NYC Taxi Fare Prediction using Exploratory Data Analysis and Machine Learning

• Analyzed New York City taxi ride data including pickup/dropoff locations, trip duration, and passenger count.
• Performed data cleaning: handled outliers, removed invalid geolocations, and engineered distance-based features using Haversine formula.
• Conducted EDA to uncover patterns in fare amounts by time of day, trip distance, and location clusters.
• Built regression models to predict taxi fares using linear regression, Ridge regression, and random forest, achieving strong performance on test data.