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Linear

The document outlines a Python script that performs linear regression analysis on the Iris dataset, focusing on the relationship between petal length and petal width. It includes data loading, preprocessing, model training, and visualization of results. The script uses libraries such as seaborn, matplotlib, and sklearn for data handling and plotting.

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2 views6 pages

Linear

The document outlines a Python script that performs linear regression analysis on the Iris dataset, focusing on the relationship between petal length and petal width. It includes data loading, preprocessing, model training, and visualization of results. The script uses libraries such as seaborn, matplotlib, and sklearn for data handling and plotting.

Uploaded by

anyalovesbunny
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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linear

September 20, 2023

[1]: print(7+3)

10

[2]: print(5+3)

[7]: import seaborn as sns

[8]: iris=sns.load_dataset('iris')

[9]: iris

[9]: sepal_length sepal_width petal_length petal_width species


0 5.1 3.5 1.4 0.2 setosa
1 4.9 3.0 1.4 0.2 setosa
2 4.7 3.2 1.3 0.2 setosa
3 4.6 3.1 1.5 0.2 setosa
4 5.0 3.6 1.4 0.2 setosa
.. … … … … …
145 6.7 3.0 5.2 2.3 virginica
146 6.3 2.5 5.0 1.9 virginica
147 6.5 3.0 5.2 2.0 virginica
148 6.2 3.4 5.4 2.3 virginica
149 5.9 3.0 5.1 1.8 virginica

[150 rows x 5 columns]

[10]: iris=iris[['petal_length','petal_width']]

[11]: iris

[11]: petal_length petal_width


0 1.4 0.2
1 1.4 0.2
2 1.3 0.2
3 1.5 0.2

1
4 1.4 0.2
.. … …
145 5.2 2.3
146 5.0 1.9
147 5.2 2.0
148 5.4 2.3
149 5.1 1.8

[150 rows x 2 columns]

[12]: x=iris['petal_length']
y=iris['petal_width']

[18]: import matplotlib.pyplot as plt


plt.scatter(x,y)
plt.xlabel("petal length")
plt.ylabel("petal width")

[18]: Text(0, 0.5, 'petal width')

2
[52]: from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.
↪4,random_state=23)

[53]: import numpy as np


x_train=np.array(x_train).reshape(-1,1)
x_test=np.array(x_test).reshape(-1,1)

[54]: from sklearn.linear_model import LinearRegression

[55]: lr=LinearRegression()

[56]: lr.fit(x_train,y_train)

[56]: LinearRegression()

[57]: c=lr.intercept_
c

[57]: -0.35113274221437507

[58]: m=lr.coef_
m

[58]: array([0.41684538])

[48]: y_pred_train=m*x_train+c
y_pred_train.flatten()

[48]: array([1.73309416, 0.31581987, 1.31624878, 0.3575044 , 1.98320139,


1.31624878, 1.64972508, 1.98320139, 1.7747787 , 1.69140962,
0.23245079, 0.31581987, 1.98320139, 0.23245079, 0.31581987,
1.94151685, 1.7747787 , 1.31624878, 0.23245079, 1.35793332,
1.85814777, 1.52467147, 2.06657046, 2.40004677, 1.44130239,
0.19076625, 1.31624878, 1.69140962, 1.69140962, 1.31624878,
0.27413533, 1.52467147, 1.52467147, 1.27456424, 1.73309416,
1.64972508, 1.2328797 , 1.7747787 , 2.27499315, 2.19162408,
0.14908171, 2.02488593, 0.8994034 , 0.27413533, 2.108255 ,
1.64972508, 0.23245079, 1.52467147, 1.39961786, 1.81646324,
0.19076625, 0.06571264, 1.10782609, 0.10739718, 1.60804055,
1.39961786, 0.14908171, 2.06657046, 1.44130239, 1.52467147,
0.31581987, 2.52510038, 1.56635601, 1.7747787 , 1.98320139,
1.60804055, 0.27413533, 0.31581987, 1.94151685, 2.06657046,
1.48298693, 0.19076625, 1.81646324, 1.02445701, 2.02488593,
1.10782609, 0.19076625, 0.27413533, 0.27413533, 1.7747787 ,
0.23245079, 0.23245079, 1.69140962, 0.23245079, 1.48298693,
0.27413533, 1.56635601, 0.27413533, 0.19076625, 1.7747787 ])

3
[59]: y_pred=lr.predict(x_train)
y_pred

[59]: array([1.73309416, 0.31581987, 1.31624878, 0.3575044 , 1.98320139,


1.31624878, 1.64972508, 1.98320139, 1.7747787 , 1.69140962,
0.23245079, 0.31581987, 1.98320139, 0.23245079, 0.31581987,
1.94151685, 1.7747787 , 1.31624878, 0.23245079, 1.35793332,
1.85814777, 1.52467147, 2.06657046, 2.40004677, 1.44130239,
0.19076625, 1.31624878, 1.69140962, 1.69140962, 1.31624878,
0.27413533, 1.52467147, 1.52467147, 1.27456424, 1.73309416,
1.64972508, 1.2328797 , 1.7747787 , 2.27499315, 2.19162408,
0.14908171, 2.02488593, 0.8994034 , 0.27413533, 2.108255 ,
1.64972508, 0.23245079, 1.52467147, 1.39961786, 1.81646324,
0.19076625, 0.06571264, 1.10782609, 0.10739718, 1.60804055,
1.39961786, 0.14908171, 2.06657046, 1.44130239, 1.52467147,
0.31581987, 2.52510038, 1.56635601, 1.7747787 , 1.98320139,
1.60804055, 0.27413533, 0.31581987, 1.94151685, 2.06657046,
1.48298693, 0.19076625, 1.81646324, 1.02445701, 2.02488593,
1.10782609, 0.19076625, 0.27413533, 0.27413533, 1.7747787 ,
0.23245079, 0.23245079, 1.69140962, 0.23245079, 1.48298693,
0.27413533, 1.56635601, 0.27413533, 0.19076625, 1.7747787 ])

[60]: import matplotlib.pyplot as plt


plt.scatter(x_train,y_train)
plt.plot(x_train,y_pred,color="red")
plt.xlabel("petal length")
plt.ylabel("petal width")

[60]: Text(0, 0.5, 'petal width')

4
[61]: y_pred_test=lr.predict(x_test)
y_pred_test

[61]: array([1.89983231, 2.14993954, 1.35793332, 0.27413533, 1.73309416,


1.69140962, 0.3575044 , 1.94151685, 0.3575044 , 1.14951063,
1.60804055, 0.31581987, 2.108255 , 0.27413533, 0.27413533,
1.7747787 , 1.52467147, 1.60804055, 2.19162408, 0.23245079,
1.85814777, 0.23245079, 0.31581987, 0.19076625, 1.98320139,
0.23245079, 0.44087348, 1.64972508, 1.48298693, 1.27456424,
0.27413533, 1.27456424, 0.19076625, 2.44173131, 0.27413533,
0.3575044 , 1.56635601, 1.02445701, 1.39961786, 2.14993954,
2.02488593, 0.44087348, 1.19119517, 0.23245079, 1.48298693,
1.73309416, 1.52467147, 2.31667769, 0.27413533, 1.35793332,
2.19162408, 1.89983231, 0.23245079, 1.98320139, 1.52467147,
1.60804055, 2.44173131, 1.39961786, 0.23245079, 1.7747787 ])

[62]: import matplotlib.pyplot as plt


plt.scatter(x_test,y_test)
plt.plot(x_test,y_pred_test,color="red")
plt.xlabel("petal length")
plt.ylabel("petal width")

5
[62]: Text(0, 0.5, 'petal width')

[ ]:

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