Walmart_Sales_Forecast_XGB-final3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pandas import read_csv\n",
    "from datetime import datetime\n",
    "from sklearn import tree\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "holidayEventData = pd.read_excel('Events_HolidaysData.xlsx')\n",
    "macroEconomicData = pd.read_excel('MacroEconomicData.xlsx')\n",
    "weatherData = pd.read_excel('WeatherData.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def encoding(pc):\n",
    "    pc = pc.strip().lower()\n",
    "    if pc == \"womenclothing\":\n",
    "        return 0\n",
    "    if pc == \"menclothing\":\n",
    "        return 1\n",
    "    return 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>ProductCategory</th>\n",
       "      <th>Sales(In ThousandDollars)</th>\n",
       "      <th>WeatherEvent</th>\n",
       "      <th>Monthly Nominal GDP Index (inMillion$)</th>\n",
       "      <th>No.of Holidays</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1755</td>\n",
       "      <td>10</td>\n",
       "      <td>14421.753</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>524</td>\n",
       "      <td>10</td>\n",
       "      <td>14421.753</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>936</td>\n",
       "      <td>10</td>\n",
       "      <td>14421.753</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1729</td>\n",
       "      <td>6</td>\n",
       "      <td>14389.200</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>496</td>\n",
       "      <td>6</td>\n",
       "      <td>14389.200</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Month  ProductCategory  Sales(In ThousandDollars)  WeatherEvent  \\\n",
       "0  2009      1                0                       1755            10   \n",
       "1  2009      1                1                        524            10   \n",
       "2  2009      1                2                        936            10   \n",
       "3  2009      2                0                       1729             6   \n",
       "4  2009      2                1                        496             6   \n",
       "\n",
       "   Monthly Nominal GDP Index (inMillion$)  No.of Holidays  \n",
       "0                               14421.753               2  \n",
       "1                               14421.753               2  \n",
       "2                               14421.753               2  \n",
       "3                               14389.200               2  \n",
       "4                               14389.200               2  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = read_csv('Train_Walmart_final3.csv', converters={2:encoding})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>ProductCategory</th>\n",
       "      <th>WeatherEvent</th>\n",
       "      <th>Monthly Nominal GDP Index (inMillion$)</th>\n",
       "      <th>No.of Holidays</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>17792.117</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>17792.117</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>17792.117</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>17931.046</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>17931.046</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Month  ProductCategory  WeatherEvent  \\\n",
       "0  2015      1                0            11   \n",
       "1  2015      1                1            11   \n",
       "2  2015      1                2            11   \n",
       "3  2015      2                0            11   \n",
       "4  2015      2                1            11   \n",
       "\n",
       "   Monthly Nominal GDP Index (inMillion$)  No.of Holidays  \n",
       "0                               17792.117               2  \n",
       "1                               17792.117               2  \n",
       "2                               17792.117               2  \n",
       "3                               17931.046               2  \n",
       "4                               17931.046               2  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = read_csv('Test_new3.csv', converters={2:encoding})\n",
    "df_test.drop(['Sales(In ThousandDollars)'],axis=1,inplace=True)\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>ProductCategory</th>\n",
       "      <th>WeatherEvent</th>\n",
       "      <th>Monthly Nominal GDP Index (inMillion$)</th>\n",
       "      <th>No.of Holidays</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>14421.753</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>14421.753</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>14421.753</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>14389.200</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>14389.200</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Month  ProductCategory  WeatherEvent  \\\n",
       "0  2009      1                0            10   \n",
       "1  2009      1                1            10   \n",
       "2  2009      1                2            10   \n",
       "3  2009      2                0             6   \n",
       "4  2009      2                1             6   \n",
       "\n",
       "   Monthly Nominal GDP Index (inMillion$)  No.of Holidays  \n",
       "0                               14421.753               2  \n",
       "1                               14421.753               2  \n",
       "2                               14421.753               2  \n",
       "3                               14389.200               2  \n",
       "4                               14389.200               2  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df['Sales(In ThousandDollars)'].fillna(df.mode().iloc[0], inplace = True)\n",
    "#df['Sales(In ThousandDollars)'].fillna(0, inplace=True)\n",
    "df_X_target = df[['Sales(In ThousandDollars)']]\n",
    "df_X_train = df.iloc[:,0:8]\n",
    "df_X_train.drop(['Sales(In ThousandDollars)'],axis=1,inplace=True)\n",
    "df_X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "walmart_data =pd.DataFrame(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = walmart_data.loc[(walmart_data.Year > 2013) \\\n",
    "                                             & (walmart_data.Year <= 2014)]\n",
    "train = walmart_data.loc[walmart_data.Year <= 2013]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>ProductCategory</th>\n",
       "      <th>WeatherEvent</th>\n",
       "      <th>Monthly Nominal GDP Index (inMillion$)</th>\n",
       "      <th>No.of Holidays</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>14421.753</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>14421.753</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>14421.753</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>14389.200</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>14389.200</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Month  ProductCategory  WeatherEvent  \\\n",
       "0  2009      1                0            10   \n",
       "1  2009      1                1            10   \n",
       "2  2009      1                2            10   \n",
       "3  2009      2                0             6   \n",
       "4  2009      2                1             6   \n",
       "\n",
       "   Monthly Nominal GDP Index (inMillion$)  No.of Holidays  \n",
       "0                               14421.753               2  \n",
       "1                               14421.753               2  \n",
       "2                               14421.753               2  \n",
       "3                               14389.200               2  \n",
       "4                               14389.200               2  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_target = train[['Sales(In ThousandDollars)']]\n",
    "y_target = test[['Sales(In ThousandDollars)']]\n",
    "X_train = train.iloc[:,0:8]\n",
    "X_train.drop(['Sales(In ThousandDollars)'],axis=1,inplace=True)\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>ProductCategory</th>\n",
       "      <th>WeatherEvent</th>\n",
       "      <th>Monthly Nominal GDP Index (inMillion$)</th>\n",
       "      <th>No.of Holidays</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>16955.609</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>16955.609</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>16955.609</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>2014</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>17028.586</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>2014</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>17028.586</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year  Month  ProductCategory  WeatherEvent  \\\n",
       "180  2014      1                0            10   \n",
       "181  2014      1                1            10   \n",
       "182  2014      1                2            10   \n",
       "183  2014      2                0            11   \n",
       "184  2014      2                1            11   \n",
       "\n",
       "     Monthly Nominal GDP Index (inMillion$)  No.of Holidays  \n",
       "180                               16955.609               2  \n",
       "181                               16955.609               2  \n",
       "182                               16955.609               2  \n",
       "183                               17028.586               2  \n",
       "184                               17028.586               2  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.drop(['Sales(In ThousandDollars)'],axis=1,inplace=True)\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = X_train\n",
    "x_target = X_target\n",
    "y_train = test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XGBoost Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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=3, 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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = XGBRegressor()\n",
    "model.fit(x_train,x_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2458,\n",
       " 556,\n",
       " 1072,\n",
       " 2921,\n",
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       " 1230,\n",
       " 4848,\n",
       " 1081,\n",
       " 1544]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict(y_train)\n",
    "y_pred = [int(round(value)) for value in y_pred]\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = df_X_train\n",
    "x_target = df_X_target\n",
    "y_train = df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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=3, 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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = XGBRegressor()\n",
    "model.fit(x_train,x_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(y_train)\n",
    "y_pred = [int(round(value)) for value in y_pred]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "submit = pd.DataFrame(y_target)\n",
    "submit.to_csv('submission_results_xgbr5.csv')"
   ]
  }
 ],
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