All data can be found in the book’s repo. Depending on when you access it, there may be more or less data available. We’ll try to clean it up to make it more clear eventually, but it’s easiest to use the code in the demonstrations to download the data directly.
Movie reviews
The movie reviews dataset was a fun way to use an LLM to create movie titles and reviews in a specific way, as well as other features. With features in hand, we then generated a rating outcome with specific feature-target relationships. It has 1000 rows and the following columns:
title: The title of the moviereview_year: The year the review was writtenage: The age of the reviewerchildren_in_home: The number of children in the reviewer’s homeeducation: The education level of the reviewer (Post-Graduate, Completed College, Completed High School)gender: The gender of the reviewer (male or female)work_status: The work status of the reviewer (Employed, Retired, Unemployed, Student)genre: The genre of the movierelease_year: The year the movie was releasedlength_minutes: The length of the movie in minutesseason: The season the movie was released (e.g. Fall, Winter)total_reviews: The total number of reviews for the movierating: The rating of the moviereview_text: The text of the reviewword_count: The number of words in the reviewreview_year_0: The review year starting from 0release_year_0: The release year starting from 0*_sc: Scaled (standardized) versions of age, length_minutes, total_reviews, and word_countrating_good: A binary version of rating, where 1 is a good rating (>= 3) and 0 is a bad rating (< 3)
Short link:
Repo File:
World Happiness Report
The World Happiness Report is a survey of the state of global happiness that ranks countries by how ‘happy’ their citizens perceive themselves to be. You can also find additional details in their supplemental documentation. Our 2018 data is from what was originally reported at that time (figure 2.2 in the corresponding report), and it also contains a life ladder score from the most recent survey, which is similar and very highly correlated.
The datasets contains the following columns:
country: The country nameyear: The year of the surveylife_ladder: The happiness scorelog_gdp_per_capita: The log of GDP per capitasocial_support: The social support scorehealthy_life_expectancy_at_birth: The healthy life expectancy at birthfreedom_to_make_life_choices: The freedom to make life choices scoregenerosity: The generosity scoreperceptions_of_corruption: The perceptions of corruption scorepositive_affect: The positive affect scorenegative_affect: The negative affect scoreconfidence_in_national_government: The confidence in national government scorehappiness_score: The happiness scoredystopia_residual: The dystopia residual score (difference from a ‘least happy’ country)
In addition there are standardized/scaled versions of the features, which are suffixed with _sc.
Short links:
Repo Files:
data/world_happiness_all_years.csvdata/world_happiness_2018.csv
Heart Disease UCI
This classic dataset comes from the UCI ML repository. We took a version from Kaggle, and features and target were renamed to be more intelligible. Here is a brief description from UCI:
This database contains 76 attributes, but all published experiments refer to using a subset of 14 of them. In particular, the Cleveland database is the only one that has been used by ML researchers to date. The “goal” field refers to the presence of heart disease in the patient. It is integer valued from 0 (no presence) to 4. Experiments with the Cleveland database have concentrated on simply attempting to distinguish presence (values 1,2,3,4) from absence (value 0).
age: Age in yearsmale: ‘yes’ or ‘no’chest_pain_type: ‘typical’, ‘atypical’, ‘non-anginal’, ‘asymptomatic’resting_bp: Resting blood pressure (mm Hg)cholesterol: Serum cholesterol (mg/dl)fasting_blood_sugar: ‘> 120 mg/dl’ or ‘<= 120 mg/dl’resting_ecg: ‘normal’, ‘left ventricular hypertrophy’, ‘ST-T wave abnormality’max_heart_rate: Maximum heart rate achievedexercise_induced_angina: ‘yes’ or ‘no’st_depression: ST depression induced by exercise relative to restslope: ‘upsloping’, ‘flat’, ‘downsloping’num_major_vessels: Number of major vessels (0-3) colored by fluoroscopythalassemia: ‘normal’, ‘fixed defect’, ‘reversible defect’heart_disease: ‘yes’ or ‘no’
Short links:
Repo Files:
data/heart_disease_processed.csvdata/heart_disease_processed_numeric_sc.csv
Fish
This is a very simple data set with a count target variable. It’s also good if you want to try your hand at zero-inflated models. The background is that state wildlife biologists want to model how many fish are being caught by fishermen at a state park.
nofish: We’ve never seen this explained. Originally 0 and 1, 0 is equivalent to livebait == ‘yes’, so it may be whether the primary motivation of the camping trip is for fishing or not.livebait: Whether live bait was used or notcamper: Whether or not they brought a camperpersons: How many total persons on the tripchild: How many children presentcount: Number of fish caught
Short Link:
Repo File: