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# Implementing Linear Regression Using Python Machine Learning | Linear Regression

Requirement

Write a python code to perform Linear regression on the data given below. You are expected to build multiple models linear, quadratic, and polynomial if ncessary. (note: you don't need to do validation. please use the whole data to generate the models)

• What model you will use for future predictions?

• Plot the dataset (original) with the regression lines in each case ( 2 and 4 degree)

```# Data:
X= [-1.32121078, -1.27082308, -1.11452161, -1.04155518, -1.03126926,
-0.82267942, -0.81628314, -0.74574417, -0.68058031, -0.59648737,
-0.53814101, -0.52397821, -0.46531586, -0.33476872,  0.03883677,
0.06342208,  0.21645035,  0.26292124,  0.33366168,  0.38973193,
0.48351149,  0.51185123,  0.55083756,  0.63525052,  0.78218406,
0.82465693,  1.18387266,  1.27117063,  1.40967043,  1.59230389]

y = [12.00737573,  7.71098446,  4.38800339,  3.95618952,  3.2581645 ,
5.47377341,  4.28830445,  4.3972834 ,  3.7360889 ,  3.1610471 ,
3.15464497,  2.36058095,  4.6522462 ,  6.24929994,  5.59728231,
7.53161638,  5.89954734,  5.86768377,  7.79154763,  6.82300825,
6.87894839,  9.19437213,  6.75357884,  7.78934968,  9.77892827,
8.37547998, 15.3373295 , 16.22611907, 26.3397566 , 53.34370424]

# show a scatter plot of the data
plt.scatter(X, y)
plt.show()```

Output:

# |Start you code here ( you can use multiple cells)

X = np.array(X)

y = np.array(y)

X = X[:, np.newaxis]

y = y[:, np.newaxis]

import numpy as np

import matplotlib.pyplot as plt

from sklearn.linear_model import LinearRegression

model = LinearRegression()

model.fit(X, y)

y_pred = model.predict(X)

```rmse = np.sqrt(mean_squared_error(y,y_pred))
r2 = r2_score(y,y_pred)
print(rmse)
print(r2)```

Output:

7.499303641920036 0.3804956217183274

```plt.scatter(X, y, color = 'red')
plt.plot(X, y_pred, color = 'blue')
plt.title('Linear Regression')
plt.xlabel('X')
plt.ylabel('y')

plt.show()

```

Output:

```import numpy as np
import matplotlib.pyplot as plt

from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.preprocessing import PolynomialFeatures```

```# Visualising the Polynomial Regression results
#Degree 2
polynomial_features= PolynomialFeatures(degree=2)
x_poly = polynomial_features.fit_transform(X)

model = LinearRegression()
model.fit(x_poly, y)
y_poly_pred = model.predict(x_poly)

rmse = np.sqrt(mean_squared_error(y,y_poly_pred))
r2 = r2_score(y,y_poly_pred)
print(rmse)
print(r2)```

output:

4.515235029553905 0.7754240599337205

```import operator
plt.scatter(X, y, s=10)
# sort the values of x before line plot
sort_axis = operator.itemgetter(0)
sorted_zip = sorted(zip(X,y_poly_pred), key=sort_axis)
X, y_poly_pred = zip(*sorted_zip)
plt.plot(X, y_poly_pred, color='m')
plt.title('Degree-2')
plt.xlabel('X')
plt.ylabel('y')

plt.show()
plt.show()```

Output:

```# Degree 4
polynomial_features= PolynomialFeatures(degree=4)
x_poly = polynomial_features.fit_transform(X)

model = LinearRegression()
model.fit(x_poly, y)
y_poly_pred = model.predict(x_poly)

rmse = np.sqrt(mean_squared_error(y,y_poly_pred))
r2 = r2_score(y,y_poly_pred)
print(rmse)
print(r2)```

Output:

1.8082575936190255 0.9639817093208918

```import operator
plt.scatter(X, y, s=10)
# sort the values of x before line plot
sort_axis = operator.itemgetter(0)
sorted_zip = sorted(zip(X,y_poly_pred), key=sort_axis)
X, y_poly_pred = zip(*sorted_zip)
plt.plot(X, y_poly_pred, color='m')
plt.title('Degree-4')
plt.xlabel('X')
plt.ylabel('y')

plt.show()
plt.show()```

Output:

It's Better to choose Degree-2 because of

• Correct fit

• Low Bias

Whereas Degree-4 might lead to over fitting for unseen data,and simple linear regression displays underfitting ,it's optimal to choose degree-2.