🚞 Skin 🚒 Cancer 🚂 Classification 🚋 is a computer ✈ vision and 🚀deep learning 🛼 that uses 🛸 Convolutional ⛱ Neural 🌈 Networks to 🚤 classify 🕌 skin lesions 🏘 and detect 🏟 potential 🏜 skin cancer 🏛 from 🏬 dermatology 🏥 images The 🚄 project aims 🏯 to support ⚽ early diagnosis ⚾ and treatment 🥎 through AI 🏐 powered image 🥊 analysis

CatIss is an intelligent tool for automatic categorization of issue reports based on the RoBERTa model.

MATLAB-based ML model for submission classification

The final project of the Data Mining course involves classifying Persian songs on Spotify as either traditional or non-traditional, based on factors such as their popularity, artist name, and song title

Mask Detection Using CNNs

A Machine Learning model to classify wether a website is phishing website or not using Python.

Film genre and named entity detection in Persian movie descriptions using NLP techniques, including traditional ML, transformers, and BERT-based models.

A repository for Datathon 2025

Machine learning project analyzing Premier League 2023/24 player data. Includes goal & assist prediction models (regression), player position classification, and statistical performance analysis across 580+ players using features like xG, npxG, progressive carries/passes, and per-90 metrics.

A machine learning system for predicting and preventing athlete injuries using advanced data analysis, risk assessment, and tailored recommendations.

A Machine Learning based Election Prediction System built using Python, Scikit-Learn, CatBoost, XGBoost, and Streamlit.

ExLibris is a Python library that provides statistical comparisons between different binary classification models. This library is specifically designed to compare the GSGP classifier with other models.

A curated set of implementations, experiments, and notes exploring algorithms and techniques in machine learning.

Machine learning models for spam email and credit card fraud detection, featuring data preprocessing, feature engineering, and classification techniques.

Student segmentation and academic risk prediction using K-Means clustering, PCA, SMOTE, and deep learning on 12K+ student records. Identifies at-risk students and predicts course outcomes using behavioral and academic data.

This repositorie contains two projects that uses different models to classify and use regression on two datasets respectively.

A machine learning project that uses Logistic Regression to classify apples as good or bad based on various features.

Machine Learning With Python - IBM Data Science