This project is a final project supervised project of Python for Machine Learning Data Science Master Class.
Aim of the project is to predict Churn column. Churn covers essential information for the company.
The data set includes information about:
Customers who left within the last month – the column is called Churn Services that each customer has signed up for phone, multiple lines, internet, online security, online backup, device protection, tech support, and streaming TV and movies Customer account information – how long they’ve been a customer, contract, payment method, paperless billing, monthly charges, and total charges Demographic info about customers – gender, age range, and if they have partners and dependents
This is a visual explanation of relation between number of months the customer has stayed with the company and the amount charged to the customer monthly according to different contract
It explains correlation between variables and customers who left within the last month.
These are confusion matrix for two different models. Below is the more accurate model for which has 78% accuracy.
https://www.kaggle.com/datasets/blastchar/telco-customer-churn
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