Mechanical Properties Prediction using Machine Learning

Overview

This project focuses on utilizing machine learning techniques to predict mechanical properties of materials. The goal is to develop accurate models that can estimate key mechanical characteristics based on input features such as material composition, processing parameters, and other relevant factors. By leveraging machine learning, we aim to streamline the prediction process, enhance accuracy, and expedite the material design and selection processes in the field of engineering.

Table of Contents

• Introduction
• Features
• Data
• Data Preprocessing
• Model Training
• Evaluation
• Results
• Future Work
• Contributing

Introduction

In many engineering applications, understanding the mechanical properties of materials is crucial for designing reliable and efficient structures. Traditional methods for predicting these properties can be time-consuming and expensive. This project explores the application of machine learning to predict mechanical properties, offering a more efficient and cost-effective approach.

Features

• Data-driven Predictions: Utilizing a dataset containing information about material composition to train machine learning models.

• Model Flexibility: Implementing various machine learning algorithms to find the most suitable model for the given dataset.

• Scalability: Designing the system to accommodate new data easily and update models for continuous improvement.

Data

The dataset used for training and testing the models is already located in the root directory. It contains information on material composition and other relevant features.

Data Preprocessing

Before training the machine learning models, the data needs preprocessing to handle missing values, normalize features, and ensure it is suitable for training.

Model Training

Machine learning models are trained using the preprocessed data. Multiple algorithms are explored, and the best-performing model is selected for further evaluation.

Evaluation

The performance of the trained models is evaluated using various metrics, such as mean absolute error, mean squared error, and R-squared score.

Results

Detailed results and insights gained from the models are documented in this section, providing transparency and allowing for comparisons between different approaches.

Regression using Sklearn models

Regression using Neural Network

Future Work

Potential improvements and extensions to the project are outlined, including additional features, model enhancements, and integration with other technologies.

Contributing

Contributions to this project are welcome. If you have ideas for improvements or find issues, please open a new issue or submit a pull request.

Feel free to reach out to me the project for any questions or clarifications.

Happy predicting! 🤖🔧