Bank_Churn_classification_ML-ANN

Bank Churn Classification using Machine Learning & Artificial Neural Networks (ML & ANN)

Project Description:

This project aims to predict customer churn in a bank using various classification techniques, including traditional Machine Learning (ML) models and Artificial Neural Networks (ANNs). Customer churn refers to when a customer stops doing business with the bank, and predicting churn helps financial institutions take proactive steps to retain valuable clients.

Objectives:

Analyze bank customer data to identify patterns related to churn behavior.

Build and compare different classification models including:

Logistic Regression

Decision Tree

Random Forest

Support Vector Machine (SVM)

K-Nearest Neighbors (KNN)

XGBoost

AdaBoost

Artificial Neural Network (ANN)

Evaluate model performance using metrics such as accuracy, precision, recall, F1-score, and ROC-AUC.

Use insights to understand key factors contributing to churn.

Key Features:

Data preprocessing (handling missing values, encoding categorical variables, feature scaling)

Exploratory Data Analysis (EDA) with visualizations

Hyperparameter tuning for model optimization

ANN built using TensorFlow/Keras for deep learning classification

Comparison of model performance to identify the most effective approach

Tools & Technologies:

Python (NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn)

TensorFlow / Keras

Jupyter Notebook / Google Colab

Outcome:

A robust and interpretable churn prediction system that helps banks reduce customer attrition and enhance customer relationship strategies.