Attrition Insight

📗 Table of Contents

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

Attrition Insight

Attrition Insight is a data application that allows users to interact with a machine learning model, view data visualizations on the data and see the values of their input saved for future use. It predicts the likelihood of an employee leaving the company based on various demographic and job-related factors.

Features

1. Age: Age of employee
2. Attrition: Employee attrition status
3. Department: Department of work
4. DistanceFromHome: what is their distance from hime
5. Education: 1-Below College; 2- College; 3-Bachelor; 4-Master; 5-Doctor;
6. EducationField: The field they studies in in the University
7. EnvironmentSatisfaction: 1-Low; 2-Medium; 3-High; 4-Very High;
8. JobSatisfaction: 1-Low; 2-Medium; 3-High; 4-Very High;
9. MaritalStatus: Whether they are married, single or divorced
10. MonthlyIncome: How much an employee makes a month
11. NumCompaniesWorked: Number of companies worked prior to IBM
12. WorkLifeBalance: 1-Bad; 2-Good; 3-Better; 4-Best;
13. YearsAtCompany: Current years of service in IBM

🛠 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

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

    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:123456
• 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

🕵🏽‍♀️ Racheal Appiah-kubi

• GitHub: GitHub Profile
• Twitter: Twitter Handle
• 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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