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Updated 4 years ago
! pip install scikit-learnRequirement already satisfied: scikit-learn in c:\users\vsneh\appdata\local\programs\python\python37\lib\site-packages (0.21.3)
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# Importing modules
from sklearn import datasets
import numpy as np# Load dataset from scikit-learn dataset library
# diabetes_X -> Features
# diabetes_Y -> Labels
diabetes = datasets.load_diabetes()
print('Dataset shape:',diabetes.data.shape)
print('Diabetes labels shape:',diabetes.target.shape)
print('Diabetes feature names:',diabetes.feature_names)Dataset shape: (442, 10)
Diabetes labels shape: (442,)
Diabetes feature names: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']
# Split Complete dataset into Train and Test sets.
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(diabetes.data, diabetes.target, test_size=0.2, random_state=1)
# This splits entire dataset into tarining and testing sets in 80% & 20% respectively.# Import Linear regression module from scikit-learn linear-model package
from sklearn.linear_model import LinearRegression
linReg = LinearRegression()
# Fit function is used to train out model on training set
linReg.fit(X_train, y_train)LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)