Project Overview

This repository contains code for building a machine learning model to predict weekly sales.

Model Explanation

How was the model built and why was this particular choice made?

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.

Deployment Strategy

How would the model be deployed in a production environment?

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 Approach

What steps would be taken to monitor the model's performance in production?

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.

Retraining Strategy

How would the model be retrained over time to keep it up to date?

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.

Evaluation Over Time

How would the model's performance be evaluated as time goes on?

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.

Continuous Improvement

How would the model be improved over time to adapt to changing patterns?

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.