This analysis explore different customer characteristics and prepare this e-commerce dataset for a predictive model of the Churn (not retained customers). We want to identify behavioral patterns and work with machine learning models for classification problems like Random Forest and XGBoost to predict customer churn.
The dataset contains the following columns:
Customer ID: A unique identifier for each customer.
Customer Name: The name of the customer (generated by Faker).
Customer Age: The age of the customer (generated by Faker).
Gender: The gender of the customer (generated by Faker).
Purchase Date: The date of each purchase made by the customer.
Product Category: The category or type of the purchased product.
Product Price: The price of the purchased product.
Quantity: The quantity of the product purchased.
Total Purchase Amount: The total amount spent by the customer in each transaction.
Payment Method: The method of payment used by the customer (e.g., credit card, PayPal).
Returns: Whether the customer returned any products from the order (binary: 0 for no return, 1 for return).
Churn: A binary column indicating whether the customer has churned (0 for retained, 1 for churned).
https://www.kaggle.com/datasets/shriyashjagtap/e-commerce-customer-for-behavior-analysis
Trello Link: https://trello.com/b/p53649s2/mid-project-customer-behavior-data