friendly reminder:
The content of this article is one of the blogger's recent major assignments. In order to complete this big assignment, the blogger spent a lot of time and learned a lot from it. This post is mainly to commemorate the great achievements of the blogger's hard work (akimbo.jpg)
So this post is not a tutorial! Not a tutorial! Not a tutorial!
So no code either! No code! No code!
Viewers who want code can leave (don't waste time looking down)
One of the purposes of this post is to commemorate the learning achievements, and the second is to share with you the idea of data analysis based on logistic regression (not very professional, just take a look, the blogger does not want to mislead the children hhhh
Digression: Maybe one day, the blogger will find that the blogger has modified the post and has a code, or uploaded the corresponding resources, it must be that the teacher does not need to do a big homework on this question (yes, the blogger is crazy suggesting not to use the blogger's Posts to deal with homework, after all, the knowledge you put into your head is priceless , just sauce, ciao~~~)
content
3.1 Analysis of Binary Variables
3.2 Outlier Analysis of Numerical Variables
3.3 Correlation analysis between continuous variables
5.1 Selection of Model Algorithms
5.2 Selection of model variables
5.3 Model prediction performance analysis
1. Introduction
2. Data s et Description
3. Exploratory Data Analysis
3.1 Analysis of Binary Variables
3.2 Outlier Analysis of Numerical Variables
3.3 Correlation analysis between continuous variables
4. Methodology
5. Results and Discussion
5.1 Selection of Model Algorithms
5.2 Selection of model variables
5.3 Model prediction performance analysis
6. Conclusion
7. References
