A machine learning classification system for early detection of Disease X based on patient symptoms using Python, Scikit-learn, and Streamlit.

📌 Projet de Data Mining sur l'amélioration du minsup dynamique dans l'algorithme Apriori, testé sur un dataset médical.

Exploratory Data Analysis of the Breast Cancer Wisconsin (Diagnostic) dataset, uncovering patterns that separate benign and malignant tumors.

A Python project that analyzes medical examination data and generates insightful visualizations. It produces categorical bar plots and correlation heatmaps to explore health-related metrics.

**Sepsis Prediction System** is a machine learning-based application designed to predict the risk of sepsis using patient health data and clinical parameters. The system analyzes medical information to identify early signs of sepsis, enabling timely intervention and supporting healthcare decision-making through predictive analytics.

The Medical Data History Analysis project focuses on analyzing patient healthcare records using SQL to extract meaningful insights from medical datasets. The project demonstrates how Structured Query Language (SQL) can be used to manage, retrieve, and analyze healthcare data efficiently for better decision-making and patient care.

Demystifying liver medical data using machine learning

This project focuses on improving the performance of classifiers on imbalanced time series clinical data using a deep learning approach, integrating advanced techniques to handle data imbalance.

This project demonstrates the use of machine learning models to predict breast cancer diagnoses. The repository covers the entire workflow from data preprocessing and feature engineering to model training and evaluation, providing insights into diagnosis prediction with various ML models.