📉 Customer Churn Prediction

Customer Churn Prediction is a Machine Learning project that predicts whether a customer is likely to leave a service (churn) or continue using it.
This project involves data preprocessing, exploratory data analysis (EDA), model building, and performance evaluation.

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📌 Project Overview

Customer churn is one of the major challenges faced by businesses such as telecom companies, banks, and subscription-based services.

Predicting customer churn helps organizations to:

• Improve customer retention
• Reduce revenue loss
• Identify high-risk customers
• Enhance customer satisfaction
• Make data-driven business decisions

This project uses machine learning algorithms to analyze customer data and predict churn behavior.

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🛠️ Technologies Used

• Python
• Pandas
• NumPy
• Scikit-learn
• Matplotlib
• Seaborn
• TensorFlow

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📂 Project Structure

Customer_Churn_Prediction/
│
├── Customer_churn_prediction.ipynb   # Main implementation file
├── Churn.csv                         # Dataset used
└── README.md                         # Project documentation

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📊 Dataset Description

The dataset contains customer-related information such as:

• Customer demographics
• Account information
• Services subscribed
• Monthly charges
• Contract type
• Tenure
• Payment method

Target Variable:

• Churn
  • 1 → Customer leaves the service
  • 0 → Customer stays

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🧹 Data Preprocessing

The following steps are applied:

• Handling missing values
• Encoding categorical variables
• Feature scaling
• Train-Test splitting

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🧠 Machine Learning Models Used

The project uses classification algorithms such as:

• Logistic Regression
• Decision Tree
• Random Forest
• Neural Network (TensorFlow)

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📊 Model Evaluation Metrics

Model performance is evaluated using:

• Accuracy Score
• Confusion Matrix
• Precision
• Recall
• F1-Score

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▶️ How to Run the Project

Step 1: Clone the Repository

git clone https://github.com/your-username/Customer_Churn_Prediction.git
cd Customer_Churn_Prediction

Step 2: Install Required Libraries

pip install pandas numpy scikit-learn matplotlib seaborn tensorflow

Step 3: Open Jupyter Notebook

jupyter notebook

Open:

Customer_churn_prediction.ipynb

Run all the cells to preprocess the data, train the model, and evaluate performance.

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📈 Output

The trained model predicts whether a customer will:

• Stay with the service
• Leave the service (Churn)

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🔮 Future Enhancements

• Deep Learning optimization
• Web application using Streamlit
• Real-time churn prediction system
• Deployment on cloud platform

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📄 License

This project is developed for educational purposes.

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Developed by:L Jitendra Kumar