In this project, students will collaborate to develop machine learning models capable of accurately identifying different materials, including resin, dyed materials, natural minerals, and rocks. They will explore various machine learning algorithms and techniques to build robust models for material classification, contributing to the development of a reliable material identification system.

Project Description:

In this project, students will work together to design, train, and evaluate machine learning models for material identification. The project consists of the following key tasks:

Data Preparation:

• Students will start with the dataset of materials, including images, spectroscopic data, or any other relevant features.
• Data preprocessing steps such as normalization, feature extraction, and data augmentation (if applicable) will be applied to prepare the dataset for model training.

Model Selection:

• Students will explore various machine learning algorithms suitable for classification tasks, such as Support Vector Machines (SVM), Random Forests, Convolutional Neural Networks (CNNs), or others depending on the data type.
• Based on the dataset and project requirements, they will select one or more algorithms to build and compare models.

Model Training and Evaluation:

• Implement and train machine learning models using the prepared dataset.
• Evaluate model performance using appropriate metrics (e.g., accuracy, precision, recall, F1-score) through cross-validation or holdout validation.

Hyperparameter Tuning:

• Optimize model hyperparameters to improve performance.
• Experiment with different hyperparameter settings to find the best configuration.

Project Deliverables:

Upon completion of the project, students will deliver the following:

1. Trained Machine Learning Models: Machine learning models for material identification based on the selected algorithms, including the code and documentation for model training.
2. Model Evaluation Report: A report summarizing the model evaluation results, including performance metrics and insights gained from the evaluation process.