This project builds a regression model that predicts the cost of producing industrial equipment. After cleaning the data and engineering meaningful features, several algorithms were evaluated. The final model helps the company estimate production expenses more accurately and plan budgets with greater confidence.
This project builds a time-series model that forecasts hourly taxi orders for a service operating in a major city. After resampling data, creating lag features and adding rolling statistics, several models were evaluated. The final solution delivers accurate short-term forecasts and helps the company plan driver allocation during high-demand periods.
This project builds and compares several regression models to predict the price of used cars. After cleaning the listings data and engineering meaningful features, different algorithms — including tree-based models and gradient boosting — were evaluated for speed and accuracy. The final model delivers strong performance and provides a reliable baseline for car-valuation systems.
