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In this liveProject, you’ll train and evaluate a machine learning model for diagnosing diabetes, and set up a pipeline for your model to run effectively. You’ll start by exploring sample data, processing features, and performing common feature engineering techniques for treating outliers or missing data. After dividing your dataset into training and testing data, you’ll train a logistic regression model using scikit-learn. You will then retrain the model with a different set of features. Finally, you’ll pick a model for scoring and build a scoring pipeline. You will test your scoring process on a scoring dataset.
This liveProject is for data scientists and engineers 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.
geekle is based on a wordle clone.