Inventory Management for Retail - Walmart Sales Forecasting

Overview

This project focuses on building a machine learning model to predict retail sales for Walmart stores, enabling intelligent inventory management. By forecasting sales, we can optimize stock levels, reducing overstock and stockouts, thus improving the efficiency of inventory operations.

Problem Statement

The primary objective is to predict Weekly Sales for different Walmart stores. Accurate sales predictions help ensure optimal inventory levels, improve supply chain operations, and minimize costs related to under- or over-stocking.

Data Source

The dataset used in this project is from Walmart Store Sales Forecasting, which includes historical data such as:

• Store ID
• Department ID
• Weekly Sales
• Date
• Store Type
• Holiday Information

Project Workflow

1. Data Loading and Preprocessing

• Data Loading: The dataset is loaded and explored for insights.
• Missing Data Handling: Missing values in numeric features are filled with the mean, and missing values in categorical features are filled with the mode.
• Feature Inspection: Key features are analyzed for consistency and relevance.

2. Feature Engineering

• Date Features: The Date column is transformed into Year, Month, and Day features for better time-based analysis.
• One-Hot Encoding: Categorical features like StoreType are converted into numerical values using one-hot encoding.

3. Modeling

• Train-Test Split: The data is split into training (80%) and testing (20%) sets.
• Model Selection: A Random Forest Regressor is used to predict sales based on the engineered features.
• Hyperparameter Tuning: Fine-tuning of model parameters to improve performance.

4. Evaluation

• Root Mean Squared Error (RMSE): A metric used to measure prediction error.
• R-Squared (R²): A metric that explains the variance in the predicted values.
• Feature Importance: Identifying which features are most influential in predicting sales.

5. Model Saving

• The trained model is saved using joblib for future use and deployment.

Key Features

• Random Forest Model: Utilizes an ensemble learning method for regression tasks.
• Feature Engineering: Transforming and optimizing data for better predictive performance.
• Feature Importance: Understanding key factors driving sales predictions through model interpretability.

Results

• RMSE on test set: 21786.298600351474
• R-Squared: -0.3413115964648241
• Feature Importance Plot: A visual representation of the most important features driving the predictions.

Future Improvements

• Hyperparameter Optimization: Further tuning to improve model performance.
• Additional Features: Include data like holidays, promotions, and external economic factors to enhance predictions.
• Model Deployment: Deploy the model in a cloud environment (AWS, GCP) for real-time sales predictions.
• API Development: Create an API for serving real-time predictions to inventory management systems.

Conclusion

This project demonstrates how machine learning can be applied to forecast retail sales, offering insights into how intelligent inventory management can reduce inefficiencies and optimize operations.