Customer Churn Prediction using Machine Learning

Overview

Customer churn is a significant challenge for businesses, impacting revenue and long-term growth. This project leverages Machine Learning (ML) techniques to predict customer churn, enabling companies to take proactive actions to retain customers.

Dataset

The dataset used for this project contains customer details, service usage patterns, and churn labels. The key features include:

• Demographics: Age, gender, location, etc.
• Account Information: Subscription tenure, contract type, billing method.
• Service Usage: Monthly charges, total charges, usage patterns.
• Churn Label: Indicates whether the customer has churned.

Project Workflow

1. Data Preprocessing:

   • Handle missing values and outliers.
   • Encode categorical variables.
   • Normalize numerical features.

2. Exploratory Data Analysis (EDA):

   • Visualize churn distribution.
   • Analyze correlations between features.
   • Identify key factors influencing churn.

3. Feature Engineering:

   • Create new informative features.
   • Perform feature selection.

4. Model Training & Evaluation:

   • Train multiple ML models (Logistic Regression, Decision Trees, Random Forest, XGBoost, etc.).
   • Use cross-validation to optimize hyperparameters.
   • Evaluate models using accuracy, precision, recall, F1-score, and ROC-AUC.

5. Model Deployment (Optional):

   • Convert the best-performing model into a deployable format (e.g., Flask API, Streamlit, etc.).

Technologies Used

• Python (pandas, numpy, scikit-learn, matplotlib, seaborn, XGBoost)
• Machine Learning Algorithms (Supervised learning techniques)
• Jupyter Notebook / Google Colab for implementation

Results

• Identified key factors influencing customer churn.
• Achieved high accuracy and F1-score using optimized models.
• Provided actionable insights to reduce churn.

How to Use

1. Clone this repository:

   git clone https://github.com/your-username/customer-churn-prediction.git
   cd customer-churn-prediction

2. Install dependencies:

   pip install -r requirements.txt

3. Run the Jupyter Notebook or execute the Python script to train the model:

   jupyter notebook CustomerChurn.ipynb

4. Modify parameters and retrain models as needed.

Future Enhancements

• Implement deep learning models for improved predictions.
• Deploy the model as a web service.
• Integrate real-time prediction capabilities.

Contributing

Contributions are welcome! Feel free to fork this repository and submit pull requests.

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Author: Muhammad Hammad Sarwar GitHub: [MHammadSarwar](https://github.com/MHammadSarwar\ LinkedIn: (https://www.linkedin.com/in/m-hammad-sarwar-84a708313/)