This was a contest to predict whether costumers were happy or not about a hotel and their service from their reviews.
You are given three files to download: train.csv, test.csv and sample_submission.csv The training data has 38932 rows and test data has 29404 rows.
| Variable | Description |
|---|---|
| User_ID | unique ID of the customer |
| Description | description of the review posted |
| Browser_Used | browser used to post the review |
| Device_Used | device used to post the review |
| Is_Response | target Variable |
A participant has to submit a csv file containing User_ID and predicted labels in a csv format. Check the sample submission file for format.
User_ID, Is_Response
id80132,happy
id80133,not_happy
id80134,not_happy
id80135,not_happy
id80136,happy
Submission will be evaluated based on Accuracy score. Higher the better. To know more, read here.
- XGBoost and NaiveBayes (R) - Click Here
- CatBoost, LightGBM, NaiveBayes (Python) - Click Here