Stars
Production-ready Arabic fake news detection system using state-of-the-art AraBERT and XGBoost models. Achieves over 96% F1-score on 13,750 verified news samples. Includes pre-trained models, full t…
ETL pipeline and data warehouse for e-commerce analytics using PostgreSQL and Python. Includes transformation scripts, schema modeling, and business insights via SQL.
A project for detecting and decoding barcodes from real-life product images using image preprocessing, contour detection, and debugging techniques. Includes a dataset, preprocessing pipeline, and p…
Rule-based expert system with 40+ logic rules and fuzzy inputs to assess and prioritize post-war building reconstruction in Gaza. Includes a professional Streamlit UI and validation notebook.
Use machine learning to predict grocery sales
Analyzes Gaza-related tweets using LLaMA-3 via GroqCloud. Combines Selenium scraping with LLM-powered sentiment analysis to surface public emotion trends.
An open-source RAG-based tool for chatting with your documents.
Sentiment analysis on BBC News headlines and descriptions using VADER. Classifies text as Positive, Negative, or Neutral based on compound scores.
Practice dataset for regression or classification modelling
Pima Indians Diabetes - ML Model Selection (83%)
Use DML and DDL scripts to build a simple HR SQL project.
A total of 170 couples are included in this dataset, along with the Divorce Predictor Scale variables (DPS), a questionnaire of 54 questions, A five-point scale was used to measure all responses.
Website that can display dynamic books while also allowing administrators to modify, delete, or add books in the SQL database.
The dataset includes 171 molecules designed for functional domains of a core clock protein, CRY1, responsible for generating circadian rhythm. 56 of the molecules are toxic and the rest are non-toxic.
Apply Association rule
In this challenge, we ask you to build a predictive model that answers the question: “what sorts of people were more likely to survive?” using passenger data (ie name, age, gender, socio-economic c…
Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. With 79 explanatory variables descri…