This repository explores the use of machine learning to predict heart attack risk factors. It analyzes a dataset of heart failure patients, examining various features like age, medical history, lab results, and the occurrence of death events.

Machine Learning project for Iris Flower Classification using Random Forest | CodeAlpha Data Science Internship

machine learning project for to identify the risk of the credit using best trained model

Finding Donors, CharityML, a Supervised Learning Machine Learning Project.

An end-to-end breast cancer tumor stage prediction system leveraging machine learning, clinical and genomic data, and a Flask-powered web interface to deliver accurate, real-time predictions.

This project is a production-ready text classification system built using BERT. It takes raw text input (e.g., customer issues) and predicts the most relevant category along with a confidence score.

modeling on loan dataset

An AI-powered Endpoint Detection and Response (EDR) simulation.

Binary classification system to detect fraudulent credit card transactions using Decision Tree and SVM models with feature analysis and evaluation metrics.

Machine learning API for Iris classification built with FastAPI and scikit-learn.

Medical condition prediction using TensorFlow neural network classifies patient conditions from clinical data using NLP-based text encoding and deep learning.

AI-powered network switch monitoring system using SNMP and anomaly detection

Machine learning model for predicting diabetes using medical data, demonstrating an end-to-end ML pipeline with training, evaluation, and model persistence.

Modeling on a timeseries dataset

End-to-end customer churn analysis and prediction using Python, Machine Learning, and Power BI with actionable business insights.

smart waste identification with DeepLearning

Multi-role AI agent system — customer service, HR portal & owner dashboard — built with LangGraph, GPT-4o, RAG, and ML predictions. Arabic + English support.

Machine learning regression project for predicting house prices using feature normalization, EDA, and model training with Scikit-learn.

A collection of data analysis notebooks exploring data cleaning, preprocessing, and machine learning using Python, Pandas, and Scikit-Learn.