This repository contains code for building a machine learning model to predict weekly sales.
I built a Random Forest Regressor model to predict weekly sales. I chose this model because it can capture non-linear relationships in the data, handle multiple features, and manage outliers effectively. Random Forests are also robust and less prone to overfitting. I've already tested a lot of machine learning algorithms, but Random Forest is still the best.
To deploy the model in production, I would save the trained model to a file and create an API or web service using any framework such as (Django, Flask, FastAPI) in Python for example . The API would take input data (store, holiday_flag, temperature, fuel_Price, cpi, unemployment, date) and return the predicted weekly sales. The choice of framework and tools for deployment would be determined by project requirements and team expertise.
Monitoring a deployed machine learning model is crucial for maintaining accuracy and reliability. I would use monitoring tools like Prometheus or Grafana to visualize metrics, implement logging for capturing key details, and track performance metrics such as response time and resource utilization. Anomaly detection and data drift monitoring would help identify unusual patterns, while regular maintenance and version tracking would ensure optimal performance.
Periodic retraining is essential to keep the model up to date. I would collect new data regularly and retrain the model on a schedule (weekly, monthly, etc.). This involves updating the model with new data while retaining knowledge learned from previous training.
I would evaluate the model's performance over time by comparing predicted values to actual weekly sales data. Metrics such as RMSE would help assess the model's accuracy on recent data and detect any degradation in performance.
Improving the model over time involves collecting diverse data, experimenting with algorithms and hyperparameters, implementing feature engineering techniques, and performing A/B testing. Regular reviews and updates would ensure the model remains aligned with changing data patterns.