🚗 Automotive Vehicle Engine Health Analysis

📌 Overview

This project analyzes vehicle engine health using data-driven techniques, including machine learning and statistical analysis. It helps predict potential failures, optimize maintenance schedules, and improve vehicle performance.

🛠️ Features

• Engine Health Assessment – Analyze key performance indicators.
• Predictive Maintenance – Forecast potential failures using data analytics.
• Real-Time Insights – Optimize vehicle maintenance and reduce downtime.
• Scalable & Efficient – Suitable for automotive diagnostics and fleet management.

🛠️ Technologies Used

• Programming: Python / MATLAB
• Data Analysis: Pandas, NumPy, SciPy
• Machine Learning: Scikit-learn, TensorFlow
• Visualization: Matplotlib, Seaborn

🚀 Getting Started

1️⃣ Clone the Repository

git clone https://github.com/yourusername/automotive-engine-health.git

2️⃣ Install Dependencies

pip install -r requirements.txt

3️⃣ Download Dataset

import kagglehub

# Download latest version
path = kagglehub.dataset_download("parvmodi/automotive-vehicles-engine-health-dataset")

print("Path to dataset files:", path)

4️⃣ Run the Notebook

Open and run automotive_vehicles_engine_health.ipynb using Jupyter Notebook.

📊 Applications

• Automotive Industry: Enhancing predictive maintenance strategies.
• Fleet Management: Reducing downtime and improving reliability.
• Individual Car Owners: Proactive vehicle health monitoring.

🤝 Contributing

Feel free to fork this repository, submit issues, or contribute improvements.

🐜 License

This project is licensed under the MIT License.