https://github.com/avijay24/customer-churn-predictions-using-classification-analysis

Customer Churn Predictions Using Classification Analysis
https://github.com/avijay24/customer-churn-predictions-using-classification-analysis

churn-analysis churn-prediction classification-analysis predictive-modeling python

Last synced: about 2 months ago
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Customer Churn Predictions Using Classification Analysis

• Host: GitHub
• URL: https://github.com/avijay24/customer-churn-predictions-using-classification-analysis
• Owner: avijay24
• Created: 2023-08-24T00:10:48.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2023-10-23T17:51:15.000Z (over 2 years ago)
• Last Synced: 2025-02-24T12:22:15.275Z (over 1 year ago)
• Topics: churn-analysis, churn-prediction, classification-analysis, predictive-modeling, python
• Language: Jupyter Notebook
• Homepage:
• Size: 2.71 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Customer Churn Predictions Using Classification Analysis

Churn refers to the number of customers who stop using a product or service over a given period of time. Customers churn for various reasons such as poor customer service, product dissatisfaction, price sensitivity, better alternatives, and changes in circumstances, e.g. relocation. A data analyst finds the factors causing churn in data and works towards preventing it.

Churn prediction is the process of using data and analytical models to identify which customers are most likely to stop doing business with or using a company’s product or service in the near future.

Data analytics professionals typically use machine learning algorithms such as logistic regression, decision trees, and support vector machines to predict customer churn using classification analysis. These algorithms analyze data such as customer demographics, purchase history, and interactions with the company to identify patterns that can predict customer churn.

Dataset used: telco-dataset 21 columns

Customers who left within the last month — the column is called Churn

Each customer has signed up for services such as 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