{"id":25905339,"url":"https://github.com/murugavl/customer_churn_prediction","last_synced_at":"2026-04-13T03:05:33.585Z","repository":{"id":280093230,"uuid":"940967282","full_name":"Murugavl/Customer_churn_Prediction","owner":"Murugavl","description":"Customer churn prediction is a crucial task in business analytics, helping companies identify customers who are likely to leave and take preventive actions. 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This project utilizes **Machine Learning (ML)** techniques to analyze customer behavior and predict churn.\n\n## 🔍 Features\n- **Data Preprocessing:** Handles missing values, encodes categorical variables, and scales numerical data.\n- **Exploratory Data Analysis (EDA):** Visualizes key trends in customer churn.\n- **Machine Learning Models:** Implements multiple ML algorithms (Logistic Regression, Random Forest, SVM, etc.).\n- **Model Evaluation:** Uses accuracy, precision, recall, and F1-score to assess performance.\n- **Deployment:** Deploys the model using **Streamlit** for an interactive user interface.\n\n## 🛠️ Technologies Used\n- **Python** \n- **Pandas, NumPy** (Data Manipulation)\n- **Matplotlib, Seaborn** (Visualization)\n- **Scikit-Learn** (Machine Learning)\n- **Streamlit** (Deployment)\n\n## 📂 Project Structure\n```\nCustomer_churn_Prediction/\n│── dataset/               # Contains the dataset (CSV file)\n│── notebooks/             # Jupyter notebooks for EDA \u0026 training\n│── app.py                 # Streamlit app for prediction\n│── requirements.txt       # List of dependencies\n│── README.md              # Project Documentation\n```\n\n## 📊 Dataset\n- The dataset includes customer information such as **demographics, account details, and usage patterns**.\n- The target variable is **Churn** (0: Not Churned, 1: Churned).\n\n## 🚀 Installation \u0026 Usage\n1. **Clone the Repository**\n   ```bash\n   git clone https://github.com/Murugavl/Customer_churn_Prediction.git\n   cd Customer_churn_Prediction\n   ```\n2. **Create a Virtual Environment** *(Optional but recommended)*\n   ```bash\n   python -m venv venv\n   source venv/bin/activate  # For macOS/Linux\n   venv\\Scripts\\activate     # For Windows\n   ```\n3. **Install Dependencies**\n   ```bash\n   pip install -r requirements.txt\n   ```\n4. **Run the Streamlit App**\n   ```bash\n   streamlit run app.py\n   ```\n\n## 📌 Model Training\nTo train the model, run the Jupyter notebook available in the `notebooks/` folder:\n```bash\njupyter notebook notebooks/Customer Churn Prediction.ipynb\n```\n\n## 💡 Future Improvements\n- Enhance feature engineering for better model performance.\n- Implement deep learning models for improved accuracy.\n- Integrate customer segmentation for targeted retention strategies.\n\n## 🤝 Contributing\nContributions are welcome! If you’d like to improve the project, fork the repository and submit a pull request.\n\n## 📜 License\nThis project is licensed under the MIT License.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmurugavl%2Fcustomer_churn_prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmurugavl%2Fcustomer_churn_prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmurugavl%2Fcustomer_churn_prediction/lists"}