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 ?
2022 project presentation

Decision Tree

overview of the dataset
  • Decision Tree 果張圖
    • Performance Analysis
      • confusion matrix
      • ROC
2022 project presentation

Naive Bayes

overview of the dataset
  • Naive Bayes 果張圖
    • Performance Analysis
      • confusion matrix
      • ROC
2022 project presentation

Logistic Regression

overview of the dataset
  • Logistic Regression 果張圖
2022 project presentation

Performance Analysis

  • Performance Analysis
    • confusion matrix
    • ROC
2022 project presentation

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 ?
2022 project presentation

Performance Analysis con't

  • how to improve ?
    • the choice of the columns may be better if other facuity involved.
    • more labelled data improves accuracy
2022 project presentation

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

2022 project presentation

why these method ?

  • [x]unsupervised modeling

    • cross out reason

      • data is already labelled
      • data is small amount and discrete

    • [x] Knn

    • [x] Kmeans

2022 project presentation

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
2022 project presentation

notes only

2022 project presentation

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- Q: Evaluation

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

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

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

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