This project builds a machine-learning model that classifies user comments as toxic or non-toxic. After cleaning the text and converting it into numerical features with TF-IDF, several algorithms were tested and tuned. The final model helps online platforms flag harmful content automatically and maintain a safer communication environment.
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.
