ML-Based-Vehicle-Predictive-Maintenance-System-with-Real-Time-Visualization

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ML-Based-Vehicle-Predictive-Maintenance-System-with-Real-Time-Visualization

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AI-driven predictive maintenance for vehicles using GBM models on real-time sensor data. Proactive fleet management, cost reduction, and efficient transportation enabled by forecasting maintenance needs and part failure probabilities through a web application interface.

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# ML-Based Vehicle Predictive Maintenance System with Real-Time Visualization

## Overview

This project implements AI-driven predictive maintenance for vehicles, leveraging machine learning techniques to forecast maintenance needs based on real-time sensor data. The system preprocesses sensor data for reliability, utilizes Gradient Boosting Machine (GBM) models for prediction, and integrates a web application interface for real-time data visualization and predictions. The goal is to enable proactive fleet management, reduce costs, and ensure efficient transportation by predicting maintenance probability, estimating maintenance dates in advance, and providing probability percentages for potential part failures.

## Features

- Collects real-time sensor data from vehicles.
- Preprocesses sensor data for reliability.
- Utilizes GBM machine learning model for predictive maintenance.
- Integrates a web application interface using Streamlit for real-time data visualization and predictions.
- Predicts maintenance probability based on model output.
- Estimates maintenance date 2-3 weeks in advance.
- Provides probability percentage for potential part failure.
- Enhances predictive accuracy and allows proactive intervention.
- Facilitates timely maintenance scheduling to minimize downtime.

## Benefits

- Performing maintenance regularly ensures optimal vehicle performance.
- Optimizes resources and prevents emergency repairs before embarking on long journeys.
- Enhances safety for both drivers and passengers.
- Improves supply chain efficiency by reducing unexpected breakdowns.
- Increases customer satisfaction by ensuring reliable transportation services.

## Repository Contents

- **engine_data.csv/**: Contains the dataset used for training the machine learning model.
- **data_preprocessing/**: Includes the code for the GBM machine learning model.
- **app.py**: Streamlit code for the web application interface deployment.

## Usage

1. Clone the repository: `git clone <repository_url>`
2. Navigate to the project directory: `cd <repository_directory>`
3. Install dependencies: `pip install -r requirements.txt`
4. Run the Streamlit application: `streamlit run app.py`
5. Access the web interface via the provided URL.

## Contributors

- Dharshan A
- Pravin R F
- Rishikaandh Devadoss
- Abinayaa M K

## Contact

For questions or inquiries, please contact dharshanathi21@gmail.com.

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