Telco Churn Prediction Gradio App

Description

This repository contains a machine learning-powered web application built using Gradio that predicts customer churn in the telecommunications industry. The app utilizes a pre-trained machine learning model to analyze input features such as customer tenure, monthly charges, and internet services to predict whether a customer is likely to churn or not.

Churn prediction is crucial for businesses to understand customer behavior and take proactive measures to retain valuable customers. This model takes into account a range of factors such as customer demographics, services used, billing information, and contract details to provide a prediction.

The application offers an intuitive and user-friendly interface for predicting customer churn, which can be a critical business challenge for telecommunications companies. The prediction model is based on historical customer data, making it a valuable tool for companies seeking to optimize their customer retention strategies.

Interface Input Features

• Gender: Gender of the customer (Male/Female).
• Partner: Whether the customer has a partner (Yes/No).
• Dependents: Whether the customer has dependents (Yes/No).
• Tenure: The number of months the customer has been with the company.
• Internet Service: Type of internet service (DSL/Fiber optic/No).
• Phone Service: Whether the customer has phone service (Yes/No).
• Multiple Lines: Whether the customer has multiple phone lines (Yes/No).
• Contract: Type of contract the customer has (Month-to-month/One year/Two year).
• Monthly Charges: Amount of monthly charges.
• Total Charges: Total amount charged to the customer.
• Paperless Billing: Whether the customer uses paperless billing (Yes/No).
• Payment Method: Payment method used by the customer (Electronic check/Mailed check/Bank transfer/Credit card).
• Online Security: Whether the customer has online security service (Yes/No).
• Online Backup: Whether the customer has online backup service (Yes/No/None).
• Device Protection: Whether the customer has device protection service (Yes/No).
• Tech Support: Whether the customer has tech support service (Yes/No).
• Streaming TV: Whether the customer uses streaming TV service (Yes/No).
• Senior Citizen: Whether the customer is a senior citizen (Yes/No).
• Streaming Movies: Whether the customer uses streaming movies service (Yes/No).

Usage

1. Clone this repository to your local machine.

Start by cloning the GitHub repository containing the Telco Churn Analysis project and the predictive app. You can do this by running the following command in your terminal:

https://github.com/AGwillmkawy1/Telco_Churn_Gradio_App-main.git

2. Navigate to the project directory.

2. Setup Virtual Environment

You need Python3 on your system to setup this app. Then you can clone this repo and being at the repo's root :: telco churn prediction gradio app> ... follow the steps below:

• Windows

    python -m venv venv; venv\Scripts\activate; python -m pip install -q --upgrade pip; python -m pip install -qr requirements.txt 

• Linux & MacOs

    python3 -m venv venv; source venv/bin/activate; python -m pip install -q --upgrade pip; python -m pip install -qr requirements.txt  

3. Install Dependencies:

Install the required Python packages within your virtual environment:

pip install -r requirements.txt

To run the Application Script

python Assets/app.py

Pretrained Model and Analysis

To better understand the modeling and analysis behind the app, you can explore the Analysis and Modeling directory here. It contains detailed information about the data preprocessing, feature engineering, model training, and evaluation that contributed to the creation of the app's predictive capabilities.

Author

Jean Plaubert