Predictive analysis and the use of historical data are essential for optimizing the operations of companies like Sweet Lift Taxi. This project demonstrates my ability to apply Machine Learning models in real-world scenarios, achieving significant results.

Project Summary

The primary objective of this project was to develop a predictive model to estimate the number of taxi orders for the next hour, using historical data collected at airports. This model helps the company make informed decisions and attract more drivers during peak hours, thereby improving service availability.

Methodology and Results

The project began with rigorous data preprocessing, including resampling into one-hour intervals to ensure temporal consistency. Multiple models with different hyperparameters were trained and evaluated, with 10% of the data used as a test sample. The goal was to minimize the root mean square error (RMSE) in the test set to less than 48, as required by the project.

Recommendations

• Use of the Optimized Model: Implement the optimized neural network to predict future taxi orders accurately and enhance operational planning.
• Continuous Monitoring: Periodically update the model with new data to maintain its accuracy over time.
• Additional Exploration: Include supplementary variables such as weather data or local events to improve model precision further.

This project exemplifies my ability to combine data analysis with advanced Machine Learning techniques to create practical solutions, helping businesses optimize their operations and overcome critical challenges. Predictive analytics has the power to revolutionize business planning! 😊