202312 Exploration Data Analysis Visualization (English version)
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This document provides an overview of exploratory data analysis (EDA) and visualization techniques that can be performed before building a machine learning model. It introduces the Iris dataset as an example and outlines the key steps of EDA, including loading the data, examining correlations, creating scatter plots, and generating distribution and box plots to understand feature statistics. As homework, students are asked to explore another dataset with a numeric target feature called "housing.tab" and explain the visualizations.
202312 Exploration Data Analysis Visualization (English version)
- 1. EDA Visualization before building model Orozco Hsu 2023-10-31 1
- 2. About me • Education • NCU (MIS)、NCCU (CS) • Work Experience • Telecom big data Innovation • AI projects • Retail marketing technology • User Group • TW Spark User Group • TW Hadoop User Group • Taiwan Data Engineer Association Director • Research • Big Data/ ML/ AIOT/ AI Columnist 2
- 3. Tutorial Content 3 Iris dataset summary EDA and visualization Homework
- 4. Code • Download materials: • https://drive.google.com/drive/folders/1Kaneenrtd2P2IWbo- PhMd3b6NvtT5FOc?usp=sharing 4
- 5. Table • Load dataset • Iirs.tab 5
- 6. The most recommend dataset • Where the independent variables are numerical and the dependent variable is categorical • The advantage of such a dataset also lies in its ease of clustering • The preferable data type for the dependent variable is binary, meaning it is either 'YES' or ‘NO • When the number of independent variables exceeds two or more, the accuracy will decrease • The most commonly used algorithm is logistic regression 6
- 8. Rank 8
- 10. Scatter plot 10
- 11. Distribution 11
- 12. Box plot 12
- 14. Homework • Change dataset, the numeric target feature • Please explain the data visualization • housing.tab 14