alexa9der / practice-with-classification

Analysis and forecast of employee retention

Data analysis project developed as part of practical work. Here is the project structure with directories, files and a description of their purpose.

Project structure

• config: contains configuration files and project settings.

• data: contains data.

• documents: contains data descriptions.

• lib: includes auxiliary libraries.

• models: store trained models, their parameters.

• my_functions: custom functions and scripts adapted for the project.

• projekt_praktyczny.ipynb: Jupyter Notebook including data analysis, model training and inference.

Dependencies

To successfully complete the project, make sure that all required dependencies are installed. A complete list of libraries and their versions can be found in the requirements.txt file.

Usage Guide

1. Clone the repository on your local machine.

2. Install the required dependencies listed in the requirements.txt file.

3. Open the Jupyter Notebook "Predictive_Analysis_of_Employee_Retention_Factors.ipynb" to start analyzing the data and training the model.

4. Use project directories and files to efficiently organize and complete tasks.

You are free to use the code and materials of this project for your own purposes.

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Author: [Oleksandr Kanalosh, Krzysztof Bobinski]