From the course: Python for AI Projects: From Data Exploration to Impact

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Preparing Customer Data for Predictions for Machine Learning

Preparing Customer Data for Predictions for Machine Learning - Python Tutorial

From the course: Python for AI Projects: From Data Exploration to Impact

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Preparing Customer Data for Predictions for Machine Learning

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- [Instructor] Welcome to our supervised machine learning notebook. After clicking on the resource link, we should be taken to this Google Colab environment. In this tutorial, we'll be covering four practice exercises and implement multiple supervised machine learning models for our Explore California case study. We'll start by analyzing the datasets for our ML models, then build scikit-learn pipelines for multi-class and binary classification to predict purchases and recommend products. Finally, we'll explore model explainability using SHAP and LIME. Before we begin, let's set up our coding environment. We can simply run this cell to install all packages and download the required data for this tutorial. This should only take one to two minutes. We'll start by loading our two datasets with pandas, focusing on product_recommendation_df to frame our ML task, predicting which product to recommend based on user interests and…

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