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

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

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

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

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.

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

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

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.

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

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.

A multimodal, context-aware health intelligence system that uses camera-based PPG, voice analysis, facial expression tracking, and environmental context to deliver real-time risk assessments.

Machine Learning project for predicting student academic performance using regression and classification models with full EDA, preprocessing, evaluation, and visualization workflow.