WALMART Sales Prediction
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WALMART Sales Prediction

Project period

10/09/2019 - 10/16/2019

Views

32

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WALMART Sales Prediction
WALMART Sales Prediction

Sales forecasting is the process or method of evaluating future sales. Perfect sales estimation forecasts enable almost every company to make informed business important decisions and predict short-term and long-term performance. Companies will be able to base their forecasts on past sales data or information, comparisons between different industries, and the economic trends. Prediction of sales gives an idea about how a company should be able to manage its workforce, cash flow, and resources. It will be easy for established firms to predict future sales based on past business data. forecasts have been a base for most Newly founded companies to focus on market research and competitive strategies to forecast their future business. Moreover, predictive sales data is playing a crucial role in businesses when focussing on acquiring investment capital. Sales forecasts perform a vital role to make a number of decisions, such as hiring and resource management to goal-setting and budgeting. Sales forecasts can also be considered as a powerful motivation tool. Sales forecasts will allow us to fix vital issues while there's still time to rectify them. According to research, it is noted that firms with perfect sales forecasts are 10% more likely to grow their revenue for the next crucial year.

Why: Problem statement

Recently information technology in this century is reaching a high level where large-scale data can be processed and easily studied to make sense or meaning of data where the normal approach is no more effective. Now, corporates need an entire view of their consumers; without that information, they will be missing the competitive edge of the market. retailers have to create the best promotions and offer to meet its sales and marketing goals, else they will fail the most important opportunities that the current market has to offer. Most of the time it is hard for the retail firms to grasp or to get a clear idea of the market condition since their stores are at different geographical locations. Big Data applications such as python enable these organizations to use past data to give a better forecast and prediction for the future year’s sales. It also helps retailers with analytical insights, specifically analyzing customers' desired products in a particular store at different locations. In this paper, we analyze the data sets of the world’s largest retailers, Walmart Store to determine the business drivers and predict sales. We have made use of Anaconda and Python to gain new information about consumer behaviors and help Walmart’s marketing efforts and their data-driven strategies through visual analytical representation data.

How: Solution description

The 2018 sales data for WalMart has been collected for 1564 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The aim is to build a predictive model and find out the sales of each product at a particular store using Numpy and Pandas.

 

Algorithms Used:

Linear Regression Model:

    Linear regression is a basic and commonly used type of predictive analysis. These regression estimates are used to explain the relationship between one dependent variable and one or more independent variables.

How is it different from competition

The major problem in the business market is that companies or organizations do not have the perfect future plans to develop their business. The unplanned way of making the business leads to the loss of business or other unwanted problems. I am going to choose and train the data using machine learning algorithms to predict sales. It is the perfect project for learning Data Analytics and apply machine learning algorithms to predict outlet production sales. We could put this model into production and turn it into an interactive that business users within the organization could use to forecast sales for any store or department for however many weeks they wanted.

In this paper, we study the usage of  Anaconda and Python version 3.6 for sales prediction. The main goal of this paper is to consider important approaches using the above-said tools for sales forecasting. This method can be used to make sales predictions when there is a small quantity of data for specific sales time series when a new product is launched. A stacking approach for building regression has been discussed. The results explain that by using stacking ideas, we will be able to improve the performance of predictive models for sales or time series forecasting. Discovering problems through forecasting has a huge impact on business plans

Who are your customers

Corporate companies, Businessmen and all others who need to predict their future sales and business growth.

Project Phases and Schedule

Phase 1: Data Exploration

Phase 2: Data Cleaning

Phase 3: Feature Engineering

Phase 4: Creating Models

Resources Required

Tools used:  Anaconda - Python version 3.6.

 

Download:
Project Code Code copy
/* Your file Name : Walmart Sale prediction.ipynb */
/* Your coding Language : python */
/* Your code snippet start here */
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    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
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      "/anaconda/lib/python3.6/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
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   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as stats\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "import seaborn as sns\n",
    "sns.set_style(\"whitegrid\")\n",
    "from sklearn import datasets, linear_model\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
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   "cell_type": "code",
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   "source": [
    "train=pd.read_csv('....../Walmart/train.csv')\n",
    "test=pd.read_csv('......./Walmart/test.csv')\n",
    "store=pd.read_csv('....../Walmart/stores.csv')\n",
    "feature=pd.read_csv('...../Walmart/features.csv')"
   ]
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       "4      1  2010-03-05        46.50       2.625        NaN        NaN   \n",
       "\n",
       "   MarkDown3  MarkDown4  MarkDown5         CPI  Unemployment IsHoliday  \n",
       "0        NaN        NaN        NaN  211.096358         8.106     False  \n",
       "1        NaN        NaN        NaN  211.242170         8.106      True  \n",
       "2        NaN        NaN        NaN  211.289143         8.106     False  \n",
       "3        NaN        NaN        NaN  211.319643         8.106     False  \n",
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       "   Store  Dept        Date  Weekly_Sales IsHoliday_x  Temperature  Fuel_Price  \\\n",
       "0      1     1  2010-02-05      24924.50       False        42.31       2.572   \n",
       "1      1     2  2010-02-05      50605.27       False        42.31       2.572   \n",
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       "4      1     5  2010-02-05      32229.38       False        42.31       2.572   \n",
       "\n",
       "   MarkDown1  MarkDown2  MarkDown3  MarkDown4  MarkDown5         CPI  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN  211.096358   \n",
       "1        NaN        NaN        NaN        NaN        NaN  211.096358   \n",
       "2        NaN        NaN        NaN        NaN        NaN  211.096358   \n",
       "3        NaN        NaN        NaN        NaN        NaN  211.096358   \n",
       "4        NaN        NaN        NaN        NaN        NaN  211.096358   \n",
       "\n",
       "   Unemployment IsHoliday_y  \n",
       "0         8.106       False  \n",
       "1         8.106       False  \n",
       "2         8.106       False  \n",
       "3         8.106       False  \n",
       "4         8.106       False  "
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       "      <td>74.280000</td>\n",
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       "      <td>0.458515</td>\n",
       "      <td>2.472</td>\n",
       "      <td>2.933000</td>\n",
       "      <td>3.45200</td>\n",
       "      <td>3.738000</td>\n",
       "      <td>4.468000</td>\n",
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       "      <td>150681.0</td>\n",
       "      <td>7246.420196</td>\n",
       "      <td>8291.221345</td>\n",
       "      <td>0.270</td>\n",
       "      <td>2240.270000</td>\n",
       "      <td>5347.45000</td>\n",
       "      <td>9210.900000</td>\n",
       "      <td>88646.760000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MarkDown2</th>\n",
       "      <td>111248.0</td>\n",
       "      <td>3334.628621</td>\n",
       "      <td>9475.357325</td>\n",
       "      <td>-265.760</td>\n",
       "      <td>41.600000</td>\n",
       "      <td>192.00000</td>\n",
       "      <td>1926.940000</td>\n",
       "      <td>104519.540000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MarkDown3</th>\n",
       "      <td>137091.0</td>\n",
       "      <td>1439.421384</td>\n",
       "      <td>9623.078290</td>\n",
       "      <td>-29.100</td>\n",
       "      <td>5.080000</td>\n",
       "      <td>24.60000</td>\n",
       "      <td>103.990000</td>\n",
       "      <td>141630.610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MarkDown4</th>\n",
       "      <td>134967.0</td>\n",
       "      <td>3383.168256</td>\n",
       "      <td>6292.384031</td>\n",
       "      <td>0.220</td>\n",
       "      <td>504.220000</td>\n",
       "      <td>1481.31000</td>\n",
       "      <td>3595.040000</td>\n",
       "      <td>67474.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MarkDown5</th>\n",
       "      <td>151432.0</td>\n",
       "      <td>4628.975079</td>\n",
       "      <td>5962.887455</td>\n",
       "      <td>135.160</td>\n",
       "      <td>1878.440000</td>\n",
       "      <td>3359.45000</td>\n",
       "      <td>5563.800000</td>\n",
       "      <td>108519.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CPI</th>\n",
       "      <td>421570.0</td>\n",
       "      <td>171.201947</td>\n",
       "      <td>39.159276</td>\n",
       "      <td>126.064</td>\n",
       "      <td>132.022667</td>\n",
       "      <td>182.31878</td>\n",
       "      <td>212.416993</td>\n",
       "      <td>227.232807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unemployment</th>\n",
       "      <td>421570.0</td>\n",
       "      <td>7.960289</td>\n",
       "      <td>1.863296</td>\n",
       "      <td>3.879</td>\n",
       "      <td>6.891000</td>\n",
       "      <td>7.86600</td>\n",
       "      <td>8.572000</td>\n",
       "      <td>14.313000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 count          mean           std       min          25%  \\\n",
       "Store         421570.0     22.200546     12.785297     1.000    11.000000   \n",
       "Dept          421570.0     44.260317     30.492054     1.000    18.000000   \n",
       "Weekly_Sales  421570.0  15981.258123  22711.183519 -4988.940  2079.650000   \n",
       "Temperature   421570.0     60.090059     18.447931    -2.060    46.680000   \n",
       "Fuel_Price    421570.0      3.361027      0.458515     2.472     2.933000   \n",
       "MarkDown1     150681.0   7246.420196   8291.221345     0.270  2240.270000   \n",
       "MarkDown2     111248.0   3334.628621   9475.357325  -265.760    41.600000   \n",
       "MarkDown3     137091.0   1439.421384   9623.078290   -29.100     5.080000   \n",
       "MarkDown4     134967.0   3383.168256   6292.384031     0.220   504.220000   \n",
       "MarkDown5     151432.0   4628.975079   5962.887455   135.160  1878.440000   \n",
       "CPI           421570.0    171.201947     39.159276   126.064   132.022667   \n",
       "Unemployment  421570.0      7.960289      1.863296     3.879     6.891000   \n",
       "\n",
       "                     50%           75%            max  \n",
       "Store           22.00000     33.000000      45.000000  \n",
       "Dept            37.00000     74.000000      99.000000  \n",
       "Weekly_Sales  7612.03000  20205.852500  693099.360000  \n",
       "Temperature     62.09000     74.280000     100.140000  \n",
       "Fuel_Price       3.45200      3.738000       4.468000  \n",
       "MarkDown1     5347.45000   9210.900000   88646.760000  \n",
       "MarkDown2      192.00000   1926.940000  104519.540000  \n",
       "MarkDown3       24.60000    103.990000  141630.610000  \n",
       "MarkDown4     1481.31000   3595.040000   67474.850000  \n",
       "MarkDown5     3359.45000   5563.800000  108519.280000  \n",
       "CPI            182.31878    212.416993     227.232807  \n",
       "Unemployment     7.86600      8.572000      14.313000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_df.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from datetime import datetime as dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2010-02-05\n",
       "1   2010-02-05\n",
       "2   2010-02-05\n",
       "3   2010-02-05\n",
       "4   2010-02-05\n",
       "Name: DateTimeObj, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_df['DateTimeObj']=[dt.strptime(x,'%Y-%m-%d') for x in list(merge_df['Date'])]\n",
    "merge_df['DateTimeObj'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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t24iIqKuri6666irq7u6mK664IvOO1atX01NPPUXPPvssPfHEE5n706ZN6ydY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\n8RAgIiIS2C8zLHQH6ShHCQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d628240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(merge_df[(merge_df.Store==1)].DateTimeObj, merge_df[(merge_df.Store==1)].Weekly_Sales, 'ro')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store  Date      \n",
       "1      2010-02-05    1643690.90\n",
       "       2010-02-12    1641957.44\n",
       "       2010-02-19    1611968.17\n",
       "       2010-02-26    1409727.59\n",
       "       2010-03-05    1554806.68\n",
       "Name: Weekly_Sales, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weeklysales=merge_df.groupby(['Store','Date'])['Weekly_Sales'].apply(lambda x:np.sum(x))\n",
    "weeklysales[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store  Dept\n",
       "1      1       3219405.18\n",
       "       2       6592598.93\n",
       "       3       1880518.36\n",
       "       4       5285874.09\n",
       "       5       3468885.58\n",
       "Name: Weekly_Sales, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weeklysaledept=merge_df.groupby(['Store','Dept'])['Weekly_Sales'].apply(lambda x:np.sum(x))\n",
    "weeklysaledept[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>Date</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-05</td>\n",
       "      <td>1643690.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-12</td>\n",
       "      <td>1641957.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>1611968.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-03-05</td>\n",
       "      <td>1554806.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store        Date  Weekly_Sales\n",
       "0      1  2010-02-05    1643690.90\n",
       "1      1  2010-02-12    1641957.44\n",
       "2      1  2010-02-19    1611968.17\n",
       "3      1  2010-02-26    1409727.59\n",
       "4      1  2010-03-05    1554806.68"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weeklyscale=weeklysales.reset_index()\n",
    "weeklyscale[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Store</th>\n",
       "      <th>Date</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>MarkDown1</th>\n",
       "      <th>MarkDown2</th>\n",
       "      <th>MarkDown3</th>\n",
       "      <th>MarkDown4</th>\n",
       "      <th>MarkDown5</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "      <th>IsHoliday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>8.106</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>1611968.17</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>211.289143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>2010-02-26</td>\n",
       "      <td>1409727.59</td>\n",
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       "      <td>2.561</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-03-05</td>\n",
       "      <td>1554806.68</td>\n",
       "      <td>46.50</td>\n",
       "      <td>2.625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>211.350143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store        Date  Weekly_Sales  Temperature  Fuel_Price  MarkDown1  \\\n",
       "0      1  2010-02-05    1643690.90        42.31       2.572        NaN   \n",
       "1      1  2010-02-12    1641957.44        38.51       2.548        NaN   \n",
       "2      1  2010-02-19    1611968.17        39.93       2.514        NaN   \n",
       "3      1  2010-02-26    1409727.59        46.63       2.561        NaN   \n",
       "4      1  2010-03-05    1554806.68        46.50       2.625        NaN   \n",
       "\n",
       "   MarkDown2  MarkDown3  MarkDown4  MarkDown5         CPI  Unemployment  \\\n",
       "0        NaN        NaN        NaN        NaN  211.096358         8.106   \n",
       "1        NaN        NaN        NaN        NaN  211.242170         8.106   \n",
       "2        NaN        NaN        NaN        NaN  211.289143         8.106   \n",
       "3        NaN        NaN        NaN        NaN  211.319643         8.106   \n",
       "4        NaN        NaN        NaN        NaN  211.350143         8.106   \n",
       "\n",
       "  IsHoliday  \n",
       "0     False  \n",
       "1      True  \n",
       "2     False  \n",
       "3     False  \n",
       "4     False  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "walmartstore=pd.merge(weeklyscale, feature, on=['Store', 'Date'], how='inner')\n",
    "walmartstore.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "walmartstoredf = walmartstore.iloc[:, list(range(5)) + list(range(10,13))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Store</th>\n",
       "      <th>Date</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
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       "      <td>46.50</td>\n",
       "      <td>2.625</td>\n",
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       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store        Date  Weekly_Sales  Temperature  Fuel_Price         CPI  \\\n",
       "0      1  2010-02-05    1643690.90        42.31       2.572  211.096358   \n",
       "1      1  2010-02-12    1641957.44        38.51       2.548  211.242170   \n",
       "2      1  2010-02-19    1611968.17        39.93       2.514  211.289143   \n",
       "3      1  2010-02-26    1409727.59        46.63       2.561  211.319643   \n",
       "4      1  2010-03-05    1554806.68        46.50       2.625  211.350143   \n",
       "\n",
       "   Unemployment IsHoliday  \n",
       "0         8.106     False  \n",
       "1         8.106      True  \n",
       "2         8.106     False  \n",
       "3         8.106     False  \n",
       "4         8.106     False  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "walmartstoredf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "walmartstoredf['DateTimeObj'] = [dt.strptime(x, '%Y-%m-%d') for x in list(walmartstoredf['Date'])]\n",
    "weekNo=walmartstoredf.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weekNo = [(x - walmartstoredf['DateTimeObj'][0]) for x in list(walmartstoredf['DateTimeObj'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "walmartstoredf['Week'] = [np.timedelta64(x, 'D').astype(int)/7 for x in weekNo]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>IsHoliday</th>\n",
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       "      <td>211.319643</td>\n",
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       "      <td>2010-02-26</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-03-05</td>\n",
       "      <td>1554806.68</td>\n",
       "      <td>46.50</td>\n",
       "      <td>2.625</td>\n",
       "      <td>211.350143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>2010-03-05</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store        Date  Weekly_Sales  Temperature  Fuel_Price         CPI  \\\n",
       "0      1  2010-02-05    1643690.90        42.31       2.572  211.096358   \n",
       "1      1  2010-02-12    1641957.44        38.51       2.548  211.242170   \n",
       "2      1  2010-02-19    1611968.17        39.93       2.514  211.289143   \n",
       "3      1  2010-02-26    1409727.59        46.63       2.561  211.319643   \n",
       "4      1  2010-03-05    1554806.68        46.50       2.625  211.350143   \n",
       "\n",
       "   Unemployment IsHoliday DateTimeObj  Week  \n",
       "0         8.106     False  2010-02-05   0.0  \n",
       "1         8.106      True  2010-02-12   1.0  \n",
       "2         8.106     False  2010-02-19   2.0  \n",
       "3         8.106     False  2010-02-26   3.0  \n",
       "4         8.106     False  2010-03-05   4.0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "walmartstoredf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UmOfyuGVlZWPidnd3o7i4GJMmTRrzPDdt6RkPVVVVXPG8JJlM5kU+9aBy+IdCKAOQp+WY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111529eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(walmartstoredf.DateTimeObj, walmartstoredf.Weekly_Sales, 'ro')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "walmartstoredf['IsHolidayInt'] = [int(x) for x in list(walmartstoredf.IsHoliday)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>Date</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "      <th>IsHoliday</th>\n",
       "      <th>DateTimeObj</th>\n",
       "      <th>Week</th>\n",
       "      <th>IsHolidayInt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-05</td>\n",
       "      <td>1643690.90</td>\n",
       "      <td>42.31</td>\n",
       "      <td>2.572</td>\n",
       "      <td>211.096358</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>2010-02-05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-12</td>\n",
       "      <td>1641957.44</td>\n",
       "      <td>38.51</td>\n",
       "      <td>2.548</td>\n",
       "      <td>211.242170</td>\n",
       "      <td>8.106</td>\n",
       "      <td>True</td>\n",
       "      <td>2010-02-12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>1611968.17</td>\n",
       "      <td>39.93</td>\n",
       "      <td>2.514</td>\n",
       "      <td>211.289143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-26</td>\n",
       "      <td>1409727.59</td>\n",
       "      <td>46.63</td>\n",
       "      <td>2.561</td>\n",
       "      <td>211.319643</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>2010-02-26</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-03-05</td>\n",
       "      <td>1554806.68</td>\n",
       "      <td>46.50</td>\n",
       "      <td>2.625</td>\n",
       "      <td>211.350143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>2010-03-05</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store        Date  Weekly_Sales  Temperature  Fuel_Price         CPI  \\\n",
       "0      1  2010-02-05    1643690.90        42.31       2.572  211.096358   \n",
       "1      1  2010-02-12    1641957.44        38.51       2.548  211.242170   \n",
       "2      1  2010-02-19    1611968.17        39.93       2.514  211.289143   \n",
       "3      1  2010-02-26    1409727.59        46.63       2.561  211.319643   \n",
       "4      1  2010-03-05    1554806.68        46.50       2.625  211.350143   \n",
       "\n",
       "   Unemployment IsHoliday DateTimeObj  Week  IsHolidayInt  \n",
       "0         8.106     False  2010-02-05   0.0             0  \n",
       "1         8.106      True  2010-02-12   1.0             1  \n",
       "2         8.106     False  2010-02-19   2.0             0  \n",
       "3         8.106     False  2010-02-26   3.0             0  \n",
       "4         8.106     False  2010-03-05   4.0             0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "walmartstoredf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "       35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "walmartstoredf.Store.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_WM, test_WM = train_test_split(walmartstoredf, test_size=0.3,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RESUuZsv+5Zdfxs2bN9Ha2hoeXNfQ0IAXX3wRzc3NyM/PR1lZGTIyMlBTUwOPxwMhBNat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\nJnsiIiLJMdkTERFJjsmeiIhIcv8Pr8dUMGPhIx0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d5c32b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(walmartstoredf[(walmartstoredf.Store==1)].Week, walmartstoredf[(walmartstoredf.Store==1)].Weekly_Sales, 'ro')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "XTrain = train_WM[['Temperature', 'Fuel_Price', 'CPI', 'Unemployment', 'Week', 'IsHolidayInt']]\n",
    "YTrain = train_WM['Weekly_Sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "XTest = test_WM[['Temperature', 'Fuel_Price', 'CPI', 'Unemployment', 'Week', 'IsHolidayInt']]\n",
    "YTest = test_WM['Weekly_Sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=True)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wmLinear = linear_model.LinearRegression(normalize=True)\n",
    "wmLinear.fit(XTrain, YTrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  -308.15266303,  -8747.78318227,  -1668.0479916 , -45058.54212884,\n",
       "         -148.87164691,  95666.79893475])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wmLinear.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Performance on the test data sets\n",
    "YHatTest = wmLinear.predict(XTest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xx0YNzmKxjOc9iVhqi8eT2uMD1B8j44suZ84kIjf3dre2t99uw5e+dA0qC3WY2rZhOCga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\niBRj4kBERESK/X9Lw3NezB/P3AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x102330278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(YTest, YHatTest,'ro')\n",
    "plt.plot(YTest, YTest,'b-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "       35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "walmartstoredf['Store'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Store_Dummies = pd.get_dummies(walmartstoredf.Store, prefix='Store').iloc[:,1:]\n",
    "walmartstoredf = pd.concat([walmartstoredf, Store_Dummies], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>Date</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "      <th>IsHoliday</th>\n",
       "      <th>DateTimeObj</th>\n",
       "      <th>Week</th>\n",
       "      <th>...</th>\n",
       "      <th>Store_36</th>\n",
       "      <th>Store_37</th>\n",
       "      <th>Store_38</th>\n",
       "      <th>Store_39</th>\n",
       "      <th>Store_40</th>\n",
       "      <th>Store_41</th>\n",
       "      <th>Store_42</th>\n",
       "      <th>Store_43</th>\n",
       "      <th>Store_44</th>\n",
       "      <th>Store_45</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-05</td>\n",
       "      <td>1643690.90</td>\n",
       "      <td>42.31</td>\n",
       "      <td>2.572</td>\n",
       "      <td>211.096358</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>2010-02-05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-12</td>\n",
       "      <td>1641957.44</td>\n",
       "      <td>38.51</td>\n",
       "      <td>2.548</td>\n",
       "      <td>211.242170</td>\n",
       "      <td>8.106</td>\n",
       "      <td>True</td>\n",
       "      <td>2010-02-12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>1611968.17</td>\n",
       "      <td>39.93</td>\n",
       "      <td>2.514</td>\n",
       "      <td>211.289143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-26</td>\n",
       "      <td>1409727.59</td>\n",
       "      <td>46.63</td>\n",
       "      <td>2.561</td>\n",
       "      <td>211.319643</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>2010-02-26</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-03-05</td>\n",
       "      <td>1554806.68</td>\n",
       "      <td>46.50</td>\n",
       "      <td>2.625</td>\n",
       "      <td>211.350143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>2010-03-05</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store        Date  Weekly_Sales  Temperature  Fuel_Price         CPI  \\\n",
       "0      1  2010-02-05    1643690.90        42.31       2.572  211.096358   \n",
       "1      1  2010-02-12    1641957.44        38.51       2.548  211.242170   \n",
       "2      1  2010-02-19    1611968.17        39.93       2.514  211.289143   \n",
       "3      1  2010-02-26    1409727.59        46.63       2.561  211.319643   \n",
       "4      1  2010-03-05    1554806.68        46.50       2.625  211.350143   \n",
       "\n",
       "   Unemployment IsHoliday DateTimeObj  Week    ...     Store_36  Store_37  \\\n",
       "0         8.106     False  2010-02-05   0.0    ...            0         0   \n",
       "1         8.106      True  2010-02-12   1.0    ...            0         0   \n",
       "2         8.106     False  2010-02-19   2.0    ...            0         0   \n",
       "3         8.106     False  2010-02-26   3.0    ...            0         0   \n",
       "4         8.106     False  2010-03-05   4.0    ...            0         0   \n",
       "\n",
       "   Store_38  Store_39  Store_40  Store_41  Store_42  Store_43  Store_44  \\\n",
       "0         0         0         0         0         0         0         0   \n",
       "1         0         0         0         0         0         0         0   \n",
       "2         0         0         0         0         0         0         0   \n",
       "3         0         0         0         0         0         0         0   \n",
       "4         0         0         0         0         0         0         0   \n",
       "\n",
       "   Store_45  \n",
       "0         0  \n",
       "1         0  \n",
       "2         0  \n",
       "3         0  \n",
       "4         0  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "walmartstoredf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_WM, test_WM = train_test_split(walmartstoredf, test_size=0.3,random_state=42)\n",
    "XTrain = train_WM.iloc[:,([3,4,5,6] + [9,10]) + list(range(11,walmartstoredf.shape[1]))]\n",
    "yTrain = train_WM.Weekly_Sales\n",
    "                                                    \n",
    "XTest = test_WM.iloc[:,([3,4,5,6] + [9,10]) + list(range(11,walmartstoredf.shape[1]))]\n",
    "yTest=test_WM.Weekly_Sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "      <th>Week</th>\n",
       "      <th>IsHolidayInt</th>\n",
       "      <th>Store_2</th>\n",
       "      <th>Store_3</th>\n",
       "      <th>Store_4</th>\n",
       "      <th>Store_5</th>\n",
       "      <th>...</th>\n",
       "      <th>Store_36</th>\n",
       "      <th>Store_37</th>\n",
       "      <th>Store_38</th>\n",
       "      <th>Store_39</th>\n",
       "      <th>Store_40</th>\n",
       "      <th>Store_41</th>\n",
       "      <th>Store_42</th>\n",
       "      <th>Store_43</th>\n",
       "      <th>Store_44</th>\n",
       "      <th>Store_45</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1288</th>\n",
       "      <td>49.96</td>\n",
       "      <td>2.828</td>\n",
       "      <td>126.496258</td>\n",
       "      <td>9.765</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1115</th>\n",
       "      <td>65.19</td>\n",
       "      <td>3.891</td>\n",
       "      <td>225.062571</td>\n",
       "      <td>5.679</td>\n",
       "      <td>114.0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>65.30</td>\n",
       "      <td>2.808</td>\n",
       "      <td>211.038853</td>\n",
       "      <td>6.465</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3647</th>\n",
       "      <td>56.94</td>\n",
       "      <td>3.851</td>\n",
       "      <td>135.265267</td>\n",
       "      <td>7.818</td>\n",
       "      <td>72.0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1564</th>\n",
       "      <td>86.49</td>\n",
       "      <td>3.638</td>\n",
       "      <td>225.829306</td>\n",
       "      <td>6.334</td>\n",
       "      <td>134.0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Temperature  Fuel_Price         CPI  Unemployment   Week  IsHolidayInt  \\\n",
       "1288        49.96       2.828  126.496258         9.765    1.0             1   \n",
       "1115        65.19       3.891  225.062571         5.679  114.0             0   \n",
       "582         65.30       2.808  211.038853         6.465   10.0             0   \n",
       "3647        56.94       3.851  135.265267         7.818   72.0             0   \n",
       "1564        86.49       3.638  225.829306         6.334  134.0             0   \n",
       "\n",
       "      Store_2  Store_3  Store_4  Store_5    ...     Store_36  Store_37  \\\n",
       "1288        0        0        0        0    ...            0         0   \n",
       "1115        0        0        0        0    ...            0         0   \n",
       "582         0        0        0        1    ...            0         0   \n",
       "3647        0        0        0        0    ...            0         0   \n",
       "1564        0        0        0        0    ...            0         0   \n",
       "\n",
       "      Store_38  Store_39  Store_40  Store_41  Store_42  Store_43  Store_44  \\\n",
       "1288         0         0         0         0         0         0         0   \n",
       "1115         0         0         0         0         0         0         0   \n",
       "582          0         0         0         0         0         0         0   \n",
       "3647         0         0         0         0         0         0         0   \n",
       "1564         0         0         0         0         0         0         0   \n",
       "\n",
       "      Store_45  \n",
       "1288         0  \n",
       "1115         0  \n",
       "582          0  \n",
       "3647         0  \n",
       "1564         0  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XTrain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=True)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wmLinear = linear_model.LinearRegression(normalize=True)\n",
    "wmLinear.fit(XTrain, YTrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qKyuFEEK8/fbbory8XAghxOOPPy6am5uF0+kUixYtEo2NjeL48eOiqKhIOJ1O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\nQERERJr9/4HthmylhgpHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d732dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Performance on the test data sets\n",
    "YHatTest = wmLinear.predict(XTest)\n",
    "plt.plot(YTest, YHatTest,'ro')\n",
    "plt.plot(YTest, YTest,'b-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.08956881121002615 26699670214.63537\n"
     ]
    }
   ],
   "source": [
    "# calculate the accuray of the model by sum of Square and mean absolute prediction error\n",
    "MAPE = np.mean(abs((YTest - YHatTest)/YTest))\n",
    "MSSE = np.mean(np.square(YHatTest - YTest))\n",
    "\n",
    "print(MAPE, MSSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Dimensionality Reduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "alphas = np.linspace(10, 20, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "testError = np.empty(10)\n",
    "\n",
    "for i, alpha in enumerate(alphas) :\n",
    "    \n",
    "    lasso = Lasso(alpha=alpha)\n",
    "    lasso.fit(XTrain, YTrain)\n",
    "    testError[i] = mean_squared_error(YTest, lasso.predict(XTest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jRpj/vflmE+5BQXZXJSINQEP0It4qK8uE+kcfwaRJ8K9/KdxFmhD14EW8zbFj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\nIiIiXkgBLyIi4oUU8CIiIl7o/wECTkdbLJYilQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e93abe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(alphas, testError, 'r-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=True)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wmLinear = linear_model.LinearRegression(normalize=True)\n",
    "wmLinear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=17, copy_X=True, fit_intercept=True, max_iter=1000,\n",
       "   normalize=False, positive=False, precompute=False, random_state=None,\n",
       "   selection='cyclic', tol=0.0001, warm_start=False)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso = Lasso(alpha=17)\n",
    "lasso.fit(XTrain, YTrain)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
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WALMART Sales Prediction

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