Kaggle Competitions

A comprehensive repository featuring high-performance solutions, scholarly explanations, and curated datasets for various Kaggle competitions, ranging from various techniques to advanced deep learning models.

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Author  ·  Overview  ·  Toolbox  ·  Competitions  ·  Certifications  ·  Structure  ·  Usage Guidelines  ·  License  ·  About  ·  Acknowledgments

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Author

Amey Thakur

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Overview

Kaggle Competitions is a curated digital laboratory documenting my trajectory through the world of competitive data science. This repository serves as a scholarly archive for machine learning solutions, featuring meticulously commented notebooks, optimized heuristics, and structured datasets.

Each competition entry is more than just code; it is an exploration of algorithmic theory, feature engineering, and model optimization. By bridging the gap between raw implementation and high-level strategy, this repository provides a transparent gateway into the mechanics of competition-winning logic.

Note

Foundational Resource: The Kaggle Book

Konrad Banachewicz and Luca Massaron
An essential reference for bridging the gap between theoretical knowledge and high-performance competitive execution.

Strategic Heuristics

Each project within this ecosystem is governed by strict design patterns ensuring clarity, reproducibility, and high-performance execution:

• Scholarly Commentary: Comprehensive technical walkthroughs detailing the theoretical underpinnings, mathematical frameworks, and architectural decisions behind each model.
• Computational Efficiency: Optimized implementation strategies tailored to meet diverse hardware constraints, ensuring low-latency inference and high-throughput training.
• Modular Architecture: Fully self-contained environments featuring integrated datasets, robust preprocessing pipelines, and verified submission utilities.

Tip

Validation Reliability

A consistent challenge in machine learning competitions is learning to prioritize local metrics over the public leaderboard. Because public scores reflect a limited sample, they often reward models that fit to noise rather than true signal. If a change hurts your local cross-validation, it should be discarded regardless of short-term leaderboard gains.> Relying completely on a strict local evaluation setup is the best method to maintain stability on the final hidden dataset.

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Kaggle Toolbox

A production-grade utility library for standardized performance optimization and automated pipeline diagnostics.

Technical Core	Functional Description
seed_everything	Full seed synchronization across Python, NumPy, PyTorch, and TensorFlow.
reduce_mem_usage	Intelligent downcasting for massive tabular datasets to prevent OOM failures.
missing_report	Comprehensive visualization and statistical breakdown of data sparsity.
find_useless_columns	Byte-level uniqueness checks to identify constant or redundant features.
check_submission	Pre-inference validation for submission format and column consistency.
timer	Context manager for precise bottleneck profiling and execution timing.
system_info	Diagnostic report of hardware constraints and environment specifications.
cv_score	Scalable cross-validation wrapper with integrated param validation.
find_correlated_features	Advanced correlation matrix filtering to mitigate multicollinearity.
find_input	Directory-level automated mapping for Kaggle dataset and competition paths.

Important

Utility Script Automation

Standardizing common diagnostic logic into a Utility Script on Kaggle ensures consistency across the competition lifecycle. This automation approach, exemplified by my Kaggle Toolbox, eliminates redundant boilerplate and enables automated updates for any dependent competition notebook.

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Competitions Index

The index below categorizes active projects by their competitive domain and foundational methodology. Each entry provides direct access to exhaustive technical documentation and the corresponding Kaggle execution environment.

#	Competition Portfolio	Medal	Domain	Technical Methodology	Documentation	Environment
1	AI Mathematical Olympiad	-	Mathematics NLP LLM	Self-Correction Symbolic Computation Agentic Inference	Analysis
2	Are You A Robot?	1/8	NLP Classification	Stylometric Analysis Gaussian Mixture Models TF-IDF	Analysis
3	BirdCLEF+ 2026	-	Bioacoustics Deep Learning Time Series	Perch v2 Model Fusion TF 2.20 Hybrid Setup Bayesian Inference	Analysis
4	CS Week Codeathon AIML (Easy Level)	-	Education Tabular Data	EDA Feature Engineering Regression	Analysis
5	Connect X		Game Theory Simulation	Minimax Alpha-Beta Pruning Heuristics Move Ordering	Analysis
6	English Scoring - Corrected Ver	-	English Proficiency NLP	Text Vectorization Regression Analysis	Analysis
7	Evading AI Detection	1/3	NLP Generative AI	Activation Steering Sparse Autoencoders Latent Feature Suppression	Analysis
8	GOSIM Spotlight 2026: Frontier Creators	-	Interpretability Uncertainty Mapping LLM	Logit Extraction Probability Tiers Entropy Visualization	Analysis	Notebook
9	Harmonizing the Data of your Data	-	Scientific Proteomics NLP	Rule-based Extraction Ontology Normalization	Analysis
10	Hedge fund - Time series forecasting	-	Financial Markets Time Series	LightGBM Ensembles Memory Optimization Weighted RMSE Rolling Validation	Analysis
11	House Prices - Advanced Regression Techniques	-	Tabular Data Regression	Deterministic Record Linkage Normalization Data Alignment	Analysis
12	LLM Classification	-	LLM Preference Ensemble Inference	Gemma-2-9B Llama-3-8B Pipeline Parallelism Logit Interpolation	Analysis
13	Measuring Progress Toward AGI	-	Attention Cognitive Control LLM	Salient Distractor Injection Selective Attention SDK Benchmarking	Analysis
14	Petals to the Metal	-	Computer Vision Deep Learning	Tensor Processing Units (TPU) Image Classification	Analysis
15	Predict Customer Churn		Binary Classification Tabular Data	XGBoost CatBoost LightGBM Optuna	Analysis
16	Stanford RNA 3D Folding	-	Structural Biology Biophysics	Hybrid TBM + Protenix-v1 Template-Based Modeling (TBM) Chunked Inference Kabsch Stitching	Analysis
17	Student Study Hours to CGPA Prediction	-	Education Regression	Polynomial Expansion Ridge/Lasso K-Fold CV MSE Optimization	Analysis
18	Titanic		Classification Forensic Analysis	Deterministic Record Linkage Normalization Data Alignment	Analysis
19	Triagegeist	-	Clinical Informatics Decision Support	Hierarchical Ensemble LightGBM + CatBoost Hemodynamic Feature Science Uncertainty-Aware Logic	Analysis

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Course Certifications

A curated collection of 17 professional certifications awarded by Kaggle, covering the full spectrum of data science and machine learning.

Note

View All 17 Certifications

Curricular Category	Professional Certification
Foundation	Intro to Programming Python Intro to SQL Advanced SQL
Machine Learning	Intro to ML Intermediate ML Feature Engineering ML Explainability
Applied Analytics	Pandas Data Visualization Data Cleaning
Specialization	Deep Learning Computer Vision Time Series Geospatial Analysis
Advanced Theory	Game AI & RL AI Ethics

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Project Structure

├── docs/                                           # Kaggle Assets
│
├── Badges/                                         # Earned Kaggle Badges (36)
│   └── README.md                                   # Badge Portfolio
│
├── BirdCLEF+ 2026/                                 # Bioacoustics: Bird Call Classification
│   ├── birdclef-2026-perch-v2-bayesian-fusion.ipynb # Verified Notebook Solution
│   └── bc26-tensorflow-2-20-0-setup.ipynb          # Environment Setup
│
├── Medals/                                         # Competition Medals
├── Tiers/                                          # Kaggle Community Tiers
│
├── Kaggle Courses/                                 # Professional Certifications (17)
│   └── README.md                                   # Certification Portfolio
│
├── Kaggle Toolbox/                                 # Production Utility Library
│   ├── kaggle_toolbox.py                           # Core Utility Functions
│   └── kaggle-toolbox-demo.ipynb                   # Library Demonstration
│
├── AI Mathematical Olympiad/                       # NLP: Mathematical Reasoning
│   ├── aimo-diagnostics-inference.ipynb            # Verified Notebook Solution
│   └── aimo-setup.ipynb                            # Environment Setup
│
├── Are You A Robot/                                # NLP: Multi-Task Essay Analysis
│   ├── README.md                                   # Technical Analysis
│   └── are_you_a_robot.ipynb                       # Verified Notebook Solution
│
├── CS Week Codeathon AIML (Easy Level)/            # Education: Academic Performance
│   ├── README.md                                   # Technical Analysis
│   └── student-final-score-prediction-with-eda-and-fe.ipynb # Verified Notebook Solution
│
├── Connect X/                                      # Simulation: Connect Four Variant
│   ├── README.md                                   # Technical Analysis
│   └── connectx-minimax-alpha-beta-agent.ipynb     # Verified Notebook Solution
│
├── English Scoring - Corrected Ver/                # NLP: English Language Proficiency
│   └── english-scoring-regression.ipynb            # Verified Notebook Solution
│
├── Evading AI Detection/                           # NLP: Generative AI & Steering
│   ├── README.md                                   # Technical Analysis
│   └── evading_ai_text_detection.ipynb             # Verified Notebook Solution
│
├── GOSIM Spotlight 2026 - Frontier Creators/       # Interpretability: Uncertainty Mapping
│   ├── README.md                                   # Technical Analysis
│   └── ai-hallucination-visualizer.ipynb           # Verified Notebook Solution
│
├── Harmonizing the Data of your Data/              # Proteomics: SDRF Metadata Extraction
│   ├── README.md                                   # Technical Analysis
│   ├── sdrf-metadata-extraction-baseline.ipynb     # Baseline Notebook Solution
│   └── harmonizing-the-data-of-your-data.ipynb     # Verified Notebook Solution
│
├── Hedge fund - Time series forecasting/           # Financial Markets: Optimization
│   ├── README.md                                   # Technical Analysis
│   └── hedge-fund-time-series-forecasting.ipynb    # Verified Notebook Solution
│
├── House Prices - Advanced Regression Techniques/  # Tabular Data: Regression
│   └── house-prices-deterministic-record-linkage.ipynb # Verified Notebook Solution
│
├── LLM Classification Finetuning/                  # LLM Preference: Chatbot Arena
│   ├── README.md                                   # Technical Analysis
│   └── llm_classification_inference.ipynb          # Verified Notebook Solution
│
├── Measuring Progress Toward AGI - Cognitive Abilities/ # Attention: Salient Distractors
│   ├── README.md                                   # Technical Analysis
│   ├── attention_dataset.csv                       # Benchmark Dataset
│   └── agi_attention_salient_distractor_benchmark.ipynb # Verified Notebook Solution
│
├── Petals to the Metal - Flower Classification on TPU/ # Computer Vision: Deep Learning
│   ├── README.md                                   # Technical Analysis
│   └── tpu-flower-classification-advanced-ensemble.ipynb # Verified Notebook Solution
│
├── Predict Customer Churn/                         # Binary Classification: Tabular Data
│   ├── README.md                                   # Technical Analysis
│   ├── predict-customer-churn-xgb-catboost-lgbm-optuna.ipynb # Verified Notebook Solution
│   ├── customer-churn-prediction-gradient-boosting.ipynb # Gradient Boosting Solution
│   ├── customer-churn-pseudo-labeled-xgboost-ensemble.ipynb # Pseudo-Labeling Strategy
│   ├── predict-customer-churn-xgb-catboost-ensemble.ipynb # XGB-CatBoost Ensemble
│   └── customer-churn-prediction-121-fe-20-cv-stacking.ipynb # Stacked Ensemble Strategy
│
├── Stanford RNA 3D Folding Part 2/                 # Structural Biology: Biophysics
│   ├── README.md                                   # Technical Analysis
│   └── stanford-rna-3d-folding-part-2-tbm-protenix-v1.ipynb # Verified Notebook Solution
│
├── Student Study Hours to CGPA Prediction/         # Education: Optimized Regression
│   ├── README.md                                   # Technical Analysis
│   └── student-study-hours-to-cgpa-prediction.ipynb # Verified Notebook Solution
│
├── Titanic - Machine Learning from Disaster/       # Classification: Forensic Analysis
│   ├── README.md                                   # Technical Analysis
│   └── titanic-passenger-survival-prediction.ipynb # Verified Notebook Solution
│
├── Triagegeist/                                    # Clinical: Hierarchical CDSS
│   ├── README.md                                   # Technical Analysis
│   └── Triagegeist_Clinical_Decision_Support_via_Multi_Tier_Acuity_Forecasting.ipynb # Verified Notebook Solution
│
├── LICENSE                                         # CC BY 4.0 (Documentation)
├── LICENSE-MIT                                     # MIT License (Source Code)
├── SECURITY.md                                     # Security Policy & Dual-Licensing
├── CITATION.cff                                    # Repository Citation Metadata
├── codemeta.json                                   # Repository Software Metadata
└── README.md                                       # Master Documentation Portal

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Usage Guidelines

This repository is openly shared to support learning and knowledge exchange across the academic community.

For Students
Use this project as reference material for understanding Data Science Pipelines, Feature Engineering, and Model Optimization workflows. The source code and notebooks are available for study to facilitate self-paced learning and exploration of Python-based competition logic and verified solutions.

For Educators
This repository may serve as a practical laboratory example or supplementary teaching resource for Statistical Modeling, Predictive Analytics, and Algorithmic Decision Making courses. Attribution is appreciated when utilizing these curated datasets and solutions.

For Researchers
The documentation and structured approach may provide insights into Academic Project Organization, Reproducible Research Environments, and State-of-the-Art implementation patterns across diverse competitive domains.

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License

This repository and all its creative and technical assets are made available under a Dual-Licensing framework. The Source Code and associated computational logic are governed by the MIT License, whereas all Technical Documentation and scholarly commentary are released under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

Refer to the LICENSE-MIT and LICENSE files for complete legal terms.

Note

Summary: You are free to share and adapt this content for any purpose, even commercially, as long as you provide appropriate attribution to the original author.

Copyright © 2026 Amey Thakur

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About This Repository

Created & Maintained by: Amey Thakur

This project features a collection of verified solutions for Kaggle competitions. It represents a personal exploration into Machine Learning, Game Theory, and Data Engineering.

Connect: Kaggle  ·  GitHub  ·  LinkedIn

Acknowledgments

Platform: Kaggle
Domain: Competitive Data Science & Machine Learning

Grateful acknowledgment to Kaggle for providing the infrastructure, datasets, and computational resources that make this continuous learning environment possible.

Special thanks to the fellow Kagglers and the global data science community. The open-source notebooks, robust discussions, and competitive algorithms provide invaluable insights and elevate the standard of every competition.

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Computer Engineering (B.E.) - University of Mumbai

Semester-wise curriculum, laboratories, projects, and academic notes.