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https://github.com/cerlymarco/medium_notebook

Repository containing notebooks of my posts on Medium
https://github.com/cerlymarco/medium_notebook

artificial-intelligence data-science deep-learning machine-learning notebooks

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Repository containing notebooks of my posts on Medium

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# MEDIUM_NoteBook

Repository containing notebooks of my posts on [MEDIUM](https://medium.com/@cerlymarco).



To be notified every time a new post is published, **SUBSCRIBE [HERE](https://medium.com/subscribe/@cerlymarco)**.



[!["Buy Me A Coffee"](https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png)](https://www.buymeacoffee.com/cerlymarco)



## Posts ordered by most recent publishing date

- Proxy SHAP: Speed Up Explainability with Simpler Models [[post](https://medium.com/towards-data-science/proxy-shap-speed-up-explainability-with-simpler-models-1aab91b79f9f)][[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Proxy_SHAP)]

- Time Series Forecasting in the Age of GenAI: Make Gradient Boosting Behaves like LLMs [[post](https://medium.com/towards-data-science/time-series-forecasting-in-the-age-of-genai-make-gradient-boosting-behaves-like-llms-674d9e22e1ce)][[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/TimeSeries_TransferLearning)]

- Hitchhiker’s Guide to MLOps for Time Series Forecasting with Sklearn [[post](https://medium.com/towards-data-science/hitchhikers-guide-to-mlops-for-time-series-forecasting-with-sklearn-d5d9728095a7)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/TimeSeries_TransferLearning)]

- Hitting Time Forecasting: The Other Way for Time Series Probabilistic Forecasting [[post](https://medium.com/towards-data-science/hitting-time-forecasting-the-other-way-for-time-series-probabilistic-forecasting-6c3b6496c353)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Hit_Time_Forecasting)]

- Forecasting with Granger Causality: Checking for Time Series Spurious Correlations [[post](https://medium.com/towards-data-science/forecasting-with-granger-causality-checking-for-time-series-spurious-correlations-5faed62c3604)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/TimeSeries_GrangerCausality)]

- Hacking Causal Inference: Synthetic Control with ML approaches [[post](https://medium.com/towards-data-science/hacking-causal-inference-synthetic-control-with-ml-approaches-7f3c19c7abfa)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Hacking_Causal_Inference)]

- Model Selection with Imbalance Data: Only AUC may Not Save you [[post](https://medium.com/towards-data-science/model-selection-with-imbalance-data-only-auc-may-not-save-you-5aed73c5efed)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/ImbalanceData_ModelSelection)]

- PCA for Multivariate Time Series: Forecasting Dynamic High-Dimensional Data [[post](https://medium.com/towards-data-science/pca-for-multivariate-time-series-forecasting-dynamic-high-dimensional-data-ab050a19e8db)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/PCA_MultivariateForecasting)]

- Hacking Statistical Significance: Hypothesis Testing with ML Approaches [[post](https://medium.com/towards-data-science/hacking-statistical-significance-hypothesis-testing-with-ml-approaches-74ff102c5ff1)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Hacking_Statistical_Significance)]

- Time Series Forecasting with Conformal Prediction Intervals: Scikit-Learn is All you Need [[post](https://medium.com/towards-data-science/time-series-forecasting-with-conformal-prediction-intervals-scikit-learn-is-all-you-need-4b68143a027a)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/TimeSeries_ConformalPredIntervals)]

- Rethinking Survival Analysis: How to Make your Model Produce Survival Curves [[post](https://medium.com/towards-data-science/rethinking-survival-analysis-how-to-make-your-model-produce-survival-curves-7a9ef112e2af)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/SurvivalClassifier)]

- Extreme Churn Prediction: Forecasting Without Features [[post](https://medium.com/towards-data-science/extreme-churn-prediction-forecasting-without-features-8ebd4a8dc8b)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Extreme_Churn_Prediction)]

- Forecast Time Series with Missing Values: Beyond Linear Interpolation [[post](https://medium.com/towards-data-science/forecast-time-series-with-missing-values-beyond-linear-interpolation-2f2adf0a0cba)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Timeseries_Missing_Forecasting)]

- Forecasting Uncertainty with Linear Models like in Deep Learning [[post](https://medium.com/towards-data-science/forecasting-uncertainty-with-linear-models-like-in-deep-learning-bc58f53938)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Forecast_Uncertainty_LinearModels)]

- Time Series Forecasting with Feature Selection: Why you may need it [[post](https://medium.com/towards-data-science/time-series-forecasting-with-feature-selection-why-you-may-need-it-696b23ecc329)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/TimeSeries_FeatureSelection)]

- Anomaly Detection in Multivariate Time Series with Network Graphs [[post](https://medium.com/towards-data-science/anomaly-detection-in-multivariate-time-series-with-network-graphs-80a84deeed9e)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Anomaly_Detection_Graph)]

- How to Improve Recursive Time Series Forecasting [[post](https://medium.com/towards-data-science/how-to-improve-recursive-time-series-forecasting-ff5b90a98eeb)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Improve_RecursiveForecasting)]

- Retrain, or not Retrain? Online Machine Learning with Gradient Boosting [[post](https://medium.com/towards-data-science/retrain-or-not-retrain-online-machine-learning-with-gradient-boosting-9ccb464415e7)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Refit_Online_Learning)]

- Data Drift Explainability: Interpretable Shift Detection with NannyML [[post](https://medium.com/towards-data-science/data-drift-explainability-interpretable-shift-detection-with-nannyml-83421319d05f)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/NannyML_Drift_Detector)]

- Word2Vec with Time Series: A Transfer Learning Approach [[post](https://medium.com/towards-data-science/word2vec-with-time-series-a-transfer-learning-approach-58017e7a019d)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/TimeSeries_Word2Vec)]

- SHAP for Drift Detection: Effective Data Shift Monitoring [[post](https://medium.com/towards-data-science/shap-for-drift-detection-effective-data-shift-monitoring-c7fb9590adb0)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Shap_Drift_Detector)]

- Forecasting with Trees: Hybrid Classifiers for Time Series [[post](https://medium.com/towards-data-science/forecasting-with-trees-hybrid-classifiers-for-time-series-b2509abf15f8)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Hybrid_Trees_Classifiers)]

- Boruta SHAP for Temporal Feature Selection [[post](https://medium.com/towards-data-science/boruta-shap-for-temporal-feature-selection-96a7840c7713)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/ShapBoruta_TemporalSelection)]

- Forecasting with Trees: Hybrid Modeling for Time Series [[post](https://medium.com/towards-data-science/forecasting-with-trees-hybrid-modeling-for-time-series-58590a113178)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Hybrid_Trees_Forecasting)]

- Recursive Feature Selection: Addition or Elimination? [[post](https://towardsdatascience.com/recursive-feature-selection-addition-or-elimination-755e5d86a791)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Shap_RFA_RFE)]

- Improve Random Forest with Linear Models [[post](https://towardsdatascience.com/improve-random-forest-with-linear-models-1fa789691e18)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/LinearForest)]

- Is Gradient Boosting good as Prophet for Time Series Forecasting? [[post](https://towardsdatascience.com/is-gradient-boosting-good-as-prophet-for-time-series-forecasting-3dcbfd03775e)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Prophet_vs_GradientBoosting)]

- Linear Boosting with Automated Features Engineering [[post](https://towardsdatascience.com/linear-boosting-with-automated-features-engineering-894962c3ba84)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/LinearBoosting_AutoFeatureEngine)]

- Improve Linear Regression for Time Series Forecasting [[post](https://towardsdatascience.com/improve-linear-regression-for-time-series-forecasting-e36f3c3e3534)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/ModelTrees_TimeSeries)]

- Boruta and SHAP for better Feature Selection [[post](https://towardsdatascience.com/boruta-and-shap-for-better-feature-selection-20ea97595f4a)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/ShapBoruta_FeatureSelection)]

- Explainable AI with Linear Trees [[post](https://towardsdatascience.com/explainable-ai-with-linear-trees-7e30a6f067d7)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/ModelTrees_Explainability)]

- SHAP for Feature Selection and HyperParameter Tuning [[post](https://towardsdatascience.com/shap-for-feature-selection-and-hyperparameter-tuning-a330ec0ea104)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Shap_FeatureSelection)]

- Model Tree: handle Data Shifts mixing Linear Model and Decision Tree [[post](https://towardsdatascience.com/model-tree-handle-data-shifts-mixing-linear-model-and-decision-tree-facfd642e42b)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/ModelTrees_DataShifts)]

- Add Prediction Intervals to your Forecasting Model [[post](https://towardsdatascience.com/add-prediction-intervals-to-your-forecasting-model-531b7c2d386c)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Prediction_Intervals)]

- Linear Tree: the perfect mix of Linear Model and Decision Tree [[post](https://towardsdatascience.com/linear-tree-the-perfect-mix-of-linear-model-and-decision-tree-2eaed21936b7)]

- ARIMA for Classification with Soft Labels [[post](https://towardsdatascience.com/arima-for-classification-with-soft-labels-29f3109d9840)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Classification_ARIMA)]

- Advanced Permutation Importance to Explain Predictions [[post](https://towardsdatascience.com/advanced-permutation-importance-to-explain-predictions-ead7de26eed4)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Advanced_Perm_Importance)]

- Time Series Bootstrap in the age of Deep Learning [[post](https://towardsdatascience.com/time-series-bootstrap-in-the-age-of-deep-learning-b98aa2aa32c4)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/TimeSeries_Bootstrap)]

- Anomaly Detection with Extreme Value Analysis [[post](https://towardsdatascience.com/anomaly-detection-with-extreme-value-analysis-b11ad19b601f)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Anomaly_Detection_ExtremeValues)]

- Time Series generation with VAE LSTM [[post](https://towardsdatascience.com/time-series-generation-with-vae-lstm-5a6426365a1c)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/VAE_TimeSeries)]

- Extreme Event Time Series Preprocessing [[post](https://towardsdatascience.com/extreme-event-time-series-preprocessing-90aa59d5630c)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Extreme_Event_PreProcessing)]

- One-Class Neural Network in Keras [[post](https://towardsdatascience.com/one-class-neural-network-in-keras-249ff56201c0)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/OneClass_NeuralNetwork)]

- Real-Time Time Series Anomaly Detection [[post](https://towardsdatascience.com/real-time-time-series-anomaly-detection-981cf1e1ca13)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Anomaly_Detection_RealTime)]

- Entropy Application in the Stock Market [[post](https://towardsdatascience.com/entropy-application-in-the-stock-market-b211914ed1f3)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Structural_Entropy)]

- Time Series Smoothing for better Forecasting [[post](https://towardsdatascience.com/time-series-smoothing-for-better-forecasting-7fbf10428b2)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/TimeSeries_Smoothing_Forecasting)]

- Time Series Smoothing for better Clustering [[post](https://towardsdatascience.com/time-series-smoothing-for-better-clustering-121b98f308e8)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/TimeSeries_Smoothing_Clustering)]

- Predictive Maintenance with ResNet [[post](https://towardsdatascience.com/predictive-maintenance-with-resnet-ebb4f4a0be3d)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Predictive_Maintenance_ResNet)]

- Neural Networks Ensemble [[post](https://towardsdatascience.com/neural-networks-ensemble-33f33bea7df3)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/NeuralNet_Ensemble)]

- Anomaly Detection in Multivariate Time Series with VAR [[post](https://towardsdatascience.com/anomaly-detection-in-multivariate-time-series-with-var-2130f276e5e9)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Anomaly_Detection_VAR)]

- Corr2Vec: a WaveNet architecture for Feature Engineering in Financial Market [[post](https://towardsdatascience.com/corr2vec-a-wavenet-architecture-for-feature-engineering-in-financial-market-94b4f8279ba6)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Corr2Vec_WaveNet)]

- Siamese and Dual BERT for Multi Text Classification [[post](https://towardsdatascience.com/siamese-and-dual-bert-for-multi-text-classification-c6552d435533)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Siamese_Dual_BERT)]

- Time Series Forecasting with Graph Convolutional Neural Network [[post](https://towardsdatascience.com/time-series-forecasting-with-graph-convolutional-neural-network-7ffb3b70afcf)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Graph_TimeSeries_Forecasting)]

- Neural Network Calibration with Keras [[post](https://towardsdatascience.com/neural-network-calibration-with-keras-76fb7c13a55)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/NeuralNet_Calibration)]

- Combine LSTM and VAR for Multivariate Time Series Forecasting [[post](https://towardsdatascience.com/combine-lstm-and-var-for-multivariate-time-series-forecasting-abdcb3c7939b)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/LSTM_VAR)]

- Feature Importance with Time Series and Recurrent Neural Network [[post](https://towardsdatascience.com/feature-importance-with-time-series-and-recurrent-neural-network-27346d500b9c)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/NeuralNetSeq_FeatureImportance)]

- Group2Vec for Advance Categorical Encoding [[post](https://towardsdatascience.com/group2vec-for-advance-categorical-encoding-54dfc7a08349)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Group2Vec)]

- Survival Analysis with Deep Learning in Keras [[post](https://towardsdatascience.com/survival-analysis-with-deep-learning-in-keras-443875c486f2)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Survival_NeuralNetwork)]

- Survival Analysis with LightGBM plus Poisson Regression [[post](https://towardsdatascience.com/survival-analysis-with-lightgbm-plus-poisson-regression-6b3cc897af82)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Survival_LGBM)]

- Predictive Maintenance: detect Faults from Sensors with CRNN and Spectrograms [[post](https://towardsdatascience.com/predictive-maintenance-detect-faults-from-sensors-with-crnn-and-spectrograms-e1e4f8c2385d)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Predictive_Maintenance_CRNN)]

- Multi-Sample Dropout in Keras [[post](https://towardsdatascience.com/multi-sample-dropout-in-keras-ea8b8a9bfd83)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Multi_Sample_Dropout)]

- When your Neural Net doesn’t know: a bayesian approach with Keras [[post](https://towardsdatascience.com/when-your-neural-net-doesnt-know-a-bayesian-approach-with-keras-4782c0818624)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/NeuralNet_BayesUncertainty)]

- Dynamic Meta Embeddings in Keras [[post](https://towardsdatascience.com/dynamic-meta-embeddings-in-keras-42393d246963)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Dynamic_Meta_Embedding)]

- Predictive Maintenance with LSTM Siamese Network [[post](https://towardsdatascience.com/predictive-maintenance-with-lstm-siamese-network-51ee7df29767)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Predictive_Maintenance_SiameseNet)]

- Text Data Augmentation makes your model stronger [[post](https://towardsdatascience.com/text-data-augmentation-makes-your-model-stronger-7232bd23704)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Text_Augmentation)]

- Anomaly Detection with Permutation Undersampling and Time Dependency [[post](https://towardsdatascience.com/anomaly-detection-with-permutation-undersampling-and-time-dependency-5919e7c695d0)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Anomaly_Detection_PermutationUndersampling)]

- Time2Vec for Time Series features encoding [[post](https://towardsdatascience.com/time2vec-for-time-series-features-encoding-a03a4f3f937e)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Time2Vec)]

- Automate Data Cleaning with Unsupervised Learning [[post](https://towardsdatascience.com/automate-data-cleaning-with-unsupervised-learning-2046ef59ac17)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Unsupervised_Text_Cleaning)]

- People Tracking with Machine Learning [[post](https://towardsdatascience.com/people-tracking-with-machine-learning-d6c54ce5bb8c)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/PeopleTracking)]

- Time Series Clustering and Dimensionality Reduction [[post](https://towardsdatascience.com/time-series-clustering-and-dimensionality-reduction-5b3b4e84f6a3)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/TimeSeries_Cluster)]

- Anomaly Detection in Images [[post](https://towardsdatascience.com/anomaly-detection-in-images-777534980aeb)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Anomaly_Detection_Image)]

- Feature Importance with Neural Network [[post](https://towardsdatascience.com/feature-importance-with-neural-network-346eb6205743)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/NeuralNet_FeatureImportance)]

- Anomaly Detection with LSTM in Keras [[post](https://towardsdatascience.com/anomaly-detection-with-lstm-in-keras-8d8d7e50ab1b)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Anomaly_Detection_LSTM)]

- Dress Segmentation with Autoencoder in Keras [[post](https://towardsdatascience.com/dress-segmentation-with-autoencoder-in-keras-497cf1fd169a)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Dress_Segmentation)]

- Extreme Event Forecasting with LSTM Autoencoders [[post](https://towardsdatascience.com/extreme-event-forecasting-with-lstm-autoencoders-297492485037)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Extreme_Event_Forecasting)]

- Zalando Dress Recommendation and Tagging [[post](https://towardsdatascience.com/zalando-dress-recomendation-and-tagging-f38e1cbfc4a9)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/ZALANDO_Recomendation_Tag)]

- Remaining Life Estimation with Keras [[post](https://towardsdatascience.com/remaining-life-estimation-with-keras-2334514f9c61)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Remaining_Life_Estimation)]

- Quality Control with Machine Learning [[post](https://towardsdatascience.com/quality-control-with-machine-learning-d7aab7382c1e)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Quality_Control)]

- Predictive Maintenance: detect Faults from Sensors with CNN [[post](https://towardsdatascience.com/predictive-maintenance-detect-faults-from-sensors-with-cnn-6c6172613371)]|[[code](https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Predictive_Maintenance)]