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Predicting Walmart store sales using various regression models and deep learning techniques. Leveraged Kaggle's Walmart Recruiting dataset for comprehensive analysis. Achieved high accuracy with Random Forest Regression leading the pack.
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keertisuryawanshi/Walmart-Store-sales-Forecasting
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Walmart-Store-sales-Forecasting
- Utilized the Walmart Recruiting - Store Sales Forecasting dataset obtained from Kaggle.
- Included three CSV files: train.csv , stores.csv , and features.csv .
Machine Learning Models:
- Implemented several regression models including Linear Regression, Random Forest Regression, K Neighbors Regression, XGBoost Regression, and a custom Deep Learning Neural Network.
Data Preprocessing:
- Addressed missing values by filling them with the median of their respective columns.
- Merged and manipulated datasets to create a comprehensive dataset.
- Split the date column into Year, Month, and Week.
- Aggregated weekly sales and detected outliers.
- One-hot-encoded categorical variables and normalized numerical columns.
- Employed Recursive Feature Elimination to select important features.
Model Training and Evaluation:
- Split the dataset into training and testing sets.
- Trained each model and evaluated their performance using metrics such as accuracy, mean absolute error, mean squared error, root mean squared error, and R2 score.
Model Comparison:
- Compared the performance of different regression models, with Random Forest Regression achieving the highest accuracy of 97.89%.
About
Predicting Walmart store sales using various regression models and deep learning techniques. Leveraged Kaggle's Walmart Recruiting dataset for comprehensive analysis. Achieved high accuracy with Random Forest Regression leading the pack.
