Question

Machine Learning

do using python on jupyter notebook
1. Linear Regression
Dataset used: Diabetes from sklearn
You are asked to solve a regression problem in the Diabetes dataset. Please review the Diabetes dataset used before creating a program to decide which attributes will be used in the regression process.
please use the cross-validation step to produce the best evaluation of the model.
All you have to do is
• Perform linear regression using the OLS (Ordinary Least Square) method (sklearn.linear_model.LinearRegression)
• Also do linear regression using additional regularization. (Lasso)
• Also do linear regression by doing gradient descent algorithm training (func: sklearn.linear_model.SGDRegressor)
• In Sklearn there are several other regression methods that can be tried for use in Diabetes problems.

Answer 1

import pandas as pd
import numpy as np
from sklearn.datasets import load_diabetes

data=load_diabetes()

data1 = pd.DataFrame(data= np.c_[data['data'], data['target']],
columns= data['feature_names'] + ['target'])

print(data1.head)

y=data1['target']
X=data1
X.drop(['target'], axis=1,inplace=True)

from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test=train_test_split(X,y)

#LinearRegression

from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
reg = LinearRegression().fit(X_train,y_train)
pred1=reg.predict(X_train)
pred2=reg.predict(X_test)
train_mse=mean_squared_error(pred1,y_train)
test_mse=mean_squared_error(pred2,y_test)

print('train mean_squared_error :',train_mse)
print('test mean_squared_error :',test_mse)

#Lasso

from sklearn import linear_model
from sklearn.metrics import mean_squared_error
reg = linear_model.Lasso()
reg.fit(X_train,y_train)
pred1=reg.predict(X_train)
pred2=reg.predict(X_test)
train_mse=mean_squared_error(pred1,y_train)
test_mse=mean_squared_error(pred2,y_test)

print('train mean_squared_error :',train_mse)
print('test mean_squared_error :',test_mse)

#SGDRegressor

from sklearn import linear_model
from sklearn.metrics import mean_squared_error
reg = linear_model.SGDRegressor()
reg.fit(X_train,y_train)
pred1=reg.predict(X_train)
pred2=reg.predict(X_test)
train_mse=mean_squared_error(pred1,y_train)
test_mse=mean_squared_error(pred2,y_test)

print('train mean_squared_error :',train_mse)
print('test mean_squared_error :',test_mse)

#Bayesian Ridge

from sklearn import linear_model
from sklearn.metrics import mean_squared_error
reg = linear_model.BayesianRidge()
reg.fit(X_train,y_train)
pred1=reg.predict(X_train)
pred2=reg.predict(X_test)
train_mse=mean_squared_error(pred1,y_train)
test_mse=mean_squared_error(pred2,y_test)

print('train mean_squared_error :',train_mse)
print('test mean_squared_error :',test_mse)