## Problem description
**Insurance companies that sell life, health, property, and casualty insurance are using machine learning (ML) to drive improvements in customer service, fraud detection, and operational efficiency.**

**CMB company provides Health Insurance to its customers. We can build a model to predict whether the customers from past year will also be interested in Vehicle Insurance provided by the company.**

**In this problem,  we invite you to help CMB company predict the binary class Responses.**


## Instruction: 

1. If you don't have Kaggle account, create yours using the link:
https://www.kaggle.com/account/login?phase=startRegisterTab&returnUrl=%2F

2. Download the train.csv, test.csv, sample solution, and dataset_description.txt.


3. Read the dataset_description.txt to understand the data you're dealing with.

4. train.csv is the data you will use to build your model.

5. Build your model. You may use any classification model, for example, a Decision tree or Nearest Neighbors. See labs #4 and #5 on elearning.

6. Read test.csv, and predict the data using the model you built in step 5.

7. Create a data frame with two columns `id` and `price_range`. The `price_range` is the predictions done in step 6.

8. Save the data frame (done in step 7) in the `submission.csv` file.

9. Submit `submission.csv`, and see your score.