In this article, I am going to explain how to use scikit-learn/sk-learn, a machine learning package in Python, to do linear regression for a set of data points.

Below is a video tutorial on this:

I am not going to explain training data, testing data, and model evaluation concepts here, but they are important.

We know that the equation of a line is given by **y=mx+b**, where **m** is the slope and **b** is the intercept.

Our goal is to find the best values of slope (**m**) and intercept (**b**) to fit our data.

Linear regression uses the ordinary least squares method to fit our data points.

`import`

statement:

`from sklearn import linear_model`

I have the height and weight data of some people. Let's use this data to do linear regression and try to predict the weight of other people.

```
height=[[4.0],[4.5],[5.0],[5.2],[5.4],[5.8],[6.1],[6.2],[6.4],[6.8]]
weight=[ 42 , 44 , 49, 55 , 53 , 58 , 60 , 64 , 66 , 69]
print("height weight")
for row in zip(height, weight):
print(row[0][0],"->",row[1])
```

Output:

```
height weight
4.0 -> 42
4.5 -> 44
5.0 -> 49
5.2 -> 55
5.4 -> 53
5.8 -> 58
6.1 -> 60
6.2 -> 64
6.4 -> 66
6.8 -> 69
```

`import`

statement to plot graph using `matplotlib`

:

`import matplotlib.pyplot as plt`

Plotting the height and weight data:

```
plt.scatter(height,weight,color='black')
plt.xlabel("height")
plt.ylabel("weight")
```

Output:

Declaring the linear regression function and call the `fit`

method to learn from data:

```
reg=linear_model.LinearRegression()
reg.fit(height,weight)
```

Slope and intercept:

```
m=reg.coef_[0]
b=reg.intercept_
print("slope=",m, "intercept=",b)
```

Output:

`slope= 10.1936218679 intercept= -0.4726651480`

```
plt.scatter(height,weight,color='black')
predicted_values = [reg.coef_ * i + reg.intercept_ for i in height]
plt.plot(height, predicted_values, 'b')
plt.xlabel("height")
plt.ylabel("weight")
```

Output:
And that's it!

**Linear regression in python scikit learn | Quick KT**

**Linear regression in python scikit learn | Quick KT**

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