https://github.com/priyanshii1511/predicting-customer-churn

Data Science mini project
https://github.com/priyanshii1511/predicting-customer-churn

confusion-matrix data-science machine-learning matplotlib numpy pandas plotly python seaborn sklearn

Last synced: about 1 month ago
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Data Science mini project

• Host: GitHub
• URL: https://github.com/priyanshii1511/predicting-customer-churn
• Owner: Priyanshii1511
• Created: 2024-03-29T15:49:17.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2024-07-21T08:52:34.000Z (7 months ago)
• Last Synced: 2024-11-13T04:41:34.302Z (3 months ago)
• Topics: confusion-matrix, data-science, machine-learning, matplotlib, numpy, pandas, plotly, python, seaborn, sklearn
• Language: Jupyter Notebook
• Homepage:
• Size: 253 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
Churn prediction means predicting which customers are at high risk of leaving a company or canceling a subscription to a service, based on their behavior with its product.

## Predicting Customer Churn

This project aims to predict the likelihood of customers churning out from an online website using Data Science.

### Dataset Description

* Each row represents a customer with a unique customer ID and each column has different information about the customer.

* The raw data contains 7043 rows (customers) and 21 columns (features).

* The columns include different features like demographics, account information, services that they have signed for and churn (customers who left within last month).

### Result

After performing Confusion Matrix and Probability, we are left with two columns:

1. Customer ID

2. Probability of the customer to churn out