Step By step
- importing libraries & functions
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- loading datasets
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]- Different feature selection techniques:
SelectKBest
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_)ExtraTreeClasssifier
model = ExtraTreesClassifier(n_estimators=10)
model.fit(X,y)
print(model.feature_importances_)Dimensionality Reduction- PCA
pca = PCA(n_components = 3)
fit = pca.fit(X,y)
print('Explained Variance: %s'% fit.explained_variance_ratio_)
print(fit.components_)best params and score findings
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_)Connect- Linkedin
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