Machine Learning in Action
This is the source code of the book Machine Learning in Action authored by Peter Harrington, which is modified to python3 version code.
System Information
Operating System: Mac OS High Sierra
Programming Language: Python 3.6.4
Instructions
Chapter 2: Classifying with k-Nearest Neighbors
knn.py: include some basic functions about the knn algorithm.
classify_person.py: the main program to test whether a person is suitable to Helen.
classify_digits.py: the main program to train and test the model to classify the handwriting 0-9.
Chapter 3: Splitting Datasets One Feature at a Time: Decision Trees
trees.py: include some basic functions about the decision tree algorithm.
tree_plotter.py: visualize the decision tree.
tree_test.py: test the example of lenses based on decision tree algorithm.
Chapter 4: Classifying with Probability Theory: Naive Bayes
bayes.py: include some basic functions about the naive bayes algorithm.
bayes_test.py: test the example in the book.
classify_email.py: classify the email based on naive bayes algorithm.
Chapter 5: Logistic Regression
logistic_regression.py: include some basic functions about the logistic regression algorithm.
logistic_test.py: test the example in the book based on logistic regression.
Chapter 6: Support Vector Machines
svm.py: include some basic functions about the Support Vector Machine algorithm.
svm_test.py: test the example in the book based on SVM algorithm.
test_digits.py: classify digits based on SVM.
Chapter 7: Improving Classification with the AdaBoost Meta-Algorithm
adaboost.py: include some basic functions about the AdaBoost algorithm.
test_adaboost.py: test the example in the book based on AdaBoost algorithm.
Chapter 8: Predicting Numeric Values: Regression
regression.py: include some basic functions about the linear regression algorithm.
regression_test.py: test the example in chapter 8
abalone.py: estimate the age of abalone.
Chapter 9: Tree-Based Regression
regression_tree.py: include some basic functions about the regression tree.
regression_tree_test.py: test for the example in the book.
example.py: comparing tree methods to standard regression.
Chapter10: Grouping Unlabeled Items Using K-Means Clustering
kmeans.py: include some basic functions about k-means clustering.
test_kmeans.py: test for the example in the book.
Chapter11: Association Analysis with the Apriori Algorithm
apriori.py: include some basic functions about Apriori algorithm.
test_apriori.py: test for the example in the book.
Chapter12: Efficiently Finding Frequent Itemsets with FP-Growth
fpgrowth.py: include some basic functions abou FP_Growth.
test_fpgrowth.py: test for the example in the book.
kosarak.py: kosarak example.
Chapter13: Using Principal Component Analysis to Simplify Data
pca.py: include some basic functions about PCA.
test_pca.py: test the example in the book.
example.py: using PCA to reduce the dimensionality of semiconductor manufacturing data.
Chapter14: Simplifying Data with the Singular Value Decomposition
svd.py: include some basic functions about the SVD.
test_svd.py: test the example in the book.
recommand.py: example: a restaurant dish recommendation engine.
compression.py: example: image compression with the SVD.