Predicting Spider Web Toughness with AutoGluon

This repository contains the code and dataset used in the development of a machine learning model to predict the structural integrity and toughness of spider webs. The project utilizes AutoGluon, an automated machine learning library, to train and optimize models capable of predicting whether a spider web will hold or rupture under various conditions such as different particle impacts and web geometries.

Project Overview

The goal of this project is to apply advanced machine learning techniques to biological materials science, specifically to predict the toughness of spider webs. This could have implications for materials science research and help in the design of new, bio-inspired materials.

Features

• Data Preprocessing: Scripts to clean and prepare the data for modeling.
• Model Training: Utilization of AutoGluon for training multiple models automatically.
• Model Evaluation: Evaluation metrics and validation results to understand model performance.
• Predictive Analysis: Use the model to predict and analyze the web's toughness under different scenarios.

Dataset

The dataset includes simulated data points of engineering stress and strain under different conditions:

• Engineering Strain
• Engineering Stress (in MPa)

Each instance in the dataset represents a different configuration and condition of the spider web during the impact tests.

Prerequisites

Before you run the project, ensure you have the following installed:

• Python 3.8 or later
• AutoGluon
• Pandas
• NumPy
• Matplotlib

You can install the necessary libraries using pip:

pip install autogluon pandas numpy matplotlib

Clone the repository:

```bash
git clone https://github.com/yourusername/spider-web-toughness-prediction.git
cd spider-web-toughness-prediction

Run the training script:

```bash
python train_model.py

This script will preprocess the data, initiate a model training session using AutoGluon, and save the best model.
Evaluate the model:

```bash
python evaluate_model.py

Run this script to load the trained model and evaluate its performance on a validation set.

![Spidytough](https://github.com/user-attachments/assets/e495a5b4-501c-40ee-be16-3446c221309c)

Contributing
Contributions to this project are welcome! Please fork the repository and submit a pull request with your features or fixes.