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Explore effect and likelihood encodings for high cardinality predictors in tidymodels, using UK museum data to demonstrate practical applications.
Learn to deploy various prediction endpoints for a Bigfoot sighting model using vetiver, including feature engineering, logistic regression, and natural language processing techniques.
Predict LEGO set piece counts using machine learning, then deploy the model with Docker for easy sharing and reproducibility.
Explore three increasingly complex approaches to analyze UK gender pay gap data, from basic comparisons to robust statistical modeling.
Explore collegiate sports expenditures using linear modeling and tidymodels, focusing on handling coefficients and intervals for insightful analysis of financial patterns.
Learn to fit zero-inflated Poisson regression models using tidymodels to analyze R package vignette counts, with practical examples and code demonstrations.
Explore advanced modeling with board game ratings data: feature engineering, XGBoost tuning, and explainability methods for data-driven insights and predictive analysis.
Explore topic modeling using R to analyze Spice Girls lyrics, uncovering hidden themes and patterns in their music through unsupervised machine learning techniques.
Explore predictive modeling for Doctor Who episode viewership using tidymodels, with a focus on flexibility and control in handling modeling objects.
Learn to predict giant pumpkin weights using tidymodels workflowsets. Explore data visualization, modeling techniques, and parallel processing for efficient analysis of #TidyTuesday pumpkin competition data.
Explore multiclass lasso modeling for economic research papers, covering data analysis, feature engineering, model tuning, and evaluation using #TidyTuesday data.
Learn to model bird bath observations in Australia using tidymodels, combining model specifications with feature engineering recipes for effective fitting and prediction.
Explore machine learning with tidymodels workflowsets to predict speaker type in Star Trek interactions, featuring data preprocessing, model evaluation, and result analysis.
Explore feature engineering for boosted tree models using Austin housing data. Learn to incorporate text information as indicator variables and tune models for accurate price predictions.
Learn to optimize xgboost models using early stopping techniques for predicting animal shelter outcomes, featuring practical examples and efficient coding strategies.
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