Training and Optimising the Accuracy of Classification Models for Churn Prediction

During this Machine Learning project at Teesside University, I trained and optimised classification models to predict customer churn.

The project involved data sourcing, processing, and exploratory data analysis (EDA) using Python, followed by the training and validation of models such as KNN, SVM, and Random Forest. I focused on improving model accuracy through hyperparameter tuning and thorough testing.

The results were documented in a scientific report, showcasing the predictive power of the models and offering insights into customer retention strategies for businesses.