Statistical Machine Learning-A3

This is a assignment for unimelb COMP90051-SML, aims to analyse and predict outcomes using the ISIC 2024 Challenge dataset with machine learning techniques.

Team Members

Name	Student ID	Email
Kejing Li	1240956	[email protected]
Wenxi Deng	1266203	[email protected]
Zhihe Ping	1238760	[email protected]

File

• data_pull.ipynb: A Jupyter Notebook that involves all commands to download data from Kaggle.
• Logistic_regression.ipynb: A Jupyter Notebook file that contains the complete code for data preprocessing, feature engineering, model training, and evaluation for Logistic Regression model.
• resnet50.ipynb: A Jupyter Notebook file that contains the complete code for image processing and argumentation, model training, and evaluation for ResNet-50 model.
• image_processing.ipynb a jupyter notebook that shows different image preprocessing, argumentation, and feature extraction methods
• Ensemble-models.ipynb : a jupyter notebook with ensemble models for data processing, training, and visualization of model performance metrics.

Installation and How to run the code

• Install the required Python libraries: os, itertools, numpy, pandas, seaborn, matplotlib, polars, sklearn, imblearn, matplotlib, opencv-python, pillow, scikit-image, torch, torchvision
• Open data_pull.ipynb to download the datasets
• Open corresponding model's Jupyter Notebook and run the desired sections