Taxi Price Regression

This project focuses on building a Machine Learning model for predicting Trip Prices based on multiple ride-related features such as distance, time of day, weather conditions, traffic conditions, and passenger count.

The dataset was carefully cleaned and preprocessed by handling missing values, encoding categorical variables using One-Hot Encoding, and preparing the data for modeling.

Two different regression models were implemented and compared:

Linear Regression

Random Forest Regression

The models were evaluated using the R² Score to measure prediction accuracy. The Random Forest model achieved better performance, demonstrating its ability to capture complex and non-linear relationships in the data.