Customer-Churn-App

An app for the deployment of the Vodafone Churn machine learning

Attrition Meter

📗 Table of Contents

• 📗 Table of Contents
• Churn App
  • 🛠 Built With
    • Tech Stack
  • Key Features
  • 💻 Getting Started
    • Prerequisites
    • Setup
    • Install
    • Usage
  • 👥 Authors
  • 🔭 Future Features
  • 🤝 Contributing
  • ⭐️ Show your support
  • 🙏 Acknowledgments
  • 📝 License

Churn App

Vodafone Churn App is a data application that allows users to interact with a machine learning model, view data visualizations on the data and see the churn rate prediction of vodafone.

Features

1. Gender -- Whether the customer is a male or a female
2. SeniorCitizen -- Whether a customer is a senior citizen or not
3. Partner -- Whether the customer has a partner or not (Yes, No)
4. Dependents -- Whether the customer has dependents or not (Yes, No)
5. Tenure -- Number of months the customer has stayed with the company
6. Phone Service -- Whether the customer has a phone service or not (Yes, No)
7. MultipleLines -- Whether the customer has multiple lines or not
8. InternetService -- Customer's internet service provider (DSL, Fiber Optic, No)
9. OnlineSecurity -- Whether the customer has online security or not (Yes, No, No Internet)
10. OnlineBackup -- Whether the customer has online backup or not (Yes, No, No Internet)
11. DeviceProtection -- Whether the customer has device protection or not (Yes, No, No internet service)
12. TechSupport -- Whether the customer has tech support or not (Yes, No, No internet)
13. StreamingTV -- Whether the customer has streaming TV or not (Yes, No, No internet service)
14. StreamingMovies -- Whether the customer has streaming movies or not (Yes, No, No Internet service)
15. Contract -- The contract term of the customer (Month-to-Month, One year, Two year)
16. PaperlessBilling -- Whether the customer has paperless billing or not (Yes, No)

🛠 Built With

Tech Stack

GUI

• Streamlit

Database

• Microsoft SQL Server

Language

• Python

Model

• Sklearn

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Key Features

• A data application that presents visualizations on both the exploratory data and the KPIs
• A predicitons page to predict by specifying the model you want to use
• View proprietory data loaded in real-time form the remote server
• Predictions are save for further analysis in the future and users can view the history of their prediction input values

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💻 Getting Started

To get a local copy up and running, follow these steps.

Prerequisites

In order to run this project you need:

• Python
• Streamlit

Setup

Clone this repository to your desired folder:

  cd my-folder
  git clone https://github.com/coderacheal/Attrition-Meter.git

Change into the cloned repository

  cd Attrition-Meter
  

Create a virtual environment

python -m venv env

Activate the virtual environment

    virtual_env/Scripts/activate

Install

Here, you need to recursively install the packages in the requirements.txt file using the command below

   pip install -r requirements.txt

Usage

To run the project, execute the following command:

    streamlit run 1_🏠_Home.py

• A webpage opens up to view the app
• Login to the app with username=coderacheal and password:abc
• Finally test a prediction by clicking on the predicitons page
• Note: Users may not be able to access the View Data page as the secrets file is not checked into git

👥 Authors

🕵🏽‍♀️ Akosua Dansoaa Danso

• GitHub: GitHub Profile
• LinkedIn: LinkedIn Profile

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🔭 Future Features

• Add a front end application for users

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🤝 Contributing

Contributions, issues, and feature requests are welcome!

Feel free to check the issues page.

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⭐️ Show your support

If you like this project kindly show some love, give it a 🌟 STAR 🌟

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🙏 Acknowledgments

I would like to thank all the free available resource made available online

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📝 License

This project is MIT licensed.

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