Pandas for Data Analysis

Data preprocessing is one of the first steps in the machine learning workflow. The main idea is to transform raw data into a format that machine learning algorithms can easily understand. The predictive performance of a machine learning model highly depends on the input data quality. Thus, it's an absolute must to know how to improve the quality of your input data by removing the features with low predictive value, engineering new ones, and dealing with multicollinearity. With this project, you'll apply these concepts to NBA data to get a high-quality dataset ready to be fed to a linear model!

You work as an analyst in a company. The company's HR boss provided you with three datasets. The first two contain information about employees' performance in offices A and B: how much they work, their salaries, the number of their projects, departments, and so on. The third one is an extensive dataset with information on the employees' satisfaction with their jobs, their latest evaluation metrics, and the current status in the company. Your task is to analyze the data and answer some of the HR’s questions.