Battery Capacity Prediction System 🔋

Overview

This project implements a machine learning pipeline for predicting battery capacity using various input parameters. The system includes data processing, model training, and a Flask web interface for making real-time predictions.

🌟 Key Features

• End-to-end ML pipeline for battery capacity prediction
• Real-time prediction API using Flask
• Comprehensive data validation and transformation
• MLflow integration for experiment tracking
• DagsHub integration for model registry
• Modular and maintainable code structure
• Logging and error handling

🏗️ Project Structure

Battery_Capacity_Prediction/
├── README.md
├── setup.py
├── src/
│   ├── __init__.py
│   ├── components/
│   │   ├── __init__.py
│   │   ├── data_ingestion.py
│   │   ├── data_transformation.py
│   │   ├── data_validation.py
│   │   ├── model_evaluation.py
│   │   └── model_trainer.py
│   ├── config/
│   │   ├── __init__.py
│   │   └── configuration.py
│   ├── constants/
│   │   └── __init__.py
│   ├── entity/
│   │   ├── __init__.py
│   │   └── config_entity.py
│   ├── pipeline/
│   │   ├── __init__.py
│   │   ├── data_ingestion_pipeline.py
│   │   ├── data_transformation_pipeline.py
│   │   ├── data_validation_pipeline.py
│   │   ├── model_evaluation_pipeline.py
│   │   ├── model_training_pipeline.py
│   │   └── prediction_pipeline.py
│   └── utils/
│       ├── __init__.py
│       └── common.py
└── tests/
    └── test_pipeline.py

🚀 Getting Started

Prerequisites

• Python 3.8 or higher
• pip package manager

Installation

1. Clone the repository:

git clone https://github.com/yourusername/Battery_Capacity_Prediction.git
cd Battery_Capacity_Prediction

2. Create and activate a virtual environment:

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

3. Install the package in development mode:

pip install -e .

This will install the package Battery_Capacity_Prediction_System and all its dependencies.

⚙️ Configuration

The project uses a modular configuration system:

• src/config/configuration.py: Main configuration management
• src/entity/config_entity.py: Configuration entities and data structures
• src/constants/__init__.py: Global constants and configurations

🔄 Training Pipeline

Running the Training Pipeline

To execute the complete training pipeline:

python main.py

This will run the following stages:

1. Data Ingestion
2. Data Validation
3. Data Transformation
4. Model Training
5. Model Evaluation

Pipeline Components

• data_ingestion.py: Handles data loading and initial processing
• data_validation.py: Validates data quality and schema
• data_transformation.py: Performs feature engineering and preprocessing
• model_trainer.py: Implements model training logic
• model_evaluation.py: Evaluates model performance using MLflow

🌐 Web Application

Running the Flask App

To start the prediction service:

python app.py

DEMO

The application will be available at http://localhost:8080

API Endpoints

• GET /: Home page with prediction form
• POST /predict: Make predictions
• POST /train: Retrigger model training

📊 Model Tracking

This project uses MLflow for experiment tracking and DagsHub for model registry. Configure your MLflow tracking URI and DagsHub credentials in the configuration files.

💡 Input Features

The model accepts the following features for prediction:

• Battery Type
• Start Time
• Ambient Temperature
• Battery ID
• Test ID
• UID
• Filename
• Re (Resistance)
• Rct (Charge Transfer Resistance)

🧪 Testing

Run the tests using:

python -m pytest tests/

🔧 Development Workflow

To modify or extend the pipeline:

1. Update configuration in src/config/configuration.py
2. Define new entities in `src/

Assignment Output

Task-1

Task-2

License

This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details.

Contact Information

For questions or collaboration opportunities: