/Churn-Prediction

Churn Predictor is a machine learning model that helps businesses identify customers likely to churn, enabling proactive measures to retain them.

Primary LanguageJupyter NotebookMIT LicenseMIT

Churn Predicton

License

A churn predictor is a machine learning model that helps identify customers who are likely to leave a business or stop using its services. This repository contains code and resources for developing a churn predictor.

Dataset

This dataset contains customer data from a telecommunications company and is focused on predicting customer churn. The dataset provides information about customers' demographics, services subscribed, and churn status.

Dataset Source

The dataset can be accessed from Kaggle at the following link: Telco Customer Churn Dataset

Description

The Telco Customer Churn dataset consists of the following columns:

  • customerID: Unique identifier for each customer
  • gender: Customer's gender (Male/Female)
  • SeniorCitizen: Indicates if the customer is a senior citizen (0: No, 1: Yes)
  • Partner: Whether the customer has a partner (Yes/No)
  • Dependents: Whether the customer has dependents (Yes/No)
  • tenure: Number of months the customer has been with the company
  • PhoneService: Whether the customer has a phone service (Yes/No)
  • MultipleLines: Whether the customer has multiple lines (Yes/No/No phone service)
  • InternetService: Type of internet service subscribed (DSL/Fiber optic/No)
  • OnlineSecurity: Whether the customer has online security (Yes/No/No internet service)
  • OnlineBackup: Whether the customer has online backup (Yes/No/No internet service)
  • DeviceProtection: Whether the customer has device protection (Yes/No/No internet service)
  • TechSupport: Whether the customer has tech support (Yes/No/No internet service)
  • StreamingTV: Whether the customer has streaming TV (Yes/No/No internet service)
  • StreamingMovies: Whether the customer has streaming movies (Yes/No/No internet service)
  • Contract: Type of contract (Month-to-month/One year/Two year)
  • PaperlessBilling: Whether the customer has opted for paperless billing (Yes/No)
  • PaymentMethod: Payment method used by the customer
  • MonthlyCharges: Monthly charges incurred by the customer
  • TotalCharges: Total charges incurred by the customer
  • Churn: Whether the customer churned (Yes/No)

Please refer to the Kaggle dataset page for more detailed information on the dataset.

Getting Started

These instructions will help you get started with the churn predictor project.

Prerequisites

Make sure you have the following prerequisites installed:

  • Python 3.x
  • Jupyter Notebook or any preferred IDE

Installation

  1. Clone the repository:

    git clone https://github.com/metarex21/churn-predicton.git