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.
Analyzed real mobile usage data to compare two legacy tariff plans and determine which one brings higher monthly revenue. The project combines data cleaning, exploratory analysis, and statistical testing to identify the more profitable plan and support a data-driven marketing strategy for the operator.
In this project, an apartment-listings dataset was analysed to identify pricing outliers and anomalies that may point to fraudulent listings. Key attributes such as area, ceiling height, distance from city centre and listing date were explored using visualisations and engineered features. The findings deliver actionable insights for real-estate platforms seeking to flag irregular listings and understand pricing patterns.
