Dataset preparation

Ludwig can train on any table-like dataset, meaning that every feature has its own column and every example its own row.

In this example, we'll use this Rotten Tomatoes dataset, a CSV file with variety of feature types and a binary target.

Download the data locally here.

Let's take a look at the first 5 rows to see how the data is arranged:

head -n 5 rotten_tomatoes.csv
import pandas as pd

df = pd.read_csv('rotten_tomatoes.csv')

Your results should look a little something like this:

movie_title content_rating genres runtime top_critic review_content recommended
Deliver Us from Evil R Action & Adventure, Horror 117.0 TRUE Director Scott Derrickson and his co-writer, Paul Harris Boardman, deliver a routine procedural with unremarkable frights. 0
Barbara PG-13 Art House & International, Drama 105.0 FALSE Somehow, in this stirring narrative, Barbara manages to keep hold of her principles, and her humanity and courage, and battles to save a dissident teenage girl whose life the Communists are trying to destroy. 1
Horrible Bosses R Comedy 98.0 FALSE These bosses cannot justify either murder or lasting comic memories, fatally compromising a farce that could have been great but ends up merely mediocre. 0
Money Monster R Drama 98.0 FALSE A satire about television that feels like it was made by the kind of people who claim they don't even watch TV. 0
Battle Royale NR Action & Adventure, Art House & International, Drama, Mystery & Suspense 114.0 FALSE Battle Royale is The Hunger Games not diluted for young audiences. 1