This repository contains my completed notebooks (.ipynb files) from various Kaggle Competitions and Kaggle Learn courses.
Each notebook includes the solutions and implementations for the official Kaggle exercises and competitions, covering a wide range of Data Science & Machine Learning topics.
- Predicting F1 Pit Stops
- Retroviral Wall Challenge
- ROGII - Wellbore Geology Prediction
- BirdCLEF+ 2026
- March Machine Learning Mania 2026
- The Gemma 4 Good Hackathon
- Measuring Progress Toward AGI - Cognitive Abilities
- The Post-Backprop Challenge: Zero-Gradient Learning for Efficiency
- IMAGINE-decoding-challenge
- Pierce the VEIL: Hack It and Crack It Simulation
- GeoHab 2026 MLWG Competition
- SPR 2026 Mammography Report Classification
- Hedge fund - Time series forecasting
- The 2026 NeuroGolf Championship
- LLM Operator Development and Optimization
- Maze Crawler
- Orbit Wars
- 3LC Multi Vehicle detection Challenge
- NVIDIA Nemotron Model Reasoning Challenge
- AI Mathematical Olympiad - Progress Prize 3
- LLM Agentic Legal Information Retrieval
- WiDS Global Datathon 2026
- Predict 1-Year US Stock Returns from Fundamentals
- ARC Prize 2026 - ARC-AGI-2
- ARC Prize 2026 - ARC-AGI-3
- Harmonizing the Data of your Data
- Predict Customer Churn
- Predicting Irrigation Need
- UMUD Challenge: Muscle Architecture in Ultrasound Data
- Triagegeist - Predict emergency severity
- Multi-view Pig Posture Recognition
- Stanford RNA 3D Folding Part 2
- CAFA 6 Protein Function Prediction
- Vesuvius Challenge - Surface Detection
- Catechol Benchmark Hackathon (NeurIPS 2025 DnB)
- Rental product recommendation system
- Recod.ai/LUC - Scientific Image Forgery Detection
- PhysioNet - Digitization of ECG Images
- CSIRO - Image2Biomass Prediction
- Predicting Student Test Scores
- Social Media Extremism Detection Challenge
- Santa 2025 - Christmas Tree Packing Challenge
- Predicting Heart Disease
- Deep Past Challenge - Translate Akkadian to English
- Diabetes Prediction Challenge
- Connect X
- Housing Prices Competition for Kaggle Learn Users
- Titanic - Machine Learning from Disaster
- Spaceship Titanic