Churn rate is a marketing metric that describes the number of customers who leave a business over a specific time period. . Every user is assigned a prediction value that estimates their state of churn at any given time. This value is based on:

User demographic information
Browsing behavior
Historical purchase data among other information

It factors in our unique and proprietary predictions of how long a user will remain a customer. This score is updated every day for all users who have a minimum of one conversion. The values assigned are between 1 and 5.

Task

Your task is to predict the churn score for a website based on the features provided in the dataset.

Data description

The dataset folder contains the following files:

train.csv: 29593 x 25
test.csv: 7399 x 24
sample_submission.csv: 5 x 2

The columns provided in the dataset are as follows:

Column name	Description
customer_id	Represents the unique identification number of a customer
Name	Represents the name of a customer
age	Represents the age of a customer
security_no	Represents a unique security number that is used to identify a person
region_category	Represents the region that a customer belongs to
membership_category	Represents the category of the membership that a customer is using
joining_date	Represents the date when a customer became a member
joined_through_referral	Represents whether a customer joined using any referral code or ID
referral_id	Represents a referral ID
preferred_offer_types	Represents the type of offer that a customer prefers
medium_of_operation	Represents the medium of operation that a customer uses for transactions
internet_option	Represents the type of internet service a customer uses
last_visit_time	Represents the last time a customer visited the website
days_since_last_login	Represents the no. of days since a customer last logged into the website
avg_time_spent	Represents the average time spent by a customer on the website
avg_transaction_value	Represents the average transaction value of a customer
avg_frequency_login_days	Represents the no. of times a customer has logged in to the website
points_in_wallet	Represents the points awarded to a customer on each transaction
used_special_discount	Represents whether a customer uses special discounts offered
offer_application_preference	Represents whether a customer prefers offers
past_complaint	Represents whether a customer has raised any complaints
complaint_status	Represents whether the complaints raised by a customer was resolved
feedback	Represents the feedback provided by a customer
churn_risk_score	Represents the churn risk score that ranges from 1 to 5

Evaluation metric

score = 100 x metrics.f1_score(actual, predicted, average="macro")

Result submission guidelines

The index is customer_id and the target is the churn_risk_score column.
The submission file must be submitted in .csv format only.
The size of this submission file must be 7399 x 2.

Note: Ensure that your submission file contains the following:

Correct index values as per the test file
Correct names of columns as provided in the sample_submission.csv file