# [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.

## 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.

## 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.