Salary Prediction using Linear Regression

Salary Prediction using Linear Regression

Gauri Shinde, Swarup Shinde, Sujal Sawant, Janhvi Tambolkar

Department of Multidisciplinary Engineering, Vishwakarma Institute of Technology, Bibwewadi, Pune

Abstract: The goal of this project is to predict the salary of employees based on their years of experience using a simple linear regression model. The input data for the predictive model is taken as years of experience which is the basis. By using years of experience as the input data we get salary as the output. In the predictive model Linear Regression method is used as there is only one variable i.e. years of experience. With the help of this model by entering the years of experience we will be getting salary as the output. The dataset used in this study includes information on the years of experience and salaries of employees in a particular organization. The data is preprocessed by checking for missing values, outliers, and transforming the variables as necessary. We then perform a simple linear regression analysis on the dataset to investigate the relationship between years of experience and salary. The results show a strong positive linear relationship between the two variables, with a high coefficient of regression (R-squared) of 0.96, indicating that 96% of the variation in salary can be explained by the years of experience.

Keywords: Linear regression, Salary Prediction, Regression Coefficient (R2)