This project aims to analyze and predict the key factors influencing employee attrition and retention within organizations. By leveraging data analysis and machine learning, we can gain actionable insights to improve talent retention and organizational success.

Follow Step-by-Step Guide: End-to-End HR Analytics: Predicting Employee Attrition with Streamlit

The project sets out to:

✅ Identify the key drivers of employee attrition within organizations.
✅ Gain insights into leveraging these factors to improve talent retention and organizational outcomes.
✅ Visualize dynamic and interactive attrition metrics using Streamlit components.
✅ Build a machine learning model to predict employee attrition based on workload, and other critical variables.
✅ Learn to effectively present insights to stakeholders for data-driven decision-making.
✅ Deploy the application on a hosted server for seamless access.

🔗 View the live app here: Attrition Prediction App

• Ensure Python is installed on your system.
• Create and activate a virtual environment.

1️⃣ Clone the GitHub repository:

git clone https://github.com/DoyinHubX/attrition.git
cd attrition

2️⃣ Install dependencies:

pip install -r requirements.txt

3️⃣ Run the Streamlit app:

streamlit run app.py

4️⃣ Interact with the app in your browser and explore insights!

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Thank you, and happy coding! 🎉