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In the Python Project, we will use Pandas and Seaborn to perform our exploratory data analysis in en effort to understand how our features impact our target. A big aspect of this is paying close attention to the distributions of our features use in our supervised machine learning project.

Follow along with this Python ML-guided project. In this beginner Python project, we build a classification model in Sklearn to predict if or not a person has early-stage diabetes in this medical prediction with machine learning.

This intro project is great for those new to Sklearn . Learn how to set up an ML workflow. From exploratory data analysis to predictions. Use pandas and seaborn in Python to perform your data analysis. Then use Sklearn to do the train test split and make your predictions. Here we also look at the R-squared score to evaluate our model.