Week 11: Programming for Data Analysis

Feb 23, 20210 likes192 views

The document discusses binary classification techniques including Naive Bayes classifiers and support vector machines (SVM). It explains that Naive Bayes classifiers use Bayes' theorem to calculate conditional probabilities for classification. Specifically, it describes how to calculate priors and fit probability distributions like binomial, multinomial, and Gaussian distributions for the Naive Bayes model. The document also outlines how SVMs find the optimal separating hyperplane between two classes by maximizing the margin between them. It provides code examples for implementing Naive Bayes and SVM classifiers and evaluating their performance.

Programming for Data
Analysis
Week 11
Dr. Ferdin Joe John Joseph
Faculty of Information Technology
Thai – Nichi Institute of Technology, Bangkok

Today’s lesson
Faculty of Information Technology, Thai - Nichi Institute of
Technology, Bangkok
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• Binary Classification
• Naïve Bayes Classifier
• Support Vector Machine

Naïve Bayes Classifier
• Conditional Probability Model of Classification
Faculty of Information Technology, Thai - Nichi Institute of
Technology, Bangkok
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Conditional Probability Model of Classification
• The conditional probability can be calculated using the joint
probability, although it would be intractable.
• Bayes Theorem provides a principled way for calculating the
conditional probability.
Faculty of Information Technology, Thai - Nichi Institute of
Technology, Bangkok
4

Bayes Theorem
• P(A|B) = P(B|A) * P(A) / P(B)
• We can frame classification as a conditional classification problem
with Bayes Theorem as follows:
P(yi | x1, x2, …, xn) = P(x1, x2, …, xn | yi) * P(yi) / P(x1, x2, …, xn)
Faculty of Information Technology, Thai - Nichi Institute of
Technology, Bangkok
5

Naïve Bayes
• For simplifying the calculation
• The Bayes Theorem assumes that each input variable is dependent
upon all other variables.
• This is a cause of complexity in the calculation.
Faculty of Information Technology, Thai - Nichi Institute of
Technology, Bangkok
6

Calculation of Prior and Conditional
Probabilities
• P(yi) = examples with yi / total examples
• In the case of categorical variables, such as counts or labels, a
multinomial distribution can be used.
• If the variables are binary, such as yes/no or true/false, a binomial
distribution can be used.
• If a variable is numerical, such as a measurement, often a Gaussian
distribution is used.
Faculty of Information Technology, Thai - Nichi Institute of
Technology, Bangkok
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Naïve Bayes Distribution
• Binomial Naïve Bayes: Naïve Bayes that uses a binomial distribution.
• Multinomial Naïve Bayes: Naïve Bayes that uses a multinomial
distribution.
• Gaussian Naïve Bayes: Naïve Bayes that uses a Gaussian distribution.
Faculty of Information Technology, Thai - Nichi Institute of
Technology, Bangkok
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Preparation of dataset
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Technology, Bangkok
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Fitting Probability Distribution
Faculty of Information Technology, Thai - Nichi Institute of
Technology, Bangkok
10

Sorting Data
Faculty of Information Technology, Thai - Nichi Institute of
Technology, Bangkok
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Calculate Priors
Faculty of Information Technology, Thai - Nichi Institute of
Technology, Bangkok
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Fit Distribution
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Technology, Bangkok
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Summary
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Technology, Bangkok
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Independent Conditional Probability
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Technology, Bangkok
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Taking a sample
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Technology, Bangkok
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Probability Scores
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Technology, Bangkok
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Wrap Up
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Technology, Bangkok
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Gaussian Naïve Bayes
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Technology, Bangkok
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Lab Exercise
• Use this source code and make a classification report which gives
accuracy, precision, recall and F1-score
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Technology, Bangkok
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Support Vector Machine
Faculty of Information Technology, Thai - Nichi Institute of
Technology, Bangkok
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SVM Objective
• A training set, S, for an SVM is comprised of m samples.
• The features, x, consist of real numbers and the classifications, y,
must be -1 or 1.
Faculty of Information Technology, Thai - Nichi Institute of
Technology, Bangkok
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SVM Hyperplane
• The SVM hyperlane is defined by the weight vector, w, and the bias, b,
and is defined as:
Faculty of Information Technology, Thai - Nichi Institute of
Technology, Bangkok
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Example for 2 Feature
• Hyperplane for two features can be written as:
Faculty of Information Technology, Thai - Nichi Institute of
Technology, Bangkok
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Libraries
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Technology, Bangkok
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Data Clusters
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Technology, Bangkok
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Data Clusters
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Technology, Bangkok
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Prepare Datasets
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Technology, Bangkok
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Learning Rate
Faculty of Information Technology, Thai - Nichi Institute of
Technology, Bangkok
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Learning Rate
Faculty of Information Technology, Thai - Nichi Institute of
Technology, Bangkok
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Draw Decision Boundary
Faculty of Information Technology, Thai - Nichi Institute of
Technology, Bangkok
31

Plot Data
Faculty of Information Technology, Thai - Nichi Institute of
Technology, Bangkok
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Plot Data
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Technology, Bangkok
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Test Classifier
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Technology, Bangkok
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Test Classifier
Faculty of Information Technology, Thai - Nichi Institute of
Technology, Bangkok
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Output
Faculty of Information Technology, Thai - Nichi Institute of
Technology, Bangkok
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Lab Exercise
• Create Confusion Matrix and classification report for SVM
Faculty of Information Technology, Thai - Nichi Institute of
Technology, Bangkok
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DSA 207 – Binary Classifier
Faculty of Information Technology, Thai - Nichi Institute of
Technology, Bangkok
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This document discusses feature engineering techniques for data analysis. It covers feature selection, construction, and engineering. Specific techniques discussed include feature imputation, handling outliers, binning, log transforms, one-hot encoding, grouping, splitting, scaling, and extracting date features. The document provides examples and explanations of these techniques to transform raw data into more useful features for machine learning models.

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This document summarizes a lesson on logistic regression for data analysis. It discusses logistic regression as a machine learning classification algorithm used to predict categorical dependent variables. It then describes assumptions of logistic regression, provides examples of its practical use, and outlines the steps taken in building a logistic regression model for a direct marketing dataset, including data preprocessing, model training and evaluation.

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This document discusses permutations, combinations, and binomial coefficients. It provides examples of calculating permutations and combinations, as well as properties like Pascal's identity. It also discusses combinations with repetition and solving combinatorics problems. The document is a lesson plan on programming for data analysis that covers permutations, combinations, and a laboratory session.

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This document outlines the topics to be covered in a programming for data analysis course at the Thai-Nichi Institute of Technology in Bangkok. The course will introduce Pandas, a data analysis library in Python. It will cover reading data from Excel/CSV files, calculating statistics, cleaning data, and visualizing data with Matplotlib. Specific topics will include reading and writing data, working with indexes, querying data, handling missing values, and correlating variables. Students will learn to import data, explore datasets, clean and transform data, and store results back into files.

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This document outlines a lesson plan on data wrangling that covers dictionaries, control statements like while loops and if/else statements, and user-defined functions in Python. It includes examples and exercises for working with dictionaries to map keys to values, using control statements with dictionaries, defining functions, and taking user input. The lesson is intended for an information technology class at Thai-Nichi Institute of Technology in Bangkok and instructs students to complete exercises on generating Fibonacci sequences and calculating factorials using Google Collab.

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This document summarizes a lesson on the NumPy library in Python. It introduces NumPy, which is a library for scientific computing that allows managing n-dimensional arrays. NumPy can be used for mathematical functions like Fourier transforms and linear algebra. It is also the foundation for data science in Python. The lesson demonstrates how to import NumPy, create different types of arrays from 0 to 3 dimensions, access and slice array elements, and perform operations like addition on arrays. Exercises include matrix multiplication and generating Pascal's triangle using NumPy.

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Deloitte Analytics - Applying Process Mining in an audit contextProcess mining Evangelist

Mieke Jans is a Manager at Deloitte Analytics Belgium. She learned about process mining from her PhD supervisor while she was collaborating with a large SAP-using company for her dissertation. Mieke extended her research topic to investigate the data availability of process mining data in SAP and the new analysis possibilities that emerge from it. It took her 8-9 months to find the right data and prepare it for her process mining analysis. She needed insights from both process owners and IT experts. For example, one person knew exactly how the procurement process took place at the front end of SAP, and another person helped her with the structure of the SAP-tables. She then combined the knowledge of these different persons.

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Week 11: Programming for Data Analysis

1. Programming for Data Analysis Week 11 Dr. Ferdin Joe John Joseph Faculty of Information Technology Thai – Nichi Institute of Technology, Bangkok

2. Today’s lesson Faculty of Information Technology, Thai - Nichi Institute of Technology, Bangkok 2 • Binary Classification • Naïve Bayes Classifier • Support Vector Machine

3. Naïve Bayes Classifier • Conditional Probability Model of Classification Faculty of Information Technology, Thai - Nichi Institute of Technology, Bangkok 3

4. Conditional Probability Model of Classification • The conditional probability can be calculated using the joint probability, although it would be intractable. • Bayes Theorem provides a principled way for calculating the conditional probability. Faculty of Information Technology, Thai - Nichi Institute of Technology, Bangkok 4

5. Bayes Theorem • P(A|B) = P(B|A) * P(A) / P(B) • We can frame classification as a conditional classification problem with Bayes Theorem as follows: P(yi | x1, x2, …, xn) = P(x1, x2, …, xn | yi) * P(yi) / P(x1, x2, …, xn) Faculty of Information Technology, Thai - Nichi Institute of Technology, Bangkok 5

6. Naïve Bayes • For simplifying the calculation • The Bayes Theorem assumes that each input variable is dependent upon all other variables. • This is a cause of complexity in the calculation. Faculty of Information Technology, Thai - Nichi Institute of Technology, Bangkok 6

7. Calculation of Prior and Conditional Probabilities • P(yi) = examples with yi / total examples • In the case of categorical variables, such as counts or labels, a multinomial distribution can be used. • If the variables are binary, such as yes/no or true/false, a binomial distribution can be used. • If a variable is numerical, such as a measurement, often a Gaussian distribution is used. Faculty of Information Technology, Thai - Nichi Institute of Technology, Bangkok 7

8. Naïve Bayes Distribution • Binomial Naïve Bayes: Naïve Bayes that uses a binomial distribution. • Multinomial Naïve Bayes: Naïve Bayes that uses a multinomial distribution. • Gaussian Naïve Bayes: Naïve Bayes that uses a Gaussian distribution. Faculty of Information Technology, Thai - Nichi Institute of Technology, Bangkok 8

9. Preparation of dataset Faculty of Information Technology, Thai - Nichi Institute of Technology, Bangkok 9

10. Fitting Probability Distribution Faculty of Information Technology, Thai - Nichi Institute of Technology, Bangkok 10

11. Sorting Data Faculty of Information Technology, Thai - Nichi Institute of Technology, Bangkok 11

12. Calculate Priors Faculty of Information Technology, Thai - Nichi Institute of Technology, Bangkok 12

13. Fit Distribution Faculty of Information Technology, Thai - Nichi Institute of Technology, Bangkok 13

14. Summary Faculty of Information Technology, Thai - Nichi Institute of Technology, Bangkok 14

15. Independent Conditional Probability Faculty of Information Technology, Thai - Nichi Institute of Technology, Bangkok 15

16. Taking a sample Faculty of Information Technology, Thai - Nichi Institute of Technology, Bangkok 16

17. Probability Scores Faculty of Information Technology, Thai - Nichi Institute of Technology, Bangkok 17

18. Wrap Up Faculty of Information Technology, Thai - Nichi Institute of Technology, Bangkok 18

19. Gaussian Naïve Bayes Faculty of Information Technology, Thai - Nichi Institute of Technology, Bangkok 19

20. Lab Exercise • Use this source code and make a classification report which gives accuracy, precision, recall and F1-score Faculty of Information Technology, Thai - Nichi Institute of Technology, Bangkok 20

21. Support Vector Machine Faculty of Information Technology, Thai - Nichi Institute of Technology, Bangkok 21

22. SVM Objective • A training set, S, for an SVM is comprised of m samples. • The features, x, consist of real numbers and the classifications, y, must be -1 or 1. Faculty of Information Technology, Thai - Nichi Institute of Technology, Bangkok 22

23. SVM Hyperplane • The SVM hyperlane is defined by the weight vector, w, and the bias, b, and is defined as: Faculty of Information Technology, Thai - Nichi Institute of Technology, Bangkok 23

24. Example for 2 Feature • Hyperplane for two features can be written as: Faculty of Information Technology, Thai - Nichi Institute of Technology, Bangkok 24

25. Libraries Faculty of Information Technology, Thai - Nichi Institute of Technology, Bangkok 25

26. Data Clusters Faculty of Information Technology, Thai - Nichi Institute of Technology, Bangkok 26

27. Data Clusters Faculty of Information Technology, Thai - Nichi Institute of Technology, Bangkok 27

28. Prepare Datasets Faculty of Information Technology, Thai - Nichi Institute of Technology, Bangkok 28

29. Learning Rate Faculty of Information Technology, Thai - Nichi Institute of Technology, Bangkok 29

30. Learning Rate Faculty of Information Technology, Thai - Nichi Institute of Technology, Bangkok 30

31. Draw Decision Boundary Faculty of Information Technology, Thai - Nichi Institute of Technology, Bangkok 31

32. Plot Data Faculty of Information Technology, Thai - Nichi Institute of Technology, Bangkok 32

33. Plot Data Faculty of Information Technology, Thai - Nichi Institute of Technology, Bangkok 33

34. Test Classifier Faculty of Information Technology, Thai - Nichi Institute of Technology, Bangkok 34

35. Test Classifier Faculty of Information Technology, Thai - Nichi Institute of Technology, Bangkok 35

36. Output Faculty of Information Technology, Thai - Nichi Institute of Technology, Bangkok 36

37. Lab Exercise • Create Confusion Matrix and classification report for SVM Faculty of Information Technology, Thai - Nichi Institute of Technology, Bangkok 37

38. DSA 207 – Binary Classifier Faculty of Information Technology, Thai - Nichi Institute of Technology, Bangkok 38

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