Predictive Analytics in Automotive Manufacturing: Testing Time Optimization

Overview:

Welcome to the repository for the project "Predictive Analytics in Automotive Manufacturing: Testing Time Optimization." This project is dedicated to leveraging predictive modeling techniques to optimize testing time in the automotive manufacturing industry. By exploring advanced machine learning methods, our goal is to reduce testing duration while considering environmental impact, contributing to a more sustainable approach in automotive testing.

Key Features:

• Diverse Predictive Models:

  • Implementation of various machine learning models, including SVM, ElasticNet, XGBoost, and Random Forest, for predictive analytics in testing time optimization.

• Innovative Feature Engineering:

  • Utilization of advanced feature engineering techniques such as feature encoding, Principal Component Analysis (PCA), and Fast Independent Component Analysis (FastICA) to enhance model performance.

• Robust Stacking Ensemble:

  • Orchestrated a powerful stacking ensemble approach, incorporating an ElasticNet meta-model for effective testing time predictions.

• Sustainable Impact:

  • Development of a predictive model aiming to minimize testing duration, contributing to a greener approach in automotive testing and reduced carbon emissions.

Getting Started:

1. Clone the Repository:

   git clone https://github.com/raghavendranhp/Predictive-Analytics-in-Automotive-Manufacturing-Testing-Time-Optimization.git

2. Install Dependencies:

   pip install -r requirements.txt

3. Explore Notebooks:

• Predictive_Model.py: Implementation of machine learning models, feature engineering, and the stacking ensemble.

Dataset:

• The project utilizes a dataset (data/train.csv and data/test.csv) containing anonymized variables representing custom features in automotive testing.
• Features include categorical variables denoted by letters and binary variables denoted by 0/1.
• The ground truth is labeled as 'y,' representing the time (in seconds) the vehicle took to pass testing for each variable.
• Visit Kaggle Dataset

Future Improvements:

• Explore additional machine learning models for further optimization.
• Incorporate real-time data for dynamic testing time predictions.
• Enhance model interpretability and explainability.

Contributors:

• Raghavendran S

License:

This project is licensed under the MIT License.

Acknowledgments:

• The project acknowledges the use of external libraries, frameworks, or Kaggle datasets that contributed to its development.
• Any inspiration or references are duly acknowledged and appreciated.