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My-kaggle-notebooks

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My kaggle notebooks

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**Author: [Carl McBride Ellis](https://www.kaggle.com/carlmcbrideellis)** ([LinkedIn](https://www.linkedin.com/in/carl-mcbride-ellis/))

The following represents a selection of my kaggle notebooks

# <center style="background-color:Gainsboro; width:80%;"> 1. eda (exploratory data analysis) </center>
* [Anscombe's quartet and the importance of EDA](https://www.kaggle.com/carlmcbrideellis/anscombe-s-quartet-and-the-importance-of-eda) (+ [dataset](https://www.kaggle.com/carlmcbrideellis/data-anscombes-quartet))
* [Absolute beginners Titanic 'EDA' using dabl](https://www.kaggle.com/carlmcbrideellis/absolute-beginners-titanic-eda-using-dabl)
* [Exploratory data analysis using pandas pivot table](https://www.kaggle.com/carlmcbrideellis/exploratory-data-analysis-using-pandas-pivot-table)
* [Pearson correlation coefficient, mutual information (MI) and Predictive Power Score (PPS)](https://www.kaggle.com/code/carlmcbrideellis/pearson-mutual-information-and-predictive-power) - a simple comparison
* Use case example: [Jane Street: EDA of day 0 and feature importance](https://www.kaggle.com/carlmcbrideellis/jane-street-eda-of-day-0-and-feature-importance)
* Use case example: [Riiid: EDA and feature importance](https://www.kaggle.com/carlmcbrideellis/riiid-eda-and-feature-importance)
* Use case example: [Ventilator Pressure: EDA and simple submission](https://www.kaggle.com/carlmcbrideellis/ventilator-pressure-eda-and-simple-submission)

# <center style="background-color:Gainsboro; width:80%;">2. data cleaning / preparation</center>

* [Filtering outliers using the Isolation Forest](https://www.kaggle.com/code/carlmcbrideellis/filtering-outliers-using-the-isolation-forest)
* [Data anonymization using Faker (Titanic example)](https://www.kaggle.com/carlmcbrideellis/data-anonymization-using-faker-titanic-example)
* [AWS PyDeequ unit tests to measure data quality](https://www.kaggle.com/code/carlmcbrideellis/aws-pydeequ-unit-tests-to-measure-data-quality)
* [Naïve Dataset Distillation](https://www.kaggle.com/code/carlmcbrideellis/ps-s3-e21-na-ve-dataset-distillation)

#  <center style="background-color:Gainsboro; width:80%;"> 3. classification / regression </center>

This is a collection of my python example scripts for either classification, using the [Titanic: Machine Learning from Disaster](https://www.kaggle.com/c/titanic) competition data, or regression, for which I use the [House Prices: Advanced Regression Techniques](https://www.kaggle.com/c/house-prices-advanced-regression-techniques) competition data:

| algorithm | classification | regression |
| :--- | --- | --- |
| Logistic regression | [link](https://www.kaggle.com/carlmcbrideellis/titanic-explainability-why-me-asks-miss-doyle) | --- |
| Generalized Additive Models (GAM) | [link](https://www.kaggle.com/carlmcbrideellis/classification-using-generalized-additive-models) | --- |
| Iterative Dichotomiser 3 (ID3) | [link](https://www.kaggle.com/carlmcbrideellis/titanic-using-the-iterative-dichotomiser-3-id3) | --- |
| Decision tree| [link](https://www.kaggle.com/carlmcbrideellis/titanic-some-sex-a-bit-of-class-and-a-tree) | --- |
| Regularized Greedy Forest (RGF) | [link](https://www.kaggle.com/carlmcbrideellis/introduction-to-the-regularized-greedy-forest) | [link](https://www.kaggle.com/carlmcbrideellis/introduction-to-the-regularized-greedy-forest) |
| XGBoost | --- | [link](https://www.kaggle.com/carlmcbrideellis/an-introduction-to-xgboost-regression)|
| TabNet | [link](https://www.kaggle.com/carlmcbrideellis/tabnet-simple-binary-classification-example) | [link](https://www.kaggle.com/carlmcbrideellis/tabnet-a-very-simple-regression-example) |
| Neural networks (using keras) | [link](https://www.kaggle.com/carlmcbrideellis/tabular-classification-with-neural-networks-keras) | [link](https://www.kaggle.com/carlmcbrideellis/very-simple-neural-network-regression) |
| Gaussian process | [link](https://www.kaggle.com/carlmcbrideellis/gaussian-process-classification-sample-script) | [link](https://www.kaggle.com/carlmcbrideellis/gaussian-process-regression-sample-script) |
| Hyperparameter grid search | [link](https://www.kaggle.com/carlmcbrideellis/hyperparameter-grid-search-sample-code) | [link](https://www.kaggle.com/carlmcbrideellis/hyperparameter-grid-search-simple-example) |

* [Classification using TensorFlow Decision Forests](https://www.kaggle.com/carlmcbrideellis/classification-using-tensorflow-decision-forests)
* [Titanic in pure H2O.ai](https://www.kaggle.com/carlmcbrideellis/titanic-in-pure-h2o-ai)
* [Predict house prices using H2O.ai (regression)](https://www.kaggle.com/carlmcbrideellis/predict-house-prices-using-h2o-ai-regression)
* [Automatic tuning of XGBoost with XGBTune](https://www.kaggle.com/carlmcbrideellis/automatic-tuning-of-xgboost-with-xgbtune)
* [MNIST with no neural network](https://www.kaggle.com/carlmcbrideellis/mnist-with-no-neural-network)
* [PyTorch Tabular: Gated Additive Tree Ensemble](https://www.kaggle.com/code/carlmcbrideellis/pytorch-tabular-gated-additive-tree-ensemble)

#  <center style="background-color:Gainsboro; width:80%;">  4. conformal prediction</center>

* [Regression prediction intervals with MAPIE](https://www.kaggle.com/code/carlmcbrideellis/regression-prediction-intervals-with-mapie)
* [Prediction intervals: Quantile Regression Forests](https://www.kaggle.com/code/carlmcbrideellis/prediction-intervals-quantile-regression-forests)
* [Locally-weighted conformal regression](https://www.kaggle.com/code/carlmcbrideellis/locally-weighted-conformal-regression)
* [Classifier probability calibration using Venn-ABERS predictors](https://www.kaggle.com/code/carlmcbrideellis/classifier-calibration-using-venn-abers) (winner of the [Gabriel Preda "Best recent Kaggle Notebook" prize](https://www.kaggle.com/discussions/general/469919))

#  <center style="background-color:Gainsboro; width:80%;"> 5. feature selection / engineering </center>
* [Feature importance using the LASSO](https://www.kaggle.com/carlmcbrideellis/feature-importance-using-the-lasso)
* [Feature selection using the Boruta-SHAP package](https://www.kaggle.com/carlmcbrideellis/feature-selection-using-the-boruta-shap-package)
* [Recursive Feature Elimination (RFE) example](https://www.kaggle.com/carlmcbrideellis/recursive-feature-elimination-rfe-example)
* [House Prices: Permutation Importance example](https://www.kaggle.com/carlmcbrideellis/house-prices-permutation-importance-example)
* [SHAP Permutation explainer + random "probe"](https://www.kaggle.com/code/carlmcbrideellis/shap-permutation-explainer-random-probe)
* [What is Adversarial Validation?](https://www.kaggle.com/carlmcbrideellis/what-is-adversarial-validation)
* [Jane Street: t-SNE using RAPIDS cuML](https://www.kaggle.com/carlmcbrideellis/jane-street-t-sne-using-rapids-cuml)
* [Synthanic feature engineering: Beware!](https://www.kaggle.com/carlmcbrideellis/synthanic-feature-engineering-beware)

#  <center style="background-color:Gainsboro; width:80%;">  6. time series and forecasting</center>

* [Time series: A simple moving average (MA) model](https://www.kaggle.com/carlmcbrideellis/time-series-a-simple-moving-average-ma-model)
* [Time series decomposition: Naive example](https://www.kaggle.com/carlmcbrideellis/time-series-decomposition-naive-example)
* [LSTM time series prediction: sine wave example](https://www.kaggle.com/carlmcbrideellis/lstm-time-series-prediction-sine-wave-example)
* [LSTM time series + stock price prediction = FAIL](https://www.kaggle.com/carlmcbrideellis/lstm-time-series-stock-price-prediction-fail)
* [Interrupted time series analysis: Causal Impact](https://www.kaggle.com/carlmcbrideellis/interrupted-time-series-analysis-causal-impact)
* [Temporal Convolutional Network using Keras-TCN](https://www.kaggle.com/carlmcbrideellis/temporal-convolutional-network-using-keras-tcn)
* [Plotting OHLC and V ticker data using mplfinance](https://www.kaggle.com/carlmcbrideellis/plotting-ohlc-and-v-ticker-data-using-mplfinance)
* [Correlograms of 14 cryptocurrencies (1 day)](https://www.kaggle.com/carlmcbrideellis/correlograms-of-14-cryptocurrencies-1-day)
* [Granger causality testing for 1 day](https://www.kaggle.com/carlmcbrideellis/granger-causality-testing-for-1-day) along with [Granger causality Part II: The Movie](https://www.kaggle.com/carlmcbrideellis/granger-causality-part-ii-the-movie)
* [Store Sales: Day of the Week model](https://www.kaggle.com/carlmcbrideellis/store-sales-day-of-the-week-model)
* [TPS Jan 2022: A simple average model (no ML)](https://www.kaggle.com/carlmcbrideellis/tps-jan-2022-a-simple-average-model-no-ml)
* [TPS Jan 2022: Prophet + holidays + GDP regressor](https://www.kaggle.com/carlmcbrideellis/tps-jan-2022-prophet-holidays-gdp-regressor)
* [Multivariable time series forecasting: Linear tree](https://www.kaggle.com/code/carlmcbrideellis/multivariable-time-series-forecasting-linear-tree)

### Prediction intervals
* [Probabilistic forecasting using GluonTS: Bitcoin](https://www.kaggle.com/carlmcbrideellis/probabilistic-forecasting-using-gluonts-bitcoin)
* [[PFI Starter] Skforecast example](https://www.kaggle.com/code/carlmcbrideellis/pfi-starter-skforecast-example) - starter notebook provided for the ["Probabilistic forecasting I: Temperature"](https://www.kaggle.com/competitions/probabilistic-forecasting-i-temperature) competition

# <center style="background-color:Gainsboro; width:80%;">  7. ensemble methods </center>

* [Ensemble methods: majority voting example](https://www.kaggle.com/carlmcbrideellis/ensemble-methods-majority-voting-example)
* [ML-Ensemble example using House Prices data](https://www.kaggle.com/carlmcbrideellis/ml-ensemble-example-using-house-prices-data)
* [Stacking ensemble using the House Prices data](https://www.kaggle.com/carlmcbrideellis/stacking-ensemble-using-the-house-prices-data)
* [Ensembling: Convex combination of model predictions (CCMP) and the `hillclimbers` package](https://www.kaggle.com/code/carlmcbrideellis/ensembling-ccmp-and-the-hillclimbers-package)

# <center style="background-color:Gainsboro; width:80%;"> 8. explainability  </center>
* [Explainability, collinearity and the variance inflation factor (VIF)](https://www.kaggle.com/code/carlmcbrideellis/variance-inflation-factor-vif-and-explainability)
* [KISS: Small and simple Titanic models](https://www.kaggle.com/carlmcbrideellis/kiss-small-and-simple-titanic-models)
* [House Prices: my score using only 'OverallQual'](https://www.kaggle.com/carlmcbrideellis/house-prices-my-score-using-only-overallqual) and also a [simple two variable model](https://www.kaggle.com/carlmcbrideellis/simple-two-variable-model)
* [Titanic explainability: Why me? asks Miss Doyle](https://www.kaggle.com/carlmcbrideellis/titanic-explainability-why-me-asks-miss-doyle)
* [TabNet and interpretability: Jane Street example](https://www.kaggle.com/carlmcbrideellis/tabnet-and-interpretability-jane-street-example)
* [GPU accelerated SHAP values: Jane Street example](https://www.kaggle.com/carlmcbrideellis/gpu-accelerated-shap-values-jane-street-example)

#  <center style="background-color:Gainsboro; width:80%;">  9. causality</center>
* [Causal Forests Double ML example using EconML](https://www.kaggle.com/carlmcbrideellis/causal-forests-double-ml-example-using-econml)
* [Interrupted time series analysis: Causal Impact](https://www.kaggle.com/carlmcbrideellis/interrupted-time-series-analysis-causal-impact)

#  <center style="background-color:Gainsboro; width:80%;"> 10. statistics </center>
* [Animated histogram of the central limit theorem](https://www.kaggle.com/carlmcbrideellis/animated-histogram-of-the-central-limit-theorem)
* [Hypothesis testing: The two sample t-test, p-value and power](https://www.kaggle.com/carlmcbrideellis/hypothesis-testing-the-t-test-p-values-and-power)
* [Explainability, collinearity and the variance inflation factor (VIF)](https://www.kaggle.com/carlmcbrideellis/explainability-collinearity-and-the-vif)

#  <center style="background-color:Gainsboro; width:80%;">11. didactic notebooks  </center>
* [Beautiful math in your notebook](https://www.kaggle.com/carlmcbrideellis/beautiful-math-in-your-notebook): a guide to using $\LaTeX$ math markup in kaggle notebooks.
* [Titanic: In all the confusion...](https://www.kaggle.com/carlmcbrideellis/titanic-in-all-the-confusion) which looks at the confusion matrix, ROC curves, $F_1$ scores etc.
* [Classification: How imbalanced is "imbalanced"?](https://www.kaggle.com/carlmcbrideellis/classification-how-imbalanced-is-imbalanced) - (mentioned in ["*Notebooks of the week: Hidden Gems*"](https://www.kaggle.com/general/273618))
* [Overfitting and underfitting the Titanic](https://www.kaggle.com/carlmcbrideellis/overfitting-and-underfitting-the-titanic)
* [False positives, false negatives and the discrimination threshold](https://www.kaggle.com/carlmcbrideellis/discrimination-threshold-false-positive-negative)
* [Introduction to the Regularized Greedy Forest](https://www.kaggle.com/carlmcbrideellis/introduction-to-the-regularized-greedy-forest) (using [rgf_python](https://github.com/RGF-team/rgf/tree/master/python-package))
* [Extrapolation: Do not stray out of the forest!](https://www.kaggle.com/carlmcbrideellis/extrapolation-do-not-stray-out-of-the-forest)
* [Titanic: some sex, a bit of class, and a tree...](https://www.kaggle.com/carlmcbrideellis/titanic-some-sex-a-bit-of-class-and-a-tree)
* [The Lehmer RNG algorithm for `seed=42`](https://www.kaggle.com/code/carlmcbrideellis/the-lehmer-rng-algorithm-for-seed-42)

#  <center style="background-color:Gainsboro; width:80%;"> 12. generative AI </center>
* [StableDiffusion: text-to-image with KerasCV](https://www.kaggle.com/code/carlmcbrideellis/stablediffusion-text-to-image-with-kerascv)
* [[Gemma] I am replacing myself with an LLM](https://www.kaggle.com/code/carlmcbrideellis/gemma-i-am-replacing-myself-with-an-llm)

#  <center style="background-color:Gainsboro; width:80%;"> 13. miscellaneous </center>
* [Titanic leaderboard: a score > 0.8 is great!](https://www.kaggle.com/carlmcbrideellis/titanic-leaderboard-a-score-0-8-is-great)
* [House Prices: How to work offline](https://www.kaggle.com/carlmcbrideellis/house-prices-how-to-work-offline) (+ [dataset](https://www.kaggle.com/carlmcbrideellis/house-prices-how-to-work-offline))
* [Pandas one-liners](https://www.kaggle.com/carlmcbrideellis/pandas-one-liners)
* [The latest trends in data science](https://www.kaggle.com/carlmcbrideellis/the-latest-trends-in-data-science)
* [The Titanic using SQL](https://www.kaggle.com/carlmcbrideellis/the-titanic-using-sql)
* [Some pretty t-SNE plots](https://www.kaggle.com/carlmcbrideellis/some-pretty-t-sne-plots)
* [Encuesta kaggle 2021: ¿España es diferente?](https://www.kaggle.com/carlmcbrideellis/encuesta-kaggle-2021-espa-a-es-diferente)
* [How much do people on kaggle earn by country (2021)](https://www.kaggle.com/carlmcbrideellis/how-much-do-people-on-kaggle-earn-by-country-2021)
* [All in a pickle: Saving the Titanic](https://www.kaggle.com/carlmcbrideellis/all-in-a-pickle-saving-the-titanic) - Saving our machine learning model to a file using pickle
* [Machine learning review papers on arXiv [polars]](https://www.kaggle.com/code/carlmcbrideellis/machine-learning-review-papers-on-arxiv-polars)

**Geospatial analysis**
* [Choropleth map of kaggle Grandmaster locations](https://www.kaggle.com/carlmcbrideellis/choropleth-map-of-kaggle-grandmaster-locations)
* [Smartphone 2022: A look at the ground truth maps](https://www.kaggle.com/code/carlmcbrideellis/smartphone-2022-a-look-at-the-ground-truth-maps)
* [Animating the path of a smartphone GPS signal](https://www.kaggle.com/code/carlmcbrideellis/animating-the-path-of-a-smartphone-gps-signal)

**Finance related**
* [S&P 500 daily returns: Normal and Cauchy fits](https://www.kaggle.com/carlmcbrideellis/s-p-500-daily-returns-normal-and-cauchy-fits)
* [Bitcoin candlestick chart (2021)](https://www.kaggle.com/carlmcbrideellis/bitcoin-candlestick-chart-2021)
* [Irrational Exuberance? S&P vs Bitcoin](https://www.kaggle.com/carlmcbrideellis/irrational-exuberance-s-p-vs-bitcoin)

#  <center style="background-color:Gainsboro; width:80%;"> fun with the meta kaggle dataset </center>
The [Meta Kaggle](https://www.kaggle.com/kaggle/meta-kaggle) dataset consists of data regarding the kaggle site 
* [Kaggle in numbers](https://www.kaggle.com/carlmcbrideellis/kaggle-in-numbers) - updated almost daily
* [Simple EDA of kaggle Grandmasters](https://www.kaggle.com/code/carlmcbrideellis/simple-eda-of-kaggle-grandmasters-scheduled)  - updated almost daily
* [Number of active Kaggle users](https://www.kaggle.com/code/carlmcbrideellis/number-of-active-kaggle-users)
* [Notebooks: Number of views, and days, per vote](https://www.kaggle.com/carlmcbrideellis/notebooks-number-of-views-and-days-per-vote)
* [kaggle discussions: busiest time of the day?](https://www.kaggle.com/carlmcbrideellis/kaggle-discussions-busiest-time-of-the-day) - (mentioned in ["*Notebooks of the week: Hidden Gems*"](https://www.kaggle.com/general/193544))
* [The kaggle working week](https://www.kaggle.com/carlmcbrideellis/the-kaggle-working-week)
* [WordCloud of gold medal winning notebook titles](https://www.kaggle.com/carlmcbrideellis/wordcloud-of-gold-medal-winning-notebook-titles)
* [Shakeup interactive scatterplot maker](https://www.kaggle.com/carlmcbrideellis/shakeup-interactive-scatterplot-maker)
* [Shakeup scatterplots: Boxes, strings and things...](https://www.kaggle.com/carlmcbrideellis/shakeup-scatterplots-boxes-strings-and-things)
* [When will my notebook get its medal?](https://www.kaggle.com/carlmcbrideellis/when-will-my-notebook-get-its-medal)

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