How To Do A Classification Tree in Python | Machine Learning in Python | Databytes

DataCamp · Beginner ·📊 Data Analytics & Business Intelligence ·3y ago
This Python tutorial for beginners will quickly walk you through how to get started with basic Python functionality. Throughout this tutorial, you will learn about operators, working with different data types, how to assign and combine variables, and more. The topics covered in this video are: 00:00 - 02:24 Classification tree theory 02:25 - 06:50 Exploring the case study 06:51 - 07:50 Splitting response and explanatory columns 07:51 - 08:41 Creating training and testing sets 08:42- 09:53 Fitting the model to the training set 09:54 - 11:01 Making predictions using the model 11:02 - 12:57 Checking model fit 12:58 - 18:06 Visualizing the classification tree [Try it yourself!] Pre-prepared workspace: https://bit.ly/3ACOVOw [More about Python] Python is the most popular programming language today and is widely used across verticals from software and web development, game development, data science, machine learning, and more. Learning Python is imperative for aspiring data scientists, data analysts, data engineers, and machine learning scientists. To learn more about Python, check out the following resources: Interesting Reads: How to Export Power BI Data to Excel | DataCamp https://www.datacamp.com/blog/how-to-export-power-bi-data-to-excel 12 of the Best Data Visualizations Tools | DataCamp https://www.datacamp.com/blog/12-of-the-best-data-visualizations-tools Financial Times recognized DataCamp as one of the Americas’ Fastest Growing Companies 2022 | https://www.datacamp.com/blog/ft-ranking-datacamp-recognized-as-one-of-the-americas-fastest-growing-companies-2022
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Chapters (7)

02:24 Classification tree theory
2:25 06:50 Exploring the case study
6:51 07:50 Splitting response and explanatory columns
7:51 08:41 Creating training and testing sets
9:54 11:01 Making predictions using the model
11:02 12:57 Checking model fit
12:58 18:06 Visualizing the classification tree
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