---

marp: true
title: Marp CLI example
description: Hosting Marp slide deck on the web
theme: uncover
paginate: true
_paginate: false
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')

footer: '2022 project presentation'

style: |
  section {
    background-color: #ccc;
    padding: 0 10vw;
  }

  footer {
    text-align: center;

  }





---

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### **slide topic?**

##### overview of the dataset
  - Male and Female proportion
    - male by age
    - female by age
  - The types of chest pain experienced among the patients
    - pie chart
  - showing the correlation between 
    chest pain and target Confusion Matrix ?

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---

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### **Decision Tree**

##### overview of the dataset
  - Decision Tree 果張圖
      - Performance Analysis
        - confusion matrix
        - ROC

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---

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### **Naive Bayes**

##### overview of the dataset
  - Naive Bayes 果張圖
      - Performance Analysis
        - confusion matrix
        - ROC

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---

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### **Logistic Regression**

##### overview of the dataset
  - Logistic Regression 果張圖

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---

### **Performance Analysis**
  - Performance Analysis
    - confusion matrix
    - ROC

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---

### **Performance Analysis con't**
  - pick a sample, bayes modeling:
    - naive_bayes_scramble.ipynb
    - column selection
      - compare column vs accurancy

  - the performance/accurancy of fewer column MAY BE better than more column
    - possible cause
      - column noise/ input data accurancy ?
      - modal overfitting ?
      - extreme case ?

<!-- - Q: Evaluation -->

---

### **Performance Analysis con't**
  - how to improve ?
    - the choice of the columns may be better if other facuity involved.
    - more labelled data improves accuracy

---

### **Performance Analysis con't**
  - disclaimer ?
    - no model can introduce 100% accurancy
    - why ?
      - extreme case
      - chaos theory ?
        - will never take all ~~ervery~~ matters into account
  
  - however, the model can be considered if accuracy above nn% in general

<!-- Q:What’s the problem you are trying to solve -->


<!-- - How’s your program solve the problem -->


<!-- - Demonstrate how your program work/ Go through the data analysis -->
<!-- - What are the interesting libraries that you used? -->
<!-- - Any complex logic in your project that you want to show off -->
<!-- - Any other interesting thing -->
<!-- - It’s not a must for you to show your code in presentation.  -->
  <!-- - Do that if you think that help you to explain things -->
<!-- - We don’t need everyone to speak. The whole group share the same score. -->
<!-- - Evaluation -->
<!-- - Good presentation flow, Clarity of your points, Pace of your presentation -->
---

### **why these method ?**
  - [x]unsupervised modeling
    - cross out reason 
      - data is already labelled
      - data is small amount and discrete
    
    - [x] Knn
    - [x] Kmeans

<!-- ref: https://www.ibm.com/cloud/blog/supervised-vs-unsupervised-learning -->

---

### **why these method ?**
  - supervised modeling / supervised learning
    - data is already labelled
      - []Decision Tree
      - []Naive Bayes
        - performance ? accuracy ? ROI ?

    - [x]multi dimensional/column difficult to understand/maintain
      - [x]Logistic Regression
      - [x]SVM

<!-- ref: https://www.ibm.com/cloud/blog/supervised-vs-unsupervised-learning -->

--- 

### notes only

<!-- Q: What’s the problem you are trying to solve -->
<!-- Q: How’s your program solve the problem -->
<!-- A: to predict humans heart disease possiblilies by modelling -->

<!-- Q: Demonstrate how your program work/ Go through the data analysis -->
<!-- 可能你要捉一捉佢路, if i were you, i won't go through the data analysis,  -->
<!-- because the sample steps every one can google e.g. data cleaning, training model, performance analysis, not the main point -->

<!-- Q: What are the interesting libraries that you used? -->
<!-- Q: Any complex logic in your project that you want to show off -->
<!-- Q: Any other interesting thing -->
<!-- bayes scrambling can help -->


<!-- Q: Evaluation -->
<!-- Performance Analysis ? -->

<!-- X -->
<!-- Q: It’s not a must for you to show your code in presentation.  -->
  <!-- -  ? Do that if you think that help you to explain things -->

<!-- Q: We don’t need everyone to speak. The whole group share the same score. -->

<!-- Q: Good presentation flow, Clarity of your points, Pace of your presentation -->