1. Introduction: Predict juice sales trends using Linear Regression for better inventory management and business decisions.
2. 2. Features: Forecast sales based on historical data, visualize trends, and analyze key factors.
3. 3. Dataset: Includes date, juice type, quantity sold, price, and advertising spend.
4. 4. Methodology: Preprocessing, EDA, Model Training, Prediction, and Visualization.
5. 5. Installation: Clone the repo, install dependencies, and run the script to predict sales.
6. Dependencies: Uses numpy, pandas, matplotlib, seaborn, and sklearn.
7. Usage: Upload sales data, run prediction script, and analyze results via charts.
8. Results: Provides an R-squared score and comparison of actual vs predicted sales.
9. Future Enhancements: Adding advanced models, time-series analysis, and web deployment.
10. Contribution: Fork the repo, submit issues, and help improve the prediction system.