Step into the shoes of an academic researcher tasked with predicting which areas will be the focus of the U.S. government’s policy-related efforts. In this liveProject, you’ll prepare for predictive modeling by exploring the policy areas and text descriptions in legislation data, using statistical visualizations and ggplot2, and identifying notable trends and outliers.

Play the role of an academic researcher preparing a machine learning model to predict the U.S. government’s focus for new policy legislation. You’ll process the legislation dataset with resampling and feature engineering techniques, employ a range of algorithms, including penalized regression and XGBoost, to fit a series of ML models, evaluate the effectiveness of the models, and tune them accordingly.

Imagine you’re an academic researcher working on a project for predicting trends in the U.S. government’s policy-making priorities. Using modern techniques for text data feature engineering, you’ll fit a set of models, subsample the training data to minimize bias, evaluate the models’ performance using a test-set of observations, and leverage a tidy workflow to explain how a model generates specific predictions.

Predict the future! You’re an academic researcher working on a project that predicts what policy areas the U.S. government will prioritize. To achieve your goal, you’ll train three kinds of deep learning neural networks on a legislation dataset (a CSV file containing one row for every bill introduced in the U.S. Congress). With the resulting text classifications, you’ll predict the area of focus for future policy bills.