Welcome to the Salary Estimating repository! This initiative focuses on forecasting average salaries across different job titles such as data scientist, data engineer, deep learning engineer, machine learning engineer, director, manager, analyst, and researcher in the United States. The predictions leverage a range of factors, encompassing company size, industry, location, company name, and job title.
Within this repository, we employ predictive modeling techniques, specifically Linear Regression and classification regression (Logistic Regression, K Nearest Neighbor (KNN), Decision Trees, Random Forest). Our goal is to estimate average salaries for various roles within the tech industry.
Data Cleansing: A meticulous process is undertaken to clean the data, with a specific emphasis on pertinent features like company rating, state, job title, and more.
Feature Importance Analysis: A comprehensive analysis is conducted to discern the influence of different factors on salary predictions.
Interactive Visualization: The creation of visual representations for salary predictions and insights is facilitated to enhance comprehension and engagement.
Predictive Modeling: The application of Linear Regression and classification regression algorithms is executed to ensure precise salary estimation.