import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error, r2_score
import pandas as pd
# Load the diabetes dataset
diabetes_data = datasets.load_diabetes()
# Print all keys and number of raw and columns
print(diabetes_data.keys, diabetes_data.data.shape)
<built-in method keys of Bunch object at 0x10b789830> (442, 10)
# Print all the feature_names in dataset
print(diabetes_data.feature_names)
['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']
df = pd.DataFrame(diabetes_data.data)
df.columns = diabetes_data.feature_names
df['target'] = diabetes_data.target
x=df.drop('target',axis=1)
# Create linear regression object
rm = linear_model.LinearRegression()
LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,
normalize=False)
print(rm.intercept_)
print(rm.coef_)
152.1334841628965
[ -10.01219782 -239.81908937 519.83978679 324.39042769 -792.18416163
476.74583782 101.04457032 177.06417623 751.27932109 67.62538639]
print(rm.predict(x)[:10])
[206.11706979 68.07234761 176.88406035 166.91796559 128.45984241
106.34908972 73.89417947 118.85378669 158.81033076 213.58408893]
plt.scatter(df.target, rm.predict(x))
plt.xlabel('old data')
plt.ylabel('predicted data')
plt.show()
