-
Taxi Service: Time-Series Demand Forecasting
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.
Project tags: Cross-Validation, Feature Engineering, Lag Features, LightGBM, Machine Learning, Matplotlib, Model Evaluation, NumPy, Pandas, Regression Modeling, Rolling Statistics, Scikit-learn, Time Series AnalysisRead more: Taxi Service: Time-Series Demand Forecasting
-
Used Car Market: Price Forecasting
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.
Project tags: CatBoost, Cross-Validation, Data Cleaning, Feature Engineering, Gradient Boosting, Handling Missing Data, LightGBM, Machine Learning, Matplotlib, Model Evaluation, NumPy, Pandas, Regression Modeling, Scikit-learnRead more: Used Car Market: Price Forecasting
-
Protecting Customer Data with Linear Transformation
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.
Project tags: Data Cleaning, Feature Engineering, Linear Algebra, Model Validation, NumPy, Pandas, Regression Modeling, Scikit-learnRead more: Protecting Customer Data with Linear Transformation
-
Modeling Gold Extraction Performance
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.
Project tags: Data Cleaning, Exploratory Data Analysis, Feature Engineering, Machine Learning, Matplotlib, Model Evaluation, NumPy, Pandas, Regression Modeling, Scikit-learn, SciPy, SeabornRead more: Modeling Gold Extraction Performance
-
Oil Field Development: Region Evaluation with ML
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.
Project tags: Bootstrapping, Data Cleaning, Feature Engineering, Machine Learning, Matplotlib, Model Evaluation, NumPy, Pandas, Regression Modeling, Risk Assessment, Scikit-learn, SciPyRead more: Oil Field Development: Region Evaluation with ML
-
Churn Forecasting for Retail Banking Clients
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.
Project tags: AUC-ROC Analysis, Classification Modeling, Cross-Validation, Data Cleaning, F1 Score Optimization, Feature Engineering, Handling Imbalanced Data, Machine Learning, Matplotlib, Model Evaluation, NumPy, Pandas, Scikit-learnRead more: Churn Forecasting for Retail Banking Clients
-
Telecom Tariff Recommendation Model
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.
Project tags: Classification Modeling, Cross-Validation, Data Cleaning, Data Visualization, Feature Engineering, Machine Learning, Model Evaluation, NumPy, Pandas, Scikit-learnRead more: Telecom Tariff Recommendation Model