sklearn stdze pca lr knn tree rf .py

import numpy as np
from sklearn.datasets import load_iris
iris = load_iris()
X = iris.data
print X

#scale the data
from sklearn.preprocessing import StandardScaler
SS=StandardScaler()
XS=SS.fit_transform(X)
print XS

#PCA
from sklearn.decomposition import PCA
#declare model and number of components
pca = PCA(n_components=2)
print pca
#fit model
pca_fit=pca.fit_transform(XS)
#explained variance for scree plot
print(pca.explained_variance_ratio_)
#eigenvalues
print(pca.components_)
#fitted projections
print(pca_fit)

#fit logistic regression
from sklearn.linear_model import LogisticRegression
Y = iris.target
print Y
#initilize
logistic=LogisticRegression()
#fit
logistic.fit(pca_fit,Y)
#intercept
print logistic.intercept_
#coeffcients
print logistic.coef_
#predict classification
print logistic.predict(pca_fit)
#predict prob
print logistic.predict_proba(pca_fit)

#KNN
from sklearn.neighbors import KNeighborsClassifier
neigh = KNeighborsClassifier(n_neighbors=3)
neigh.fit(pca_fit, y)
print(neigh.predict(pca_fit))

#Tree
from sklearn.tree import DecisionTreeClassifier
tree = DecisionTreeClassifier(max_depth=2)
tree.fit(pca_fit,y)
print(tree.predict(pca_fit))

#RandomForest
from sklearn.ensemble import RandomForestClassifier
RFC = RandomForestClassifier(n_estimators=10, max_depth=2)
RFC.fit(pca_fit,y)
print(RFC.predict(pca_fit))