{"id":29543971,"url":"https://github.com/shubhamkumar0786/customer_churn_prediction","last_synced_at":"2026-07-02T21:31:35.235Z","repository":{"id":304592894,"uuid":"1018454306","full_name":"ShubhamKumar0786/Customer_Churn_Prediction","owner":"ShubhamKumar0786","description":"This project uses deep learning models to predict whether a customer is likely to churn or not. 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The application provides a web interface using **Streamlit**, enabling users to input customer details and receive churn probability predictions.\n\n---\n\n## 🚀 Features\n\n- Deep learning model built with TensorFlow/Keras\n- Data preprocessing using scikit-learn (scaling, encoding)\n- User-friendly web interface with Streamlit\n- Real-time churn probability prediction\n- Visual and interactive input for customer parameters\n\n---\n\n## 🧰 Tech Stack\n\n- **Python**\n- **TensorFlow / Keras**\n- **Pandas / NumPy**\n- **Scikit-learn**\n- **Streamlit**\n- **Matplotlib (for visualization, if needed)**\n- **Pickle** (for model and encoder serialization)\n\n---\n\n## 📁 Project Structure\n\n├── app.py # Streamlit application\n------\n├── model.h5 # Trained Keras model\n------\n├── label_encoder_gender.pkl # LabelEncoder for Gender\n------\n├── onehot_encoder_geo.pkl # OneHotEncoder for Geography\n------\n├── scaler.pkl # Scaler for feature normalization\n------\n├── Churn_Modelling.csv # Original dataset\n------\n├── requirements.txt # Python dependencies\n------\n├── prediction.ipynb # Model training notebook\n------\n├── experiments.ipynb # Experimentation and EDA\n------\n\n\n---\n\n## 📝 Usage Instructions\n\n1. **Install dependencies**\n\n```bash\npip install -r requirements.txt\nRun the Streamlit app\n\nstreamlit run app.py\nInteract with the Web Interface\n\nChoose values for customer details such as Geography, Gender, Age, etc.\n\nGet real-time churn prediction and probability.\n------------------------------------------------------------------------------------------------------------------------------------------\n☁️ Deploying on Streamlit Cloud\nYou can easily deploy this app to Streamlit Cloud by following these steps:\n\nPush your code to a GitHub repository.\n\nGo to Streamlit Cloud and log in with your GitHub account.\n\nClick on \"New app\" and connect your repository.\n\nIn the deployment form:\n\nRepository: Select your GitHub repo\n\nBranch: main or whichever branch your code is on\n\nMain file path: app.py\n\nClick \"Deploy\" and wait for it to launch.\n-----------------------------------------------------------------------------------------------------------\n📝 Make sure that:\n\nAll model files (model.h5, .pkl files) are in the repo.\n\nYou’ve added a requirements.txt with all the necessary packages.\n\n\n--------------------------------------------------------------------------------------------------------------\n📊 Input Features Used\nCredit Score\n\nGender (Encoded)\n\nAge\n\nTenure\n\nBalance\n\nNumber of Products\n\nHas Credit Card\n\nIs Active Member\n\nEstimated Salary\n\nGeography (One-hot encoded)\n------------------------------------------------------------------------------------------------------------------------\n🧠 Model Overview\nTrained using Keras with TensorFlow backend.\n\nBinary classification output (Churn or Not Churn).\n\nModel input data is scaled and encoded to match training configuration.\n-------------------------------------------------------------------------------------------------------------------\n📦 Dependencies\nSee requirements.txt:\n\ntensorflow\npandas\nnumpy\nscikit-learn\ntensorboard\nmatplotlib\nstreamlit\nscikeras\n\n```\n\n\n📬 Contact\nFeel free to connect or raise an issue if you have suggestions or questions about the project\n- 🌐 [GitHub Profile](https://github.com/ShubhamKumar0786https://github.com/ShubhamKumar0786)  \n- 📧 Email:shubhamkashyap9501@gmail.com\n- LinkedIn: [Linkedin_link](https://www.linkedin.com/in/shubham0786/)\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshubhamkumar0786%2Fcustomer_churn_prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshubhamkumar0786%2Fcustomer_churn_prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshubhamkumar0786%2Fcustomer_churn_prediction/lists"}