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louiscklaw
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Man1130/jupyter/Man1130-python-comission/slides/slides.md
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Man1130/jupyter/Man1130-python-comission/slides/slides.md
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---
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marp: true
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title: Marp CLI example
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description: Hosting Marp slide deck on the web
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theme: uncover
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paginate: true
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_paginate: false
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backgroundImage: url('https://www.google.com/url?sa=i&url=https%3A%2F%2Fcommons.wikimedia.org%2Fwiki%2FFile%3AHelloWorld.svg&psig=AOvVaw0d3lmyaMphPi0ANeGIEJOw&ust=1670049479380000&source=images&cd=vfe&ved=0CBAQjRxqFwoTCJjxx6Gp2vsCFQAAAAAdAAAAABAE')
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footer: '2022 project presentation'
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style: |
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section {
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background-color: #ccc;
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padding: 0 10vw;
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}
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footer {
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text-align: center;
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}
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---
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<!-- lead-invert-red -->
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### **slide topic?**
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##### overview of the dataset
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- Male and Female proportion
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- male by age
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- female by age
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- The types of chest pain experienced among the patients
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- pie chart
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- showing the correlation between
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chest pain and target Confusion Matrix ?
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<!-- HTML comment recognizes as a presenter note per pages. -->
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<!-- You may place multiple comments in a single page. -->
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<!--
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Also supports multiline.
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We bet these comments would help your presentation...
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-->
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---
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<!-- lead-invert-red -->
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### **Decision Tree**
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##### overview of the dataset
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- Decision Tree 果張圖
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- Performance Analysis
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- confusion matrix
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- ROC
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<!-- HTML comment recognizes as a presenter note per pages. -->
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<!-- You may place multiple comments in a single page. -->
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<!--
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Also supports multiline.
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We bet these comments would help your presentation...
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-->
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---
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<!-- lead-invert-red -->
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### **Naive Bayes**
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##### overview of the dataset
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- Naive Bayes 果張圖
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- Performance Analysis
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- confusion matrix
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- ROC
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<!-- HTML comment recognizes as a presenter note per pages. -->
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<!-- You may place multiple comments in a single page. -->
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<!--
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Also supports multiline.
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We bet these comments would help your presentation...
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-->
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---
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<!-- lead-invert-red -->
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### **Logistic Regression**
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##### overview of the dataset
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- Logistic Regression 果張圖
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<!-- HTML comment recognizes as a presenter note per pages. -->
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<!-- You may place multiple comments in a single page. -->
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<!--
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Also supports multiline.
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We bet these comments would help your presentation...
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-->
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---
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### **Performance Analysis**
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- Performance Analysis
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- confusion matrix
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- ROC
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<!-- HTML comment recognizes as a presenter note per pages. -->
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<!-- You may place multiple comments in a single page. -->
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<!--
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Also supports multiline.
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We bet these comments would help your presentation...
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-->
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---
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### **Performance Analysis con't**
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- pick a sample, bayes modeling:
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- naive_bayes_scramble.ipynb
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- column selection
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- compare column vs accurancy
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- the performance/accurancy of fewer column MAY BE better than more column
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- possible cause
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- column noise/ input data accurancy ?
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- modal overfitting ?
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- extreme case ?
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<!-- - Q: Evaluation -->
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---
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### **Performance Analysis con't**
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- how to improve ?
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- the choice of the columns may be better if other facuity involved.
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- more labelled data improves accuracy
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---
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### **Performance Analysis con't**
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- disclaimer ?
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- no model can introduce 100% accurancy
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- why ?
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- extreme case
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- chaos theory ?
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- will never take all ~~ervery~~ matters into account
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- however, the model can be considered if accuracy above nn% in general
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<!-- Q:What’s the problem you are trying to solve -->
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<!-- - How’s your program solve the problem -->
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<!-- - Demonstrate how your program work/ Go through the data analysis -->
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<!-- - What are the interesting libraries that you used? -->
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<!-- - Any complex logic in your project that you want to show off -->
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<!-- - Any other interesting thing -->
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<!-- - It’s not a must for you to show your code in presentation. -->
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<!-- - Do that if you think that help you to explain things -->
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<!-- - We don’t need everyone to speak. The whole group share the same score. -->
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<!-- - Evaluation -->
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<!-- - Good presentation flow, Clarity of your points, Pace of your presentation -->
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---
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### **why these method ?**
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- [x]unsupervised modeling
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- cross out reason
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- data is already labelled
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- data is small amount and discrete
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- [x] Knn
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- [x] Kmeans
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<!-- ref: https://www.ibm.com/cloud/blog/supervised-vs-unsupervised-learning -->
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---
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### **why these method ?**
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- supervised modeling / supervised learning
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- data is already labelled
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- []Decision Tree
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- []Naive Bayes
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- performance ? accuracy ? ROI ?
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- [x]multi dimensional/column difficult to understand/maintain
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- [x]Logistic Regression
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- [x]SVM
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<!-- ref: https://www.ibm.com/cloud/blog/supervised-vs-unsupervised-learning -->
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---
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### notes only
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<!-- Q: What’s the problem you are trying to solve -->
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<!-- Q: How’s your program solve the problem -->
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<!-- A: to predict humans heart disease possiblilies by modelling -->
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<!-- Q: Demonstrate how your program work/ Go through the data analysis -->
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<!-- 可能你要捉一捉佢路, if i were you, i won't go through the data analysis, -->
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<!-- because the sample steps every one can google e.g. data cleaning, training model, performance analysis, not the main point -->
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<!-- Q: What are the interesting libraries that you used? -->
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<!-- Q: Any complex logic in your project that you want to show off -->
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<!-- Q: Any other interesting thing -->
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<!-- bayes scrambling can help -->
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<!-- Q: Evaluation -->
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<!-- Performance Analysis ? -->
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<!-- X -->
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<!-- Q: It’s not a must for you to show your code in presentation. -->
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<!-- - ? Do that if you think that help you to explain things -->
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<!-- Q: We don’t need everyone to speak. The whole group share the same score. -->
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<!-- Q: Good presentation flow, Clarity of your points, Pace of your presentation -->
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