This project develops a machine learning model to estimate used car market values for a pricing app. Using pandas for data manipulation and models like Random Forest, Gradient Boosting, and Linear Regression, it aims to balance prediction quality, speed, and training time. It compares multiple models to find the best fit for predicting car prices

This project forecasts hourly taxi demand for peak times using historical data from airports. It uses pandas for data preparation and scikit-learn for building and evaluating predictive models like Random Forest and Gradient Boosting. The project aims to enhance driver availability during rush hours by predicting the number of future taxi orders