# Polynomial Regression in Python In this article, we will see the step-by-step process of Polynomial Regression using Python.

If you don’t like to read, here’s the video on the same.

## Polynomial Regression in Python

You can read all the steps or can get the code on GitHub.

Let’s start coding step by step.

Importing the dataset and libraries.

```# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Importing the dataset
dataset.info()
dataset.dropna(inplace=True)
X = dataset.iloc[:, 1:-1].values
y = dataset.iloc[:, -1].values
```

As the data is very small, we have trained the model on whole dataset.Training and predicting from Polynomial Regresssion.

```from sklearn.linear_model import LinearRegression

from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(degree = 4)
X_poly = poly_reg.fit_transform(X)
lin_reg_2 = LinearRegression()
lin_reg_2.fit(X_poly, y)
```

Predicting unseen data if you have any.

```X_test1=poly_reg.transform([])
y_pred=lin_reg_2.predict(X_test1)
print(y_pred)
```

Plotting the graph.

```plt.scatter(X, y, color = 'red')
plt.plot(X, lin_reg_2.predict(poly_reg.fit_transform(X)), color = 'blue')
plt.title('Truth or Bluff (Polynomial Regression)')
plt.xlabel('Position level')
plt.ylabel('Salary')
plt.show()
```

If you want to see some other actual time problems codes (DNN, SVM), feel free to check out my github.

Also Read: Multiple Linear Regression in Python