Overview: This project focuses on predicting sales for different product families across various stores using machine learning techniques. The goal is to develop robust models that can accurately forecast sales, leveraging historical data and external factors.

Loading Data: Import transaction, store, oil price, and holiday data.

Feature Engineering: Create features such as moving averages for oil prices and incorporate holiday events.

Data Merging: Combine datasets to form a comprehensive feature set for training and testing.

Model Selection: Use linear regression, support vector regression (SVR), and other machine learning models.

Training: Train models on historical sales data.

Feature Matrix Creation: Construct feature matrices for both training and testing phases.

Prediction: Generate sales predictions for the test set using the trained models.

Performance Metrics: Evaluate model performance using metrics such as R² score and mean squared error (MSE).

Comparison: Compare the performance of different models to select the best-performing one.

Visualization: Plot and visualize the results to understand model performance and sales trends.