Shinkansen Travel Experience

Problem Statement Goal

The goal of the problem is to predict whether a passenger was satisfied or not considering his/her overall experience of traveling on the Shinkansen Bullet Train.

Dataset

The problem consists of two separate datasets: Travel data and Survey data. Travel data has information related to passengers and attributes related to the Shinkansen train in which they traveled. The survey data is aggregated data of surveys indicating the post-service experience. You are expected to treat both these datasets as raw data and perform any necessary data cleaning/validation steps as required.

The data has been split into two groups and provided in the Dataset folder. The folder contains both train and test data separately:

• Train_Data
• Test_Data

Target Variable

• Overall_Experience: 1 represents ‘satisfied’, and 0 represents ‘not satisfied’.

The training set can be used to build your machine learning model. The training set has labels for the target column - Overall_Experience. The testing set should be used to see how well your model performs on unseen data. For the test set, it is expected to predict the ‘Overall_Experience’ level for each participant.

Data Dictionary

All the data is self-explanatory. The survey levels are explained in the Data Dictionary file.

Submission File Format

You will need to submit a CSV file with exactly 35,602 entries plus a header row. The file should have exactly two columns:

• ID
• Overall_Experience (contains 0 & 1 values, 1 represents ‘Satisfied’, and 0 represents ‘Not Satisfied’)

Evaluation Criteria

Accuracy Score: The evaluation metric is simply the percentage of predictions made by the model that turned out to be correct. This is also called the accuracy of the model. It will be calculated as the total number of correct predictions (True Positives + True Negatives) divided by the total number of observations in the dataset. In other words, the best possible accuracy is 100% (or 1) and the worst possible accuracy is 0%.