Customerware model

📗 Table of Contents

• 📖 About the Project
  • 🛠 Built With
    • Tech Stack
    • Key Features
  • 🚀 Live Demo
• 💻 Getting Started
  • Setup
  • Prerequisites
  • Install
  • Deployment
• 👥 Authors
• 🔭 Future Features
• 🤝 Contributing
• ⭐️ Show your support
• 🙏 Acknowledgements
• ❓ FAQ

📖 [Customerware]

[customerware] is a model that predicts whether customers will leave a company or not and more specifically in the telecommunication industry

• Video Link
• Deployed Model
• Backend Repositiory
• Frontend Repository
• Colab Link

🛠 Built With

Tech Stack

Model

For the model we mainly used Jupyter notebook for dataset cleaning, feature engineering, feature selection and model traning,validation, valuation and sample deployment. Tools:pandas,matplotlib,scitlearn,xgboost,numpy,seaborn

Client

The frontend is implemented in HML,CSS and Vanilla Javascript

Server

The server is implemented in flask with scitlearn, pandas,joblib,flask-cors as dependencies

Key Features

• [Prediction]
• [GoodUI]
• [ReliableOutput]

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🚀 Live Demo

• Live Demo Link

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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:

• A google account in order to run colab
• scit-learn, flask-cors,Flask, xgboost for the server
• You should have python installed

sudo apt-get update
sudo apt-get install python3.6

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Clone this repository to your desired folder:

Example commands:

  cd my-folder
  git clone git@github.com:myaccount/customerware-submission.git.git

Install

• To run colab use the link:colab link

• To run the [frontend] head to this repository: customerware frontend and clone to choice directory,

  cd choice directory
  code .

click Go Live if live server is installed

• To run [server] head to the repository:customerware and clone it to choice directory

 cd choice directory
 git clone https://github.com/Ednah-Akoth/AI_flask

  pip install scit-learn, flask-cors,Flask, xgboost

  python main.py

Deployment

You can deploy this project using: -streamlit -vercel -railwayapp

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👥 Authors

👤 Samuel Wanza

• GitHub: @githubhandle
• Twitter: @twitterhandle
• LinkedIn: LinkedIn

👤 Ednah Akoth

• GitHub: @githubhandle
• Twitter: @twitterhandle
• LinkedIn: LinkedIn

👤 Myra Lugwiri

• GitHub: @githubhandle
• Twitter: @twitterhandle
• LinkedIn: LinkedIn

👤 Spencer Kamayo

• GitHub: @githubhandle
• LinkedIn: LinkedIn

👤 Ahmed Mohamed

• GitHub: @githubhandle
• LinkedIn: LinkedIn

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🤝 Contributing

Contributions, issues, and feature requests are welcome!

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⭐️ Show your support

If you like this project kindly star it

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🙏 Acknowledgments

We would like to appreciate everyone that shared their ideas to the success of this project

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❓ FAQ

• [In Which industry is this model valid]

  • [The telecommunications industry given the dataset used]

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