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.
This project develops a secure data-transformation method that protects sensitive customer information while keeping model performance intact. A linear regression model was trained on the original data and then re-validated on data transformed with an invertible matrix. Identical R² scores confirmed that the transformation preserves predictive power while preventing reconstruction of personal data.
This project builds a regression model that predicts the recovery rate of gold from raw ore during extraction and refinement. The analysis covers process parameters, intermediate outputs and final concentrate characteristics. The final model helps mining operations assess ore quality early and avoid launching unprofitable production cycles.
This project evaluates three potential regions for drilling new oil wells. A regression model predicts reserves for future wells, while a bootstrapped risk analysis estimates expected profit and loss probability. The final recommendation balances predicted revenue with financial risk, helping the company choose the safest and most profitable region for development.
