Four-Project Series

Machine Learning with R for Text Data you own this product

intermediate R, particularly tidyverse • basic understanding of text data structures • intermediate ML • intermediate DL
skills learned
import and clean data • tokenize and clean text data • feature engineering • fit models using a tidy workflow • evaluate classification models • tune ML models • resample datasets • implement pre-trained word embeddings in an ML workflow • explain how an ML model generates specific predictions
Benjamin Soltoff
4 weeks · 4-6 hours per week average · INTERMEDIATE

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Picture this: You’re an academic researcher tasked with helping social scientists determine the U.S. government’s responsiveness to public demands. A clear expression of this responsiveness is examining the types of policies legislators seek to advance. In this series of liveProjects, you’ll apply machine learning to generate predictions of the policy focus of each congressional bill in a legislation dataset. Leveraging tools widely used by data scientists and academic researchers—including R, the tidymodels framework, feature engineering techniques, and ML algorithms—you’ll perform exploratory data analysis (EDA) to prepare for predictive modeling, preprocess the text data, develop core ML models, and train DL models.

These projects are designed for learning purposes and are not complete, production-ready applications or solutions.

Mentor Fengyi Zheng shares what she likes about the Manning liveProject platform.

here's what's included

Project 1 EDA for Text

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.

Project 2 ML for Text Classification

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.

Project 3 Extending ML for Text Classification

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.

Project 4 DL for Text Classification

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.

book resources

When you start each of the projects in this series, you'll get full access to the following book for 90 days.

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project author

Benjamin Soltoff
Benjamin Soltoff is an assistant senior instructional professor in computational social science at the University of Chicago. He’s the associate director of the Masters in Computational Social Science program and teaches courses in research design, programming in R, data visualization, and machine learning. He holds a PhD in political science from Pennsylvania State University. He develops training workshops for learners in academia and industry on data science techniques using R with an emphasis on reproducible workflows, and he’s an RStudio-certified trainer. For more information, you can view his personal site.


These liveProjects are for data analysts familiar with writing basic code in R and who have prior experience working with machine learning techniques. To begin these liveProjects, you’ll need to be familiar with the following:

  • Basic knowledge of R, particularly tidyverse
  • Basic understanding of text data structures
  • Intermediate understanding of machine learning workflows (machine learning algorithms, loss functions, cross-validation, metrics and model evaluation)
  • Intermediate understanding of deep learning models

you will learn

In this liveProject series, you’ll learn to use feature engineering, machine learning workflows, and deep learning techniques to generate predictions.

  • Import and clean data
  • Generate basic data visualizations for time-series datasets
  • Tokenize and clean text data
  • Calculate summary statistics for text data
  • Resample datasets for unbiased measures of model performance
  • Feature engineering for text data
  • Fit models using a tidy framework
  • Evaluate classification models using appropriate metrics
  • Tune machine learning models to maximize their effectiveness
  • Resample datasets for unbiased measures of model performance
  • Generate feature hashes for categorical variables
  • Implement pre-trained word embeddings in a machine learning workflow
  • Subsample an unbalanced dataset to minimize bias
  • Fit models using Keras
  • Tune hyperparameters to maximize model performance
  • Explain how a machine learning model generates specific predictions


You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.