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.
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.
This project explores historical videogame sales, ratings and genre data to understand what makes a game successful across global markets. The analysis highlights regional preferences, platform differences, and key factors shaping sales performance. It also includes hypothesis testing to compare user ratings between platforms and genres in a statistically sound way.
