object\n",
      "transferred    73283 non-null object\n",
      "dcoilwtico     336867 non-null float64\n",
      "dtypes: float64(2), int64(4), object(8)\n",
      "memory usage: 38.6+ MB\n"
     ]
    }
   ],
   "source": [
    "Salesdf.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "transferred    263584\n",
       "locale         263584\n",
       "type_y         263584\n",
       "dcoilwtico          0\n",
       "perishable          0\n",
       "family              0\n",
       "cluster             0\n",
       "type_x              0\n",
       "city                0\n",
       "onpromotion         0\n",
       "unit_sales          0\n",
       "item_nbr            0\n",
       "store_nbr           0\n",
       "date                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Salesdf.isnull().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape :  (336867, 14) \n",
      "\n",
      "Type :  \n",
      " date            object\n",
      "store_nbr        int64\n",
      "item_nbr         int64\n",
      "unit_sales     float64\n",
      "onpromotion     object\n",
      "city            object\n",
      "type_x          object\n",
      "cluster          int64\n",
      "family          object\n",
      "perishable       int64\n",
      "type_y          object\n",
      "locale          object\n",
      "transferred     object\n",
      "dcoilwtico     float64\n",
      "dtype: object\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>store_nbr</th>\n",
       "      <th>item_nbr</th>\n",
       "      <th>unit_sales</th>\n",
       "      <th>cluster</th>\n",
       "      <th>perishable</th>\n",
       "      <th>dcoilwtico</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>336867.000000</td>\n",
       "      <td>3.368670e+05</td>\n",
       "      <td>336867.000000</td>\n",
       "      <td>336867.000000</td>\n",
       "      <td>336867.000000</td>\n",
       "      <td>336867.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>28.313305</td>\n",
       "      <td>1.170498e+06</td>\n",
       "      <td>8.043790</td>\n",
       "      <td>8.709102</td>\n",
       "      <td>0.258402</td>\n",
       "      <td>48.146043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>16.309769</td>\n",
       "      <td>5.868558e+05</td>\n",
       "      <td>22.929827</td>\n",
       "      <td>4.563188</td>\n",
       "      <td>0.437757</td>\n",
       "      <td>1.467865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.699500e+04</td>\n",
       "      <td>-36.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.780000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>6.925310e+05</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>46.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>1.209720e+06</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>48.580000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>44.000000</td>\n",
       "      <td>1.576330e+06</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>49.470000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>54.000000</td>\n",
       "      <td>2.127114e+06</td>\n",
       "      <td>3952.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>50.210000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           store_nbr      item_nbr     unit_sales        cluster  \\\n",
       "count  336867.000000  3.368670e+05  336867.000000  336867.000000   \n",
       "mean       28.313305  1.170498e+06       8.043790       8.709102   \n",
       "std        16.309769  5.868558e+05      22.929827       4.563188   \n",
       "min         1.000000  9.699500e+04     -36.000000       1.000000   \n",
       "25%        13.000000  6.925310e+05       2.000000       5.000000   \n",
       "50%        29.000000  1.209720e+06       4.000000       9.000000   \n",
       "75%        44.000000  1.576330e+06       8.000000      13.000000   \n",
       "max        54.000000  2.127114e+06    3952.000000      17.000000   \n",
       "\n",
       "          perishable     dcoilwtico  \n",
       "count  336867.000000  336867.000000  \n",
       "mean        0.258402      48.146043  \n",
       "std         0.437757       1.467865  \n",
       "min         0.000000      45.780000  \n",
       "25%         0.000000      46.400000  \n",
       "50%         0.000000      48.580000  \n",
       "75%         1.000000      49.470000  \n",
       "max         1.000000      50.210000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Shape\n",
    "print('Shape : ', Salesdf.shape, '\\n')\n",
    "\n",
    "#Type\n",
    "print('Type : ', '\\n', Salesdf.dtypes)\n",
    "\n",
    "#Summary\n",
    "Salesdf.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>date</th>\n",
       "      <th>store_nbr</th>\n",
       "      <th>item_nbr</th>\n",
       "      <th>unit_sales</th>\n",
       "      <th>onpromotion</th>\n",
       "      <th>city</th>\n",
       "      <th>type_x</th>\n",
       "      <th>cluster</th>\n",
       "      <th>family</th>\n",
       "      <th>perishable</th>\n",
       "      <th>type_y</th>\n",
       "      <th>locale</th>\n",
       "      <th>transferred</th>\n",
       "      <th>dcoilwtico</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>220265</th>\n",
       "      <td>2017-07-29</td>\n",
       "      <td>13</td>\n",
       "      <td>1239908</td>\n",
       "      <td>6.0</td>\n",
       "      <td>False</td>\n",
       "      <td>Latacunga</td>\n",
       "      <td>C</td>\n",
       "      <td>15</td>\n",
       "      <td>DAIRY</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.9650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230155</th>\n",
       "      <td>2017-07-30</td>\n",
       "      <td>41</td>\n",
       "      <td>890825</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>Machala</td>\n",
       "      <td>D</td>\n",
       "      <td>4</td>\n",
       "      <td>GROCERY I</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.0875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99418</th>\n",
       "      <td>2017-07-29</td>\n",
       "      <td>23</td>\n",
       "      <td>1909483</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>Ambato</td>\n",
       "      <td>D</td>\n",
       "      <td>9</td>\n",
       "      <td>HOME AND KITCHEN II</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.9650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19077</th>\n",
       "      <td>2017-08-02</td>\n",
       "      <td>20</td>\n",
       "      <td>1463783</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>Quito</td>\n",
       "      <td>B</td>\n",
       "      <td>6</td>\n",
       "      <td>BEVERAGES</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114306</th>\n",
       "      <td>2017-07-31</td>\n",
       "      <td>13</td>\n",
       "      <td>1373037</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>Latacunga</td>\n",
       "      <td>C</td>\n",
       "      <td>15</td>\n",
       "      <td>HOME AND KITCHEN II</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.2100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287861</th>\n",
       "      <td>2017-08-12</td>\n",
       "      <td>53</td>\n",
       "      <td>936994</td>\n",
       "      <td>6.0</td>\n",
       "      <td>False</td>\n",
       "      <td>Manta</td>\n",
       "      <td>D</td>\n",
       "      <td>13</td>\n",
       "      <td>GROCERY I</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275581</th>\n",
       "      <td>2017-07-20</td>\n",
       "      <td>9</td>\n",
       "      <td>2047752</td>\n",
       "      <td>6.0</td>\n",
       "      <td>False</td>\n",
       "      <td>Quito</td>\n",
       "      <td>B</td>\n",
       "      <td>6</td>\n",
       "      <td>CLEANING</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46.7300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100110</th>\n",
       "      <td>2017-08-14</td>\n",
       "      <td>40</td>\n",
       "      <td>788172</td>\n",
       "      <td>3.0</td>\n",
       "      <td>False</td>\n",
       "      <td>Machala</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>CLEANING</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.5900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34409</th>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>42</td>\n",
       "      <td>1012788</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>Cuenca</td>\n",
       "      <td>D</td>\n",
       "      <td>2</td>\n",
       "      <td>GROCERY I</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.5800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207282</th>\n",
       "      <td>2017-08-09</td>\n",
       "      <td>34</td>\n",
       "      <td>255161</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>Guayaquil</td>\n",
       "      <td>B</td>\n",
       "      <td>6</td>\n",
       "      <td>GROCERY I</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.5900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              date  store_nbr  item_nbr  unit_sales onpromotion       city  \\\n",
       "220265  2017-07-29         13   1239908         6.0       False  Latacunga   \n",
       "230155  2017-07-30         41    890825         1.0       False    Machala   \n",
       "99418   2017-07-29         23   1909483         2.0       False     Ambato   \n",
       "19077   2017-08-02         20   1463783         2.0       False      Quito   \n",
       "114306  2017-07-31         13   1373037         2.0       False  Latacunga   \n",
       "287861  2017-08-12         53    936994         6.0       False      Manta   \n",
       "275581  2017-07-20          9   2047752         6.0       False      Quito   \n",
       "100110  2017-08-14         40    788172         3.0       False    Machala   \n",
       "34409   2017-07-26         42   1012788         2.0       False     Cuenca   \n",
       "207282  2017-08-09         34    255161         1.0        True  Guayaquil   \n",
       "\n",
       "       type_x  cluster               family  perishable type_y locale  \\\n",
       "220265      C       15                DAIRY           1    NaN    NaN   \n",
       "230155      D        4            GROCERY I           0    NaN    NaN   \n",
       "99418       D        9  HOME AND KITCHEN II           0    NaN    NaN   \n",
       "19077       B        6            BEVERAGES           0    NaN    NaN   \n",
       "114306      C       15  HOME AND KITCHEN II           0    NaN    NaN   \n",
       "287861      D       13            GROCERY I           0    NaN    NaN   \n",
       "275581      B        6             CLEANING           0    NaN    NaN   \n",
       "100110      C        3             CLEANING           0    NaN    NaN   \n",
       "34409       D        2            GROCERY I           0    NaN    NaN   \n",
       "207282      B        6            GROCERY I           0    NaN    NaN   \n",
       "\n",
       "       transferred  dcoilwtico  \n",
       "220265         NaN     49.9650  \n",
       "230155         NaN     50.0875  \n",
       "99418          NaN     49.9650  \n",
       "19077          NaN     49.6000  \n",
       "114306         NaN     50.2100  \n",
       "287861         NaN     48.2000  \n",
       "275581         NaN     46.7300  \n",
       "100110         NaN     47.5900  \n",
       "34409          NaN     48.5800  \n",
       "207282         NaN     49.5900  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#5 random lines\n",
    "Salesdf.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py:1460: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  stat_data = remove_na(group_data)\n"
     ]
    },
    {
     "data": {
      "image/png": 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sR9RvZ9LRKmbWO/5/kHSeu/pRK6NJKwmkntKoZbR/d+BHpK7pgqph/bO374/2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\nAzuedW9Dkl9oAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d786708e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='store_nbr', data=Salesdf);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The the above plot shows the number of stores according to each store type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Series.unique of 0          633977\n",
       "1         1991151\n",
       "2          968935\n",
       "3         1909453\n",
       "4         1695875\n",
       "5          884878\n",
       "6          322095\n",
       "7          915980\n",
       "8          939131\n",
       "9         2013621\n",
       "10         838216\n",
       "11        1146783\n",
       "12        1473428\n",
       "13         940590\n",
       "14         938126\n",
       "15        2027777\n",
       "16         916886\n",
       "17        1660194\n",
       "18         404334\n",
       "19         214381\n",
       "20        1114761\n",
       "21         414426\n",
       "22        2035581\n",
       "23         513855\n",
       "24        2010766\n",
       "25        1473511\n",
       "26        1112840\n",
       "27        1037857\n",
       "28         769312\n",
       "29         510054\n",
       "           ...   \n",
       "336837    1987383\n",
       "336838     273528\n",
       "336839     315320\n",
       "336840    1473476\n",
       "336841    1321497\n",
       "336842     872317\n",
       "336843    1456881\n",
       "336844    1967038\n",
       "336845     552050\n",
       "336846    1695848\n",
       "336847     839362\n",
       "336848     979197\n",
       "336849     360705\n",
       "336850    1920284\n",
       "336851    1464088\n",
       "336852    1281011\n",
       "336853     957096\n",
       "336854    1914786\n",
       "336855    1153584\n",
       "336856     812751\n",
       "336857    1986680\n",
       "336858    2048358\n",
       "336859    1150362\n",
       "336860    1367273\n",
       "336861     557285\n",
       "336862    1070962\n",
       "336863    1347811\n",
       "336864    1456916\n",
       "336865    1471462\n",
       "336866     671079\n",
       "Name: item_nbr, Length: 336867, dtype: int64>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Salesdf.item_nbr.unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1254013,     203],\n",
       "       [2042947,     195],\n",
       "       [ 847863,     190],\n",
       "       ..., \n",
       "       [1047709,       1],\n",
       "       [2011032,       1],\n",
       "       [1360013,       1]], dtype=int64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=itemfreq(Salesdf.item_nbr)\n",
    "x=x[x[:,1].argsort()[::-1]]\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1254013, 2042947,  847863, 1473475, 1489899,  115894,  273528,\n",
       "       1342003, 1157329, 1080021], dtype=int64)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[:,0][0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 633977, 1991151,  968935, ..., 1456916, 1471462,  671079], dtype=int64)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s=Salesdf.item_nbr.as_matrix()\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 633977],\n",
       "       [1991151],\n",
       "       [ 968935],\n",
       "       ..., \n",
       "       [1456916],\n",
       "       [1471462],\n",
       "       [ 671079]], dtype=int64)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t=s.reshape(s.shape[0],1)\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: elementwise == comparison failed; this will raise an error in the future.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([], dtype=int64),)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1=np.where(t==x)\n",
    "t1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Product Purchase Trend"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  6.a. Fetching the N most purchased products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "labels_filtered_index[0].shape =  (3735,)\n"
     ]
    }
   ],
   "source": [
    "#Fetching the N most purchased products#\n",
    "def N_most_labels(data, variable , N , all='TRUE'):\n",
    "    labels_freq_pd = itemfreq(data[variable])\n",
    "    labels_freq_pd = labels_freq_pd[labels_freq_pd[:, 1].argsort()[::-1]] #[::-1] ==> to sort in descending order\n",
    "    \n",
    "    if all == 'FALSE':\n",
    "        main_labels = labels_freq_pd[:,0][0:N]\n",
    "    else: \n",
    "        main_labels = labels_freq_pd[:,0][:]\n",
    "        \n",
    "    labels_raw_np = data[variable].as_matrix() #transform in numpy\n",
    "    labels_raw_np = labels_raw_np.reshape(labels_raw_np.shape[0],1)\n",
    "\n",
    "    labels_filtered_index = np.where(labels_raw_np == main_labels)\n",
    "    \n",
    "    return labels_freq_pd, labels_filtered_index\n",
    "\n",
    "label_freq, labels_filtered_index = N_most_labels(data = Salesdf, variable = \"item_nbr\", N = 20, all='FALSE')\n",
    "print(\"labels_filtered_index[0].shape = \", labels_filtered_index[0].shape)\n",
    "\n",
    "Salesdf_filtered = Salesdf.loc[labels_filtered_index[0],:]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of most purchased products gives us the idea of looking for the sales for those products."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code in the document by Source Kaggle is licensed under the MIT License https://opensource.org/licenses/MIT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1254013,     203],\n",
       "       [2042947,     195],\n",
       "       [ 847863,     190],\n",
       "       [1473475,     189],\n",
       "       [1489899,     189],\n",
       "       [ 115894,     188],\n",
       "       [ 273528,     187],\n",
       "       [1342003,     186],\n",
       "       [1157329,     186],\n",
       "       [1080021,     186]], dtype=int64)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_freq[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>date</th>\n",
       "      <th>store_nbr</th>\n",
       "      <th>item_nbr</th>\n",
       "      <th>unit_sales</th>\n",
       "      <th>onpromotion</th>\n",
       "      <th>city</th>\n",
       "      <th>type_x</th>\n",
       "      <th>cluster</th>\n",
       "      <th>family</th>\n",
       "      <th>perishable</th>\n",
       "      <th>type_y</th>\n",
       "      <th>locale</th>\n",
       "      <th>transferred</th>\n",
       "      <th>dcoilwtico</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20977</th>\n",
       "      <td>2017-07-19</td>\n",
       "      <td>13</td>\n",
       "      <td>1489899</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>Latacunga</td>\n",
       "      <td>C</td>\n",
       "      <td>15</td>\n",
       "      <td>PRODUCE</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74654</th>\n",
       "      <td>2017-07-22</td>\n",
       "      <td>11</td>\n",
       "      <td>581078</td>\n",
       "      <td>22.0</td>\n",
       "      <td>False</td>\n",
       "      <td>Cayambe</td>\n",
       "      <td>B</td>\n",
       "      <td>6</td>\n",
       "      <td>CLEANING</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91572</th>\n",
       "      <td>2017-08-10</td>\n",
       "      <td>32</td>\n",
       "      <td>1254013</td>\n",
       "      <td>5.0</td>\n",
       "      <td>False</td>\n",
       "      <td>Guayaquil</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>PERSONAL CARE</td>\n",
       "      <td>0</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>National</td>\n",
       "      <td>True</td>\n",
       "      <td>48.540</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             date  store_nbr  item_nbr  unit_sales onpromotion       city  \\\n",
       "20977  2017-07-19         13   1489899         1.0       False  Latacunga   \n",
       "74654  2017-07-22         11    581078        22.0       False    Cayambe   \n",
       "91572  2017-08-10         32   1254013         5.0       False  Guayaquil   \n",
       "\n",
       "      type_x  cluster         family  perishable   type_y    locale  \\\n",
       "20977      C       15        PRODUCE           1      NaN       NaN   \n",
       "74654      B        6       CLEANING           0      NaN       NaN   \n",
       "91572      C        3  PERSONAL CARE           0  Holiday  National   \n",
       "\n",
       "      transferred  dcoilwtico  \n",
       "20977         NaN      47.100  \n",
       "74654         NaN      45.995  \n",
       "91572        True      48.540  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Salesdf_filtered.sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of na_index_Salesdf :  2933 \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "date           0\n",
       "store_nbr      0\n",
       "item_nbr       0\n",
       "unit_sales     0\n",
       "onpromotion    0\n",
       "city           0\n",
       "type_x         0\n",
       "cluster        0\n",
       "family         0\n",
       "perishable     0\n",
       "type_y         0\n",
       "locale         0\n",
       "transferred    0\n",
       "dcoilwtico     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fill in cells if there is no holiday by the value : \"no_holyday\"\n",
    "na_index_Salesdf = Salesdf_filtered[Salesdf_filtered['type_y'].isnull() == True].index.values\n",
    "print(\"Size of na_index_Salesdf : \", len(na_index_Salesdf), '\\n')\n",
    "\n",
    "Salesdf_filtered.loc[Salesdf_filtered['type_y'].isnull(), 'type_y'] = \"no_holyday\"\n",
    "Salesdf_filtered.loc[Salesdf_filtered['locale'].isnull(), 'locale'] = \"no_locale\"\n",
    "Salesdf_filtered.loc[Salesdf_filtered['transferred'].isnull(), 'transferred'] = \"no_holyday\"\n",
    "    \n",
    "#check is there is NA\n",
    "Salesdf_filtered.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_month_year(df):\n",
    "    df['month'] = df.date.apply(lambda x: x.split('-')[1])\n",
    "    df['year'] = df.date.apply(lambda x: x.split('-')[0])\n",
    "    \n",
    "    return df\n",
    "\n",
    "get_month_year(Salesdf_filtered);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Salesdf_filtered['date'] = pd.to_datetime(Salesdf_filtered['date'])\n",
    "Salesdf_filtered['day'] = Salesdf_filtered['date'].dt.weekday_name\n",
    "Salesdf_filtered = Salesdf_filtered.drop('date', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>store_nbr</th>\n",
       "      <th>item_nbr</th>\n",
       "      <th>unit_sales</th>\n",
       "      <th>onpromotion</th>\n",
       "      <th>city</th>\n",
       "      <th>type_x</th>\n",
       "      <th>cluster</th>\n",
       "      <th>family</th>\n",
       "      <th>perishable</th>\n",
       "      <th>type_y</th>\n",
       "      <th>locale</th>\n",
       "      <th>transferred</th>\n",
       "      <th>dcoilwtico</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>215670</th>\n",
       "      <td>46</td>\n",
       "      <td>1146785</td>\n",
       "      <td>21.000</td>\n",
       "      <td>False</td>\n",
       "      <td>Quito</td>\n",
       "      <td>A</td>\n",
       "      <td>14</td>\n",
       "      <td>GROCERY I</td>\n",
       "      <td>0</td>\n",
       "      <td>no_holyday</td>\n",
       "      <td>no_locale</td>\n",
       "      <td>no_holyday</td>\n",
       "      <td>48.2000</td>\n",
       "      <td>08</td>\n",
       "      <td>2017</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110589</th>\n",
       "      <td>20</td>\n",
       "      <td>1087269</td>\n",
       "      <td>38.000</td>\n",
       "      <td>True</td>\n",
       "      <td>Quito</td>\n",
       "      <td>B</td>\n",
       "      <td>6</td>\n",
       "      <td>PERSONAL CARE</td>\n",
       "      <td>0</td>\n",
       "      <td>no_holyday</td>\n",
       "      <td>no_locale</td>\n",
       "      <td>no_holyday</td>\n",
       "      <td>49.6000</td>\n",
       "      <td>08</td>\n",
       "      <td>2017</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180340</th>\n",
       "      <td>49</td>\n",
       "      <td>2042947</td>\n",
       "      <td>225.000</td>\n",
       "      <td>False</td>\n",
       "      <td>Quito</td>\n",
       "      <td>A</td>\n",
       "      <td>11</td>\n",
       "      <td>BEVERAGES</td>\n",
       "      <td>0</td>\n",
       "      <td>no_holyday</td>\n",
       "      <td>no_locale</td>\n",
       "      <td>no_holyday</td>\n",
       "      <td>46.0200</td>\n",
       "      <td>07</td>\n",
       "      <td>2017</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286490</th>\n",
       "      <td>31</td>\n",
       "      <td>1473475</td>\n",
       "      <td>15.848</td>\n",
       "      <td>False</td>\n",
       "      <td>Babahoyo</td>\n",
       "      <td>B</td>\n",
       "      <td>10</td>\n",
       "      <td>PRODUCE</td>\n",
       "      <td>1</td>\n",
       "      <td>no_holyday</td>\n",
       "      <td>no_locale</td>\n",
       "      <td>no_holyday</td>\n",
       "      <td>46.0200</td>\n",
       "      <td>07</td>\n",
       "      <td>2017</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288516</th>\n",
       "      <td>16</td>\n",
       "      <td>1080021</td>\n",
       "      <td>1.000</td>\n",
       "      <td>False</td>\n",
       "      <td>Santo Domingo</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>GROCERY I</td>\n",
       "      <td>0</td>\n",
       "      <td>no_holyday</td>\n",
       "      <td>no_locale</td>\n",
       "      <td>no_holyday</td>\n",
       "      <td>47.1000</td>\n",
       "      <td>07</td>\n",
       "      <td>2017</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245710</th>\n",
       "      <td>28</td>\n",
       "      <td>1958181</td>\n",
       "      <td>15.000</td>\n",
       "      <td>False</td>\n",
       "      <td>Guayaquil</td>\n",
       "      <td>E</td>\n",
       "      <td>10</td>\n",
       "      <td>BEVERAGES</td>\n",
       "      <td>0</td>\n",
       "      <td>no_holyday</td>\n",
       "      <td>no_locale</td>\n",
       "      <td>no_holyday</td>\n",
       "      <td>49.9650</td>\n",
       "      <td>07</td>\n",
       "      <td>2017</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212031</th>\n",
       "      <td>52</td>\n",
       "      <td>1146785</td>\n",
       "      <td>2.000</td>\n",
       "      <td>False</td>\n",
       "      <td>Manta</td>\n",
       "      <td>A</td>\n",
       "      <td>11</td>\n",
       "      <td>GROCERY I</td>\n",
       "      <td>0</td>\n",
       "      <td>no_holyday</td>\n",
       "      <td>no_locale</td>\n",
       "      <td>no_holyday</td>\n",
       "      <td>46.0200</td>\n",
       "      <td>07</td>\n",
       "      <td>2017</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58369</th>\n",
       "      <td>25</td>\n",
       "      <td>273528</td>\n",
       "      <td>4.000</td>\n",
       "      <td>False</td>\n",
       "      <td>Salinas</td>\n",
       "      <td>D</td>\n",
       "      <td>1</td>\n",
       "      <td>PERSONAL CARE</td>\n",
       "      <td>0</td>\n",
       "      <td>no_holyday</td>\n",
       "      <td>no_locale</td>\n",
       "      <td>no_holyday</td>\n",
       "      <td>49.5700</td>\n",
       "      <td>08</td>\n",
       "      <td>2017</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249646</th>\n",
       "      <td>10</td>\n",
       "      <td>1342003</td>\n",
       "      <td>16.000</td>\n",
       "      <td>False</td>\n",
       "      <td>Quito</td>\n",
       "      <td>C</td>\n",
       "      <td>15</td>\n",
       "      <td>GROCERY I</td>\n",
       "      <td>0</td>\n",
       "      <td>no_holyday</td>\n",
       "      <td>no_locale</td>\n",
       "      <td>no_holyday</td>\n",
       "      <td>50.0875</td>\n",
       "      <td>07</td>\n",
       "      <td>2017</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23922</th>\n",
       "      <td>53</td>\n",
       "      <td>2042947</td>\n",
       "      <td>223.000</td>\n",
       "      <td>True</td>\n",
       "      <td>Manta</td>\n",
       "      <td>D</td>\n",
       "      <td>13</td>\n",
       "      <td>BEVERAGES</td>\n",
       "      <td>0</td>\n",
       "      <td>no_holyday</td>\n",
       "      <td>no_locale</td>\n",
       "      <td>no_holyday</td>\n",
       "      <td>50.0875</td>\n",
       "      <td>07</td>\n",
       "      <td>2017</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        store_nbr  item_nbr  unit_sales onpromotion           city type_x  \\\n",
       "215670         46   1146785      21.000       False          Quito      A   \n",
       "110589         20   1087269      38.000        True          Quito      B   \n",
       "180340         49   2042947     225.000       False          Quito      A   \n",
       "286490         31   1473475      15.848       False       Babahoyo      B   \n",
       "288516         16   1080021       1.000       False  Santo Domingo      C   \n",
       "245710         28   1958181      15.000       False      Guayaquil      E   \n",
       "212031         52   1146785       2.000       False          Manta      A   \n",
       "58369          25    273528       4.000       False        Salinas      D   \n",
       "249646         10   1342003      16.000       False          Quito      C   \n",
       "23922          53   2042947     223.000        True          Manta      D   \n",
       "\n",
       "        cluster         family  perishable      type_y     locale transferred  \\\n",
       "215670       14      GROCERY I           0  no_holyday  no_locale  no_holyday   \n",
       "110589        6  PERSONAL CARE           0  no_holyday  no_locale  no_holyday   \n",
       "180340       11      BEVERAGES           0  no_holyday  no_locale  no_holyday   \n",
       "286490       10        PRODUCE           1  no_holyday  no_locale  no_holyday   \n",
       "288516        3      GROCERY I           0  no_holyday  no_locale  no_holyday   \n",
       "245710       10      BEVERAGES           0  no_holyday  no_locale  no_holyday   \n",
       "212031       11      GROCERY I           0  no_holyday  no_locale  no_holyday   \n",
       "58369         1  PERSONAL CARE           0  no_holyday  no_locale  no_holyday   \n",
       "249646       15      GROCERY I           0  no_holyday  no_locale  no_holyday   \n",
       "23922        13      BEVERAGES           0  no_holyday  no_locale  no_holyday   \n",
       "\n",
       "        dcoilwtico month  year        day  \n",
       "215670     48.2000    08  2017   Saturday  \n",
       "110589     49.6000    08  2017  Wednesday  \n",
       "180340     46.0200    07  2017     Monday  \n",
       "286490     46.0200    07  2017   Saturday  \n",
       "288516     47.1000    07  2017  Wednesday  \n",
       "245710     49.9650    07  2017   Saturday  \n",
       "212031     46.0200    07  2017   Saturday  \n",
       "58369      49.5700    08  2017     Friday  \n",
       "249646     50.0875    07  2017     Sunday  \n",
       "23922      50.0875    07  2017     Sunday  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Salesdf_filtered.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "strain = Salesdf.sample(frac=0.01,replace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py:1460: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  stat_data = remove_na(group_data)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d780cfeb00>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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I6wau31Tn+k1dg7xuMAXWL1JK/c6DJEmSJEmSJEmStFqsab85JkmSJEmSJEmS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\nTRvuHDMzMzMzMzMzMzMzM7Np4/8BFLp3e3liALsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d780d00240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Sales per Item Family\n",
    "fig, (axis1) = plt.subplots(1,1,figsize=(30,4))\n",
    "sns.barplot(x='family', y='unit_sales', data=strain, ax=axis1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The above plot shows the sales of products per Item family. The average unit_sales ranges somewhere between 0 to 20 with the maximum for the Food and Beverages item family."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py:1460: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  stat_data = remove_na(group_data)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d780d2ca20>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ClnyaaHffOO/rpn0pglX12KraMu/rzqp65YI5z6iqr82b89rv9vUAAADY3XKODO433f35\nJMckSVWtS3JTkoumTP1Yd58wZRwAAIB9sC8XkNlfnpXkC91949BBAAAAxmKQI4MLnJzkHYtse0pV\nfSbJzUl+rbuvWTihqjYl2ZQkj370o2cWEgDgvspNvYFpBj0yWFUHJHlekndP2XxlkiO7++gkb07y\nl9Oeo7vP6+4N3b1hbm5udmEBAO6jdt3Ue9u2bUNHAVaRoU8TfU6SK7v71oUbuvvO7v76ZPmSJPev\nqoNXOiAAAMBaNHQZPCWLnCJaVYdUVU2Wj83OrF9ewWwAAABr1mCfGayqByV5dpJfnDf28iTp7nOT\nnJTkl6pqR5JvJjm5u3vacwHjcvD33JNkx+QRAIDvxmBlsLu/keR7F4ydO2/5nCTnrHQuYPX7tSd8\ndegIAAD3eUOfJgoAAMAAVsOtJQAAYHTOedVfDx1hVfjq7f/8nUc/k+S0N/6nFXstZZCp3I8IgLXg\nsqc9fegIq8I3169LqvLNrVv9TJI8/aOXDR0BVgVlkKl23Y8IAABYm3xmEAAAYISUQQAAgBFSBgEA\nAEZIGQQAABghZRAAAGCElEEAAIARcmsJAIA17mHduz0CJMogAMCa97N33zN0BGAVcpooAADACDky\nuMCT/sdbh46wKhx0+11Zl+Sfbr/LzyTJFW/4r0NHAACA/UoZBAAABnPgAQ/Z7ZGVowwCAACD2fiD\nLxg6wmj5zCAAAMAIDVYGq+qLVXVVVW2pqs1TtldV/X5VXV9Vn62qJw6REwAAYC0a+jTRZ3b37Yts\ne06Sx0y+npzkDyePAAAA7KPVfJroiUne2jv9fZKHVdWhQ4cCAABYC4Ysg53kg1V1RVVtmrL9sCRf\nmre+dTK2m6raVFWbq2rz9u3bZxQVAABgbRmyDG7s7idm5+mgr6iqpy3YXlP26T0Gus/r7g3dvWFu\nbm4WOQEAANacwcpgd988ebwtyUVJjl0wZWuSI+atH57k5pVJxz0HHJi7H/CQ3HPAgUNHAQAAZmCQ\nC8hU1YFJ7tfdd02Wj0ty1oJpFyc5raremZ0Xjvlad9+ywlFH658fc9zQEQAAgBka6mqij0pyUVXt\nyvBn3f3+qnp5knT3uUkuSfLcJNcn+UaSlwyUFQAAYM0ZpAx29w1Jjp4yfu685U7yipXMBQAAMBar\n+dYSAAAAzIgyCAAAMELKIAAAwAgpgwAAACOkDAIAAIyQMggAADBCyiAAAMAIKYMAAAAjpAwCAACM\nkDIIAAAwQsogAADACCmDAAAAI6QMAgAAjJAyCAAAMELKIAAAwAgpgwAAACOkDAIAAIyQMggAADBC\ng5TBqjqiqj5cVddW1TVV9StT5jyjqr5WVVsmX68dIisAAMBatH6g192R5FXdfWVVHZTkiqq6tLs/\nt2Dex7r7hAHyAQAArGmDHBns7lu6+8rJ8l1Jrk1y2BBZAAAAxmjwzwxW1VFJfjjJp6ZsfkpVfaaq\n3ldV/36R/TdV1eaq2rx9+/YZJgUAAFg7Bi2DVfXgJO9J8sruvnPB5iuTHNndRyd5c5K/nPYc3X1e\nd2/o7g1zc3OzDQwAALBGDFYGq+r+2VkE397d7124vbvv7O6vT5YvSXL/qjp4hWMCAACsSUNdTbSS\nnJ/k2u5+0yJzDpnMS1Udm51Zv7xyKQEAANauoa4mujHJi5NcVVVbJmOvSfLoJOnuc5OclOSXqmpH\nkm8mObm7e4iwAAAAa80gZbC7P56k7mXOOUnOWZlEAAAA4zL41UQBAABYecogAADACCmDAAAAI6QM\nAgAAjJAyCAAAMELKIAAAwAgpgwAAACOkDAIAAIyQMggAADBCyiAAAMAIKYMAAAAjpAwCAACMkDII\nAAAwQsogAADACCmDAAAAI6QMAgAAjJAyCAAAMEKDlcGqOr6qPl9V11fVGVO2P6Cq3jXZ/qmqOmrl\nUwIAAKxNg5TBqlqX5A+SPCfJ45KcUlWPWzDtpUm+0t3/JsnvJHn9yqYEAABYu4Y6Mnhskuu7+4bu\n/laSdyY5ccGcE5NcOFn+iyTPqqpawYwAAABrVnX3yr9o1UlJju/ul03WX5zkyd192rw5V0/mbJ2s\nf2Ey5/YFz7UpyabJ6mOTfH4FvoWxODjJ7fc6C4bh/clq5b3JauW9yWrm/bn/HNndc0uZuH7WSRYx\n7Qjfwla6lDnp7vOSnLc/QrG7qtrc3RuGzgHTeH+yWnlvslp5b7KaeX8OY6jTRLcmOWLe+uFJbl5s\nTlWtT/LQJHesSDoAAIA1bqgyeHmSx1TV91fVAUlOTnLxgjkXJzl1snxSkr/tIc5pBQAAWIMGOU20\nu3dU1WlJPpBkXZILuvuaqjoryebuvjjJ+UneVlXXZ+cRwZOHyDpyTr9lNfP+ZLXy3mS18t5kNfP+\nHMAgF5ABAABgWIPddB4AAIDhKIMAAAAjpAyyh6o6pKreWVVfqKrPVdUlVfVvh84FVXV3VW2pqs9U\n1ZVV9dShM8F8VfVTVdVV9e+GzgK7zPvdec3k9+evVpX/A7IqzHt/7vo6Y+hMY+Izg+ymqirJ3yW5\nsLvPnYwdk+Sg7v7YoOEYvar6enc/eLL8k0le091PHzgWfEdV/XmSQ5N8qLt/Y+A4kGSP352PTPJn\nST7R3WcOmwx2f3+y8vxViIWemeTbu4pgknT3FkWQVeghSb4ydAjYpaoenGRjkpfGFbBZpbr7tiSb\nkpw2+QMwMGKD3FqCVe3xSa4YOgQs4oFVtSXJ92Tn0ZcfHzgPzPf8JO/v7n+sqjuq6ondfeXQoWCh\n7r5hcproI5PcOnQeRm/Xv+27/O/uftdgaUZGGQTuS77Z3cckSVU9Jclbq+rx7Xx3VodTkvzuZPmd\nk3VlkNXKUUFWi+/8287KUwZZ6JokJw0dAu5Nd3+yqg5OMpfktqHzMG5V9b3ZeaT68VXVSdYl6ao6\n3R8rWG2q6geS3B2/O2H0fGaQhf42yQOq6hd2DVTVj1SVi3Swqkyu1rguyZeHzgLZ+Ue0t3b3kd19\nVHcfkeT/JvmxgXPBbqpqLsm5Sc7xhwrAkUF2091dVT+V5Hcnl/b9lyRfTPLKQYPBTvM/V1BJTu3u\nu4cMBBOnJHndgrH3JPmZJC7AxdB2/e68f5IdSd6W5E3DRoLvWPiZwfd3t9tLrBC3lgAAABghp4kC\nAACMkDIIAAAwQsogAADACCmDAAAAI6QMAgAAjJAyCADzVNXDquqXh84BALOmDALA7h6WRBkEYM1T\nBgFgd69L8oNVtaWq3l1VJ+7aUFVvr6rnVdXPVdVfVdX7q+rzVXXmvDk/W1Wfnuz/R1W1btqLVNWR\nVXVdVR1cVferqo9V1XEr8P0BQBJlEAAWOiPJF7r7mCTnJHlJklTVQ5M8Ncklk3nHJvkvSY5J8sKq\n2lBVP5TkRUk2Tva/ezJnD919Y5LXJzk3yauSfK67Pziz7woAFlg/dAAAWK26+7Kq+oOqemSSFyR5\nT3fvqKokubS7v5wkVfXeJD+WZEeSJyW5fDLngUlu28vz/0lVvTDJy7OzVALAilEGAWDv3padR/dO\nTvLz88Z7wbxOUkku7O5XL+WJq+pBSQ6frD44yV37FhUAls5pogCwu7uSHDRv/S1JXpkk3X3NvPFn\nV9UjquqBSZ6f5BNJPpTkpMmRxEy2H7mX13p9krcneW2SP95v3wEALIEyCADzTE79/ERVXV1Vb+ju\nW5Ncm+RPF0z9eHYeNdySnaePbu7uzyX59SQfrKrPJrk0yaHTXqeqnp7kR5K8vrvfnuRbVfWS2XxX\nALCn6l54lgsAsMvkVM6rkjyxu782Gfu5JBu6+7QhswHAvnBkEAAWUVU/keQfkrx5VxEEgLXCkUEA\nmLGq+lSSBywYfnF3XzVEHgBIlEEAAIBRcpooAADACCmDAAAAI6QMAgAAjJAyCAAAMELKIAAAwAj9\nP82CZ0UH6lI6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d780d00908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Sales per Store Type\n",
    "fig, (axis1) = plt.subplots(1,1,figsize=(15,4))\n",
    "sns.barplot(x='type_x', y='unit_sales', data=strain, ax=axis1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The bar plot of Total Sales per store type shows that Store type B has the maximum sales over other store types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py:1460: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  stat_data = remove_na(group_data)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d780d00780>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d7816910b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Stores in Cities\n",
    "fig, (axis1) = plt.subplots(1,1,figsize=(30,4))\n",
    "sns.countplot(x=stores['city'], data=stores, ax=axis1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The City of Quito has the most number of Stores and the second being the city of Guayaquil."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py:1460: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  stat_data = remove_na(group_data)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d781596860>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d7815a0f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Stores in States\n",
    "fig, (axis1) = plt.subplots(1,1,figsize=(30,4))\n",
    "sns.countplot(x=stores['state'], data=stores, ax=axis1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The States of Pichincha Santo and the state of Guayas has the most number of Stores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ufn5+mD59Onr06IH4+HgcOHBAemOV/4ZQnitXrmDixIkwNzfHzZs3cfjwYQwe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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d781645978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Stacked Barplots of Types against clusters\n",
    "plt.style.use('seaborn-white')\n",
    "#plt.style.use('dark_background')\n",
    "type_cluster = stores.groupby(['type','cluster']).size()\n",
    "type_cluster.unstack().plot(kind='bar',stacked=True, colormap= 'PuBu', figsize=(13,11),  grid=False)\n",
    "plt.title('Stacked Barplot of Store types and their cluster distribution', fontsize=18)\n",
    "plt.ylabel('Count of clusters in a particular store type', fontsize=16)\n",
    "plt.xlabel('Store type', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The above bar plot shows the cluster distribution across the store types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EREREFVgcEBERERERABYHRERERERUgcUBEREREREBYHFA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d781649278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Stacked barplot of types of stores across the different cities\n",
    "plt.style.use('seaborn-white')\n",
    "city_cluster = stores.groupby(['city','type']).store_nbr.size()\n",
    "city_cluster.unstack().plot(kind='bar',stacked=True, colormap= 'viridis', figsize=(13,11),  grid=False)\n",
    "plt.title('Stacked Barplot of Store types opened for each city')\n",
    "plt.ylabel('Count of stores for a particular city')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QzQAAAIAJohkAAAAwwSXnkGNaliiU0yMAAPDM+KzCqCxd30eHR2Tp+v7XcaQZ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\nIiIiEsJv+Qrr4Wpm470AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d782a10588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Holiday Events data\n",
    "plt.style.use('seaborn-white')\n",
    "# plt.style.use('dark_background')\n",
    "holiday_local_type = holidays.groupby(['locale_name', 'type']).size()\n",
    "holiday_local_type.unstack().plot(kind='bar',stacked=True, colormap= 'magma_r', figsize=(12,10),  grid=False)\n",
    "plt.title('Stacked Barplot of locale name against event type')\n",
    "plt.ylabel('Count of entries')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. Data Transformation Techniques"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8.a One hot Encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is One Hot Encoding?\n",
    "A one hot encoding is a representation of categorical variables as binary vectors.\n",
    "This first requires that the categorical values be mapped to integer values.\n",
    "Then, each integer value is represented as a binary vector that is all zero values except the index of the integer, which is marked with a 1.\n",
    "\n",
    "## Why Use a One Hot Encoding?\n",
    "A one hot encoding allows the representation of categorical data to be more expressive.\n",
    "Many machine learning algorithms cannot work with categorical data directly. The categories must be converted into numbers. This is required for both input and output variables that are categorical.\n",
    "\n",
    "\n",
    "## The text in the document by Source Wikipedia is licensed under CC BY 3.0 https://creativecommons.org/licenses/by/3.0/us/\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## One hot encoding using get_dummies on pandas dataframe.\n",
    "dummy_variables = ['onpromotion','city','type_x','cluster','store_nbr','item_nbr',\n",
    "                'family','perishable','type_y', 'locale', 'transferred', 'month', 'day']\n",
    "\n",
    "for var in dummy_variables:\n",
    "    dummy = pd.get_dummies(Salesdf_filtered[var], prefix = var, drop_first = False)\n",
    "    Salesdf_filtered = pd.concat([Salesdf_filtered, dummy], axis = 1)\n",
    "\n",
    "Salesdf_filtered = Salesdf_filtered.drop(dummy_variables, axis = 1)\n",
    "Salesdf_filtered = Salesdf_filtered.drop(['year'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3735 entries, 135 to 336838\n",
      "Columns: 149 entries, unit_sales to day_Wednesday\n",
      "dtypes: float64(2), uint8(147)\n",
      "memory usage: 623.7 KB\n"
     ]
    }
   ],
   "source": [
    "Salesdf_filtered.info()\n",
    "#Salesdf_filtered.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>day_Thursday</th>\n",
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       "     unit_sales  dcoilwtico  onpromotion_False  onpromotion_True  city_Ambato  \\\n",
       "135        27.0     49.5900                  1                 0            0   \n",
       "183        18.0     50.0875                  1                 0            0   \n",
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       "\n",
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       "\n",
       "         ...        transferred_no_holyday  month_07  month_08  day_Friday  \\\n",
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       "396      ...                             1         1         0           0   \n",
       "423      ...                             1         0         1           0   \n",
       "\n",
       "     day_Monday  day_Saturday  day_Sunday  day_Thursday  day_Tuesday  \\\n",
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       "339           0             0           1             0            0   \n",
       "396           0             1           0             0            0   \n",
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       "\n",
       "     day_Wednesday  \n",
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       "183              0  \n",
       "339              0  \n",
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       "\n",
       "[5 rows x 149 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Salesdf_filtered.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The above dataframe contains data after the one hot encoding technique is applied to the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Re-scale\n",
    "#We keep this value to re-scale the predicted unit_sales values in the following lines of code.\n",
    "min_train, max_train = Salesdf_filtered['unit_sales'].min(), Salesdf_filtered['unit_sales'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scalable_variables = ['unit_sales','dcoilwtico']\n",
    "\n",
    "for var in scalable_variables:\n",
    "    mini, maxi = Salesdf_filtered[var].min(), Salesdf_filtered[var].max()\n",
    "    Salesdf_filtered.loc[:,var] = (Salesdf_filtered[var] - mini) / (maxi - mini)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73894</th>\n",
       "      <td>0.009127</td>\n",
       "      <td>0.408578</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>...</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 149 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        unit_sales  dcoilwtico  onpromotion_False  onpromotion_True  \\\n",
       "169470    0.002335    0.860045                  1                 0   \n",
       "245710    0.009127    0.944695                  1                 0   \n",
       "34727     0.030603    0.408578                  1                 0   \n",
       "212031    0.001101    0.054176                  1                 0   \n",
       "187197    0.009127    0.404063                  1                 0   \n",
       "213221    0.006816    0.546275                  1                 0   \n",
       "230420    0.008509    0.054176                  1                 0   \n",
       "38399     0.007892    0.944695                  0                 1   \n",
       "277634    0.004746    0.072799                  1                 0   \n",
       "73894     0.009127    0.408578                  1                 0   \n",
       "\n",
       "        city_Ambato  city_Babahoyo  city_Cayambe  city_Cuenca  city_Daule  \\\n",
       "169470            0              0             0            0           0   \n",
       "245710            0              0             0            0           0   \n",
       "34727             0              0             0            0           0   \n",
       "212031            0              0             0            0           0   \n",
       "187197            0              0             0            0           0   \n",
       "213221            0              0             0            0           0   \n",
       "230420            1              0             0            0           0   \n",
       "38399             0              0             0            0           0   \n",
       "277634            0              0             0            0           0   \n",
       "73894             0              0             0            0           0   \n",
       "\n",
       "        city_El Carmen      ...        transferred_no_holyday  month_07  \\\n",
       "169470               0      ...                             1         0   \n",
       "245710               0      ...                             1         1   \n",
       "34727                0      ...                             1         0   \n",
       "212031               0      ...                             1         1   \n",
       "187197               0      ...                             0         0   \n",
       "213221               0      ...                             1         0   \n",
       "230420               0      ...                             1         1   \n",
       "38399                0      ...                             1         1   \n",
       "277634               0      ...                             0         1   \n",
       "73894                0      ...                             1         0   \n",
       "\n",
       "        month_08  day_Friday  day_Monday  day_Saturday  day_Sunday  \\\n",
       "169470         1           0           0             0           0   \n",
       "245710         0           0           0             1           0   \n",
       "34727          1           0           1             0           0   \n",
       "212031         0           0           0             1           0   \n",
       "187197         1           0           0             0           0   \n",
       "213221         1           0           0             1           0   \n",
       "230420         0           0           0             1           0   \n",
       "38399          0           0           0             1           0   \n",
       "277634         0           0           0             0           1   \n",
       "73894          1           0           1             0           0   \n",
       "\n",
       "        day_Thursday  day_Tuesday  day_Wednesday  \n",
       "169470             0            0              1  \n",
       "245710             0            0              0  \n",
       "34727              0            0              0  \n",
       "212031             0            0              0  \n",
       "187197             0            1              0  \n",
       "213221             0            0              0  \n",
       "230420             0            0              0  \n",
       "38399              0            0              0  \n",
       "277634             0            0              0  \n",
       "73894              0            0              0  \n",
       "\n",
       "[10 rows x 149 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Shape : ', Salesdf_filtered.shape)\n",
    "Salesdf_filtered.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "unit_sales                0\n",
       "dcoilwtico                0\n",
       "onpromotion_False         0\n",
       "onpromotion_True          0\n",
       "city_Ambato               0\n",
       "city_Babahoyo             0\n",
       "city_Cayambe              0\n",
       "city_Cuenca               0\n",
       "city_Daule                0\n",
       "city_El Carmen            0\n",
       "city_Esmeraldas           0\n",
       "city_Guaranda             0\n",
       "city_Guayaquil            0\n",
       "city_Ibarra               0\n",
       "city_Latacunga            0\n",
       "city_Libertad             0\n",
       "city_Loja                 0\n",
       "city_Machala              0\n",
       "city_Manta                0\n",
       "city_Playas               0\n",
       "city_Puyo                 0\n",
       "city_Quevedo              0\n",
       "city_Quito                0\n",
       "city_Riobamba             0\n",
       "city_Salinas              0\n",
       "city_Santo Domingo        0\n",
       "type_x_A                  0\n",
       "type_x_B                  0\n",
       "type_x_C                  0\n",
       "type_x_D                  0\n",
       "                         ..\n",
       "item_nbr_1958181          0\n",
       "item_nbr_2042941          0\n",
       "item_nbr_2042947          0\n",
       "family_BEVERAGES          0\n",
       "family_BREAD/BAKERY       0\n",
       "family_CLEANING           0\n",
       "family_GROCERY I          0\n",
       "family_PERSONAL CARE      0\n",
       "family_PRODUCE            0\n",
       "perishable_0              0\n",
       "perishable_1              0\n",
       "type_y_Additional         0\n",
       "type_y_Holiday            0\n",
       "type_y_Transfer           0\n",
       "type_y_no_holyday         0\n",
       "locale_Local              0\n",
       "locale_National           0\n",
       "locale_no_locale          0\n",
       "transferred_False         0\n",
       "transferred_True          0\n",
       "transferred_no_holyday    0\n",
       "month_07                  0\n",
       "month_08                  0\n",
       "day_Friday                0\n",
       "day_Monday                0\n",
       "day_Saturday              0\n",
       "day_Sunday                0\n",
       "day_Thursday              0\n",
       "day_Tuesday               0\n",
       "day_Wednesday             0\n",
       "Length: 149, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Salesdf_filtered.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape X : (3735, 148)\n",
      "Shape y : (3735,)\n"
     ]
    }
   ],
   "source": [
    "#train database without unit_sales\n",
    "Salesdf_filtered = Salesdf_filtered.reset_index(drop=True)  #we reset the index\n",
    "y = Salesdf_filtered['unit_sales']\n",
    "X = Salesdf_filtered.drop(['unit_sales'], axis = 1)\n",
    "\n",
    "print('Shape X :', X.shape)\n",
    "print('Shape y :', y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape : (2988, 148)\n",
      "y_train shape : (2988,)\n",
      "X_test shape : (747, 148)\n",
      "y_test shape : (747,)\n"
     ]
    }
   ],
   "source": [
    "num_test = 0.20\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=num_test, random_state=15)\n",
    "print('X_train shape :', X_train.shape)\n",
    "print('y_train shape :', y_train.shape)\n",
    "print('X_test shape :', X_test.shape)\n",
    "print('y_test shape :', y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9. Regression Model fitting techniques."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9.a. Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression\n",
    "\n",
    "Linear Regression is a linear approach for modelling the relationship between a scalar dependent variable y and one or more explanatory variables (or independent variables) denoted X. The case of one explanatory variable is called simple linear regression. For more than one explanatory variable, the process is called multiple linear regression.\n",
    "\n",
    "Linear regression models are often fitted using the least squares approach, but they may also be fitted in other ways, such as by minimizing the \"lack of fit\" in some other norm (as with least absolute deviations regression), or by minimizing a penalized version of the least squares cost function as in ridge regression (L2-norm penalty) and lasso (L1-norm penalty). \n",
    "\n",
    "\n",
    "## The text in the document by Source Wikipedia is licensed under CC BY 3.0 https://creativecommons.org/licenses/by/3.0/us/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)\n"
     ]
    }
   ],
   "source": [
    "# Fit the linear model\n",
    "model = linear_model.LinearRegression()\n",
    "results = model.fit(X_train, y_train)\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-10077717828.6 [  5.77278827e-03  -4.44002834e+11  -4.44002834e+11  -1.20049145e+11\n",
      "   6.71849673e+11  -3.00197319e+11  -4.10525006e+11  -2.19780004e+11\n",
      "  -3.50107920e+11  -8.38620072e+10  -1.07196630e+11  -6.50316918e+10\n",
      "   9.76924707e+10   1.79310313e+11  -3.68439881e+11   3.24373052e+11\n",
      "   6.87498201e+10  -1.73268895e+11   5.88248972e+10   1.63766182e+11\n",
      "   1.05804503e+11  -3.17490270e+10  -4.24909495e+11  -5.72211037e+11\n",
      "  -1.78411474e+10  -1.05642561e+11  -3.35504730e+11   5.95024592e+10\n",
      "   1.64092747e+11  -4.61227293e+10  -1.03174077e+11   2.64559275e+11\n",
      "  -4.43826214e+10  -3.55476150e+11   1.51662781e+11   1.59024917e+11\n",
      "   1.51153503e+11  -3.34901373e+11  -2.07913878e+11   1.43898354e+11\n",
      "  -1.15761973e+11   2.48465599e+10  -2.17076115e+11  -1.58991247e+11\n",
      "  -3.25466957e+11   5.84658769e+10  -2.52456020e+10  -1.08276286e+11\n",
      "  -1.08276286e+11   9.54897184e+09  -1.17438523e+11   1.62158689e+10\n",
      "  -1.08276286e+11   9.54897184e+09   9.54897184e+09   1.52201578e+10\n",
      "   1.04704843e+11   2.83668450e+11  -1.06354497e+11  -1.06354497e+11\n",
      "   2.12448510e+10  -2.47366547e+10  -1.90287372e+11  -2.45608674e+11\n",
      "   1.15779198e+11   1.80152446e+11   1.52201578e+10   1.31227816e+09\n",
      "  -5.67430826e+11  -2.91384053e+10  -1.88895660e+11   3.18283686e+11\n",
      "  -4.35968091e+11  -3.41473475e+10  -2.25752615e+11  -2.25752615e+11\n",
      "  -1.43096828e+11  -6.73251980e+11  -1.43096828e+11  -3.13933022e+11\n",
      "   4.85028226e+10  -2.66953417e+11   7.76555740e+10  -2.11135697e+11\n",
      "  -3.25998331e+11   3.93996137e+11  -2.76878340e+11  -7.03750985e+10\n",
      "  -2.11135697e+11  -2.06922300e+11  -2.07279874e+11   6.01448797e+10\n",
      "   1.03374154e+11   1.03374154e+11   1.03374154e+11   6.01448797e+10\n",
      "   1.91674272e+11   2.91117304e+09   2.01664748e+11   3.32435820e+10\n",
      "   1.41979400e+11  -1.07870288e+10  -1.07870288e+10  -1.83366254e+11\n",
      "   1.18473410e+11  -1.07870288e+10   3.14003851e+10  -1.07870288e+10\n",
      "  -1.07870288e+10  -1.83366254e+11  -1.07870288e+10  -1.07870288e+10\n",
      "  -1.83366254e+11  -1.07870288e+10  -1.07870288e+10  -1.07870288e+10\n",
      "  -2.66270526e+10  -2.66270526e+10   7.73519835e+10   7.73519835e+10\n",
      "   7.73519835e+10  -8.89932410e+09   7.35492465e+10   3.70522744e+10\n",
      "   7.92396883e+10   2.51818914e+11   2.18649710e+11   6.58957301e+10\n",
      "  -5.76742674e+10   3.48214561e+11   3.48214561e+11   7.70501633e+10\n",
      "  -4.62273833e+10  -3.10223932e+11  -3.90595346e+10  -4.62273833e+10\n",
      "   4.96650263e+11   2.25485865e+11   6.27095658e+11   2.32482914e+10\n",
      "   2.32482914e+10  -4.51483388e+10  -4.51483388e+10  -4.51483388e+10\n",
      "  -4.51483388e+10  -4.51483388e+10  -4.51483388e+10  -4.51483388e+10]\n"
     ]
    }
   ],
   "source": [
    "# Print the coefficients\n",
    "print (results.intercept_, results.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\compat\\pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>unit_sales</td>    <th>  R-squared:         </th>  <td>   0.354</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.335</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   18.69</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 21 Apr 2018</td> <th>  Prob (F-statistic):</th>  <td>2.22e-213</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:36:28</td>     <th>  Log-Likelihood:    </th>  <td>  6716.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  2988</td>      <th>  AIC:               </th> <td>-1.326e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  2902</td>      <th>  BIC:               </th> <td>-1.275e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    85</td>      <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>               <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dcoilwtico</th>             <td>    0.0057</td> <td>    0.002</td> <td>    3.394</td> <td> 0.001</td> <td>    0.002</td> <td>    0.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>onpromotion_False</th>      <td>   -0.0037</td> <td>    0.001</td> <td>   -5.395</td> <td> 0.000</td> <td>   -0.005</td> <td>   -0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>onpromotion_True</th>       <td>    0.0082</td> <td>    0.001</td> <td>    9.787</td> <td> 0.000</td> <td>    0.007</td> <td>    0.010</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>city_Ambato</th>            <td>   -0.0014</td> <td>    0.002</td> <td>   -0.839</td> <td> 0.401</td> <td>   -0.005</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>city_Babahoyo</th>          <td>    0.0022</td> <td>    0.001</td> <td>    1.594</td> <td> 0.111</td> <td>   -0.000</td> <td>    0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>city_Cayambe</th>           <td>    0.0024</td> <td>    0.002</td> <td>    1.401</td> <td> 0.161</td> <td>   -0.001</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>city_Cuenca</th>            <td>    0.0013</td> <td>    0.001</td> <td>    0.887</td> <td> 0.375</td> <td>   -0.002</td> <td>    0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>city_Daule</th>             <td>   -0.0002</td> <td>    0.002</td> <td>   -0.137</td> <td> 0.891</td> <td>   -0.003</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>city_El Carmen</th>         <td>   -0.0013</td> <td>    0.002</td> <td>   -0.692</td> <td> 0.489</td> <td>   -0.005</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>city_Esmeraldas</th>        <td> -1.01e-05</td> <td>    0.002</td> <td>   -0.006</td> <td> 0.995</td> <td>   -0.003</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>city_Guaranda</th>          <td>    0.0002</td> <td>    0.002</td> <td>    0.154</td> <td> 0.878</td> <td>   -0.003</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>city_Guayaquil</th>         <td> 2.858e-05</td> <td>    0.001</td> <td>    0.024</td> <td> 0.981</td> <td>   -0.002</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>city_Ibarra</th>            <td>   -0.0008</td> <td>    0.001</td> <td>   -0.533</td> <td> 0.594</td> <td>   -0.004</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>city_Latacunga</th>         <td>-4.487e-06</td> <td>    0.002</td> <td>   -0.003</td> <td> 0.998</td> <td>   -0.003</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>city_Libertad</th>          <td> 6.975e-05</td> <td>    0.002</td> <td>    0.040</td> <td> 0.968</td> <td>   -0.003</td> <td>    0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>city_Loja</th>              <td>    0.0010</td> <td>    0.002</td> <td>    0.575</td> <td> 0.565</td> <td>   -0.002</td> <td>    0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>city_Machala</th>           <td>    0.0013</td> <td>    0.002</td> <td>    0.802</td> <td> 0.423</td> <td>   -0.002</td> <td>    0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>city_Manta</th>             <td>   -0.0015</td> <td>    0.001</td> <td>   -1.015</td> <td> 0.310</td> <td>   -0.004</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>city_Playas</th>            <td>    0.0004</td> <td>    0.002</td> <td>    0.261</td> <td> 0.794</td> <td>   -0.003</td> <td>    0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>city_Puyo</th>              <td>   -0.0007</td> <td>    0.001</td> <td>   -0.589</td> <td> 0.556</td> <td>   -0.003</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>city_Quevedo</th>           <td>    0.0007</td> <td>    0.002</td> <td>    0.427</td> <td> 0.669</td> <td>   -0.002</td> <td>    0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>city_Quito</th>             <td>    0.0037</td> <td>    0.001</td> <td>    3.598</td> <td> 0.000</td> <td>    0.002</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>city_Riobamba</th>          <td>   -0.0004</td> <td>    0.001</td> <td>   -0.330</td> <td> 0.741</td> <td>   -0.003</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>city_Salinas</th>           <td>   -0.0027</td> <td>    0.002</td> <td>   -1.580</td> <td> 0.114</td> <td>   -0.006</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>city_Santo Domingo</th>     <td>  4.72e-05</td> <td>    0.002</td> <td>    0.030</td> <td> 0.976</td> <td>   -0.003</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>type_x_A</th>               <td>    0.0101</td> <td>    0.001</td> <td>   12.258</td> <td> 0.000</td> <td>    0.008</td> <td>    0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>type_x_B</th>               <td>   -0.0025</td> <td>    0.001</td> <td>   -2.781</td> <td> 0.005</td> <td>   -0.004</td> <td>   -0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>type_x_C</th>               <td>   -0.0036</td> <td>    0.001</td> <td>   -4.989</td> <td> 0.000</td> <td>   -0.005</td> <td>   -0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>type_x_D</th>               <td>   -0.0012</td> <td>    0.001</td> <td>   -1.394</td> <td> 0.164</td> <td>   -0.003</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>type_x_E</th>               <td>    0.0016</td> <td>    0.001</td> <td>    1.326</td> <td> 0.185</td> <td>   -0.001</td> <td>    0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cluster_1</th>              <td>    0.0029</td> <td>    0.001</td> <td>    2.053</td> <td> 0.040</td> <td>    0.000</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cluster_2</th>              <td>   -0.0002</td> <td>    0.001</td> <td>   -0.165</td> <td> 0.869</td> <td>   -0.003</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cluster_3</th>              <td>   -0.0004</td> <td>    0.001</td> <td>   -0.359</td> <td> 0.719</td> <td>   -0.002</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cluster_4</th>              <td>    0.0005</td> <td>    0.001</td> <td>    0.318</td> <td> 0.750</td> <td>   -0.002</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cluster_5</th>              <td>    0.0105</td> <td>    0.002</td> <td>    6.967</td> <td> 0.000</td> <td>    0.008</td> <td>    0.013</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cluster_6</th>              <td>   -0.0003</td> <td>    0.001</td> <td>   -0.296</td> <td> 0.767</td> <td>   -0.003</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cluster_7</th>              <td>   -0.0011</td> <td>    0.001</td> <td>   -1.114</td> <td> 0.265</td> <td>   -0.003</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cluster_8</th>              <td>    0.0035</td> <td>    0.001</td> <td>    2.428</td> <td> 0.015</td> <td>    0.001</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cluster_9</th>              <td>    0.0003</td> <td>    0.001</td> <td>    0.207</td> <td> 0.836</td> <td>   -0.003</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cluster_10</th>             <td>   -0.0023</td> <td>    0.001</td> <td>   -1.774</td> <td> 0.076</td> <td>   -0.005</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cluster_11</th>             <td>    0.0016</td> <td>    0.001</td> <td>    1.219</td> <td> 0.223</td> <td>   -0.001</td> <td>    0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cluster_12</th>             <td>   -0.0001</td> <td>    0.002</td> <td>   -0.068</td> <td> 0.946</td> <td>   -0.003</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cluster_13</th>             <td>   -0.0021</td> <td>    0.001</td> <td>   -1.563</td> <td> 0.118</td> <td>   -0.005</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cluster_14</th>             <td>   -0.0019</td> <td>    0.001</td> <td>   -1.457</td> <td> 0.145</td> <td>   -0.004</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cluster_15</th>             <td>   -0.0021</td> <td>    0.001</td> <td>   -1.878</td> <td> 0.060</td> <td>   -0.004</td> <td> 9.15e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cluster_16</th>             <td>   -0.0043</td> <td>    0.001</td> <td>   -3.014</td> <td> 0.003</td> <td>   -0.007</td> <td>   -0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cluster_17</th>             <td>   -0.0001</td> <td>    0.001</td> <td>   -0.079</td> <td> 0.937</td> <td>   -0.003</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_1</th>            <td>   -0.0064</td> <td>    0.003</td> <td>   -2.317</td> <td> 0.021</td> <td>   -0.012</td> <td>   -0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_2</th>            <td>    0.0004</td> <td>    0.003</td> <td>    0.135</td> <td> 0.893</td> <td>   -0.005</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_3</th>            <td>    0.0076</td> <td>    0.003</td> <td>    2.783</td> <td> 0.005</td> <td>    0.002</td> <td>    0.013</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_4</th>            <td>    0.0028</td> <td>    0.002</td> <td>    1.236</td> <td> 0.216</td> <td>   -0.002</td> <td>    0.007</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_5</th>            <td>   -0.0019</td> <td>    0.002</td> <td>   -0.800</td> <td> 0.423</td> <td>   -0.006</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_6</th>            <td>    0.0005</td> <td>    0.003</td> <td>    0.179</td> <td> 0.858</td> <td>   -0.005</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_7</th>            <td>   -0.0028</td> <td>    0.003</td> <td>   -0.922</td> <td> 0.356</td> <td>   -0.009</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_8</th>            <td>   -0.0014</td> <td>    0.003</td> <td>   -0.453</td> <td> 0.650</td> <td>   -0.007</td> <td>    0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_9</th>            <td>   -0.0012</td> <td>    0.003</td> <td>   -0.374</td> <td> 0.708</td> <td>   -0.008</td> <td>    0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_10</th>           <td>   -0.0015</td> <td>    0.002</td> <td>   -0.630</td> <td> 0.529</td> <td>   -0.006</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_11</th>           <td>    0.0024</td> <td>    0.002</td> <td>    1.401</td> <td> 0.161</td> <td>   -0.001</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_12</th>           <td>    0.0012</td> <td>    0.003</td> <td>    0.464</td> <td> 0.643</td> <td>   -0.004</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_13</th>           <td>   -0.0012</td> <td>    0.003</td> <td>   -0.442</td> <td> 0.659</td> <td>   -0.007</td> <td>    0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_14</th>           <td>   -0.0004</td> <td>    0.001</td> <td>   -0.330</td> <td> 0.741</td> <td>   -0.003</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_15</th>           <td>   -0.0008</td> <td>    0.001</td> <td>   -0.533</td> <td> 0.594</td> <td>   -0.004</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_16</th>           <td>    0.0013</td> <td>    0.003</td> <td>    0.482</td> <td> 0.630</td> <td>   -0.004</td> <td>    0.007</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_17</th>           <td>   -0.0001</td> <td>    0.002</td> <td>   -0.068</td> <td> 0.946</td> <td>   -0.003</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_18</th>           <td>   -0.0043</td> <td>    0.001</td> <td>   -3.014</td> <td> 0.003</td> <td>   -0.007</td> <td>   -0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_19</th>           <td>    0.0002</td> <td>    0.002</td> <td>    0.154</td> <td> 0.878</td> <td>   -0.003</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_20</th>           <td>   -0.0040</td> <td>    0.004</td> <td>   -1.139</td> <td> 0.255</td> <td>   -0.011</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_21</th>           <td>    0.0006</td> <td>    0.003</td> <td>    0.228</td> <td> 0.819</td> <td>   -0.005</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_22</th>           <td>   -0.0007</td> <td>    0.001</td> <td>   -0.589</td> <td> 0.556</td> <td>   -0.003</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_23</th>           <td>   -0.0025</td> <td>    0.002</td> <td>   -1.161</td> <td> 0.246</td> <td>   -0.007</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_24</th>           <td>    0.0058</td> <td>    0.002</td> <td>    2.386</td> <td> 0.017</td> <td>    0.001</td> <td>    0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_25</th>           <td>   -0.0027</td> <td>    0.002</td> <td>   -1.580</td> <td> 0.114</td> <td>   -0.006</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_26</th>           <td>   -0.0060</td> <td>    0.002</td> <td>   -2.700</td> <td> 0.007</td> <td>   -0.010</td> <td>   -0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_27</th>           <td>   -0.0002</td> <td>    0.002</td> <td>   -0.137</td> <td> 0.891</td> <td>   -0.003</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_28</th>           <td>    0.0029</td> <td>    0.003</td> <td>    1.119</td> <td> 0.263</td> <td>   -0.002</td> <td>    0.008</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_29</th>           <td>   -0.0014</td> <td>    0.002</td> <td>   -0.568</td> <td> 0.570</td> <td>   -0.006</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_30</th>           <td>   -0.0011</td> <td>    0.003</td> <td>   -0.375</td> <td> 0.707</td> <td>   -0.007</td> <td>    0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_31</th>           <td>    0.0022</td> <td>    0.001</td> <td>    1.594</td> <td> 0.111</td> <td>   -0.000</td> <td>    0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_32</th>           <td>   -0.0005</td> <td>    0.003</td> <td>   -0.164</td> <td> 0.870</td> <td>   -0.006</td> <td>    0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_33</th>           <td>    0.0007</td> <td>    0.002</td> <td>    0.427</td> <td> 0.669</td> <td>   -0.002</td> <td>    0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_34</th>           <td>    0.0004</td> <td>    0.003</td> <td>    0.135</td> <td> 0.893</td> <td>   -0.005</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_35</th>           <td>    0.0004</td> <td>    0.002</td> <td>    0.261</td> <td> 0.794</td> <td>   -0.003</td> <td>    0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_36</th>           <td> 6.975e-05</td> <td>    0.002</td> <td>    0.040</td> <td> 0.968</td> <td>   -0.003</td> <td>    0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_37</th>           <td>   -0.0006</td> <td>    0.003</td> <td>   -0.215</td> <td> 0.830</td> <td>   -0.006</td> <td>    0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_38</th>           <td>    0.0010</td> <td>    0.002</td> <td>    0.575</td> <td> 0.565</td> <td>   -0.002</td> <td>    0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_39</th>           <td>    0.0015</td> <td>    0.002</td> <td>    0.704</td> <td> 0.481</td> <td>   -0.003</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_40</th>           <td>-1.067e-05</td> <td>    0.002</td> <td>   -0.005</td> <td> 0.996</td> <td>   -0.005</td> <td>    0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_41</th>           <td>    0.0013</td> <td>    0.002</td> <td>    0.579</td> <td> 0.563</td> <td>   -0.003</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_42</th>           <td>    0.0004</td> <td>    0.003</td> <td>    0.137</td> <td> 0.891</td> <td>   -0.005</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_43</th>           <td> -1.01e-05</td> <td>    0.002</td> <td>   -0.006</td> <td> 0.995</td> <td>   -0.003</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_44</th>           <td>    0.0105</td> <td>    0.002</td> <td>    6.967</td> <td> 0.000</td> <td>    0.008</td> <td>    0.013</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_45</th>           <td>    0.0020</td> <td>    0.003</td> <td>    0.746</td> <td> 0.456</td> <td>   -0.003</td> <td>    0.007</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_46</th>           <td>   -0.0066</td> <td>    0.003</td> <td>   -2.208</td> <td> 0.027</td> <td>   -0.012</td> <td>   -0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_47</th>           <td>    0.0056</td> <td>    0.003</td> <td>    1.879</td> <td> 0.060</td> <td>   -0.000</td> <td>    0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_48</th>           <td>   -0.0020</td> <td>    0.003</td> <td>   -0.643</td> <td> 0.520</td> <td>   -0.008</td> <td>    0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_49</th>           <td>    0.0045</td> <td>    0.003</td> <td>    1.730</td> <td> 0.084</td> <td>   -0.001</td> <td>    0.010</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_50</th>           <td>    0.0011</td> <td>    0.002</td> <td>    0.479</td> <td> 0.632</td> <td>   -0.003</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_51</th>           <td>   -0.0001</td> <td>    0.001</td> <td>   -0.079</td> <td> 0.937</td> <td>   -0.003</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_52</th>           <td>   -0.0049</td> <td>    0.002</td> <td>   -2.343</td> <td> 0.019</td> <td>   -0.009</td> <td>   -0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_53</th>           <td>    0.0034</td> <td>    0.002</td> <td>    1.596</td> <td> 0.111</td> <td>   -0.001</td> <td>    0.008</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>store_nbr_54</th>           <td>   -0.0013</td> <td>    0.002</td> <td>   -0.692</td> <td> 0.489</td> <td>   -0.005</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>item_nbr_115894</th>        <td>   -0.0011</td> <td>    0.002</td> <td>   -0.510</td> <td> 0.610</td> <td>   -0.005</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>item_nbr_165988</th>        <td>   -0.0006</td> <td>    0.002</td> <td>   -0.310</td> <td> 0.756</td> <td>   -0.005</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>item_nbr_273528</th>        <td>    0.0018</td> <td>    0.002</td> <td>    0.997</td> <td> 0.319</td> <td>   -0.002</td> <td>    0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>item_nbr_315474</th>        <td>   -0.0013</td> <td>    0.001</td> <td>   -1.562</td> <td> 0.118</td> <td>   -0.003</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>item_nbr_414353</th>        <td>    0.0043</td> <td>    0.002</td> <td>    2.079</td> <td> 0.038</td> <td>    0.000</td> <td>    0.008</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>item_nbr_581078</th>        <td>   -0.0017</td> <td>    0.001</td> <td>   -1.813</td> <td> 0.070</td> <td>   -0.004</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>item_nbr_847863</th>        <td>   -0.0041</td> <td>    0.002</td> <td>   -1.987</td> <td> 0.047</td> <td>   -0.008</td> <td>-5.49e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>item_nbr_1080021</th>       <td>   -0.0019</td> <td>    0.002</td> <td>   -0.896</td> <td> 0.370</td> <td>   -0.006</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>item_nbr_1087269</th>       <td>   -0.0060</td> <td>    0.002</td> <td>   -3.301</td> <td> 0.001</td> <td>   -0.010</td> <td>   -0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>item_nbr_1146785</th>       <td>   -0.0014</td> <td>    0.002</td> <td>   -0.693</td> <td> 0.489</td> <td>   -0.005</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>item_nbr_1157329</th>       <td>   -0.0022</td> <td>    0.002</td> <td>   -1.061</td> <td> 0.289</td> <td>   -0.006</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>item_nbr_1254013</th>       <td>   -0.0013</td> <td>    0.002</td> <td>   -0.725</td> <td> 0.468</td> <td>   -0.005</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>item_nbr_1342003</th>       <td>   -0.0006</td> <td>    0.002</td> <td>   -0.295</td> <td> 0.768</td> <td>   -0.005</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>item_nbr_1418845</th>       <td>    0.0024</td> <td>    0.002</td> <td>    1.139</td> <td> 0.255</td> <td>   -0.002</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>item_nbr_1418846</th>       <td>   -0.0027</td> <td>    0.002</td> <td>   -1.311</td> <td> 0.190</td> <td>   -0.007</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>item_nbr_1473475</th>       <td>    0.0008</td> <td>    0.002</td> <td>    0.517</td> <td> 0.606</td> <td>   -0.002</td> <td>    0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>item_nbr_1489899</th>       <td>   -0.0012</td> <td>    0.002</td> <td>   -0.778</td> <td> 0.436</td> <td>   -0.004</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>item_nbr_1958181</th>       <td>   -0.0275</td> <td>    0.002</td> <td>  -15.197</td> <td> 0.000</td> <td>   -0.031</td> <td>   -0.024</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>item_nbr_2042941</th>       <td>    0.0266</td> <td>    0.002</td> <td>   15.028</td> <td> 0.000</td> <td>    0.023</td> <td>    0.030</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>item_nbr_2042947</th>       <td>    0.0222</td> <td>    0.002</td> <td>   12.534</td> <td> 0.000</td> <td>    0.019</td> <td>    0.026</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>family_BEVERAGES</th>       <td>    0.0213</td> <td>    0.001</td> <td>   26.313</td> <td> 0.000</td> <td>    0.020</td> <td>    0.023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>family_BREAD/BAKERY</th>    <td>   -0.0013</td> <td>    0.001</td> <td>   -1.562</td> <td> 0.118</td> <td>   -0.003</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>family_CLEANING</th>        <td>   -0.0017</td> <td>    0.001</td> <td>   -1.813</td> <td> 0.070</td> <td>   -0.004</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>family_GROCERY I</th>       <td>   -0.0079</td> <td>    0.001</td> <td>  -12.099</td> <td> 0.000</td> <td>   -0.009</td> <td>   -0.007</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>family_PERSONAL CARE</th>   <td>   -0.0055</td> <td>    0.001</td> <td>   -6.887</td> <td> 0.000</td> <td>   -0.007</td> <td>   -0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>family_PRODUCE</th>         <td>   -0.0004</td> <td>    0.001</td> <td>   -0.545</td> <td> 0.586</td> <td>   -0.002</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>perishable_0</th>           <td>    0.0061</td> <td>    0.000</td> <td>   12.723</td> <td> 0.000</td> <td>    0.005</td> <td>    0.007</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>perishable_1</th>           <td>   -0.0017</td> <td>    0.001</td> <td>   -3.275</td> <td> 0.001</td> <td>   -0.003</td> <td>   -0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>type_y_Additional</th>      <td>    0.0019</td> <td>    0.002</td> <td>    1.065</td> <td> 0.287</td> <td>   -0.002</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>type_y_Holiday</th>         <td>    0.0021</td> <td>    0.001</td> <td>    1.539</td> <td> 0.124</td> <td>   -0.001</td> <td>    0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>type_y_Transfer</th>        <td>   -0.0007</td> <td>    0.002</td> <td>   -0.438</td> <td> 0.661</td> <td>   -0.004</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>type_y_no_holyday</th>      <td>    0.0011</td> <td>    0.000</td> <td>    3.206</td> <td> 0.001</td> <td>    0.000</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>locale_Local</th>           <td>    0.0007</td> <td>    0.001</td> <td>    0.823</td> <td> 0.411</td> <td>   -0.001</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>locale_National</th>        <td>    0.0026</td> <td>    0.001</td> <td>    2.128</td> <td> 0.033</td> <td>    0.000</td> <td>    0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>locale_no_locale</th>       <td>    0.0011</td> <td>    0.000</td> <td>    3.206</td> <td> 0.001</td> <td>    0.000</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>transferred_False</th>      <td> 7.331e-05</td> <td>    0.001</td> <td>    0.059</td> <td> 0.953</td> <td>   -0.002</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>transferred_True</th>       <td>    0.0033</td> <td>    0.002</td> <td>    2.103</td> <td> 0.036</td> <td>    0.000</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>transferred_no_holyday</th> <td>    0.0011</td> <td>    0.000</td> <td>    3.206</td> <td> 0.001</td> <td>    0.000</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_07</th>               <td>    0.0035</td> <td>    0.001</td> <td>    5.935</td> <td> 0.000</td> <td>    0.002</td> <td>    0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_08</th>               <td>    0.0009</td> <td>    0.001</td> <td>    1.295</td> <td> 0.195</td> <td>   -0.000</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_Friday</th>             <td>   -0.0005</td> <td>    0.001</td> <td>   -0.324</td> <td> 0.746</td> <td>   -0.003</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_Monday</th>             <td>    0.0014</td> <td>    0.001</td> <td>    1.217</td> <td> 0.224</td> <td>   -0.001</td> <td>    0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_Saturday</th>           <td>    0.0029</td> <td>    0.001</td> <td>    2.497</td> <td> 0.013</td> <td>    0.001</td> <td>    0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_Sunday</th>             <td>    0.0055</td> <td>    0.001</td> <td>    4.660</td> <td> 0.000</td> <td>    0.003</td> <td>    0.008</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_Thursday</th>           <td>   -0.0038</td> <td>    0.001</td> <td>   -2.645</td> <td> 0.008</td> <td>   -0.007</td> <td>   -0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_Tuesday</th>            <td>    0.0006</td> <td>    0.001</td> <td>    0.471</td> <td> 0.638</td> <td>   -0.002</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_Wednesday</th>          <td>   -0.0017</td> <td>    0.001</td> <td>   -1.312</td> <td> 0.189</td> <td>   -0.004</td> <td>    0.001</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>5975.960</td> <th>  Durbin-Watson:     </th>   <td>   1.994</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>30308766.074</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td>15.644</td>  <th>  Prob(JB):          </th>   <td>    0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td>495.407</td> <th>  Cond. No.          </th>   <td>1.71e+16</td>  \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             unit_sales   R-squared:                       0.354\n",
       "Model:                            OLS   Adj. R-squared:                  0.335\n",
       "Method:                 Least Squares   F-statistic:                     18.69\n",
       "Date:                Sat, 21 Apr 2018   Prob (F-statistic):          2.22e-213\n",
       "Time:                        11:36:28   Log-Likelihood:                 6716.8\n",
       "No. Observations:                2988   AIC:                        -1.326e+04\n",
       "Df Residuals:                    2902   BIC:                        -1.275e+04\n",
       "Df Model:                          85                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==========================================================================================\n",
       "                             coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------------\n",
       "dcoilwtico                 0.0057      0.002      3.394      0.001       0.002       0.009\n",
       "onpromotion_False         -0.0037      0.001     -5.395      0.000      -0.005      -0.002\n",
       "onpromotion_True           0.0082      0.001      9.787      0.000       0.007       0.010\n",
       "city_Ambato               -0.0014      0.002     -0.839      0.401      -0.005       0.002\n",
       "city_Babahoyo              0.0022      0.001      1.594      0.111      -0.000       0.005\n",
       "city_Cayambe               0.0024      0.002      1.401      0.161      -0.001       0.006\n",
       "city_Cuenca                0.0013      0.001      0.887      0.375      -0.002       0.004\n",
       "city_Daule                -0.0002      0.002     -0.137      0.891      -0.003       0.003\n",
       "city_El Carmen            -0.0013      0.002     -0.692      0.489      -0.005       0.002\n",
       "city_Esmeraldas         -1.01e-05      0.002     -0.006      0.995      -0.003       0.003\n",
       "city_Guaranda              0.0002      0.002      0.154      0.878      -0.003       0.003\n",
       "city_Guayaquil          2.858e-05      0.001      0.024      0.981      -0.002       0.002\n",
       "city_Ibarra               -0.0008      0.001     -0.533      0.594      -0.004       0.002\n",
       "city_Latacunga         -4.487e-06      0.002     -0.003      0.998      -0.003       0.003\n",
       "city_Libertad           6.975e-05      0.002      0.040      0.968      -0.003       0.004\n",
       "city_Loja                  0.0010      0.002      0.575      0.565      -0.002       0.004\n",
       "city_Machala               0.0013      0.002      0.802      0.423      -0.002       0.005\n",
       "city_Manta                -0.0015      0.001     -1.015      0.310      -0.004       0.001\n",
       "city_Playas                0.0004      0.002      0.261      0.794      -0.003       0.004\n",
       "city_Puyo                 -0.0007      0.001     -0.589      0.556      -0.003       0.002\n",
       "city_Quevedo               0.0007      0.002      0.427      0.669      -0.002       0.004\n",
       "city_Quito                 0.0037      0.001      3.598      0.000       0.002       0.006\n",
       "city_Riobamba             -0.0004      0.001     -0.330      0.741      -0.003       0.002\n",
       "city_Salinas              -0.0027      0.002     -1.580      0.114      -0.006       0.001\n",
       "city_Santo Domingo       4.72e-05      0.002      0.030      0.976      -0.003       0.003\n",
       "type_x_A                   0.0101      0.001     12.258      0.000       0.008       0.012\n",
       "type_x_B                  -0.0025      0.001     -2.781      0.005      -0.004      -0.001\n",
       "type_x_C                  -0.0036      0.001     -4.989      0.000      -0.005      -0.002\n",
       "type_x_D                  -0.0012      0.001     -1.394      0.164      -0.003       0.000\n",
       "type_x_E                   0.0016      0.001      1.326      0.185      -0.001       0.004\n",
       "cluster_1                  0.0029      0.001      2.053      0.040       0.000       0.006\n",
       "cluster_2                 -0.0002      0.001     -0.165      0.869      -0.003       0.003\n",
       "cluster_3                 -0.0004      0.001     -0.359      0.719      -0.002       0.002\n",
       "cluster_4                  0.0005      0.001      0.318      0.750      -0.002       0.003\n",
       "cluster_5                  0.0105      0.002      6.967      0.000       0.008       0.013\n",
       "cluster_6                 -0.0003      0.001     -0.296      0.767      -0.003       0.002\n",
       "cluster_7                 -0.0011      0.001     -1.114      0.265      -0.003       0.001\n",
       "cluster_8                  0.0035      0.001      2.428      0.015       0.001       0.006\n",
       "cluster_9                  0.0003      0.001      0.207      0.836      -0.003       0.003\n",
       "cluster_10                -0.0023      0.001     -1.774      0.076      -0.005       0.000\n",
       "cluster_11                 0.0016      0.001      1.219      0.223      -0.001       0.004\n",
       "cluster_12                -0.0001      0.002     -0.068      0.946      -0.003       0.003\n",
       "cluster_13                -0.0021      0.001     -1.563      0.118      -0.005       0.001\n",
       "cluster_14                -0.0019      0.001     -1.457      0.145      -0.004       0.001\n",
       "cluster_15                -0.0021      0.001     -1.878      0.060      -0.004    9.15e-05\n",
       "cluster_16                -0.0043      0.001     -3.014      0.003      -0.007      -0.001\n",
       "cluster_17                -0.0001      0.001     -0.079      0.937      -0.003       0.003\n",
       "store_nbr_1               -0.0064      0.003     -2.317      0.021      -0.012      -0.001\n",
       "store_nbr_2                0.0004      0.003      0.135      0.893      -0.005       0.006\n",
       "store_nbr_3                0.0076      0.003      2.783      0.005       0.002       0.013\n",
       "store_nbr_4                0.0028      0.002      1.236      0.216      -0.002       0.007\n",
       "store_nbr_5               -0.0019      0.002     -0.800      0.423      -0.006       0.003\n",
       "store_nbr_6                0.0005      0.003      0.179      0.858      -0.005       0.006\n",
       "store_nbr_7               -0.0028      0.003     -0.922      0.356      -0.009       0.003\n",
       "store_nbr_8               -0.0014      0.003     -0.453      0.650      -0.007       0.005\n",
       "store_nbr_9               -0.0012      0.003     -0.374      0.708      -0.008       0.005\n",
       "store_nbr_10              -0.0015      0.002     -0.630      0.529      -0.006       0.003\n",
       "store_nbr_11               0.0024      0.002      1.401      0.161      -0.001       0.006\n",
       "store_nbr_12               0.0012      0.003      0.464      0.643      -0.004       0.006\n",
       "store_nbr_13              -0.0012      0.003     -0.442      0.659      -0.007       0.004\n",
       "store_nbr_14              -0.0004      0.001     -0.330      0.741      -0.003       0.002\n",
       "store_nbr_15              -0.0008      0.001     -0.533      0.594      -0.004       0.002\n",
       "store_nbr_16               0.0013      0.003      0.482      0.630      -0.004       0.007\n",
       "store_nbr_17              -0.0001      0.002     -0.068      0.946      -0.003       0.003\n",
       "store_nbr_18              -0.0043      0.001     -3.014      0.003      -0.007      -0.001\n",
       "store_nbr_19               0.0002      0.002      0.154      0.878      -0.003       0.003\n",
       "store_nbr_20              -0.0040      0.004     -1.139      0.255      -0.011       0.003\n",
       "store_nbr_21               0.0006      0.003      0.228      0.819      -0.005       0.006\n",
       "store_nbr_22              -0.0007      0.001     -0.589      0.556      -0.003       0.002\n",
       "store_nbr_23              -0.0025      0.002     -1.161      0.246      -0.007       0.002\n",
       "store_nbr_24               0.0058      0.002      2.386      0.017       0.001       0.011\n",
       "store_nbr_25              -0.0027      0.002     -1.580      0.114      -0.006       0.001\n",
       "store_nbr_26              -0.0060      0.002     -2.700      0.007      -0.010      -0.002\n",
       "store_nbr_27              -0.0002      0.002     -0.137      0.891      -0.003       0.003\n",
       "store_nbr_28               0.0029      0.003      1.119      0.263      -0.002       0.008\n",
       "store_nbr_29              -0.0014      0.002     -0.568      0.570      -0.006       0.003\n",
       "store_nbr_30              -0.0011      0.003     -0.375      0.707      -0.007       0.004\n",
       "store_nbr_31               0.0022      0.001      1.594      0.111      -0.000       0.005\n",
       "store_nbr_32              -0.0005      0.003     -0.164      0.870      -0.006       0.005\n",
       "store_nbr_33               0.0007      0.002      0.427      0.669      -0.002       0.004\n",
       "store_nbr_34               0.0004      0.003      0.135      0.893      -0.005       0.006\n",
       "store_nbr_35               0.0004      0.002      0.261      0.794      -0.003       0.004\n",
       "store_nbr_36            6.975e-05      0.002      0.040      0.968      -0.003       0.004\n",
       "store_nbr_37              -0.0006      0.003     -0.215      0.830      -0.006       0.005\n",
       "store_nbr_38               0.0010      0.002      0.575      0.565      -0.002       0.004\n",
       "store_nbr_39               0.0015      0.002      0.704      0.481      -0.003       0.006\n",
       "store_nbr_40           -1.067e-05      0.002     -0.005      0.996      -0.005       0.005\n",
       "store_nbr_41               0.0013      0.002      0.579      0.563      -0.003       0.006\n",
       "store_nbr_42               0.0004      0.003      0.137      0.891      -0.005       0.006\n",
       "store_nbr_43            -1.01e-05      0.002     -0.006      0.995      -0.003       0.003\n",
       "store_nbr_44               0.0105      0.002      6.967      0.000       0.008       0.013\n",
       "store_nbr_45               0.0020      0.003      0.746      0.456      -0.003       0.007\n",
       "store_nbr_46              -0.0066      0.003     -2.208      0.027      -0.012      -0.001\n",
       "store_nbr_47               0.0056      0.003      1.879      0.060      -0.000       0.012\n",
       "store_nbr_48              -0.0020      0.003     -0.643      0.520      -0.008       0.004\n",
       "store_nbr_49               0.0045      0.003      1.730      0.084      -0.001       0.010\n",
       "store_nbr_50               0.0011      0.002      0.479      0.632      -0.003       0.006\n",
       "store_nbr_51              -0.0001      0.001     -0.079      0.937      -0.003       0.003\n",
       "store_nbr_52              -0.0049      0.002     -2.343      0.019      -0.009      -0.001\n",
       "store_nbr_53               0.0034      0.002      1.596      0.111      -0.001       0.008\n",
       "store_nbr_54              -0.0013      0.002     -0.692      0.489      -0.005       0.002\n",
       "item_nbr_115894           -0.0011      0.002     -0.510      0.610      -0.005       0.003\n",
       "item_nbr_165988           -0.0006      0.002     -0.310      0.756      -0.005       0.003\n",
       "item_nbr_273528            0.0018      0.002      0.997      0.319      -0.002       0.005\n",
       "item_nbr_315474           -0.0013      0.001     -1.562      0.118      -0.003       0.000\n",
       "item_nbr_414353            0.0043      0.002      2.079      0.038       0.000       0.008\n",
       "item_nbr_581078           -0.0017      0.001     -1.813      0.070      -0.004       0.000\n",
       "item_nbr_847863           -0.0041      0.002     -1.987      0.047      -0.008   -5.49e-05\n",
       "item_nbr_1080021          -0.0019      0.002     -0.896      0.370      -0.006       0.002\n",
       "item_nbr_1087269          -0.0060      0.002     -3.301      0.001      -0.010      -0.002\n",
       "item_nbr_1146785          -0.0014      0.002     -0.693      0.489      -0.005       0.003\n",
       "item_nbr_1157329          -0.0022      0.002     -1.061      0.289      -0.006       0.002\n",
       "item_nbr_1254013          -0.0013      0.002     -0.725      0.468      -0.005       0.002\n",
       "item_nbr_1342003          -0.0006      0.002     -0.295      0.768      -0.005       0.003\n",
       "item_nbr_1418845           0.0024      0.002      1.139      0.255      -0.002       0.006\n",
       "item_nbr_1418846          -0.0027      0.002     -1.311      0.190      -0.007       0.001\n",
       "item_nbr_1473475           0.0008      0.002      0.517      0.606      -0.002       0.004\n",
       "item_nbr_1489899          -0.0012      0.002     -0.778      0.436      -0.004       0.002\n",
       "item_nbr_1958181          -0.0275      0.002    -15.197      0.000      -0.031      -0.024\n",
       "item_nbr_2042941           0.0266      0.002     15.028      0.000       0.023       0.030\n",
       "item_nbr_2042947           0.0222      0.002     12.534      0.000       0.019       0.026\n",
       "family_BEVERAGES           0.0213      0.001     26.313      0.000       0.020       0.023\n",
       "family_BREAD/BAKERY       -0.0013      0.001     -1.562      0.118      -0.003       0.000\n",
       "family_CLEANING           -0.0017      0.001     -1.813      0.070      -0.004       0.000\n",
       "family_GROCERY I          -0.0079      0.001    -12.099      0.000      -0.009      -0.007\n",
       "family_PERSONAL CARE      -0.0055      0.001     -6.887      0.000      -0.007      -0.004\n",
       "family_PRODUCE            -0.0004      0.001     -0.545      0.586      -0.002       0.001\n",
       "perishable_0               0.0061      0.000     12.723      0.000       0.005       0.007\n",
       "perishable_1              -0.0017      0.001     -3.275      0.001      -0.003      -0.001\n",
       "type_y_Additional          0.0019      0.002      1.065      0.287      -0.002       0.006\n",
       "type_y_Holiday             0.0021      0.001      1.539      0.124      -0.001       0.005\n",
       "type_y_Transfer           -0.0007      0.002     -0.438      0.661      -0.004       0.002\n",
       "type_y_no_holyday          0.0011      0.000      3.206      0.001       0.000       0.002\n",
       "locale_Local               0.0007      0.001      0.823      0.411      -0.001       0.002\n",
       "locale_National            0.0026      0.001      2.128      0.033       0.000       0.005\n",
       "locale_no_locale           0.0011      0.000      3.206      0.001       0.000       0.002\n",
       "transferred_False       7.331e-05      0.001      0.059      0.953      -0.002       0.003\n",
       "transferred_True           0.0033      0.002      2.103      0.036       0.000       0.006\n",
       "transferred_no_holyday     0.0011      0.000      3.206      0.001       0.000       0.002\n",
       "month_07                   0.0035      0.001      5.935      0.000       0.002       0.005\n",
       "month_08                   0.0009      0.001      1.295      0.195      -0.000       0.002\n",
       "day_Friday                -0.0005      0.001     -0.324      0.746      -0.003       0.002\n",
       "day_Monday                 0.0014      0.001      1.217      0.224      -0.001       0.004\n",
       "day_Saturday               0.0029      0.001      2.497      0.013       0.001       0.005\n",
       "day_Sunday                 0.0055      0.001      4.660      0.000       0.003       0.008\n",
       "day_Thursday              -0.0038      0.001     -2.645      0.008      -0.007      -0.001\n",
       "day_Tuesday                0.0006      0.001      0.471      0.638      -0.002       0.003\n",
       "day_Wednesday             -0.0017      0.001     -1.312      0.189      -0.004       0.001\n",
       "==============================================================================\n",
       "Omnibus:                     5975.960   Durbin-Watson:                   1.994\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):         30308766.074\n",
       "Skew:                          15.644   Prob(JB):                         0.00\n",
       "Kurtosis:                     495.407   Cond. No.                     1.71e+16\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The smallest eigenvalue is 5.51e-29. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as sm\n",
    "model = sm.OLS(y_train, X_train)\n",
    "results = model.fit()\n",
    "# Statsmodels gives R-like statistical output\n",
    "results.summary()\n",
    "# Here the Ordinalry Least Squares method has given us the r^2 value of 0.354 which is poor for model to be predicted on these variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Linear regression model gives a low R- square value of 0.354"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Checking for VIF for eliminating multicollinearity and overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Implementing VIF (Variance Inflation Factor) to check whether the selected independent variables are correct for prediction \n",
    "# or not. Also, 'item_nbr', 'perishable' and 'dcoilwtico' had very close levels of co-relation with price which makes us to investigate whether all \n",
    "# three are important or not.\n",
    "indep=['dcoilwtico','perishable','item_nbr','store_nbr','cluster']\n",
    "X=Salesdf[indep]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vif : [11.526623747491566, 1.3507403432685345, 4.9699125171435918, 4.0093263521352034, 4.6349900352112581]\n",
      "dcoilwtico\n",
      "Final variables:  ['perishable', 'item_nbr', 'store_nbr', 'cluster']\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor  \n",
    "thresh=10 #Setting a threshold of 10 as a sign of serious and sever multi-collinearity\n",
    "for i in np.arange(0,len(indep)):\n",
    "    vif=[variance_inflation_factor(X[indep].values,ix)\n",
    "    for ix in range(X[indep].shape[1])]\n",
    "    maxloc=vif.index(max(vif))\n",
    "    if max(vif) > thresh:\n",
    "        print (\"vif :\", vif)\n",
    "        print( X[indep].columns[maxloc] )\n",
    "        del indep[maxloc]\n",
    "    else:\n",
    "        break\n",
    "        \n",
    "    print ('Final variables: ', indep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>perishable</th>\n",
       "      <th>item_nbr</th>\n",
       "      <th>store_nbr</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>633977</td>\n",
       "      <td>22</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1991151</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>968935</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1909453</td>\n",
       "      <td>18</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1695875</td>\n",
       "      <td>52</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   perishable  item_nbr  store_nbr  cluster\n",
       "0           1    633977         22        7\n",
       "1           0   1991151          5        4\n",
       "2           0    968935          5        4\n",
       "3           0   1909453         18       16\n",
       "4           1   1695875         52       11"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[indep].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X=Salesdf[['perishable', 'item_nbr', 'store_nbr', 'cluster']]\n",
    "y=Salesdf[[\"unit_sales\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross Validation using Scikit Learn\n",
    "\n",
    "R^2 value is basically dependant on the way the data is split. Hence, there may be times when the R^2 value may not be able to\n",
    "represent the model's ability to generalize. For this we perform cross validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.41958907  0.42895752  0.39221205  0.20780525  0.27409657]\n",
      "0.344532091925\n",
      "0.0879658700132\n"
     ]
    }
   ],
   "source": [
    "reg=linear_model.LinearRegression()\n",
    "cv_results=cross_val_score(reg,X_train,y_train,cv=5)\n",
    "print(cv_results)\n",
    "print(np.mean(cv_results))\n",
    "print(np.std(cv_results))\n",
    "#Using cross validation of score 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regularization\n",
    "\n",
    "We perform regularization in order to alter the loss function to penalize it for having higher coefficients for each feature variable. And as we know, large coefficients leads to overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.40352756968925618"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge = Ridge(alpha=0.1, normalize = True)\n",
    "ridge.fit(X_train,y_train)\n",
    "ridge_pred=ridge.predict(X_test)\n",
    "ridge.score(X_test,y_test)\n",
    "#The score is pretty much similar to the linear model built which ensures that the model has passed the Ridge regression test\n",
    "# for regularization\n",
    "#Ridge is used to penalize the loss function by adding the OLS loss function to the square of each coefficient multiplied by alpha."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.b DecisionTree Regressor\n",
    "\n",
    "A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dtr=DecisionTreeRegressor(max_depth=10,min_samples_leaf=5,max_leaf_nodes=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score =  0.705856948908 / 1.0\n",
      "MSE score =  0.000293228502691 / 0.0\n"
     ]
    }
   ],
   "source": [
    "dtr.fit(X_train,y_train)\n",
    "y_pred=dtr.predict(X_test)\n",
    "\n",
    "print('R2 score = ',r2_score(y_test, y_pred), '/ 1.0')\n",
    "print('MSE score = ',mean_squared_error(y_test, y_pred), '/ 0.0')\n",
    "\n",
    "##using a decision tree greatly improves the accurancy of model prediction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.c. ExtraTreesRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Extra-trees differ from classic decision trees in the way they are built. When looking for the best split to separate the samples of a node into two groups, random splits are drawn for each of the max_features randomly selected features and the best split among those is chosen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 54 candidates, totalling 162 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   20.3s\n",
      "[Parallel(n_jobs=-1)]: Done 162 out of 162 | elapsed:  1.2min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=15,\n",
       "          max_features='auto', max_leaf_nodes=None,\n",
       "          min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "          min_samples_leaf=1, min_samples_split=10,\n",
       "          min_weight_fraction_leaf=0.0, n_estimators=5, n_jobs=1,\n",
       "          oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "etr = ExtraTreesRegressor()\n",
    "\n",
    "# Choose some parameter combinations to try\n",
    "\n",
    "parameters = {'n_estimators': [5,10,100],\n",
    "              'criterion': ['mse'],\n",
    "              'max_depth': [5,10,15], \n",
    "              'min_samples_split': [2,5,10],\n",
    "              'min_samples_leaf': [1,5]\n",
    "             }\n",
    "#We have to use RandomForestRegressor's own scorer (which is R^2 score)\n",
    "\n",
    "#Determines the cross-validation splitting strategy /to specify the number of folds in a (Stratified)KFold\n",
    "\n",
    "grid_obj = GridSearchCV(etr, parameters,\n",
    "                        cv=3, \n",
    "                        n_jobs=-1, #Number of jobs to run in parallel\n",
    "                        verbose=1)\n",
    "grid_obj = grid_obj.fit(X_train, y_train)\n",
    "\n",
    "# Set the clf to the best combination of parameters\n",
    "etr = grid_obj.best_estimator_\n",
    "\n",
    "# Fit the best algorithm to the data. \n",
    "etr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score =  0.826619399995 / 1.0\n",
      "MSE score =  0.000172841525735 / 0.0\n"
     ]
    }
   ],
   "source": [
    "y_pred = etr.predict(X_test)\n",
    "\n",
    "print('R2 score = ',r2_score(y_test, y_pred), '/ 1.0')\n",
    "print('MSE score = ',mean_squared_error(y_test, y_pred), '/ 0.0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.d Random Forest Regressor\n",
    "\n",
    "Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks, that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.Random decision forests correct for decision trees' habit of overfitting to their training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 6 candidates, totalling 30 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  30 out of  30 | elapsed:   32.2s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=5, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Choose the type of classifier. \n",
    "RFR = RandomForestRegressor()\n",
    "\n",
    "# Choose some parameter combinations to try\n",
    "parameters = {'n_estimators': [5, 10, 100],\n",
    "              'min_samples_leaf': [1,5]\n",
    "             }\n",
    "\n",
    "\n",
    "#We have to use RandomForestRegressor's own scorer (which is R^2 score)\n",
    "\n",
    "#Determines the cross-validation splitting strategy /to specify the number of folds in a (Stratified)KFold\n",
    "grid_obj = GridSearchCV(RFR, parameters,\n",
    "                        cv=5, \n",
    "                        n_jobs=-1, #Number of jobs to run in parallel\n",
    "                        verbose=1)\n",
    "grid_obj = grid_obj.fit(X_train, y_train)\n",
    "\n",
    "# Set the clf to the best combination of parameters\n",
    "RFR = grid_obj.best_estimator_\n",
    "\n",
    "# Fit the best algorithm to the data. \n",
    "RFR.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score =  0.794068854354 / 1.0\n",
      "MSE score =  0.000205290865349 / 0.0\n"
     ]
    }
   ],
   "source": [
    "y_pred = RFR.predict(X_test)\n",
    "\n",
    "print('R2 score = ',r2_score(y_test, y_pred), '/ 1.0')\n",
    "print('MSE score = ',mean_squared_error(y_test, y_pred), '/ 0.0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 54 candidates, totalling 270 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   20.8s\n",
      "[Parallel(n_jobs=-1)]: Done 192 tasks      | elapsed:  1.2min\n",
      "[Parallel(n_jobs=-1)]: Done 270 out of 270 | elapsed:  1.9min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=10,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=10,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFR = RandomForestRegressor()\n",
    "\n",
    "# Choose some parameter combinations to try\n",
    "parameters = {'n_estimators': [5,10,100],\n",
    "              'criterion': ['mse'],\n",
    "              'max_depth': [5,10,15], \n",
    "              'min_samples_split': [2,5,10],\n",
    "              'min_samples_leaf': [1,5]\n",
    "             }\n",
    "\n",
    "\n",
    "#We have to use RandomForestRegressor's own scorer (which is R^2 score)\n",
    "\n",
    "#Determines the cross-validation splitting strategy /to specify the number of folds in a (Stratified)KFold\n",
    "grid_obj = GridSearchCV(RFR, parameters,\n",
    "                        cv=5, \n",
    "                        n_jobs=-1, #Number of jobs to run in parallel\n",
    "                        verbose=1)\n",
    "grid_obj = grid_obj.fit(X_train, y_train)\n",
    "\n",
    "# Set the clf to the best combination of parameters\n",
    "RFR = grid_obj.best_estimator_\n",
    "\n",
    "# Fit the best algorithm to the data. \n",
    "RFR.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score =  0.840477379305 / 1.0\n",
      "MSE score =  0.000159026633599 / 0.0\n"
     ]
    }
   ],
   "source": [
    "y_pred = RFR.predict(X_test)\n",
    "\n",
    "print('R2 score = ',r2_score(y_test, y_pred), '/ 1.0')\n",
    "print('MSE score = ',mean_squared_error(y_test, y_pred), '/ 0.0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.e. Gradient Boosting Regressor\n",
    "\n",
    "The idea of boosting came out of the idea of whether a weak learner can be modified to become better.\n",
    "A weak hypothesis or weak learner is defined as one whose performance is at least slightly better than random chance.\n",
    "Hypothesis boosting was the idea of filtering observations, leaving those observations that the weak learner can handle and focusing on developing new weak learns to handle the remaining difficult observations.\n",
    "\n",
    "## How Gradient Boosting Works\n",
    "\n",
    "Gradient boosting involves three elements:\n",
    "\n",
    "-- A loss function to be optimized.\n",
    "\n",
    "-- A weak learner to make predictions.\n",
    "\n",
    "-- An additive model to add weak learners to minimize the loss function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 16 candidates, totalling 80 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   17.9s\n",
      "[Parallel(n_jobs=-1)]: Done  80 out of  80 | elapsed:   32.5s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='mse', init=None,\n",
       "             learning_rate=0.1, loss='huber', max_depth=10,\n",
       "             max_features=None, max_leaf_nodes=None,\n",
       "             min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "             min_samples_leaf=1, min_samples_split=5,\n",
       "             min_weight_fraction_leaf=0.0, n_estimators=10, presort='auto',\n",
       "             random_state=None, subsample=1.0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#gbr = GradientBoostingRegressor(loss='huber',learning_rate=0.3,n_estimators=100,max_depth=5,min_samples_split=3)\n",
    "gbr=GradientBoostingRegressor()\n",
    "\n",
    "parameters = {'n_estimators': [5,10],\n",
    "              'loss':['huber'],\n",
    "              'criterion': ['mse'],\n",
    "              'max_depth': [5,10], \n",
    "              'min_samples_split': [2,5],\n",
    "              'min_samples_leaf': [1,5]\n",
    "             }\n",
    "\n",
    "#Determines the cross-validation splitting strategy /to specify the number of folds in a (Stratified)KFold\n",
    "grid_obj = GridSearchCV(gbr, parameters,\n",
    "                        cv=5, \n",
    "                        n_jobs=-1, #Number of jobs to run in parallel\n",
    "                        verbose=1)\n",
    "grid_obj = grid_obj.fit(X_train, y_train)\n",
    "\n",
    "# Set the clf to the best combination of parameters\n",
    "gbr = grid_obj.best_estimator_\n",
    "\n",
    "# Fit the best algorithm to the data. \n",
    "gbr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score using Gradient Boosting=  0.501852330509 / 1.0\n",
      "MSE score using Gradient Boosting=  0.000496598830744 / 0.0\n"
     ]
    }
   ],
   "source": [
    "y_pred = gbr.predict(X_test)\n",
    "\n",
    "print('R2 score using Gradient Boosting= ',r2_score(y_test, y_pred), '/ 1.0')\n",
    "print('MSE score using Gradient Boosting= ',mean_squared_error(y_test, y_pred), '/ 0.0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 48 candidates, totalling 240 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:  1.3min\n",
      "[Parallel(n_jobs=-1)]: Done 192 tasks      | elapsed:  6.8min\n",
      "[Parallel(n_jobs=-1)]: Done 240 out of 240 | elapsed:  9.4min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='mse', init=None,\n",
       "             learning_rate=0.1, loss='huber', max_depth=10,\n",
       "             max_features=None, max_leaf_nodes=None,\n",
       "             min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "             min_samples_leaf=1, min_samples_split=5,\n",
       "             min_weight_fraction_leaf=0.0, n_estimators=150,\n",
       "             presort='auto', random_state=None, subsample=1.0, verbose=0,\n",
       "             warm_start=False)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbr = GradientBoostingRegressor(loss='ls',learning_rate=0.1,n_estimators=150,max_depth=10,min_samples_split=5)\n",
    "\n",
    "\n",
    "parameters = {'n_estimators': [5,15,150],\n",
    "              'loss':['ls','huber'],\n",
    "              'criterion': ['mse'],\n",
    "              'max_depth': [10,15], \n",
    "              'min_samples_split': [2,5],\n",
    "              'min_samples_leaf': [1,5]\n",
    "             }\n",
    "\n",
    "#Determines the cross-validation splitting strategy /to specify the number of folds in a (Stratified)KFold\n",
    "grid_obj = GridSearchCV(gbr, parameters,\n",
    "                        cv=5, \n",
    "                        n_jobs=-1, #Number of jobs to run in parallel\n",
    "                        verbose=1)\n",
    "grid_obj = grid_obj.fit(X_train, y_train)\n",
    "\n",
    "# Set the clf to the best combination of parameters\n",
    "gbr = grid_obj.best_estimator_\n",
    "\n",
    "# Fit the best algorithm to the data. \n",
    "gbr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score using Gradient Boosting=  0.840477379305 / 1.0\n",
      "MSE score using Gradient Boosting=  0.000159026633599 / 0.0\n"
     ]
    }
   ],
   "source": [
    "y_pred = RFR.predict(X_test)\n",
    "\n",
    "print('R2 score using Gradient Boosting= ',r2_score(y_test, y_pred), '/ 1.0')\n",
    "print('MSE score using Gradient Boosting= ',mean_squared_error(y_test, y_pred), '/ 0.0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.f. XGBOOST\n",
    "\n",
    "XGBoost (eXtreme Gradient Boosting) is a direct application of Gradient Boosting for decision trees\n",
    "\n",
    "Main advantages are as follows:\n",
    "1. Easy to use\n",
    "2. Computational efficiency\n",
    "3. Model Accuracy\n",
    "4. Feasibility — easy to tune parameters and modify objectives."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model=XGBRegressor(max_depth=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n",
       "       max_depth=5, min_child_weight=1, missing=None, n_estimators=100,\n",
       "       n_jobs=1, nthread=None, objective='reg:linear', random_state=0,\n",
       "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "       silent=True, subsample=1)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score using XG Boost=  0.797564020916 / 1.0\n",
      "MSE score using XG Boost=  0.000201806565945 / 0.0\n"
     ]
    }
   ],
   "source": [
    "print('R2 score using XG Boost= ',r2_score(y_test, y_pred), '/ 1.0')\n",
    "print('MSE score using XG Boost= ',mean_squared_error(y_test, y_pred), '/ 0.0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10. LGBM\n",
    "\n",
    "Light GBM is a fast, distributed, high-performance gradient boosting framework based on decision tree algorithm, used for ranking, classification and many other machine learning tasks.\n",
    "\n",
    "Since it is based on decision tree algorithms, it splits the tree leaf wise with the best fit whereas other boosting algorithms split the tree depth wise or level wise rather than leaf-wise. So when growing on the same leaf in Light GBM, the leaf-wise algorithm can reduce more loss than the level-wise algorithm and hence results in much better accuracy which can rarely be achieved by any of the existing boosting algorithms. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n",
       "       learning_rate=0.1, max_depth=5, min_child_samples=20,\n",
       "       min_child_weight=0.001, min_split_gain=0.0, n_estimators=100,\n",
       "       n_jobs=-1, num_leaves=31, objective=None, random_state=None,\n",
       "       reg_alpha=0.0, reg_lambda=0.0, silent=True, subsample=1.0,\n",
       "       subsample_for_bin=200000, subsample_freq=1)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from lightgbm import LGBMRegressor\n",
    "lgbm=LGBMRegressor(max_depth=5)\n",
    "lgbm.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred=lgbm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score using LGBM =  0.759040161358 / 1.0\n",
      "MSE score using LGBM =  0.000240210647273 / 0.0\n"
     ]
    }
   ],
   "source": [
    "print('R2 score using LGBM = ',r2_score(y_test, y_pred), '/ 1.0')\n",
    "print('MSE score using LGBM = ',mean_squared_error(y_test, y_pred), '/ 0.0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>store_nbr</th>\n",
       "      <th>item_nbr</th>\n",
       "      <th>unit_sales</th>\n",
       "      <th>cluster</th>\n",
       "      <th>perishable</th>\n",
       "      <th>dcoilwtico</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>store_nbr</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.008087</td>\n",
       "      <td>0.051609</td>\n",
       "      <td>0.033256</td>\n",
       "      <td>0.007572</td>\n",
       "      <td>0.002356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item_nbr</th>\n",
       "      <td>0.008087</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.025643</td>\n",
       "      <td>-0.002518</td>\n",
       "      <td>0.043440</td>\n",
       "      <td>-0.000529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unit_sales</th>\n",
       "      <td>0.051609</td>\n",
       "      <td>0.025643</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.018465</td>\n",
       "      <td>0.052492</td>\n",
       "      <td>0.006032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <td>0.033256</td>\n",
       "      <td>-0.002518</td>\n",
       "      <td>0.018465</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.003042</td>\n",
       "      <td>0.003812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perishable</th>\n",
       "      <td>0.007572</td>\n",
       "      <td>0.043440</td>\n",
       "      <td>0.052492</td>\n",
       "      <td>-0.003042</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.000616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dcoilwtico</th>\n",
       "      <td>0.002356</td>\n",
       "      <td>-0.000529</td>\n",
       "      <td>0.006032</td>\n",
       "      <td>0.003812</td>\n",
       "      <td>-0.000616</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            store_nbr  item_nbr  unit_sales   cluster  perishable  dcoilwtico\n",
       "store_nbr    1.000000  0.008087    0.051609  0.033256    0.007572    0.002356\n",
       "item_nbr     0.008087  1.000000    0.025643 -0.002518    0.043440   -0.000529\n",
       "unit_sales   0.051609  0.025643    1.000000  0.018465    0.052492    0.006032\n",
       "cluster      0.033256 -0.002518    0.018465  1.000000   -0.003042    0.003812\n",
       "perishable   0.007572  0.043440    0.052492 -0.003042    1.000000   -0.000616\n",
       "dcoilwtico   0.002356 -0.000529    0.006032  0.003812   -0.000616    1.000000"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Salesdf.corr()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 11. Creating a neural network\n",
    "\n",
    "Multi-layer Perceptron (MLP) is a supervised learning algorithm that learns a function  by training on a dataset, where  is the number of dimensions for input and  is the number of dimensions for output.\n",
    "\n",
    "The advantages of Multi-layer Perceptron are:\n",
    "\n",
    "-- Capability to learn non-linear models.\n",
    "\n",
    "-- Capability to learn models in real-time (on-line learning) using partial_fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.44920993  0.          1.         ...,  0.          1.          0.        ]\n",
      " [ 0.05417607  1.          0.         ...,  0.          0.          0.        ]\n",
      " [ 1.          1.          0.         ...,  0.          0.          0.        ]\n",
      " ..., \n",
      " [ 0.85553047  1.          0.         ...,  0.          0.          0.        ]\n",
      " [ 0.85553047  1.          0.         ...,  0.          0.          0.        ]\n",
      " [ 0.85553047  1.          0.         ...,  0.          0.          0.        ]]\n",
      "[[ 0.0078918 ]\n",
      " [ 0.01838703]\n",
      " [ 0.00480496]\n",
      " [ 0.01134967]\n",
      " [ 0.0023355 ]\n",
      " [ 0.00770288]\n",
      " [ 0.01097863]\n",
      " [ 0.00171813]\n",
      " [ 0.0171523 ]\n",
      " [ 0.00110076]]\n"
     ]
    }
   ],
   "source": [
    "# Convert data as np.array\n",
    "features = np.array(X_train)\n",
    "#targets = np.array(y_train.reshape(y_train.shape[0],1))\n",
    "targets = np.array(y_train.values.reshape(y_train.shape[0],1))\n",
    "features_validation= np.array(X_test)\n",
    "#targets_validation = np.array(y_test.reshape(y_test.shape[0],1))\n",
    "targets_validation = np.array(y_test.values.reshape(y_test.shape[0],1))\n",
    "\n",
    "print(features[:10])\n",
    "print(targets[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 32)                4768      \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 16)                528       \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 5,313\n",
      "Trainable params: 5,313\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Building the model\n",
    "model = Sequential()\n",
    "model.add(Dense(32, activation='relu', input_shape=(X_train.shape[1],)))\n",
    "model.add(Dropout(.2))\n",
    "model.add(Dense(16, activation='relu'))\n",
    "model.add(Dropout(.1))\n",
    "model.add(Dense(1))\n",
    "\n",
    "# Compiling the model\n",
    "model.compile(loss = 'mse', optimizer='adam', metrics=['mse']) #mse: mean_square_error\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step :  0 / 1000\n",
      "2988/2988 [==============================] - 0s 55us/step\n",
      "Training MSE: 8.39290174089e-05\n",
      "747/747 [==============================] - 0s 23us/step\n",
      "Validation MSE: 0.000179130502091 \n",
      "\n",
      "Step :  250 / 1000\n",
      "2988/2988 [==============================] - 0s 37us/step\n",
      "Training MSE: 4.61727727154e-05\n",
      "747/747 [==============================] - 0s 37us/step\n",
      "Validation MSE: 0.000149936103996 \n",
      "\n",
      "Step :  500 / 1000\n",
      "2988/2988 [==============================] - 0s 50us/step\n",
      "Training MSE: 2.88912075386e-05\n",
      "747/747 [==============================] - 0s 45us/step\n",
      "Validation MSE: 0.000184745042915 \n",
      "\n",
      "Step :  750 / 1000\n",
      "2988/2988 [==============================] - 0s 40us/step\n",
      "Training MSE: 5.02470094963e-05\n",
      "747/747 [==============================] - 0s 36us/step\n",
      "Validation MSE: 0.000170684710265 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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6g1JEJJN9Qh7V5EVE0tkm5LWQFxHJZJ+QNwzdQikiksY2Ie8wwFTKi4iksE3IOw2DhKmQ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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d782a8b6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Training the model\n",
    "epochs_tot = 1000\n",
    "epochs_step = 250\n",
    "epochs_ratio = int(epochs_tot / epochs_step)\n",
    "hist =np.array([])\n",
    "\n",
    "for i in range(epochs_ratio):\n",
    "    history = model.fit(features, targets, epochs=epochs_step, batch_size=100, verbose=0)\n",
    "    \n",
    "    # Evaluating the model on the training and testing set\n",
    "    print(\"Step : \" , i * epochs_step, \"/\", epochs_tot)\n",
    "    score = model.evaluate(features, targets)\n",
    "    print(\"Training MSE:\", score[1])\n",
    "    score = model.evaluate(features_validation, targets_validation)\n",
    "    print(\"Validation MSE:\", score[1], \"\\n\")\n",
    "    hist = np.concatenate((hist, np.array(history.history['mean_squared_error'])), axis = 0)\n",
    "    \n",
    "# plot metrics\n",
    "plt.plot(hist)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score =  0.828782939913 / 1.0\n",
      "MSE score =  0.000170684712687 / 0.0\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(features_validation, verbose=0)\n",
    "\n",
    "print('R2 score = ',r2_score(y_test, y_pred), '/ 1.0')\n",
    "print('MSE score = ',mean_squared_error(y_test, y_pred), '/ 0.0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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skw8E5w96p+DK1+1u9Uv53nzkz77q3vpWeVlW/o7s/ayfzttG0hQJ+Qvd39vY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d782ab2550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Lets plot the  first 50 predictions\n",
    "plt.plot(y_test.as_matrix()[0:50], '+', color ='blue', alpha=0.7)\n",
    "plt.plot(y_pred[0:50], 'ro', color ='red', alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Light GBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_train = pd.read_csv(\n",
    "    'train.csv', usecols=[1, 2, 3, 4, 5],\n",
    "    dtype={'onpromotion': bool},\n",
    "    converters={'unit_sales': lambda u: np.log1p(\n",
    "        float(u)) if float(u) > 0 else 0},\n",
    "    parse_dates=[\"date\"],\n",
    "    skiprows=range(1, 66458909)  # 2016-01-01\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>date</th>\n",
       "      <th>store_nbr</th>\n",
       "      <th>item_nbr</th>\n",
       "      <th>unit_sales</th>\n",
       "      <th>onpromotion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>25</td>\n",
       "      <td>105574</td>\n",
       "      <td>2.564949</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>25</td>\n",
       "      <td>105575</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>25</td>\n",
       "      <td>105857</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>25</td>\n",
       "      <td>108634</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>25</td>\n",
       "      <td>108701</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  store_nbr  item_nbr  unit_sales  onpromotion\n",
       "0 2016-01-01         25    105574    2.564949        False\n",
       "1 2016-01-01         25    105575    2.302585        False\n",
       "2 2016-01-01         25    105857    1.386294        False\n",
       "3 2016-01-01         25    108634    1.386294        False\n",
       "4 2016-01-01         25    108701    1.098612         True"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(59038132, 5)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>date</th>\n",
       "      <th>store_nbr</th>\n",
       "      <th>item_nbr</th>\n",
       "      <th>unit_sales</th>\n",
       "      <th>onpromotion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>59038127</th>\n",
       "      <td>2017-08-15</td>\n",
       "      <td>54</td>\n",
       "      <td>2089339</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59038128</th>\n",
       "      <td>2017-08-15</td>\n",
       "      <td>54</td>\n",
       "      <td>2106464</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59038129</th>\n",
       "      <td>2017-08-15</td>\n",
       "      <td>54</td>\n",
       "      <td>2110456</td>\n",
       "      <td>5.262690</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59038130</th>\n",
       "      <td>2017-08-15</td>\n",
       "      <td>54</td>\n",
       "      <td>2113914</td>\n",
       "      <td>5.293305</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59038131</th>\n",
       "      <td>2017-08-15</td>\n",
       "      <td>54</td>\n",
       "      <td>2116416</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               date  store_nbr  item_nbr  unit_sales  onpromotion\n",
       "59038127 2017-08-15         54   2089339    1.609438        False\n",
       "59038128 2017-08-15         54   2106464    0.693147         True\n",
       "59038129 2017-08-15         54   2110456    5.262690        False\n",
       "59038130 2017-08-15         54   2113914    5.293305         True\n",
       "59038131 2017-08-15         54   2116416    1.098612        False"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_test = pd.read_csv(\n",
    "    \"test.csv\", usecols=[0, 1, 2, 3, 4],\n",
    "    dtype={'onpromotion': bool},\n",
    "    parse_dates=[\"date\"]  # , date_parser=parser\n",
    ").set_index(\n",
    "    ['store_nbr', 'item_nbr', 'date']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>onpromotion</th>\n",
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       "    <tr>\n",
       "      <th>store_nbr</th>\n",
       "      <th>item_nbr</th>\n",
       "      <th>date</th>\n",
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       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>96995</th>\n",
       "      <th>2017-08-16</th>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>99197</th>\n",
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       "    <tr>\n",
       "      <th>103501</th>\n",
       "      <th>2017-08-16</th>\n",
       "      <td>125497042</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>103520</th>\n",
       "      <th>2017-08-16</th>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>103665</th>\n",
       "      <th>2017-08-16</th>\n",
       "      <td>125497044</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      id  onpromotion\n",
       "store_nbr item_nbr date                              \n",
       "1         96995    2017-08-16  125497040        False\n",
       "          99197    2017-08-16  125497041        False\n",
       "          103501   2017-08-16  125497042        False\n",
       "          103520   2017-08-16  125497043        False\n",
       "          103665   2017-08-16  125497044        False"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we want to predict unit sales for last 15 days of Aug 2017\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "items = pd.read_csv(\n",
    "    \"items.csv\",\n",
    ").set_index(\"item_nbr\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4100, 3)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_2017 = df_train[df_train.date.isin(\n",
    "    pd.date_range(\"2017-05-31\", periods=7 * 11))].copy()\n",
    "del df_train\n",
    "#dates range from May 31st to 15th Aug~ 77 days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8125670, 5)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2017.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>unit_sales</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50912462</th>\n",
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       "      <th>50912463</th>\n",
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       "      <td>0.693147</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>50912464</th>\n",
       "      <td>2017-05-31</td>\n",
       "      <td>1</td>\n",
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       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50912465</th>\n",
       "      <td>2017-05-31</td>\n",
       "      <td>1</td>\n",
       "      <td>103665</td>\n",
       "      <td>2.197225</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50912466</th>\n",
       "      <td>2017-05-31</td>\n",
       "      <td>1</td>\n",
       "      <td>105574</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               date  store_nbr  item_nbr  unit_sales  onpromotion\n",
       "50912462 2017-05-31          1     96995    0.693147        False\n",
       "50912463 2017-05-31          1     99197    0.693147        False\n",
       "50912464 2017-05-31          1    103520    1.386294        False\n",
       "50912465 2017-05-31          1    103665    2.197225        False\n",
       "50912466 2017-05-31          1    105574    1.386294        False"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2017.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date           0\n",
       "store_nbr      0\n",
       "item_nbr       0\n",
       "unit_sales     0\n",
       "onpromotion    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2017.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "promo_2017_train = df_2017.set_index(\n",
    "    [\"store_nbr\", \"item_nbr\", \"date\"])[[\"onpromotion\"]].unstack(\n",
    "        level=-1).fillna(False)\n",
    "#pivots the table and we want last level of index which is date in our case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>date</th>\n",
       "      <th>2017-05-31</th>\n",
       "      <th>2017-06-01</th>\n",
       "      <th>2017-06-02</th>\n",
       "      <th>2017-06-03</th>\n",
       "      <th>2017-06-04</th>\n",
       "      <th>2017-06-05</th>\n",
       "      <th>2017-06-06</th>\n",
       "      <th>2017-06-07</th>\n",
       "      <th>2017-06-08</th>\n",
       "      <th>2017-06-09</th>\n",
       "      <th>...</th>\n",
       "      <th>2017-08-06</th>\n",
       "      <th>2017-08-07</th>\n",
       "      <th>2017-08-08</th>\n",
       "      <th>2017-08-09</th>\n",
       "      <th>2017-08-10</th>\n",
       "      <th>2017-08-11</th>\n",
       "      <th>2017-08-12</th>\n",
       "      <th>2017-08-13</th>\n",
       "      <th>2017-08-14</th>\n",
       "      <th>2017-08-15</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>store_nbr</th>\n",
       "      <th>item_nbr</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>103520</th>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>103665</th>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 77 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   onpromotion                                              \\\n",
       "date                2017-05-31 2017-06-01 2017-06-02 2017-06-03 2017-06-04   \n",
       "store_nbr item_nbr                                                           \n",
       "1         96995          False      False      False      False      False   \n",
       "          99197          False      False      False      False      False   \n",
       "          103520         False      False      False      False      False   \n",
       "          103665         False      False      False      False      False   \n",
       "          105574         False      False      False      False      False   \n",
       "\n",
       "                                                                           \\\n",
       "date               2017-06-05 2017-06-06 2017-06-07 2017-06-08 2017-06-09   \n",
       "store_nbr item_nbr                                                          \n",
       "1         96995         False      False      False      False      False   \n",
       "          99197         False      False      False      False      False   \n",
       "          103520        False      False      False      False      False   \n",
       "          103665        False      False      False      False      False   \n",
       "          105574        False      False      False      False      False   \n",
       "\n",
       "                      ...                                                  \\\n",
       "date                  ...     2017-08-06 2017-08-07 2017-08-08 2017-08-09   \n",
       "store_nbr item_nbr    ...                                                   \n",
       "1         96995       ...          False      False      False      False   \n",
       "          99197       ...          False      False      False      False   \n",
       "          103520      ...          False      False      False      False   \n",
       "          103665      ...          False      False      False      False   \n",
       "          105574      ...          False      False      False      False   \n",
       "\n",
       "                                                                           \\\n",
       "date               2017-08-10 2017-08-11 2017-08-12 2017-08-13 2017-08-14   \n",
       "store_nbr item_nbr                                                          \n",
       "1         96995         False      False      False      False      False   \n",
       "          99197         False      False      False      False      False   \n",
       "          103520        False      False      False      False      False   \n",
       "          103665        False      False      False      False      False   \n",
       "          105574        False      False      False      False      False   \n",
       "\n",
       "                               \n",
       "date               2017-08-15  \n",
       "store_nbr item_nbr             \n",
       "1         96995         False  \n",
       "          99197         False  \n",
       "          103520        False  \n",
       "          103665        False  \n",
       "          105574        False  \n",
       "\n",
       "[5 rows x 77 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "promo_2017_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "promo_2017_train.columns = promo_2017_train.columns.get_level_values(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2017-05-31', '2017-06-01', '2017-06-02', '2017-06-03',\n",
       "               '2017-06-04', '2017-06-05', '2017-06-06', '2017-06-07',\n",
       "               '2017-06-08', '2017-06-09', '2017-06-10', '2017-06-11',\n",
       "               '2017-06-12', '2017-06-13', '2017-06-14', '2017-06-15',\n",
       "               '2017-06-16', '2017-06-17', '2017-06-18', '2017-06-19',\n",
       "               '2017-06-20', '2017-06-21', '2017-06-22', '2017-06-23',\n",
       "               '2017-06-24', '2017-06-25', '2017-06-26', '2017-06-27',\n",
       "               '2017-06-28', '2017-06-29', '2017-06-30', '2017-07-01',\n",
       "               '2017-07-02', '2017-07-03', '2017-07-04', '2017-07-05',\n",
       "               '2017-07-06', '2017-07-07', '2017-07-08', '2017-07-09',\n",
       "               '2017-07-10', '2017-07-11', '2017-07-12', '2017-07-13',\n",
       "               '2017-07-14', '2017-07-15', '2017-07-16', '2017-07-17',\n",
       "               '2017-07-18', '2017-07-19', '2017-07-20', '2017-07-21',\n",
       "               '2017-07-22', '2017-07-23', '2017-07-24', '2017-07-25',\n",
       "               '2017-07-26', '2017-07-27', '2017-07-28', '2017-07-29',\n",
       "               '2017-07-30', '2017-07-31', '2017-08-01', '2017-08-02',\n",
       "               '2017-08-03', '2017-08-04', '2017-08-05', '2017-08-06',\n",
       "               '2017-08-07', '2017-08-08', '2017-08-09', '2017-08-10',\n",
       "               '2017-08-11', '2017-08-12', '2017-08-13', '2017-08-14',\n",
       "               '2017-08-15'],\n",
       "              dtype='datetime64[ns]', name='date', freq=None)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "promo_2017_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Repeat same steps for test dataset\n",
    "promo_2017_test = df_test[[\"onpromotion\"]].unstack(level=-1).fillna(False)\n",
    "promo_2017_test.columns = promo_2017_test.columns.get_level_values(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(210654, 16)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "promo_2017_test.head()\n",
    "promo_2017_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "promo_2017_test = promo_2017_test.reindex(promo_2017_train.index).fillna(False)\n",
    "#Reseting the index same as promo_2017_train\n",
    "promo_2017 = pd.concat([promo_2017_train, promo_2017_test], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del promo_2017_test,promo_2017_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_2017 = df_2017.set_index(\n",
    "    [\"store_nbr\", \"item_nbr\", \"date\"])[[\"unit_sales\"]].unstack(\n",
    "        level=-1).fillna(0)\n",
    "df_2017.columns = df_2017.columns.get_level_values(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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>date</th>\n",
       "      <th>2017-05-31 00:00:00</th>\n",
       "      <th>2017-06-01 00:00:00</th>\n",
       "      <th>2017-06-02 00:00:00</th>\n",
       "      <th>2017-06-03 00:00:00</th>\n",
       "      <th>2017-06-04 00:00:00</th>\n",
       "      <th>2017-06-05 00:00:00</th>\n",
       "      <th>2017-06-06 00:00:00</th>\n",
       "      <th>2017-06-07 00:00:00</th>\n",
       "      <th>2017-06-08 00:00:00</th>\n",
       "      <th>2017-06-09 00:00:00</th>\n",
       "      <th>...</th>\n",
       "      <th>2017-08-22 00:00:00</th>\n",
       "      <th>2017-08-23 00:00:00</th>\n",
       "      <th>2017-08-24 00:00:00</th>\n",
       "      <th>2017-08-25 00:00:00</th>\n",
       "      <th>2017-08-26 00:00:00</th>\n",
       "      <th>2017-08-27 00:00:00</th>\n",
       "      <th>2017-08-28 00:00:00</th>\n",
       "      <th>2017-08-29 00:00:00</th>\n",
       "      <th>2017-08-30 00:00:00</th>\n",
       "      <th>2017-08-31 00:00:00</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>store_nbr</th>\n",
       "      <th>item_nbr</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>96995</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99197</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103520</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103665</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105574</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 93 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "date                2017-05-31  2017-06-01  2017-06-02  2017-06-03  \\\n",
       "store_nbr item_nbr                                                   \n",
       "1         96995          False       False       False       False   \n",
       "          99197          False       False       False       False   \n",
       "          103520         False       False       False       False   \n",
       "          103665         False       False       False       False   \n",
       "          105574         False       False       False       False   \n",
       "\n",
       "date                2017-06-04  2017-06-05  2017-06-06  2017-06-07  \\\n",
       "store_nbr item_nbr                                                   \n",
       "1         96995          False       False       False       False   \n",
       "          99197          False       False       False       False   \n",
       "          103520         False       False       False       False   \n",
       "          103665         False       False       False       False   \n",
       "          105574         False       False       False       False   \n",
       "\n",
       "date                2017-06-08  2017-06-09     ...      2017-08-22  \\\n",
       "store_nbr item_nbr                             ...                   \n",
       "1         96995          False       False     ...           False   \n",
       "          99197          False       False     ...           False   \n",
       "          103520         False       False     ...           False   \n",
       "          103665         False       False     ...           False   \n",
       "          105574         False       False     ...           False   \n",
       "\n",
       "date                2017-08-23  2017-08-24  2017-08-25  2017-08-26  \\\n",
       "store_nbr item_nbr                                                   \n",
       "1         96995          False       False       False       False   \n",
       "          99197          False       False       False       False   \n",
       "          103520         False       False       False       False   \n",
       "          103665         False       False       False       False   \n",
       "          105574         False       False       False       False   \n",
       "\n",
       "date                2017-08-27  2017-08-28  2017-08-29  2017-08-30  2017-08-31  \n",
       "store_nbr item_nbr                                                              \n",
       "1         96995          False       False       False       False       False  \n",
       "          99197          False       False       False       False       False  \n",
       "          103520         False       False       False       False       False  \n",
       "          103665         False       False       False       False       False  \n",
       "          105574         False       False       False       False       False  \n",
       "\n",
       "[5 rows x 93 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "promo_2017.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "items = items.reindex(df_2017.index.get_level_values(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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>family</th>\n",
       "      <th>class</th>\n",
       "      <th>perishable</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item_nbr</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>96995</th>\n",
       "      <td>GROCERY I</td>\n",
       "      <td>1093</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99197</th>\n",
       "      <td>GROCERY I</td>\n",
       "      <td>1067</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103520</th>\n",
       "      <td>GROCERY I</td>\n",
       "      <td>1028</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103665</th>\n",
       "      <td>BREAD/BAKERY</td>\n",
       "      <td>2712</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105574</th>\n",
       "      <td>GROCERY I</td>\n",
       "      <td>1045</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                family  class  perishable\n",
       "item_nbr                                 \n",
       "96995        GROCERY I   1093           0\n",
       "99197        GROCERY I   1067           0\n",
       "103520       GROCERY I   1028           0\n",
       "103665    BREAD/BAKERY   2712           1\n",
       "105574       GROCERY I   1045           0"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_timespan(df, dt, minus, periods):\n",
    "    return df[\n",
    "        pd.date_range(dt - timedelta(days=minus), periods=periods)\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def prepare_dataset(t2017, is_train=True):\n",
    "    X = pd.DataFrame({\n",
    "        \"mean_3_2017\": get_timespan(df_2017, t2017, 3, 3).mean(axis=1).values,\n",
    "        \"mean_7_2017\": get_timespan(df_2017, t2017, 7, 7).mean(axis=1).values,\n",
    "        \"mean_14_2017\": get_timespan(df_2017, t2017, 14, 14).mean(axis=1).values,\n",
    "        \"promo_14_2017\": get_timespan(promo_2017, t2017, 14, 14).sum(axis=1).values\n",
    "    })\n",
    "    for i in range(16):\n",
    "        X[\"promo_{}\".format(i)] = promo_2017[\n",
    "            t2017 + timedelta(days=i)].values.astype(np.uint8)\n",
    "    if is_train:\n",
    "        y = df_2017[\n",
    "            pd.date_range(t2017, periods=16)\n",
    "        ].values\n",
    "        return X, y\n",
    "    return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preparing dataset...\n"
     ]
    }
   ],
   "source": [
    "print(\"Preparing dataset...\")\n",
    "t2017 = date(2017, 6, 21)\n",
    "X_l, y_l = [], []\n",
    "for i in range(4):\n",
    "    delta = timedelta(days=7 * i)\n",
    "    X_tmp, y_tmp = prepare_dataset(\n",
    "        t2017 + delta\n",
    "    )\n",
    "    X_l.append(X_tmp)\n",
    "    y_l.append(y_tmp)\n",
    "X_train = pd.concat(X_l, axis=0)\n",
    "y_train = np.concatenate(y_l, axis=0)\n",
    "del X_l, y_l\n",
    "X_val, y_val = prepare_dataset(date(2017, 7, 26))\n",
    "X_test = prepare_dataset(date(2017, 8, 16), is_train=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training and predicting models...\n"
     ]
    }
   ],
   "source": [
    "print(\"Training and predicting models...\")\n",
    "params = {\n",
    "    'num_leaves': 2**5 - 1,\n",
    "    'objective': 'regression_l2',\n",
    "    'max_depth': 8,\n",
    "    'min_data_in_leaf': 50,\n",
    "    'learning_rate': 0.05,\n",
    "    'feature_fraction': 0.75,\n",
    "    'bagging_fraction': 0.75,\n",
    "    'bagging_freq': 1,\n",
    "    'metric': 'l2',\n",
    "    'num_threads': 4\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "MAX_ROUNDS = 1000\n",
    "val_pred = []\n",
    "test_pred = []\n",
    "cate_vars = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================================================\n",
      "Step 1\n",
      "==================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\lightgbm\\basic.py:1036: UserWarning: Using categorical_feature in Dataset.\n",
      "  warnings.warn('Using categorical_feature in Dataset.')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 50 rounds.\n",
      "[50]\ttraining's l2: 0.345097\tvalid_1's l2: 0.341325\n",
      "[100]\ttraining's l2: 0.333329\tvalid_1's l2: 0.330014\n",
      "[150]\ttraining's l2: 0.331387\tvalid_1's l2: 0.328774\n",
      "[200]\ttraining's l2: 0.330338\tvalid_1's l2: 0.328296\n",
      "[250]\ttraining's l2: 0.329482\tvalid_1's l2: 0.327889\n",
      "[300]\ttraining's l2: 0.328796\tvalid_1's l2: 0.327648\n",
      "[350]\ttraining's l2: 0.328179\tvalid_1's l2: 0.327391\n",
      "[400]\ttraining's l2: 0.32762\tvalid_1's l2: 0.32721\n",
      "[450]\ttraining's l2: 0.327135\tvalid_1's l2: 0.327108\n",
      "[500]\ttraining's l2: 0.326678\tvalid_1's l2: 0.327039\n",
      "[550]\ttraining's l2: 0.326257\tvalid_1's l2: 0.326976\n",
      "[600]\ttraining's l2: 0.325865\tvalid_1's l2: 0.32694\n",
      "[650]\ttraining's l2: 0.325471\tvalid_1's l2: 0.32692\n",
      "[700]\ttraining's l2: 0.325077\tvalid_1's l2: 0.326862\n",
      "[750]\ttraining's l2: 0.32472\tvalid_1's l2: 0.326886\n",
      "Early stopping, best iteration is:\n",
      "[700]\ttraining's l2: 0.325077\tvalid_1's l2: 0.326862\n",
      "mean_14_2017: 1986449.84\n",
      "mean_7_2017: 1457763.98\n",
      "mean_3_2017: 315685.86\n",
      "promo_0: 138311.77\n",
      "promo_14_2017: 55771.15\n",
      "promo_7: 20798.22\n",
      "promo_14: 7760.52\n",
      "promo_3: 5673.91\n",
      "promo_15: 3997.14\n",
      "promo_9: 2805.85\n",
      "promo_2: 2164.39\n",
      "promo_13: 1999.63\n",
      "promo_4: 1896.85\n",
      "promo_6: 1442.07\n",
      "promo_1: 1237.74\n",
      "promo_11: 1209.49\n",
      "promo_5: 1037.48\n",
      "promo_8: 1029.53\n",
      "promo_10: 878.85\n",
      "promo_12: 786.74\n",
      "==================================================\n",
      "Step 2\n",
      "==================================================\n",
      "Training until validation scores don't improve for 50 rounds.\n",
      "[50]\ttraining's l2: 0.362083\tvalid_1's l2: 0.373936\n",
      "[100]\ttraining's l2: 0.352839\tvalid_1's l2: 0.36388\n",
      "[150]\ttraining's l2: 0.350732\tvalid_1's l2: 0.361879\n",
      "[200]\ttraining's l2: 0.349587\tvalid_1's l2: 0.361007\n",
      "[250]\ttraining's l2: 0.348683\tvalid_1's l2: 0.360553\n",
      "[300]\ttraining's l2: 0.3479\tvalid_1's l2: 0.36017\n",
      "[350]\ttraining's l2: 0.347234\tvalid_1's l2: 0.360051\n",
      "[400]\ttraining's l2: 0.346659\tvalid_1's l2: 0.359872\n",
      "[450]\ttraining's l2: 0.346122\tvalid_1's l2: 0.359842\n",
      "[500]\ttraining's l2: 0.34562\tvalid_1's l2: 0.359741\n",
      "[550]\ttraining's l2: 0.345179\tvalid_1's l2: 0.359637\n",
      "Early stopping, best iteration is:\n",
      "[544]\ttraining's l2: 0.345234\tvalid_1's l2: 0.359619\n",
      "mean_14_2017: 1868914.77\n",
      "mean_7_2017: 992464.75\n",
      "mean_3_2017: 246905.29\n",
      "promo_1: 99350.78\n",
      "promo_14_2017: 44172.54\n",
      "promo_3: 12750.79\n",
      "promo_0: 6779.88\n",
      "promo_2: 6583.47\n",
      "promo_4: 5325.07\n",
      "promo_5: 4631.52\n",
      "promo_7: 2156.05\n",
      "promo_8: 1831.46\n",
      "promo_6: 1827.78\n",
      "promo_9: 1776.41\n",
      "promo_14: 1612.61\n",
      "promo_15: 1062.18\n",
      "promo_13: 950.48\n",
      "promo_11: 766.67\n",
      "promo_12: 695.36\n",
      "promo_10: 610.23\n",
      "==================================================\n",
      "Step 3\n",
      "==================================================\n",
      "Training until validation scores don't improve for 50 rounds.\n",
      "[50]\ttraining's l2: 0.380875\tvalid_1's l2: 0.402351\n",
      "[100]\ttraining's l2: 0.367498\tvalid_1's l2: 0.39009\n",
      "[150]\ttraining's l2: 0.364512\tvalid_1's l2: 0.388904\n",
      "[200]\ttraining's l2: 0.363052\tvalid_1's l2: 0.387965\n",
      "[250]\ttraining's l2: 0.361938\tvalid_1's l2: 0.387665\n",
      "[300]\ttraining's l2: 0.361065\tvalid_1's l2: 0.387599\n",
      "[350]\ttraining's l2: 0.360329\tvalid_1's l2: 0.387381\n",
      "[400]\ttraining's l2: 0.359635\tvalid_1's l2: 0.387359\n",
      "[450]\ttraining's l2: 0.359046\tvalid_1's l2: 0.387275\n",
      "[500]\ttraining's l2: 0.35847\tvalid_1's l2: 0.387182\n",
      "[550]\ttraining's l2: 0.357961\tvalid_1's l2: 0.387104\n",
      "[600]\ttraining's l2: 0.3575\tvalid_1's l2: 0.387178\n",
      "Early stopping, best iteration is:\n",
      "[566]\ttraining's l2: 0.357817\tvalid_1's l2: 0.387091\n",
      "mean_14_2017: 2319488.79\n",
      "mean_7_2017: 966966.91\n",
      "mean_3_2017: 272105.14\n",
      "promo_2: 142008.91\n",
      "promo_14_2017: 60744.28\n",
      "promo_9: 14380.12\n",
      "promo_3: 9424.53\n",
      "promo_5: 6225.54\n",
      "promo_0: 5800.61\n",
      "promo_4: 5396.82\n",
      "promo_1: 5007.91\n",
      "promo_7: 4463.36\n",
      "promo_14: 3223.84\n",
      "promo_10: 2505.59\n",
      "promo_6: 2371.64\n",
      "promo_13: 2257.02\n",
      "promo_15: 1714.15\n",
      "promo_11: 1644.68\n",
      "promo_12: 1350.52\n",
      "promo_8: 1083.30\n",
      "==================================================\n",
      "Step 4\n",
      "==================================================\n",
      "Training until validation scores don't improve for 50 rounds.\n",
      "[50]\ttraining's l2: 0.41088\tvalid_1's l2: 0.414437\n",
      "[100]\ttraining's l2: 0.398636\tvalid_1's l2: 0.402885\n",
      "[150]\ttraining's l2: 0.395403\tvalid_1's l2: 0.400838\n",
      "[200]\ttraining's l2: 0.393721\tvalid_1's l2: 0.400026\n",
      "[250]\ttraining's l2: 0.392571\tvalid_1's l2: 0.39955\n",
      "[300]\ttraining's l2: 0.391653\tvalid_1's l2: 0.399163\n",
      "[350]\ttraining's l2: 0.390913\tvalid_1's l2: 0.39899\n",
      "[400]\ttraining's l2: 0.390209\tvalid_1's l2: 0.398832\n",
      "[450]\ttraining's l2: 0.38955\tvalid_1's l2: 0.39859\n",
      "[500]\ttraining's l2: 0.388929\tvalid_1's l2: 0.39851\n",
      "[550]\ttraining's l2: 0.388391\tvalid_1's l2: 0.39842\n",
      "[600]\ttraining's l2: 0.387866\tvalid_1's l2: 0.398311\n",
      "[650]\ttraining's l2: 0.38739\tvalid_1's l2: 0.398177\n",
      "[700]\ttraining's l2: 0.386894\tvalid_1's l2: 0.398144\n",
      "[750]\ttraining's l2: 0.386481\tvalid_1's l2: 0.398138\n",
      "[800]\ttraining's l2: 0.386046\tvalid_1's l2: 0.39809\n",
      "[850]\ttraining's l2: 0.385606\tvalid_1's l2: 0.398011\n",
      "[900]\ttraining's l2: 0.385208\tvalid_1's l2: 0.398025\n",
      "Early stopping, best iteration is:\n",
      "[850]\ttraining's l2: 0.385606\tvalid_1's l2: 0.398011\n",
      "mean_14_2017: 2699240.14\n",
      "mean_7_2017: 1108185.73\n",
      "mean_3_2017: 318024.54\n",
      "promo_3: 84253.36\n",
      "promo_14_2017: 48070.58\n",
      "promo_2: 12630.11\n",
      "promo_5: 8872.43\n",
      "promo_4: 8629.52\n",
      "promo_0: 8202.50\n",
      "promo_1: 7135.85\n",
      "promo_7: 6907.18\n",
      "promo_14: 4039.79\n",
      "promo_6: 3344.26\n",
      "promo_9: 2780.04\n",
      "promo_8: 2739.25\n",
      "promo_10: 2535.59\n",
      "promo_13: 1718.36\n",
      "promo_15: 1632.95\n",
      "promo_11: 1444.55\n",
      "promo_12: 971.72\n",
      "==================================================\n",
      "Step 5\n",
      "==================================================\n",
      "Training until validation scores don't improve for 50 rounds.\n",
      "[50]\ttraining's l2: 0.439576\tvalid_1's l2: 0.437491\n",
      "[100]\ttraining's l2: 0.425773\tvalid_1's l2: 0.425185\n",
      "[150]\ttraining's l2: 0.422249\tvalid_1's l2: 0.422508\n",
      "[200]\ttraining's l2: 0.420476\tvalid_1's l2: 0.42152\n",
      "[250]\ttraining's l2: 0.419141\tvalid_1's l2: 0.420784\n",
      "[300]\ttraining's l2: 0.418091\tvalid_1's l2: 0.420299\n",
      "[350]\ttraining's l2: 0.417272\tvalid_1's l2: 0.419958\n",
      "[400]\ttraining's l2: 0.416513\tvalid_1's l2: 0.41973\n",
      "[450]\ttraining's l2: 0.415835\tvalid_1's l2: 0.419579\n",
      "[500]\ttraining's l2: 0.415178\tvalid_1's l2: 0.419386\n",
      "[550]\ttraining's l2: 0.414574\tvalid_1's l2: 0.419117\n",
      "[600]\ttraining's l2: 0.414011\tvalid_1's l2: 0.418952\n",
      "[650]\ttraining's l2: 0.413479\tvalid_1's l2: 0.418839\n",
      "[700]\ttraining's l2: 0.412944\tvalid_1's l2: 0.418794\n",
      "[750]\ttraining's l2: 0.412487\tvalid_1's l2: 0.418727\n",
      "Early stopping, best iteration is:\n",
      "[733]\ttraining's l2: 0.412644\tvalid_1's l2: 0.418721\n",
      "mean_14_2017: 2900562.14\n",
      "mean_7_2017: 940343.68\n",
      "mean_3_2017: 555576.71\n",
      "promo_4: 86413.56\n",
      "promo_14_2017: 44620.25\n",
      "promo_5: 11967.47\n",
      "promo_3: 11050.45\n",
      "promo_7: 9011.18\n",
      "promo_2: 7906.63\n",
      "promo_0: 6428.12\n",
      "promo_1: 6227.03\n",
      "promo_6: 5886.70\n",
      "promo_14: 4861.32\n",
      "promo_11: 4145.03\n",
      "promo_9: 4013.79\n",
      "promo_8: 3018.28\n",
      "promo_10: 2167.46\n",
      "promo_15: 2053.96\n",
      "promo_13: 1956.13\n",
      "promo_12: 1914.83\n",
      "==================================================\n",
      "Step 6\n",
      "==================================================\n",
      "Training until validation scores don't improve for 50 rounds.\n",
      "[50]\ttraining's l2: 0.409584\tvalid_1's l2: 0.423971\n",
      "[100]\ttraining's l2: 0.398291\tvalid_1's l2: 0.410411\n",
      "[150]\ttraining's l2: 0.395342\tvalid_1's l2: 0.407794\n",
      "[200]\ttraining's l2: 0.39379\tvalid_1's l2: 0.406887\n",
      "[250]\ttraining's l2: 0.392667\tvalid_1's l2: 0.406418\n",
      "[300]\ttraining's l2: 0.391736\tvalid_1's l2: 0.406087\n",
      "[350]\ttraining's l2: 0.390967\tvalid_1's l2: 0.405975\n",
      "[400]\ttraining's l2: 0.390286\tvalid_1's l2: 0.405775\n",
      "[450]\ttraining's l2: 0.389664\tvalid_1's l2: 0.405608\n",
      "[500]\ttraining's l2: 0.389064\tvalid_1's l2: 0.405492\n",
      "[550]\ttraining's l2: 0.388476\tvalid_1's l2: 0.405527\n",
      "Early stopping, best iteration is:\n",
      "[503]\ttraining's l2: 0.389017\tvalid_1's l2: 0.405467\n",
      "mean_14_2017: 2380586.50\n",
      "mean_7_2017: 832178.27\n",
      "mean_3_2017: 369471.68\n",
      "promo_5: 90672.57\n",
      "promo_14_2017: 43199.02\n",
      "promo_3: 14850.07\n",
      "promo_6: 11417.65\n",
      "promo_7: 10118.18\n",
      "promo_2: 5299.55\n",
      "promo_4: 5149.10\n",
      "promo_1: 4358.04\n",
      "promo_0: 4328.28\n",
      "promo_9: 3430.97\n",
      "promo_8: 3336.90\n",
      "promo_14: 2825.31\n",
      "promo_12: 2248.26\n",
      "promo_11: 2046.16\n",
      "promo_10: 1638.87\n",
      "promo_13: 1559.84\n",
      "promo_15: 1140.49\n",
      "==================================================\n",
      "Step 7\n",
      "==================================================\n",
      "Training until validation scores don't improve for 50 rounds.\n",
      "[50]\ttraining's l2: 0.399217\tvalid_1's l2: 0.499347\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100]\ttraining's l2: 0.387503\tvalid_1's l2: 0.484477\n",
      "[150]\ttraining's l2: 0.38451\tvalid_1's l2: 0.483293\n",
      "Early stopping, best iteration is:\n",
      "[133]\ttraining's l2: 0.385266\tvalid_1's l2: 0.483197\n",
      "mean_14_2017: 2173915.37\n",
      "mean_7_2017: 779085.54\n",
      "mean_3_2017: 309418.75\n",
      "promo_6: 153438.12\n",
      "promo_14_2017: 48445.64\n",
      "promo_3: 14017.86\n",
      "promo_7: 9137.57\n",
      "promo_13: 8640.51\n",
      "promo_5: 7108.16\n",
      "promo_0: 4701.30\n",
      "promo_1: 3898.41\n",
      "promo_4: 3646.02\n",
      "promo_9: 3257.32\n",
      "promo_2: 2908.48\n",
      "promo_14: 2786.73\n",
      "promo_8: 1924.80\n",
      "promo_15: 943.23\n",
      "promo_12: 873.16\n",
      "promo_11: 850.42\n",
      "promo_10: 779.03\n",
      "==================================================\n",
      "Step 8\n",
      "==================================================\n",
      "Training until validation scores don't improve for 50 rounds.\n",
      "[50]\ttraining's l2: 0.38804\tvalid_1's l2: 0.460395\n",
      "[100]\ttraining's l2: 0.375671\tvalid_1's l2: 0.44255\n",
      "[150]\ttraining's l2: 0.372894\tvalid_1's l2: 0.439745\n",
      "[200]\ttraining's l2: 0.371495\tvalid_1's l2: 0.438764\n",
      "[250]\ttraining's l2: 0.370359\tvalid_1's l2: 0.438271\n",
      "[300]\ttraining's l2: 0.369416\tvalid_1's l2: 0.437744\n",
      "[350]\ttraining's l2: 0.368715\tvalid_1's l2: 0.437619\n",
      "[400]\ttraining's l2: 0.368041\tvalid_1's l2: 0.437442\n",
      "[450]\ttraining's l2: 0.36745\tvalid_1's l2: 0.437331\n",
      "[500]\ttraining's l2: 0.366861\tvalid_1's l2: 0.437071\n",
      "[550]\ttraining's l2: 0.366324\tvalid_1's l2: 0.437007\n",
      "[600]\ttraining's l2: 0.365809\tvalid_1's l2: 0.436936\n",
      "[650]\ttraining's l2: 0.365316\tvalid_1's l2: 0.436796\n",
      "[700]\ttraining's l2: 0.364857\tvalid_1's l2: 0.436676\n",
      "[750]\ttraining's l2: 0.364437\tvalid_1's l2: 0.436579\n",
      "[800]\ttraining's l2: 0.364046\tvalid_1's l2: 0.436594\n",
      "Early stopping, best iteration is:\n",
      "[751]\ttraining's l2: 0.364428\tvalid_1's l2: 0.436565\n",
      "mean_14_2017: 2384937.96\n",
      "mean_7_2017: 800275.74\n",
      "mean_3_2017: 264449.48\n",
      "promo_7: 198969.97\n",
      "promo_14_2017: 43102.32\n",
      "promo_0: 40277.13\n",
      "promo_14: 25346.38\n",
      "promo_5: 8275.66\n",
      "promo_3: 7146.90\n",
      "promo_9: 6885.87\n",
      "promo_6: 5602.21\n",
      "promo_8: 4957.82\n",
      "promo_4: 4043.85\n",
      "promo_2: 3830.85\n",
      "promo_1: 3345.19\n",
      "promo_15: 3162.98\n",
      "promo_10: 2194.94\n",
      "promo_13: 2140.94\n",
      "promo_11: 1299.25\n",
      "promo_12: 1084.98\n",
      "==================================================\n",
      "Step 9\n",
      "==================================================\n",
      "Training until validation scores don't improve for 50 rounds.\n",
      "[50]\ttraining's l2: 0.39501\tvalid_1's l2: 0.439575\n",
      "[100]\ttraining's l2: 0.38446\tvalid_1's l2: 0.426923\n",
      "[150]\ttraining's l2: 0.381702\tvalid_1's l2: 0.425458\n",
      "[200]\ttraining's l2: 0.380266\tvalid_1's l2: 0.425137\n",
      "[250]\ttraining's l2: 0.379208\tvalid_1's l2: 0.42483\n",
      "[300]\ttraining's l2: 0.37831\tvalid_1's l2: 0.424731\n",
      "[350]\ttraining's l2: 0.377586\tvalid_1's l2: 0.424813\n",
      "Early stopping, best iteration is:\n",
      "[322]\ttraining's l2: 0.377986\tvalid_1's l2: 0.424682\n",
      "mean_14_2017: 2075947.93\n",
      "mean_7_2017: 655683.84\n",
      "mean_3_2017: 222744.11\n",
      "promo_8: 131624.44\n",
      "promo_14_2017: 49753.23\n",
      "promo_10: 16232.78\n",
      "promo_9: 9035.94\n",
      "promo_7: 8227.44\n",
      "promo_3: 5610.98\n",
      "promo_11: 4952.09\n",
      "promo_12: 4276.68\n",
      "promo_5: 3849.07\n",
      "promo_13: 3707.71\n",
      "promo_0: 3655.26\n",
      "promo_14: 3170.06\n",
      "promo_1: 2449.77\n",
      "promo_6: 2227.24\n",
      "promo_2: 2209.61\n",
      "promo_15: 2121.89\n",
      "promo_4: 1531.99\n",
      "==================================================\n",
      "Step 10\n",
      "==================================================\n",
      "Training until validation scores don't improve for 50 rounds.\n",
      "[50]\ttraining's l2: 0.411047\tvalid_1's l2: 0.442473\n",
      "[100]\ttraining's l2: 0.397624\tvalid_1's l2: 0.429152\n",
      "[150]\ttraining's l2: 0.394107\tvalid_1's l2: 0.427581\n",
      "[200]\ttraining's l2: 0.392325\tvalid_1's l2: 0.427375\n",
      "[250]\ttraining's l2: 0.391099\tvalid_1's l2: 0.427094\n",
      "[300]\ttraining's l2: 0.390115\tvalid_1's l2: 0.426925\n",
      "[350]\ttraining's l2: 0.389301\tvalid_1's l2: 0.42691\n",
      "[400]\ttraining's l2: 0.388546\tvalid_1's l2: 0.426784\n",
      "[450]\ttraining's l2: 0.387796\tvalid_1's l2: 0.426701\n",
      "[500]\ttraining's l2: 0.387151\tvalid_1's l2: 0.4266\n",
      "[550]\ttraining's l2: 0.386535\tvalid_1's l2: 0.426631\n",
      "Early stopping, best iteration is:\n",
      "[531]\ttraining's l2: 0.386772\tvalid_1's l2: 0.426497\n",
      "mean_14_2017: 2346893.69\n",
      "mean_7_2017: 767571.67\n",
      "mean_3_2017: 253304.63\n",
      "promo_9: 183229.22\n",
      "promo_14_2017: 65895.09\n",
      "promo_2: 21899.03\n",
      "promo_10: 11392.59\n",
      "promo_7: 9644.36\n",
      "promo_8: 6755.37\n",
      "promo_14: 6300.28\n",
      "promo_12: 5457.72\n",
      "promo_11: 5087.14\n",
      "promo_0: 3239.42\n",
      "promo_13: 2844.98\n",
      "promo_1: 2706.35\n",
      "promo_6: 2621.03\n",
      "promo_15: 2161.14\n",
      "promo_4: 1552.31\n",
      "promo_5: 1527.53\n",
      "promo_3: 1435.12\n",
      "==================================================\n",
      "Step 11\n",
      "==================================================\n",
      "Training until validation scores don't improve for 50 rounds.\n",
      "[50]\ttraining's l2: 0.44642\tvalid_1's l2: 0.449231\n",
      "[100]\ttraining's l2: 0.433665\tvalid_1's l2: 0.43933\n",
      "[150]\ttraining's l2: 0.429949\tvalid_1's l2: 0.438557\n",
      "[200]\ttraining's l2: 0.42814\tvalid_1's l2: 0.438385\n",
      "[250]\ttraining's l2: 0.426818\tvalid_1's l2: 0.438375\n",
      "Early stopping, best iteration is:\n",
      "[218]\ttraining's l2: 0.427628\tvalid_1's l2: 0.43829\n",
      "mean_14_2017: 2674856.55\n",
      "mean_7_2017: 866909.51\n",
      "mean_3_2017: 282468.04\n",
      "promo_10: 115296.28\n",
      "promo_14_2017: 52248.98\n",
      "promo_9: 15729.62\n",
      "promo_12: 11478.02\n",
      "promo_14: 9985.99\n",
      "promo_7: 9855.92\n",
      "promo_11: 8609.79\n",
      "promo_8: 7361.15\n",
      "promo_13: 4811.76\n",
      "promo_3: 4763.15\n",
      "promo_15: 3875.72\n",
      "promo_0: 2518.23\n",
      "promo_1: 2478.58\n",
      "promo_2: 2453.78\n",
      "promo_6: 2430.77\n",
      "promo_5: 2144.93\n",
      "promo_4: 1879.64\n",
      "==================================================\n",
      "Step 12\n",
      "==================================================\n",
      "Training until validation scores don't improve for 50 rounds.\n",
      "[50]\ttraining's l2: 0.473531\tvalid_1's l2: 0.478164\n",
      "[100]\ttraining's l2: 0.459874\tvalid_1's l2: 0.467236\n",
      "[150]\ttraining's l2: 0.455961\tvalid_1's l2: 0.465847\n",
      "[200]\ttraining's l2: 0.454032\tvalid_1's l2: 0.464987\n",
      "[250]\ttraining's l2: 0.452477\tvalid_1's l2: 0.464666\n",
      "[300]\ttraining's l2: 0.451408\tvalid_1's l2: 0.464392\n",
      "[350]\ttraining's l2: 0.450497\tvalid_1's l2: 0.464058\n",
      "[400]\ttraining's l2: 0.44964\tvalid_1's l2: 0.463948\n",
      "[450]\ttraining's l2: 0.448839\tvalid_1's l2: 0.463859\n",
      "[500]\ttraining's l2: 0.44812\tvalid_1's l2: 0.463847\n",
      "[550]\ttraining's l2: 0.44744\tvalid_1's l2: 0.463719\n",
      "[600]\ttraining's l2: 0.44681\tvalid_1's l2: 0.463732\n",
      "Early stopping, best iteration is:\n",
      "[558]\ttraining's l2: 0.447327\tvalid_1's l2: 0.463674\n",
      "mean_14_2017: 2863451.91\n",
      "mean_7_2017: 793941.57\n",
      "mean_3_2017: 454939.35\n",
      "promo_11: 121096.13\n",
      "promo_14_2017: 51788.18\n",
      "promo_10: 13365.51\n",
      "promo_14: 12763.64\n",
      "promo_9: 12649.93\n",
      "promo_12: 12085.60\n",
      "promo_4: 11290.30\n",
      "promo_13: 9057.12\n",
      "promo_7: 8929.03\n",
      "promo_8: 6839.25\n",
      "promo_0: 4212.43\n",
      "promo_15: 3631.61\n",
      "promo_6: 3165.39\n",
      "promo_2: 2612.85\n",
      "promo_1: 2457.32\n",
      "promo_5: 2426.51\n",
      "promo_3: 2229.57\n",
      "==================================================\n",
      "Step 13\n",
      "==================================================\n",
      "Training until validation scores don't improve for 50 rounds.\n",
      "[50]\ttraining's l2: 0.439663\tvalid_1's l2: 0.442017\n",
      "[100]\ttraining's l2: 0.427572\tvalid_1's l2: 0.43144\n",
      "[150]\ttraining's l2: 0.424178\tvalid_1's l2: 0.430049\n",
      "[200]\ttraining's l2: 0.422529\tvalid_1's l2: 0.429607\n",
      "[250]\ttraining's l2: 0.421276\tvalid_1's l2: 0.429385\n",
      "[300]\ttraining's l2: 0.4202\tvalid_1's l2: 0.429375\n",
      "[350]\ttraining's l2: 0.419331\tvalid_1's l2: 0.429369\n",
      "Early stopping, best iteration is:\n",
      "[343]\ttraining's l2: 0.419456\tvalid_1's l2: 0.429322\n",
      "mean_14_2017: 2374488.91\n",
      "mean_7_2017: 704889.17\n",
      "mean_3_2017: 339685.82\n",
      "promo_12: 106997.97\n",
      "promo_14_2017: 47577.68\n",
      "promo_13: 27859.67\n",
      "promo_10: 13382.15\n",
      "promo_14: 12542.50\n",
      "promo_9: 8178.92\n",
      "promo_15: 5883.99\n",
      "promo_5: 5882.48\n",
      "promo_11: 5750.61\n",
      "promo_7: 5431.63\n",
      "promo_8: 4317.89\n",
      "promo_0: 2690.25\n",
      "promo_6: 2671.94\n",
      "promo_3: 2307.56\n",
      "promo_2: 1666.13\n",
      "promo_1: 1419.02\n",
      "promo_4: 1202.69\n",
      "==================================================\n",
      "Step 14\n",
      "==================================================\n",
      "Training until validation scores don't improve for 50 rounds.\n",
      "[50]\ttraining's l2: 0.424673\tvalid_1's l2: 0.424792\n",
      "[100]\ttraining's l2: 0.412572\tvalid_1's l2: 0.415371\n",
      "[150]\ttraining's l2: 0.409087\tvalid_1's l2: 0.414212\n",
      "[200]\ttraining's l2: 0.407458\tvalid_1's l2: 0.413845\n",
      "[250]\ttraining's l2: 0.406269\tvalid_1's l2: 0.413571\n",
      "[300]\ttraining's l2: 0.405294\tvalid_1's l2: 0.413507\n",
      "[350]\ttraining's l2: 0.404425\tvalid_1's l2: 0.413497\n",
      "Early stopping, best iteration is:\n",
      "[319]\ttraining's l2: 0.404951\tvalid_1's l2: 0.413424\n",
      "mean_14_2017: 2204252.88\n",
      "mean_7_2017: 637532.89\n",
      "mean_3_2017: 294400.81\n",
      "promo_13: 188805.49\n",
      "promo_14_2017: 48903.87\n",
      "promo_12: 14636.31\n",
      "promo_14: 14418.49\n",
      "promo_6: 13514.03\n",
      "promo_10: 10371.92\n",
      "promo_9: 6203.03\n",
      "promo_7: 5976.85\n",
      "promo_0: 5537.58\n",
      "promo_8: 4816.48\n",
      "promo_15: 4104.16\n",
      "promo_11: 3981.07\n",
      "promo_2: 2367.04\n",
      "promo_1: 1767.85\n",
      "promo_5: 1491.46\n",
      "promo_4: 1365.14\n",
      "promo_3: 1214.20\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================================================\n",
      "Step 15\n",
      "==================================================\n",
      "Training until validation scores don't improve for 50 rounds.\n",
      "[50]\ttraining's l2: 0.410919\tvalid_1's l2: 0.410371\n",
      "[100]\ttraining's l2: 0.397115\tvalid_1's l2: 0.398748\n",
      "[150]\ttraining's l2: 0.394313\tvalid_1's l2: 0.397684\n",
      "[200]\ttraining's l2: 0.392711\tvalid_1's l2: 0.397309\n",
      "[250]\ttraining's l2: 0.391556\tvalid_1's l2: 0.397216\n",
      "[300]\ttraining's l2: 0.390632\tvalid_1's l2: 0.397001\n",
      "[350]\ttraining's l2: 0.389878\tvalid_1's l2: 0.396751\n",
      "[400]\ttraining's l2: 0.389201\tvalid_1's l2: 0.396668\n",
      "[450]\ttraining's l2: 0.388549\tvalid_1's l2: 0.396577\n",
      "Early stopping, best iteration is:\n",
      "[443]\ttraining's l2: 0.388637\tvalid_1's l2: 0.396544\n",
      "mean_14_2017: 2325703.66\n",
      "mean_7_2017: 697625.27\n",
      "mean_3_2017: 244009.25\n",
      "promo_14: 232375.05\n",
      "promo_14_2017: 50039.63\n",
      "promo_7: 32257.52\n",
      "promo_0: 29105.04\n",
      "promo_15: 20072.46\n",
      "promo_13: 11033.72\n",
      "promo_9: 9036.77\n",
      "promo_12: 8426.44\n",
      "promo_10: 6103.29\n",
      "promo_2: 4641.07\n",
      "promo_6: 4357.55\n",
      "promo_11: 3179.79\n",
      "promo_8: 2955.70\n",
      "promo_4: 2468.38\n",
      "promo_1: 1808.81\n",
      "promo_5: 1281.50\n",
      "promo_3: 943.46\n",
      "==================================================\n",
      "Step 16\n",
      "==================================================\n",
      "Training until validation scores don't improve for 50 rounds.\n",
      "[50]\ttraining's l2: 0.419219\tvalid_1's l2: 0.432307\n",
      "[100]\ttraining's l2: 0.408384\tvalid_1's l2: 0.423714\n",
      "[150]\ttraining's l2: 0.405477\tvalid_1's l2: 0.422287\n",
      "[200]\ttraining's l2: 0.403823\tvalid_1's l2: 0.421915\n",
      "[250]\ttraining's l2: 0.402596\tvalid_1's l2: 0.421392\n",
      "[300]\ttraining's l2: 0.401565\tvalid_1's l2: 0.421407\n",
      "Early stopping, best iteration is:\n",
      "[260]\ttraining's l2: 0.402333\tvalid_1's l2: 0.421319\n",
      "mean_14_2017: 2017786.49\n",
      "mean_7_2017: 597065.44\n",
      "mean_3_2017: 209361.65\n",
      "promo_15: 183397.47\n",
      "promo_14_2017: 49204.78\n",
      "promo_14: 9435.88\n",
      "promo_7: 4710.27\n",
      "promo_10: 4474.12\n",
      "promo_9: 4468.63\n",
      "promo_0: 4304.73\n",
      "promo_13: 3390.71\n",
      "promo_12: 3341.79\n",
      "promo_8: 2719.90\n",
      "promo_11: 2431.73\n",
      "promo_1: 2089.00\n",
      "promo_2: 1943.76\n",
      "promo_3: 1874.32\n",
      "promo_6: 1558.04\n",
      "promo_4: 1111.01\n",
      "promo_5: 1062.91\n"
     ]
    }
   ],
   "source": [
    "for i in range(16):\n",
    "    print(\"=\" * 50)\n",
    "    print(\"Step %d\" % (i+1))\n",
    "    print(\"=\" * 50)\n",
    "    dtrain = lgb.Dataset(\n",
    "        X_train, label=y_train[:, i],\n",
    "        categorical_feature=cate_vars,\n",
    "        weight=pd.concat([items[\"perishable\"]] * 4) * 0.25 + 1\n",
    "    )\n",
    "    dval = lgb.Dataset(\n",
    "        X_val, label=y_val[:, i], reference=dtrain,\n",
    "        weight=items[\"perishable\"] * 0.25 + 1,\n",
    "        categorical_feature=cate_vars)\n",
    "    bst = lgb.train(\n",
    "        params, dtrain, num_boost_round=MAX_ROUNDS,\n",
    "        valid_sets=[dtrain, dval], early_stopping_rounds=50, verbose_eval=50\n",
    "    )\n",
    "    print(\"\\n\".join((\"%s: %.2f\" % x) for x in sorted(\n",
    "        zip(X_train.columns, bst.feature_importance(\"gain\")),\n",
    "        key=lambda x: x[1], reverse=True\n",
    "    )))\n",
    "    val_pred.append(bst.predict(\n",
    "        X_val, num_iteration=bst.best_iteration or MAX_ROUNDS))\n",
    "    test_pred.append(bst.predict(\n",
    "        X_test, num_iteration=bst.best_iteration or MAX_ROUNDS))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation mse: 0.413994342653\n"
     ]
    }
   ],
   "source": [
    "print(\"Validation mse:\", mean_squared_error(\n",
    "    y_val, np.array(val_pred).transpose()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Making submission...\n"
     ]
    }
   ],
   "source": [
    "print(\"Making submission...\")\n",
    "y_test = np.array(test_pred).transpose()\n",
    "df_preds = pd.DataFrame(\n",
    "    y_test, index=df_2017.index,\n",
    "    columns=pd.date_range(\"2017-08-16\", periods=16)\n",
    ").stack().to_frame(\"unit_sales\")\n",
    "df_preds.index.set_names([\"store_nbr\", \"item_nbr\", \"date\"], inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submission = df_test[[\"id\"]].join(df_preds, how=\"left\").fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "ename": "PermissionError",
     "evalue": "[Errno 13] Permission denied: 'lgb.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mPermissionError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-114-858473924a67>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0msubmission\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"unit_sales\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexpm1\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msubmission\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"unit_sales\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0msubmission\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'lgb.csv'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfloat_format\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'%.4f'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mto_csv\u001b[1;34m(self, path_or_buf, sep, na_rep, float_format, columns, header, index, index_label, mode, encoding, compression, quoting, quotechar, line_terminator, chunksize, tupleize_cols, date_format, doublequote, escapechar, decimal)\u001b[0m\n\u001b[0;32m   1522\u001b[0m                                      \u001b[0mdoublequote\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdoublequote\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1523\u001b[0m                                      escapechar=escapechar, decimal=decimal)\n\u001b[1;32m-> 1524\u001b[1;33m         \u001b[0mformatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1525\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1526\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mpath_or_buf\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\formats\\format.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1635\u001b[0m             f, handles = _get_handle(self.path_or_buf, self.mode,\n\u001b[0;32m   1636\u001b[0m                                      \u001b[0mencoding\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1637\u001b[1;33m                                      compression=self.compression)\n\u001b[0m\u001b[0;32m   1638\u001b[0m             \u001b[0mclose\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1639\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36m_get_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text)\u001b[0m\n\u001b[0;32m    388\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mencoding\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    389\u001b[0m             \u001b[1;31m# Python 3 and encoding\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 390\u001b[1;33m             \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath_or_buf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    391\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mis_text\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    392\u001b[0m             \u001b[1;31m# Python 3 and no explicit encoding\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mPermissionError\u001b[0m: [Errno 13] Permission denied: 'lgb.csv'"
     ]
    }
   ],
   "source": [
    "submission[\"unit_sales\"] = np.clip(np.expm1(submission[\"unit_sales\"]), 0, 1000)\n",
    "submission.to_csv('lgb.csv', float_format='%.4f', index=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 12. Conclusion\n",
    "We are getting following results on applying data set on different models:\n",
    "\n",
    "Model                                    \n",
    "R2 Score\n",
    "\n",
    "Linear Regression                  \n",
    "0.354\n",
    "\n",
    "Decision Tree Regression           \n",
    "0.705\n",
    "\n",
    "Extra Tree Regression              \n",
    "0.825\n",
    "\n",
    "Random Forest Regression           \n",
    "0.836\n",
    "\n",
    "Gradient Boosting Regression       \n",
    "0.836\n",
    "\n",
    "XG Boost                           \n",
    "0.797\n",
    "\n",
    "LGBM                               \n",
    "0.759"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 13. References and Attributions\n",
    "\n",
    "[1] Cui, G., Wong, M. L., & Lui, H. K. (2006). Machine learning for direct marketing response models:\n",
    "Bayesian networks with evolutionary programming.Management Science, 52(4), 597-612\n",
    "\n",
    "[2] Taylor, E. L. (2014). Predicting Consumer Behavior. Research World, 2014(46), 67-68\n",
    "\n",
    "[3] Morwitz, V. G., Steckel, J. H., & Gupta, A. (2007). When do purchase intentions predict sales?.\n",
    "International Journal of Forecasting, 23(3), 347-364\n",
    "\n",
    "[4] https://www.kaggle.com/c/favorita-grocery-sales-forecasting/data\n",
    "\n",
    "[5] https://en.wikipedia.org/wiki/Xgboost\n",
    "\n",
    "[6] https://en.wikipedia.org/wiki/Random_forest\n",
    "\n",
    "[7] https://en.wikipedia.org/wiki/Decision_tree\n",
    "\n",
    "[8]https://www.analyticsvidhya.com/blog/2017/06/which-algorithm-takes-the-crown-light-gbm-vsxgboost/\n",
    "\n",
    "[9] https://www.tutorialspoint.com/sales_forecasting/sales_forecasting_discussion.html\n",
    "\n",
    "The text in the document by Source Wikipedia is licensed under CC BY 3.0 https://creativecommons.org/licenses/by/3.0/us/\n",
    "\n",
    "The text in the document by Analytics Vidhya is licensed under CC BY 3.0 https://creativecommons.org/licenses/by/3.0/us/\n",
    "\n",
    "The text in the document by towards data science is licensed under CC BY 3.0 https://creativecommons.org/licenses/by/3.0/us/\n",
    "\n",
    "The code in the document by Source Kaggle is licensed under the MIT License https://opensource.org/licenses/MIT"
   ]
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