Customer Churn Prediction

In this customer churn prediction project for an e-commerce store, I initiated the analysis by thorough data cleaning and null value removal. Subsequently, I performed descriptive statistics to gain valuable insights into the dataset. One key aspect of the analysis involved determining the churn frequency and creating visualizations based on factors such as gender, tenure, and other relevant subjects. To further enhance the predictive capabilities, I employed K-means cluster models, leveraging their advanced clustering techniques to effectively forecast customer churn.