https://github.com/anas436/customer-churn-prediction-using-logistic-regression-with-python
https://github.com/anas436/customer-churn-prediction-using-logistic-regression-with-python
jupyterlab linear-models logistic-regression matplotlib metrics numpy pandas pylab python3 scikit-learn scipy sklearn
Last synced: about 1 month ago
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- Host: GitHub
- URL: https://github.com/anas436/customer-churn-prediction-using-logistic-regression-with-python
- Owner: Anas436
- Created: 2022-09-05T13:21:25.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2022-09-05T13:23:58.000Z (over 2 years ago)
- Last Synced: 2025-02-01T15:30:57.943Z (3 months ago)
- Topics: jupyterlab, linear-models, logistic-regression, matplotlib, metrics, numpy, pandas, pylab, python3, scikit-learn, scipy, sklearn
- Language: Jupyter Notebook
- Homepage:
- Size: 33.2 KB
- Stars: 0
- Watchers: 2
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Customer-Churn-Prediction-using-Logistic-Regression-with-Python
## Objectives
After completing this lab you will be able to:
* Use scikit Logistic Regression to classify
* Understand confusion matrix
In this notebook, you will learn Logistic Regression, and then, you'll create a model for a telecommunication company, to predict when its customers will leave for a competitor, so that they can take some action to retain the customers.
Table of contents
- About the dataset
- Data pre-processing and selection
- Modeling (Logistic Regression with Scikit-learn)
- Evaluation
- Practice
About the dataset
We will use a telecommunications dataset for predicting customer churn. This is a historical customer dataset where each row represents one customer. The data is relatively easy to understand, and you may uncover insights you can use immediately. Typically it is less expensive to keep customers than acquire new ones, so the focus of this analysis is to predict the customers who will stay with the company.
This data set provides information to help you predict what behavior will help you to retain customers. You can analyze all relevant customer data and develop focused customer retention programs.
The dataset 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 had 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