Problem Statement

Customer Happiness has become the top priority for service & product based companies. To an extent, some of the companies even appoint CHOs (Customer Happiness Officer) to ensure delivering a kickass and delightful customer experience.

Well, machine learning is now playing a pivotal role in delivering that experience. The ability to predict happy and unhappy customers give companies a nice head-start to improve their experience.

TripAdvisor is the world's largest travel site where you can compare and book hotels, flights, restaurants etc. The data set provided in this challenge consists of a sample of hotel reviews provided by the customers. Analysing customers reviews will help them understand about the hotels listed on their website i.e. if they are treating customers well or if they are providing hospitality services as expected.

In this challenge, you have to predict if a customer is happy or not happy.

Download Dataset

Data Description

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

Submission

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

Evaluation Metric

Submission will be evaluated based on Accuracy score. Higher the better. To know more, read here.

Scripts

XGBoost and NaiveBayes (R) - Click Here
CatBoost, LightGBM, NaiveBayes (Python) - Click Here