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 builds a machine-learning model that predicts which banking clients are likely to churn. Using demographic and behavioural data, several classification algorithms were trained and tuned on an imbalanced dataset. The final model achieved a strong F1 score and AUC-ROC, helping the bank prioritise retention efforts and improve customer lifetime value.
This project builds a classification model that recommends the most suitable mobile tariff for telecom users based on their monthly activity. Call duration, messages and internet usage were analysed to train and evaluate several algorithms. The final model reaches strong accuracy and provides a practical tool for upgrading users to better-fitting plans.
