⚔️ISIC Competition 🧪Feature Extraction and Ensemble
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Updated
Jun 6, 2025 - Jupyter Notebook
⚔️ISIC Competition 🧪Feature Extraction and Ensemble
98% accurate - This stroke risk prediction Machine Learning model utilises ensemble machine learning (Random Forest, Gradient Boosting, XBoost) combined via voting classifier. We tune parameters with Stratified K-Fold Cross Validation, ROC-AUC, Precision-Recall Curves and feature importance analysis.
The objective is to develop a model that accurately predicts whether users will cancel their tickets. Each cancellation incurs a fine for the ticket registration site from the passenger company.
Effective deep learning approaches for animal classification.
This project utilizes advanced data analysis and machine learning techniques to predict equipment failures before they occur. The goal is to detect anomalies and possible defects in equipment and processes to enable preemptive maintenance, thereby reducing downtime and costs.
Skin Cancer Detection Using both image and corresponding metadata. Used ISIC 2024 Dataset from Kaggle.
A model to identify hate speech (racist or sexist tweets) on Twitter
This repo's only used to archive the syntaxs i used for my thesis and yes, they're available for public use!
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