| Summary: | In this video, you will learn regression techniques in Python using ordinary least squares, ridge, lasso, decision trees, and neural networks. We start by exploring a census dataset that captures sales from a business in various counties across the United States. We briefly explore the dataset before moving onto model assumptions and feature engineering. We then implement a linear regression, which is a simple model that is easy to interpret, then move through more complex models to see what best makes predictions on our dataset. To avoid overfitting, we split our dataset and to optimize predictions, we tune hyperparameters with k-folds cross validation. We move through models that are more complex until we arrive at a neural network model. We then use the model with the lowest error metrics on the test dataset and make predictions on a new dataset. Using these predictions, we make a recommendation to the company's shareholders who want to expand the business about which counties to expand to next. This modeling process will be done in Python 3 on a Jupyter notebook, so it's a good idea to have Anaconda installed on your computer so you can follow along. We will structure our notebook to be easy-to-read by others on our team who may want to expand on our analysis. What You Will Learn Explore a dataset with Pandas Transform variables in a dataset to account for non-linearities and optimize predictions Tune model hyperparameters and score model performance to determine the best model for a given dataset Use statistical modeling to make recommendations to shareholders Who This Video Is For Software professionals with knowledge of Python basics and data scientists looking to apply data science to industry.
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