🏦 Customer Churn Prediction — Churn Modelling Dataset

📌 Objective

Predict which bank customers are likely to leave (churn) using machine learning and interactive data analysis.
This project is Task 2 of my Data Science Internship at DevelopersHub Corporation.

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📊 Dataset

• Churn Modelling Dataset (Kaggle link)
• 10,000 records, 14 features
• Target: Exited (1 = Churned, 0 = Stayed)

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🔧 Approach

1. Data Cleaning: Removed irrelevant columns, handled categorical variables.
2. Feature Engineering: Encoded categorical features (Geography, Gender).
3. Exploratory Data Analysis (EDA): Interactive visualizations with Plotly for demographics, financials, and activity.
4. Feature Scaling: Standardized numerical features.
5. Model Training: Compared Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting.
6. Evaluation: Used accuracy, F1, precision, recall, ROC curve, and confusion matrix.
7. Feature Importance: Identified key drivers of churn.
8. Business Insights: Actionable recommendations for customer retention.

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📈 Results

Model	Accuracy	F1 Score	AUC
Logistic Regression	~81%	~0.45	~0.77
Decision Tree	~79%	~0.50	~0.72
Random Forest	~86%	~0.58	~0.86
Gradient Boosting	~86%	~0.60	~0.87

• Best Model: Gradient Boosting (~86% accuracy, ~0.60 F1)

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💡 Key Insights

• Age is the strongest predictor — older customers churn more.
• Geography: German customers have 2x higher churn.
• Inactive members and those with 3+ products are at highest risk.
• Credit score and salary have minimal impact on churn.

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🛠️ Tech Stack

• Python 3.x
• Pandas, NumPy
• Plotly (interactive EDA & model evaluation)
• Scikit-learn

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🚀 How to Run

1. Clone the repository:

   git clone https://github.com/kinzaemann/churn-modelling.git
   cd churn-modelling

2. Install dependencies:

   pip install -r requirements.txt

3. Open and run the notebook:
   • customer_churn_prediction.ipynb

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📂 Project Structure

├── README.md
├── requirements.txt
├── customer_churn_prediction.ipynb   # Main notebook
└── data/
    └── Churn_Modelling.csv           # Dataset (add or download from Kaggle)

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📬 Contact

For questions or feedback, please open an issue or connect on LinkedIn.

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*Completed as part of Data Science Internship at Developers Hub Corporation).