This study uses machine learning to predict and understand employee attrition, aiming to provide actionable insights for proactive human resource strategies based on diverse employee attributes.
Tools used:
i. Google Collab (Collaboratory): Google Collab is a cloud-based platform provided by Google that allows users to write and execute Python code in a Jupyter Notebook environment.
ii. Jupyter Notebook: Jupyter Notebook is an open-source web application that enables interactive computing. It allows users to create and share documents containing live code, equations, visualizations, and explanatory text.
iii. Power BI (Business Intelligence): Power BI is a business analytics tool developed by Microsoft. It allows users to visualize and share insights from their data through interactive reports and dashboards.
iv. Dash: Dash is a Python framework built on top of Flask
v. Flask: Flask is a lightweight and flexible web application framework for Python. It is designed to make getting started with web development quick and easy, with the ability to scale up to complex
vi. Plotly: Plotly is an open-source, interactive data visualization library for creating a wide variety of graphical plots and charts.
Models and techniques:
Logistic Regression Random Forest SVM Decision Tree How To run the file:
In split terminal run both app.py and eda.py as python app.py and python eda.py at the same time to view the full project. The server will run on http://127.0.0.1:5000 app.py consists of the
The Overview of the project The SVM prediction model Decision Tree Comparison of ML Models Attrition of high performance employees with reason eda.py consists of the Simple HR Dashboard analysis by attrition Observaton of the data for example outliers, correlation etc,.
Dataset Download link: https://www.kaggle.com/datasets/pavansubhasht/ibm-hr-analytics-attrition-dataset
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent