Business-Analytics-Data-Mining

1. The task or goal of this project was to predict churn of subscribers for KKbox which is a music streaming service and KKbox being a subscription business, accurately predicting churn is critical to long term success and any variation in churn can drastically affect profits. So, coming up with a model to accurately predict churn can help KKbox try and retain more customers and increase their profits.

2. We used supervised machine learning techniques such as Logistic regression, Decision trees, neural networks, SVM and random forest to predict the target class “churn” and show the obtained results. We compare these results and evaluate the models in this project. We also visualize different aspects of data to determine any trends or patterns.

3. Detailed report and instructions to use these models is given in Report and Instructions file.

4. Dataset can be obtained from Kaggle.com