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the below  is an example of diabetes dataset import matplotlib.pyplot as plt import numpy as np from sklearn.datasets import load_diabetes from sklearn import linear_model d = load_diabetes() d_X = d.data[:, np.newaxis, 2] dx_train = d_X[:-20] dy_train = d.target[:-20] dx_test = d_X[-20:] dy_test = d.target[-20:] lr = linear_model.LinearRegression() lr.fit(dx_train, dy_train) mse = np.mean((lr.predict(dx_test) - dy_test) **2) lr_score = lr.score(dx_test, dy_test) print(lr.coef_) print(mse) print(lr_score) plt.scatter(dx_test, dy_test) plt.plot(dx_test, lr.predict(dx_test), c='r') plt.show()

the below  is an example of diabetes dataset import matplotlib.pyplot as plt import numpy as np from sklearn.datasets import load_diabetes from sklearn import linear_model d = load_diabetes() d_X = d.data[:, np.newaxis, 2] dx_train = d_X[:-20] dy_train = d.target[:-20] dx_test = d_X[-20:] dy_test = d.target[-20:] lr = linear_model.LinearRegression() lr.fit(dx_train, dy_train) mse = np.mean((lr.predict(dx_test) - dy_test) **2) lr_score = lr.score(dx_test, dy_test) print(lr.coef_) print(mse) print(lr_score) plt.scatter(dx_test, dy_test) plt.plot(dx_test, lr.predict(dx_test), c='r') plt.show()

Database System Concepts
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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the below  is an example of diabetes dataset

import matplotlib.pyplot as plt
import numpy as np
from sklearn.datasets import load_diabetes
from sklearn import linear_model

d = load_diabetes()
d_X = d.data[:, np.newaxis, 2]
dx_train = d_X[:-20]
dy_train = d.target[:-20]
dx_test = d_X[-20:]
dy_test = d.target[-20:]

lr = linear_model.LinearRegression()
lr.fit(dx_train, dy_train)

mse = np.mean((lr.predict(dx_test) - dy_test) **2)
lr_score = lr.score(dx_test, dy_test)

print(lr.coef_)
print(mse)
print(lr_score)
plt.scatter(dx_test, dy_test)
plt.plot(dx_test, lr.predict(dx_test), c='r')
plt.show()

• Create a Python file called linear Regression.py. • In this task you will use the diabetes dataset mentioned above to perform linear regression to find the best fit line through the data. Reserve the last 20 observations for testing and use the rest for training your model. Instead of using linear_model.LinearRegression () from sklearn, write a function and make use of numpy to calculate the gradient and the y-intercept of the best fit line, which has equation y = mx + b. The equations below describe how both the gradient and the y-intercept can be calculated from the training data and labels. Note: when you calculate the gradient, you will need to reshape the x array to remove an extra dimension of 1 from its shape (it has this as the dataset was formatted for use with the sklearn functions, which require this extra dimension). You can easily do this by applying squeeze () to the x array when you pass it as an argument to the method. Hint: if the line doesn't look like it fits the data well, there is a bug in your code. 0 m = (µ(x) * µ(y) − µ(x * y))/((µ(x))² − µ(x²)) - o b = µ(y) = m* µ(x) Where u is a mean function • Use these values to produce a figure with the following: o Scatter plot of training data colored red. o Scatter plot of testing data colored green. O Line graph for the best-fit line colored blue. o Legend.
Transcribed Image Text:• Create a Python file called linear Regression.py. • In this task you will use the diabetes dataset mentioned above to perform linear regression to find the best fit line through the data. Reserve the last 20 observations for testing and use the rest for training your model. Instead of using linear_model.LinearRegression () from sklearn, write a function and make use of numpy to calculate the gradient and the y-intercept of the best fit line, which has equation y = mx + b. The equations below describe how both the gradient and the y-intercept can be calculated from the training data and labels. Note: when you calculate the gradient, you will need to reshape the x array to remove an extra dimension of 1 from its shape (it has this as the dataset was formatted for use with the sklearn functions, which require this extra dimension). You can easily do this by applying squeeze () to the x array when you pass it as an argument to the method. Hint: if the line doesn't look like it fits the data well, there is a bug in your code. 0 m = (µ(x) * µ(y) − µ(x * y))/((µ(x))² − µ(x²)) - o b = µ(y) = m* µ(x) Where u is a mean function • Use these values to produce a figure with the following: o Scatter plot of training data colored red. o Scatter plot of testing data colored green. O Line graph for the best-fit line colored blue. o Legend.
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