In this liveProject, you’ll process raw data to make it ready for a machine learning model to diagnose diabetes rates. You’ll use feature engineering techniques to generate ML features from raw data. To make sense of your data, you will undertake data profiling, exploratory data analysis, analyze independent/dependent variables, and visualize data patterns. You’ll evaluate the correlation between dependent and independent variables to identify relevant features. You’ll even generate additional features as needed. Additionally, you will apply feature engineering techniques such as treating missing values and outliers to make your features ready for model training.
This liveProject is for data scientists who are familiar with Python, the basics of machine learning, and data modeling. To begin this liveProject you will need to be familiar with the following:
In this liveProject, you’ll master common Python libraries for the important task of machine learning feature engineering.