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https://github.com/alfarias/awesome-kaggle-kernels
Compilation of good Kaggle Kernels.
https://github.com/alfarias/awesome-kaggle-kernels
List: awesome-kaggle-kernels
data-science deep-learning kaggle machine-learning
Last synced: 16 days ago
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Compilation of good Kaggle Kernels.
- Host: GitHub
- URL: https://github.com/alfarias/awesome-kaggle-kernels
- Owner: alfarias
- License: mit
- Created: 2020-08-22T15:38:28.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2020-09-08T14:27:06.000Z (over 4 years ago)
- Last Synced: 2024-05-22T19:05:58.324Z (7 months ago)
- Topics: data-science, deep-learning, kaggle, machine-learning
- Homepage:
- Size: 9.77 KB
- Stars: 54
- Watchers: 6
- Forks: 8
- Open Issues: 0
-
Metadata Files:
- Readme: Readme.md
- License: LICENSE
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- ultimate-awesome - awesome-kaggle-kernels - Compilation of good Kaggle Kernels. (Other Lists / Monkey C Lists)
README
# Kaggle Kernels Compilation
[](https://github.com/sindresorhus/awesome) [](https://github.com/alfarias/awesome-kaggle-kernels/blob/master/LICENSE) [](https://github.com/alfarias/awesome-kaggle-kernels/commits/master)
\
*Image Source: [Wikimedia](https://commons.wikimedia.org/wiki/File:Kaggle_logo.png
)*
Disclaimer: This compilation is still a work in progress.
- [✔] Converting urls to text hyperlinks.
- [✔] Clean Kernel Titles.
- [✔] Write Introduction.
- [X] Better Topic Division.
## Table of Contents
1. [Introduction](#Introduction)
2. [General Machine Learning](#General-Machine-Learning)
3. [Data Visualization](#Data-Visualization)
4. [Forecasting](#Data-Visualization)
5. [Natural Language Processing](#Natural-Language-Processing)
6. [Computer Vision](#Computer-Vision)
7. [Recommendation Systems](#Recommendation-Systems)
8. [Clustering](#Clustering)
9. [Reinforcement Learning](#Reinforcement-Learning)
10. [Competitions (Kernel Examples)](#Competitions-(Kernel-Examples))
## Introduction
In this compilation you will find curated Kaggle Kernels to aid on your Data Science Learning Journey. The list is weekly reviewed.\
I wiil search on Kaggle for interesting and didactic Kernel, if is good will be added here.\
If any author doesn't want his work on this compilation, open an issue and I will remove the requested Kernel.\
Most Kernels are written in Python, but if any of them is written in R, the tag `[R]` will appear in the title end.
## General Machine Learning
- [**Data Science Tutorial for Beginners**](https://www.kaggle.com/kanncaa1/data-sciencetutorial-for-beginners)
- [**Machine Learning Tutorial for Beginners**](https://www.kaggle.com/kanncaa1/machine-learning-tutorial-for-beginners)
- [**Statistical Learning Tutorial for Beginners**](https://www.kaggle.com/kanncaa1/statistical-learning-tutorial-for-beginners)
- [**A Data Science Framework: To Achieve 99% Accuracy**](https://www.kaggle.com/ldfreeman3/a-data-science-framework-to-achieve-99-accuracy)
- [**A Comprehensive ML Workflow with Python**](https://www.kaggle.com/mjbahmani/a-comprehensive-ml-workflow-with-python)
- [**Guided path of Learning ML-DS**](https://www.kaggle.com/ambarish/guided-path-of-learning-ml-ds)
- [**Exploration of data step by step**](https://www.kaggle.com/artgor/exploration-of-data-step-by-step)
- [**Python walkthrough for Titanic data analysis**](https://www.kaggle.com/headsortails/pytanic)
- [**How to not overfit?**](https://www.kaggle.com/artgor/how-to-not-overfit)
- [**OOP approach to FE and models**](https://www.kaggle.com/artgor/oop-approach-to-fe-and-models)
- [**Predictive Power Score vs Correlation**](https://www.kaggle.com/frtgnn/predictive-power-score-vs-correlation)
- [**Pseudo Labeling - QDA**](https://www.kaggle.com/cdeotte/pseudo-labeling-qda-0-969)
- [**Bayesian Learning Basics | Tutorial**](https://www.kaggle.com/upadorprofzs/bayesian-learning-basics-tutorial)
- [**Data Analysis using SQL:**](https://www.kaggle.com/dimarudov/data-analysis-using-sql)
- [**SQLalchemy and ML with sklearn demo**](https://www.kaggle.com/aawiegel/sqlalchemy-and-ml-with-sklearn-demo)
- [**Pyspark ML tutorial for beginners:**](https://www.kaggle.com/fatmakursun/pyspark-ml-tutorial-for-beginners)
- [**Getting started with H2O (AutoML)**](https://www.kaggle.com/sudalairajkumar/getting-started-with-h2o)
- [**XGBoost in H2O! (AutoML)**](https://www.kaggle.com/brandenkmurray/xgboost-in-h2o)
- [**CatBoost: A Deeper Dive**](https://www.kaggle.com/abhinand05/catboost-a-deeper-dive)
- [**GMEAN of low correlation**](https://www.kaggle.com/paulorzp/gmean-of-low-correlation-lb-0-952x)
- [**Resampling strategies for imbalanced datasets**](https://www.kaggle.com/rafjaa/resampling-strategies-for-imbalanced-datasets)
## Deeep Learning
- [**Deep Learning Tutorial for Beginners**](https://www.kaggle.com/kanncaa1/deep-learning-tutorial-for-beginners)
- [**In-Depth Guide to Convolutional Neural Networks**](https://www.kaggle.com/abhinand05/in-depth-guide-to-convolutional-neural-networks)
- [**Introduction to Pytorch (a very gentle start)**](https://www.kaggle.com/frtgnn/introduction-to-pytorch-a-very-gentle-start)
## Data Visualization
- [**Creating a Good Analytics Report**](https://www.kaggle.com/jpmiller/creating-a-good-analytics-report)
- [**Visual data analysis in Python**](https://www.kaggle.com/kashnitsky/topic-2-visual-data-analysis-in-python)
- [**Patterns of Missing Data**](https://www.kaggle.com/jpmiller/patterns-of-missing-data)
- [**Basic EDA with Images**](https://www.kaggle.com/jpmiller/basic-eda-with-images)
- [**Matplotlib Plotting Guide**](https://www.kaggle.com/grroverpr/matplotlib-plotting-guide)
- [**Plotting with Python: learn 80 plots STEP by STEP**](https://www.kaggle.com/python10pm/plotting-with-python-learn-80-plots-step-by-step)
- [**Seaborn and Plotly**](https://www.kaggle.com/kashnitsky/topic-2-part-2-seaborn-and-plotly)
- [**Seaborn Tutorial for Beginners**](https://www.kaggle.com/kanncaa1/seaborn-tutorial-for-beginners)
- [**Altair visualization: 2018 StackOverflow survey**](https://www.kaggle.com/notslush/altair-visualization-2018-stackoverflow-survey)
- [**Plotly Tutorial for Beginners**](https://www.kaggle.com/kanncaa1/plotly-tutorial-for-beginners)
- [**Visualization: Bokeh Tutorial Part 1**](https://www.kaggle.com/kanncaa1/visualization-bokeh-tutorial-part-1)
- [**Interactive Bokeh Tutorial Part 2**](https://www.kaggle.com/kanncaa1/interactive-bokeh-tutorial-part-2)
- [**Tutorial: Interactive data visualizations [R]**](https://www.kaggle.com/tavoosi/tutorial-interactive-data-visualizations)
- [**Beginners guide to Highchart Visual in R**](https://www.kaggle.com/nulldata/beginners-guide-to-highchart-visual-in-r)
- [**Interactive Viz - UFC with Altair**](https://www.kaggle.com/subinium/interactive-viz-1-ufc-with-altair)
- [**EDA using Pyviz**](https://www.kaggle.com/deepanshusemwal/eda-using-pyviz)
- [**Interactive Exploratory Data Analysis**](https://www.kaggle.com/dcstang/interactive-exploratory-data-analysis-eda)
- [**Advanced Pyspark for Exploratory Data Analysis**](https://www.kaggle.com/tientd95/advanced-pyspark-for-exploratory-data-analysis)
## Forecasting
- [**Time Series Prediction Tutorial with EDA**](https://www.kaggle.com/kanncaa1/time-series-prediction-tutorial-with-eda)
- [**Back to (predict) the future - Interactive M5 EDA [R]**](https://www.kaggle.com/headsortails/back-to-predict-the-future-interactive-m5-eda)
- [**EDA + Prophet + MLP Neural Network Forecasting**](https://www.kaggle.com/arindamgot/eda-prophet-mlp-neural-network-forecasting)
- [**Predicting stock movement**](https://www.kaggle.com/zikazika/predicting-stock-movement/)
## Natural Language Processing
- [**Data Science with DL & NLP: Advanced Techniques**](https://www.kaggle.com/vbmokin/data-science-with-dl-nlp-advanced-techniques)
- [**A mind map for of NLP**](https://www.kaggle.com/rftexas/ml-cheatsheet-a-mind-map-for-nlp)
- [**NLP Cheatsheet - Master NLP**](https://www.kaggle.com/rftexas/nlp-cheatsheet-master-nlp)
- [**Approaching (Almost) Any NLP Problem on Kaggle**](https://www.kaggle.com/abhishek/approaching-almost-any-nlp-problem-on-kaggle)
- [**Regex Primer**](https://www.kaggle.com/adityaecdrid/regex-primer-annoying-artgor-xd)
- [**How to Preprocessing when using embeddings**](https://www.kaggle.com/christofhenkel/how-to-preprocessing-when-using-embeddings)
- [**How To Preprocessing for GloVe Part 1: EDA**](https://www.kaggle.com/christofhenkel/how-to-preprocessing-for-glove-part1-eda)
- [**How To Preprocessing for GloVe Part 2: Usage**](https://www.kaggle.com/christofhenkel/how-to-preprocessing-for-glove-part2-usage)
- [**Gensim Word2Vec Tutorial**](https://www.kaggle.com/pierremegret/gensim-word2vec-tutorial)
- [**Hitchhiker's Guide to NLP in spaCy**](https://www.kaggle.com/nirant/hitchhiker-s-guide-to-nlp-in-spacy/)
- [**Tutorial on topic modelling-LDA&NLP**](https://www.kaggle.com/zikazika/tutorial-on-topic-modelling-lda-nlp?scriptVersionId=9699432)
- [**Treemap House of Horror: Spooky EDA/LDA/Features [R]**](https://www.kaggle.com/headsortails/treemap-house-of-horror-spooky-eda-lda-features)
- [**Movie Review Sentiment Analysis EDA and models**](https://www.kaggle.com/artgor/movie-review-sentiment-analysis-eda-and-models)
- [**Loading BERT using pytorch (with tokenizer & apex)**](https://www.kaggle.com/christofhenkel/loading-bert-using-pytorch-with-tokenizer-apex/notebook)
- [**Text modelling in Pytorch v2**](https://www.kaggle.com/artgor/text-modelling-in-pytorch-v2)
- [**CNN in keras on folds**](https://www.kaggle.com/artgor/cnn-in-keras-on-folds)
- [**BERT for Humans: Tutorial+Baseline (Version 2)**](https://www.kaggle.com/abhinand05/bert-for-humans-tutorial-baseline-version-2)
- [**Clickbait News - BERT PyTorch**](https://www.kaggle.com/kashnitsky/clickbait-news-bert-pytorch)
- [**Bert-base TF2.0 (now Huggingface transformer)**](https://www.kaggle.com/akensert/bert-base-tf2-0-now-huggingface-transformer)
- [**DistilBert + Catalyst, amazon product reviews**](https://www.kaggle.com/kashnitsky/distillbert-catalyst-amazon-product-reviews)
- [**Vowpal Wabbit tutorial: blazingly fast learnin**](https://www.kaggle.com/kashnitsky/vowpal-wabbit-tutorial-blazingly-fast-learning)
## Computer Vision
- [**Convolutional Neural Network (CNN) Tutorial**](https://www.kaggle.com/kanncaa1/convolutional-neural-network-cnn-tutorial)
- [**Image classification with Convolutional Neural Networks (Fast.ai)**](https://www.kaggle.com/hortonhearsafoo/fast-ai-lesson-1)
- [**A complete ML pipeline (Fast.ai)**](https://www.kaggle.com/qitvision/a-complete-ml-pipeline-fast-ai)
- [**Introduction to CNN Keras**](https://www.kaggle.com/yassineghouzam/introduction-to-cnn-keras-0-997-top-6)
- [**Indian way to learn CNN**](https://www.kaggle.com/shahules/indian-way-to-learn-cnn)
- [**Beginners guide to MNIST with fast.ai**](https://www.kaggle.com/christianwallenwein/beginners-guide-to-mnist-with-fast-ai)
- [**Practical Deep Learning Using PyTorch**](https://www.kaggle.com/ankitjha/practical-deep-learning-using-pytorch)
- [**Classification in catalyst with utility scripts**](https://www.kaggle.com/artgor/classification-in-catalyst-with-utility-scripts)
- [**Pytorch utils for images**](https://www.kaggle.com/artgor/pytorch-utils-for-images)
- [**RSNA Intracranial Hemorrhage Basic EDA + Data Visualization**](https://www.kaggle.com/marcovasquez/basic-eda-data-visualization/notebook)
- [**Severstal: Simple 2-step pipeline**](https://www.kaggle.com/xhlulu/severstal-simple-2-step-pipeline)
- [**GAN Introduction**](https://www.kaggle.com/jesucristo/gan-introduction)
- [**Kuzushiji Recognition Complete Guide**](https://www.kaggle.com/jesucristo/kuzushiji-recognition-complete-guide)
- [**Train Simple XRay CNN**](https://www.kaggle.com/kmader/train-simple-xray-cnn)
## Recommendation Systems
- [**Recommendation Systems Tutorial**](https://www.kaggle.com/kanncaa1/recommendation-systems-tutorial)
- [**How To Recommend Anything/Deep Recommender**](https://www.kaggle.com/morrisb/how-to-recommend-anything-deep-recommender)
- [**Tutorial: Collaborative filtering with PySpark**](https://www.kaggle.com/vchulski/tutorial-collaborative-filtering-with-pyspark)
## Clustering
- [**Unsupervised Learning: Clustering (Tutorial)**](https://www.kaggle.com/maximgolovatchev/unsupervised-learning-clustering-tutorial/data)
- [**In-depth EDA and K-Means Clustering**](https://www.kaggle.com/thebrownviking20/in-depth-eda-and-k-means-clustering)
- [**Unsupervised learning: PCA and clustering**](https://www.kaggle.com/kashnitsky/topic-7-unsupervised-learning-pca-and-clustering)
## Reinforcement Learning
- [**Reinforcement Learning for Meal Planning in Python**](https://www.kaggle.com/osbornep/reinforcement-learning-for-meal-planning-in-python)
- [**Learn by example Reinforcement Learning with Gym**](https://www.kaggle.com/charel/learn-by-example-reinforcement-learning-with-gym)
- [**Crash Course in Reinforcement Learning**](https://www.kaggle.com/blairyoung/crash-course-in-reinforcement-learning)
- [**Deep Reinforcement Learning on Stock Data**](https://www.kaggle.com/itoeiji/deep-reinforcement-learning-on-stock-data)
- [**RL from Scratch Part 1: Defining the Environment**](https://www.kaggle.com/osbornep/rl-from-scratch-part-1-defining-the-environment)
- [**RL from Scratch Part 2: Understanding RL Parameters**](https://www.kaggle.com/osbornep/rl-from-scratch-part-2-understanding-rl-paramters)
## Competitions (Kernel Examples)
- [**NFL Injury Analysis**](https://www.kaggle.com/aleksandradeis/nfl-injury-analysis)
- [**NFL Punt Analytics**](https://www.kaggle.com/jpmiller/nfl-punt-analytics)
- [**IEEE-CIS Fraud Detection**](https://www.kaggle.com/artgor/eda-and-models)
- [**Elo Merchant Category Recommendation**](https://www.kaggle.com/artgor/elo-eda-and-models)
- [**Santander Customer Transaction Prediction**](https://www.kaggle.com/artgor/santander-eda-fe-fs-and-models)
- [**Porto Seguro’s Safe Driver Prediction [R]**](https://www.kaggle.com/headsortails/steering-wheel-of-fortune-porto-seguro-eda)
- [**Starter: ASHRAE Great Energy Predictor**](https://www.kaggle.com/jesucristo/starter-great-energy-predictor)
- [**Vowpal Wabbit starter Microsoft Malware Prediction**](https://www.kaggle.com/kashnitsky/training-while-reading-vowpal-wabbit-starter)
- [**PUBG Data Exploration + Random Forest**](https://www.kaggle.com/carlolepelaars/pubg-data-exploration-rf-funny-gifs)