mridul0703/Cab-fare-Prediction

Our machine learning project focuses on building and evaluating predictive models for cab fare prediction. We perform extensive data processing, cleaning, and feature extraction to prepare the dataset for model training. This project aims to predict cab fares accurately based on various input parameters such as location, no. of passengers and time.

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Experimental

This project helps predict New York City cab fares accurately. It takes details like pickup/drop-off locations, trip distance, and time of day, then processes them to estimate the final fare. This would be useful for ride-sharing companies, city planners, or data analysts in transportation to understand and forecast pricing.

No commits in the last 6 months.

Use this if you need to predict taxi fares based on various trip parameters and want to understand the factors influencing pricing.

Not ideal if you're looking for a simple fare calculator for personal use or need real-time, dynamic pricing for a live application.

transportation-analytics fare-forecasting pricing-strategy urban-planning logistics-optimization
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 5 / 25
Maturity 16 / 25
Community 0 / 25

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12

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Language

Jupyter Notebook

License

MIT

Last pushed

May 10, 2024

Commits (30d)

0

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