🛠 Predictive Maintenance System (Flask Web App)

This is a machine learning-based web application that predicts machine failure using real-time sensor inputs. Built with Flask, the app uses a Random Forest Classifier trained on predictive maintenance data.

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🚀 Features

• Predicts equipment failure based on 8 real-time sensor inputs.
• Web interface built with Flask and styled with CSS.
• Integrates a trained ML model (rf_model.pkl) using joblib.
• Fully deployable on Render or Heroku.

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📂 Project Structure

predictive-maintenance-system/
│
├── data/                    # Raw and processed datasets
├── models/                  # Trained models (saved as .pkl or .h5)
├── notebooks/               # Jupyter notebooks for EDA & training
├── app/                     # Flask app
│   ├── static/              # CSS, JS files
│   ├── templates/           # HTML files
│   └── app.py               # Flask application
├── requirements.txt         # All dependencies
├── README.md                # Project overview
└── train_model.py           # Script to train the model

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💻 How to Run Locally

1. Clone the Repository

git clone https://github.com/yourusername/predictive-maintenance-app.git
cd predictive-maintenance-app

2. Create and Activate a Virtual Environment

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install Dependencies

pip install -r requirements.txt

4. Run the Flask App

python app.py

Open your browser and go to http://127.0.0.1:5000/

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🌐 Deployment on Render

1. Push your project to a GitHub repository.
2. Create a free Render account at render.com.
3. Create a new web service and connect your repo.
4. Fill in:
   • Build Command: pip install -r requirements.txt
   • Start Command: gunicorn app:app
   • Environment: Python 3.x
5. Click Deploy.

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📊 Model Details

• Algorithm: Random Forest Classifier
• Accuracy: 99.7%
• Metrics: Precision = 94%, Recall = 97%, F1-score = 95%
• Raw Dataset: Machine Predictive Maintanence Classification Dataset
• Trained On: Preprocessed machine sensor data (processed_data.csv)
• Features: Temperature, Torque, Tool Wear, Rotational Speed, etc.

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📧 Contact

Built by Abhimantr Singh
📫 Email: abhimantrsingh@gmail.com
🏫 VIT Chennai | Dept. of CSE

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📄 License

This project is for educational and demonstration purposes only.