Machine learning project for NYC Yellow Taxi fare prediction. Complete data pipeline with DuckDB/Polars ETL, exploratory analysis of 34M trips, feature engineering, and ML model preparation. Achieves 0.954 correlation between distance and fare through comprehensive 2023 dataset analysis.

Developed a machine learning model to predict airline ticket prices using ensemble learning (Random Forest Regressor). Applied categorical feature encoding, outlier detection, and data preprocessing to improve data quality. Optimized model performance through hyperparameter tuning with cross‑validation.

React - fare calculator for carriers, taking into account excess baggage, children's discounts (for excess and for everything)

Predict NYC taxi fares with AI! Built with React, Django, and Extra Trees Regressor (R² 0.94). Try it out!

The objective is to Predict the cab fare amount using historical pilot data in the city.

This is a project on designing a Machine Learning Algorithm to predict fare of the largest taxi company "Uber" using python. The main objective of the project is to design an algorithm which will tell the fare to be charged for a passenger.

Machine Learning assignments: Uber Fare Prediction using Linear Regression & Diabetes Classification using K-Nearest Neighbors (KNN) — built with scikit-learn on Google Colab.

Flight Fare Prediction using Machine Learning and Data Analysis including a Flask Website

Academic ML project

A Python and Streamlit powered ML application that predicts key cost-of-living components—rent, PG accommodation, and travel fares—using trained regression models.

A fare prediction app built on Streamlit