sikit-learn

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

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.

smart waste identification with DeepLearning

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

modeling on loan dataset

End-to-end Machine Learning project for predicting candidate job-change intention using Logistic Regression, class balancing, cross-validation, and feature analysis.

An AI-powered recruitment screening system using Machine Learning and Streamlit.

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.

Exploratory data analysis (EDA) of the UCI Iris dataset examining measurement variations, feature distributions, and pairwise correlations across flower species.

NLP text classification project for emotion detection using spaCy, TF-IDF, Logistic Regression, and Random Forest.

Handwritten digit classification comparing from-scratch NumPy kNN, linear and polynomial regression with PCA and a Decision Tree baseline.

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.

End-to-end ML system for network throughput prediction, from data analysis and modeling to an interactive Streamlit application.

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

A beginner-friendly repository for learning TensorFlow and basic machine learning concepts using Google Colab.

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

Benchmark regression analysis on the scikit-learn diabetes dataset evaluating baseline clinical predictors for one-year disease progression using Ridge Regression ($R^2 = 0.457$).

Time-series regression modeling using Ridge Regression to forecast daily bike demand from calendar and weather features, evaluated on a chronological holdout ($R^2 = 0.613$).