This project analyzes employee salary data from San Francisco, examining various aspects of compensation over the years 2011 to 2014. Using Python and Pandas, the analysis provides insights into salary trends, job titles, and overtime pay.

Project Final Data Analyst Bootcamp by Human Iniative x Dibimbing

Study about salaries in data scientists roles applying the following techniques: QDA, LDA, Naive-Bayes approach, Random Forests, KNN, SVM, (Logistic, Lasso and Ridge) Regression and Elastic Nets. Also, study of the database considering linear, simple and multivariate models.

This project performs a detailed analysis of public employee salaries in San Francisco using the SF Salaries Dataset. It explores salary distributions, missing data, job titles, compensation patterns, and year-over-year trends using Python and Pandas.