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')
df.head()
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 |