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Taxi Fare Predictor June 09th 2024
Description: Developed a machine learning model using linear regression to predict taxi fares based on vendor, distance, time to reach, and rush hour status. The project aimed to create an intelligent system that can accurately estimate taxi fares based on various factors, enabling users to make informed decisions. The model was trained on a dataset of historical taxi fares and was able to achieve high accuracy of 96% in predicting fares for new, unseen data. Technologies Used: Python, scikit-learn, Pandas, Numpy.
