This project focuses on performing salary analysis using the Ordinary Least Squares (OLS) regression technique. The goal is to explore the relationship between various factors such as education, job position, and location, and their impact on salary.
In this project, we employ the OLS regression model to analyze salary data. By using statistical techniques, we aim to uncover the factors that significantly influence salary. This analysis can provide valuable insights into the relationships between education, job position, location, and income.
The dataset used in this project contains information about individuals' salaries, along with corresponding attributes such as education level, job position, and location. Before conducting the analysis, we preprocess the dataset by handling missing values, reindexing columns, and adding dummy variables.
To run the code and reproduce the analysis, follow these steps:
- Clone this repository to your local machine.
- Install the required dependencies listed in the
requirements.txtfile. - Execute the notebook OLS_Final.ipynb to perform the OLS regression analysis.
Modify the OLS_Final.ipynb script according to your specific requirements. You can customize the variables, add or remove features, and fine-tune the analysis parameters to suit your needs.
After training the OLS regression model, we evaluate its performance and interpret the coefficients to understand the impact of each variable on salary. The results are presented in the form of statistical metrics and visualizations, enabling a comprehensive understanding of the salary analysis.
We welcome contributions from the community to enhance this project. If you have any suggestions, bug fixes, or new features to propose, feel free to submit a pull request.
This project is licensed under the MIT License, which allows for personal and commercial use.
For a detailed explanation of Ordinary Least Squares (OLS) regression, you can refer to the corresponding Medium article here.