//-------------------------------------------------------------------------------------------------------------------------------------
//[0] Anaconda Install
https://www.continuum.io/downloads
scikit - Jupyter
<<Book>>
파이썬 라이브러리를 활용한 머신러닝 : 사이킷런 핵심 개발자가 쓴 머신러닝과 데이터 과학 실무서
http://www.aladin.co.kr/shop/wproduct.aspx?ItemId=112158396
<<Monty Python's Eric Idle>>
https://www.youtube.com/watch?v=jiu0lYQIPqE
<<tensorflow>>
python 3.50
<<ML 3 Keywords>>
1. 알고리즘 [KNN, Tree, Linear Perceptual, MLP(Not-Linear) ]
2. Data (Pandas)
3. Tools (SCIKit)
<<Monty Python's Eric Idle>>
https://www.youtube.com/watch?v=jiu0lYQIPqE
//-------------------------------------------------------------------------------------------------------------------------------------
//[1] python etc
import this
import antigravity
import sklearn
dir (sklearn)
%who
!dir
import sklearn
from sklearn.datasets import load_iris
import inspect
print (inspect.getsource(load_iris))
<<단축 Key>>
shift + Enter : 한줄 실행
shift + Tab : Help
//-------------------------------------------------------------------------------------------------------------------------------------
//[2] Load sample data : https://en.wikipedia.org/wiki/Iris_flower_data_set
import sklearn
from sklearn.datasets import load_iris
iris = load_iris()
print (iris)
type (load_iris)
iris.keys()
iris.target_names
print (iris.DESCR)
len (iris)
//-------------------------------------------------------------------------------------------------------------------------------------
//[3] Split
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
X_train, X_test, Y_train, Y_test = train_test_split(iris.data, iris.target, test_size=0.3)
print (X_train)
len (X_train)
//-------------------------------------------------------------------------------------------------------------------------------------
//[3] DecisionTreeClassifier
*** INTANCE ***
from sklearn.tree import DecisionTreeClassifier
type(DecisionTreeClassifier)
tree = DecisionTreeClassifier()
tree
*** Trainning FIT ***
%timeit tree.fit(X_train, Y_train)
*** Predict ***
tree.predict( [[3, 3, 3, 3]])
iris.target_names
//-------------------------------------------------------------------------------------------------------------------------------------
//[4] KNN
*** INTANCE ***
from sklearn.neighbors import KNeighborsClassifier
neighbors = KNeighborsClassifier()
neighbors
*** Trainning FIT ***
%timeit neighbors.fit(X_train, Y_train)
*** Predict ***
print (neighbors.predict( [[3, 3, 3, 3]]))
//-------------------------------------------------------------------------------------------------------------------------------------
//[5] Multi Layer Perceptual
*** INTANCE ***
from sklearn.neural_network import MLPClassifier
network = MLPClassifier()
network
*** Trainning FIT ***
%timeit network.fit(X_train, Y_train)
*** Predict ***
print (network.predict( [[3, 3, 3, 3]]))
//-------------------------------------------------------------------------------------------------------------------------------------
//[6] classification_report
from sklearn.metrics import classification_report
print (classification_report(y_test, tree.predict(X_test)))
//-------------------------------------------------------------------------------------------------------------------------------------
//[7] confusion_matrix
from sklearn.metrics import confusion_matrix
print confusion_matrix(y_test, tree.predict(X_test)))
//-------------------------------------------------------------------------------------------------------------------------------------
//[8] Automation
from sklearn.model_selection import GridSearchCV
dir (GridSearchCV)
//-------------------------------------------------------------------------------------------------------------------------------------
//[9] Pandas
import pandas as pd
import seaborn as sns
data2 = pd.DataFrom
data3 = pd.DataFrame(data.target, columns
sns.pairplot(data4, hue='species')