Problem Statement:

Use machine learning to create a model that predicts which customers will pass the next funnel cycle

The Challenge:

In this case study, we ask you to build a predictive model that answers the question: “what sorts of people were more likely to pass the next funnel cycle?” using customer data (ie age, gender, etc)

Data:

In this case study, you’ll gain access to two similar datasets that include customer information like age, gender, etc. One dataset is titled train.csv and the other is titled test.csv.

Train.csv will contain the details of a subset of the customer information and importantly, will reveal whether they passed the next funnel or not, also known as the “ground truth”.

The test.csv dataset contains similar information but does not disclose the “ground truth” for each customer. It’s your job to predict these outcomes.

Using the patterns you find in the train.csv data, predict whether the other customers (found in test.csv) converted.