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`python-m5p` - M5 Prime regression trees in python, compliant with scikit-learn
Sylvain Marié
Algorithms, Predictive Modelling, Science

`python-m5p` is an implementation of the M5P algorithm compliant with scikit-learn.

Honey, I shrunk the target variable! Common pitfalls when transforming the target variable and how to exploit transformations.
Florian Wilhelm
Math, Predictive Modelling, Statistics

Honey, I shrunk the target variable! Common pitfalls when transforming the target variable and how to exploit transformations.

How a simple streamlit dashboard will help to put your machine learning model in production
Daniël Willemsen, Welmoet Verbaan
Best Practice, Data Visualization, Predictive Modelling

Have you struggled getting your valuable machine learning model into the hands of users? A simple streamlit monitoring dashboard can help!

Inpsect and try to interpret your scikit-learn machine-learning models
Guillaume Lemaitre
Predictive Modelling, Statistics, Transparency / Interpretability

Inspect and try to interpret your scikit-learn machine-learning models

Introduction to MLOps with MLflow
Tobias Sterbak
Best Practice, Predictive Modelling, Reproducibility

Learn the basics of MLops with MLflow to manage the machine learning life-cycle.

Introduction to Uplift Modeling
Dr. Juan Orduz
Algorithms, Predictive Modelling, Statistics

In this talk we introduce uplift modelling, a method to estimate conditional average treatment effects (CATE) using machine learning estimators.

Predictive Maintenance and Anomaly Detection for Wind Energy
Tobias Hoinka
Predictive Modelling, Statistics, Time Series

This talk will describe predictive modeling applications in wind turbine maintenance, the challenges of anomaly detection and ways to move to more automatic diagnoses by modeling past documented defects.

sktime - python toolbox for time series: advanced forecasting - probabilistic, global and hierarchical
Franz Kiraly
Algorithms, Predictive Modelling, Time Series

The forecasting module of sktime provides a unified, sklearn-compatible, and composable interface. This tutorial covers advanced topics in forecasting using sktime: probabilistic forecasting, and forecasting with panel data, including global/hierarchical forecasting.

Unsupervised shallow learning for fraud detection on marketplaces
Andreu Mora
Algorithms, Best Practice, Predictive Modelling

Tune in to learn how @adyen uses ML and open source over python to combat fraud and wrongdoings over large marketplaces such as @gofundme or @eBay

Your data, your insights: creating personal data projects to (re-)own the data you share
Paula Gonzalez Avalos
Data Visualization, Predictive Modelling

Your data, your insights: 3 examples to illustrate how we can apply common data science libraries together with data shared via mobile apps or collected manually to build little data visualization projects that provide unique, contextual and intmiate insights.

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