Time Series Forecasting of Walmart Sales Data using Deep Learning and Machine Learning

Walmart Recruiting - Store Sales Forecasting downloaded from https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting

• train.csv - CSV Data file containing following attributes
  • Store
  • Dept
  • Date
  • Weekly_Sales
  • IsHoliday

115064 Data rows

• stores.csv - CSV Data File containing following attributes
  • Store
  • Type
  • Size

45 Data rows

• features.csv - CSV Data file containing following attributes
  • Store
  • Date
  • Temperature
  • Fuel_Price
  • MarkDown1
  • MarkDown2
  • MarkDown3
  • MarkDown4
  • MarkDown5
  • CPI
  • Unemployment
  • IsHoliday

8190 Data rows

• Linear Regression Model
• Random Forest Regression Model
• K Neighbors Regression Model
• XGBoost Regression Model
• Custom Deep Learning Neural Network

• Handling Missing Values

  • CPI, Unemployment of features dataset had 585 null values.
  • MarkDown1 had 4158 null values
  • MarkDown2 had 5269 null values
  • MarkDown3 had 4577 null values
  • MarkDown4 had 4726 null values
  • MarkDown5 had 4140 null values All missing values were filled using median of respective columns.

• Merging Datasets

  • Main Dataset merged with stores dataset
  • Resulting Dataset merged with features dataset
  • Total data rows 421570 and 15 attributes
  • Date column converted into datetime data type
  • Date attribute set as index of combined dataset

• Splitting Date Column

  • Split the Date column into Year, Month, Week

• Aggregate Weekly Sales

  • Create max, min, mean, median, std of weekly sales

• Outlier Detection and Other abnormalities

  • Markdowns were summed into Total_MarkDown
  • Outliers were removed using z-score
  • Data rows 375438 and 20 columns
  • Negative weekly sales were removed
  • 374247 Data rows and 20 columns

• One-hot-encoding

  • Store, Dept, Type columns were one-hot-encoded using get_dummies method
  • After one-hot-encoding, no. of columns becomes 145

• Data Normalization

  • Numerical columns normalized using MinMaxScaler in the range 0 to 1

• Recursive Feature Elimination

  • Random Forest Regressor used to calculate feature ranks and importance with 23 estimators
  • Features selected to retain
    • mean, median, Week, Temperature, max, CPI, Fuel_Price, min, std, Unemployment, Month, Total_MarkDown, Dept_16, Dept_18, IsHoliday, Dept_3, Size, Dept_9, Year, Dept_11, Dept_1, Dept_5, Dept_56
  • No. of attributes after feature elimination - 24

• Dataset was splitted into 80% for training and 20% for testing
• Target feature - Weekly_Sales

• Linear Regressor Accuracy - 92.28
• Mean Absolute Error - 0.030057
• Mean Squared Error - 0.0034851
• Root Mean Squared Error - 0.059
• R2 - 0.9228
• LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)

• Random Forest Regressor Accuracy - 97.889
• Mean Absolute Error - 0.015522
• Mean Squared Error - 0.000953
• Root Mean Squared Error - 0.03087
• R2 - 0.9788
• n_estimators - 100
• RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse', max_depth=None, max_features='auto', max_leaf_nodes=None, max_samples=None, min_impurity_decrease=0.0, min_impurity_split=None, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=None, oob_score=False, random_state=None, verbose=0, warm_start=False)

• KNeigbhbors Regressor Accuracy - 91.9726
• Mean Absolute Error - 0.0331221
• Mean Squared Error - 0.0036242
• Root Mean Squared Error - 0.060202
• R2 - 0.919921
• Neighbors - 1
• KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=None, n_neighbors=1, p=2, weights='uniform')

• XGBoost Regressor Accuracy - 94.21152
• Mean Absolute Error - 0.0267718
• Mean Squared Error - 0.0026134
• Root Mean Squared Error - 0.051121
• R2 - 0.942115235
• Learning Rate - 0.1
• n_estimators - 100
• XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1, gamma=0, importance_type='gain', learning_rate=0.1, max_delta_step=0, max_depth=3, min_child_weight=1, missing=None, n_estimators=100, n_jobs=1, nthread=None, objective='reg:linear', random_state=0, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None, silent=None, subsample=1, verbosity=1)

• Deep Neural Network accuracy - 90.50328
• Mean Absolute Error - 0.033255
• Mean Squared Error - 0.003867
• Root Mean Squared Error - 0.06218
• R2 - 0.9144106
• Kernel Initializer - normal
• Optimizer - adam
• Input layer with 23 dimensions and 64 output dimensions and activation function as relu
• 1 hidden layer with 32 nodes
• Output layer with 1 node
• Batch Size - 5000
• Epochs -100

• Linear Regressor Accuracy - 92.280797
• Random Forest Regressor Accuracy - 97.889071
• K Neighbors Regressor Accuracy - 91.972603
• XGBoost Accuracy - 94.211523
• DNN Accuracy - 90.503287

• Walmart Recruiting - Store Sales Forecasting https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting

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