020. classifier(svm, random forest, etc)
# @
# I can see various kind of classifiers by searching "scikit-learn classifier"
# If data can be separated by linear line, you can use svm
# On the contrary, if data can't be separated by linear line
# or if data can be separated by circle line
# or if data can be separated by grid line,
# you should use other methods
# You can see pictures here
# scikit-learn.org/stable/_images/sphx_glr_plot_classifier_comparison_001.png
# scikit-learn.org/stable/auto_example/classification/plot_classifier_comparison.html
# @
# You can test all methods with proper amount of data
# and choose method which gives highest performance and precision
# @
# Let's talk about random forest
# Random forest creates randomly generated forest
# at every running time into output result
# So, whenever you run 'this random forest model',
# accuracy can be changed a little bit
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
# For learning
clf = RandomForestClassifier()
clf.fit(train_data, train_label)
# For predicting
predict = clf.predict(test_data)