bank-churning-model

The primary objective of this project was to develop a predictive model capable of accurately determining whether a bank customer will maintain or close their account. Kaggle Playground Series S4E1

Preprocessing a bank's customer Data and using an Artificial Neural Network to predict the customer churn rate.

This project involves the development of a machine learning model to predict customer churn for a banking institution. By utilizing historical customer data, the model aims to identify at-risk customers, enabling the bank to take proactive measures to retain them and improve customer loyalty.

EDA and comparisons of various on Bank customer data using pycaret

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Predicting whether or not a bank customer will churn (close their account) by using an XGBoost classifier

Bank customer churn analysis (10k accounts) using Power Query and Power BI. Identifies a 20.38% churn rate ($185.68M lost) driven by Germany (32.44%), ages 51–60 (56.21%), and 3+ product holders (82%–100%).

Predict customer churn with ease using this machine learning-based system. Input customer data, get predictions, and gain valuable insights.

Predict Churning customers