This repository showcases a comprehensive data analysis project focused on predicting customer churn in banking sector using Machine learning models.The project was a data warrior scholarship by Datum Discovery for creating a Power Bi Dashboard but I decide to also build a model. This is my first dashboard and its available through this link.
- Dashboard Design The effectiveness of the dashboard in presenting clear, insightful visualisations tat are easy to interpret. Integration of key metrics relevant to customer churn, ensuring intuitive navigation and usability for stakeholders.
- Data Analysis The accuracy and rlevance of insights derived from the dataset, ensuring they reflect meaningful patterns and trends related to customer churn. Demonstration of strong analytical skills with attention to detail in the cleaning,preparation and exploration of data.
The idea behind this project is to develop a predictive model to identify customers who are likely to churn.This involves analyzing customer behaviors, demographics and financial data to uncover the factors influencing churn. This project is a Classfication Problem because there are two possible outcomes:
- 1 (Churn): The customer is predicted to leave the bank
- 0 (No Churn): The customer is predicted to stay
I will build a couple of models and evaluate to determine which one provides the most accurate predictions. The models will be compared based on their performance metrics and the best fitting model will be selected for deployment.