import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.linear_model import Lasso from sklearn.model_selection import GridSearchCV from sklearn.feature_selection import SelectKBest from sklearn.feature_selection import f_classif from sklearn.ensemble import ExtraTreesClassifier from sklearn.decomposition import PCA import os from sklearn.feature_selection import RFE from sklearn.linear_model import LogisticRegression

file = os.getcwd()+"/datasets_228_482_diabetes.csv" names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class'] df = pd.read_csv(file, names = names) array = df.values X = array[:, 0:8] y = array[:,8]

test = SelectKBest(score_func = f_classif, k=4) fit = test.fit(X,y) features = fit.transform(X) corr_p = df['skin'].corr(df['class']) print(corr_p) print(features[0:5,:]) model = LogisticRegression(solver = 'lbfgs') rfe = RFE(model, 3) fit = rfe.fit(X,y) print('Num features: %d' % fit.n_features_) print('Selected features: %s' % fit.support_) print('feature ranking: %s' % fit.ranking_)

model = ExtraTreesClassifier(n_estimators=10) model.fit(X,y) print(model.feature_importances_)

pca = PCA(n_components = 3) fit = pca.fit(X,y) print('Explained Variance: %s'% fit.explained_variance_ratio_) print(fit.components_)

lasso = Lasso() parameters = {'alpha': [1e-15,1e-10, 1e-8, 1e-4, 1e-3,1e-2,1,5,10,20]} lasso_regressor = GridSearchCV(lasso, parameters, scoring = 'neg_mean_squared_error', cv=5) lasso_regressor.fit(X,y) print(lasso_regressor.best_params_) print(lasso_regressor.best_score_)