🏦 Predicting Bank Customer Churn Using Machine Learning

📌 Overview

This project analyzes bank customer churn using machine learning models. The goal is to predict whether a customer will leave the bank based on their demographic, financial, and behavioral data.

The dataset consists of 10,000 bank customers, with features such as:

• Credit Score, Age, Balance, Number of Products, IsActiveMember, and more.

By applying Exploratory Data Analysis (EDA) and Supervised Machine Learning models, we aim to identify key factors that contribute to customer churn and build a predictive model.

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🎯 Objectives

• 📊 Perform Exploratory Data Analysis (EDA) to find patterns in customer churn.
• 🏆 Train and evaluate machine learning models to predict churn.
• 🔎 Identify key features that influence customer retention.
• 🚀 Optimize model performance for better accuracy.

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📂 Dataset

• File Name: churn.csv
• Rows: 10,000
• Columns: 14
• Target Variable: Exited (1 = Churned, 0 = Retained)

🔑 Key Features:

Feature	Description
CreditScore	Customer's credit score
Geography	Country (France, Spain, Germany)
Gender	Male or Female
Age	Age of customer
Tenure	Number of years with the bank
Balance	Account balance
NumOfProducts	Number of products customer has with the bank
HasCrCard	Whether the customer has a credit card (1 = Yes, 0 = No)
IsActiveMember	Whether the customer is active (1 = Yes, 0 = No)
EstimatedSalary	Customer's estimated salary
Exited	(Target Variable) 1 = Churned, 0 = Retained

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🔍 Exploratory Data Analysis (EDA)

• ✅ Summary statistics & data distribution
• 📊 Visualizations (Histograms, Boxplots, Correlation heatmaps)
• 🔎 Feature importance analysis

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⚙️ Machine Learning Models

We experiment with different classification models:

1. Logistic Regression 🤖
2. Random Forest Classifier 🌲
3. Gradient Boosting (XGBoost, LightGBM) 🚀
4. Neural Networks (MLPClassifier) 🔥

📊 Model Evaluation Metrics:

• Accuracy
• Precision & Recall
• F1 Score
• ROC-AUC Curve

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🚀 Results & Key Insights

• Feature Importance: The most influential factors in churn prediction were:
  • Age (Older customers churn more)
  • Number of Products (Customers with 1 product churn more)
  • IsActiveMember (Inactive members are more likely to leave)
  • Geography (Churn rate varies by country)

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