Skip to content

Custiner Lifetime Value Optimization

Industry Department Role
Retail & Online Marketing Marketing Lead

Processed Dataset

Data

This is a dataset for binary sentiment classification containing a set of 25,000 highly popular movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well. Raw text and already processed bag of words formats are provided.

Features informations

  • review
  • sentiment

MindsDB Code example

import mindsdb
from sklearn.metrics import accuracy_score


predictor = mindsdb.Predictor(name='movie_sentiment_predictor')
predictor.learn(from_data='train.tsv', to_predict=['sentiment'])

accuracy_data = predictions.test('test.tsv', accuracy_score)

accuracy_pct = accuracy_data['sentiment_accuracy'] * 100
print(f'Accuracy of {accuracy_pct}% !')

Mindsdb accuracy

Accuraccy Backend Last run MindsDB Version Latest Version
0.8573 Lightwood 06 February 2020 MindsDB PyPi Version