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[Solved] Linear Regression on Pandas DataFrame using Sklearn ( Index: tuple index out of range)

Hello Guys, How are you all? Hope You all Are Fine. Today I get the following error Linear Regression on Pandas DataFrame using Sklearn ( Index: tuple index out of range) in python. So Here I am Explain to you all the possible solutions here.

Without wasting your time, Let’s start This Article to Solve This Error.

How Linear Regression on Pandas DataFrame using Sklearn ( Index: tuple index out of range) Error Occurs?

Today I get the following error Linear Regression on Pandas DataFrame using Sklearn ( Index: tuple index out of range) in python.

How To Solve Linear Regression on Pandas DataFrame using Sklearn ( Index: tuple index out of range) Error ?

  1. How To Solve Linear Regression on Pandas DataFrame using Sklearn ( Index: tuple index out of range) Error ?

    To Solve Linear Regression on Pandas DataFrame using Sklearn ( Index: tuple index out of range) Error The LinearRegression has coef_ and intercept_ attributes.

  2. Linear Regression on Pandas DataFrame using Sklearn ( Index: tuple index out of range)

    To Solve Linear Regression on Pandas DataFrame using Sklearn ( Index: tuple index out of range) Error The LinearRegression has coef_ and intercept_ attributes.

Solution 1

Let’s assume your csv looks something like:

c1,c2
0.000000,0.968012
1.000000,2.712641
2.000000,11.958873
3.000000,10.889784
...

I generated the data as such:

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

length = 10
x = np.arange(length, dtype=float).reshape((length, 1))
y = x + (np.random.rand(length)*10).reshape((length, 1))

This data is saved to test.csv (just so you know where it came from, obviously you’ll use your own).

data = pd.read_csv('test.csv', index_col=False, header=0)
x = data.c1.values
y = data.c2.values
print x # prints: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9.]

You need to take a look at the shape of the data you are feeding into .fit().

Here x.shape = (10,) but we need it to be (10, 1), see sklearn. Same goes for y. So we reshape:

x = x.reshape(length, 1)
y = y.reshape(length, 1)

Now we create the regression object and then call fit():

regr = linear_model.LinearRegression()
regr.fit(x, y)

# plot it as in the example at http://scikit-learn.org/
plt.scatter(x, y,  color='black')
plt.plot(x, regr.predict(x), color='blue', linewidth=3)
plt.xticks(())
plt.yticks(())
plt.show()

See sklearn linear regression example. enter image description here

Solution 2

make predictions based on the result?

To predict,

lr = linear_model.LinearRegression().fit(X,Y)
lr.predict(X)

Is there any way I can view details of the regression?

The LinearRegression has coef_ and intercept_ attributes.

lr.coef_
lr.intercept_

show the slope and intercept.

Summery

It’s all About this issue. Hope all solution helped you a lot. Comment below Your thoughts and your queries. Also, Comment below which solution worked for you? Thank You.

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