# Blueprint to Polynomial Regression

WTF is a polynomial?

polylinear. Adjective. (not comparable) (mathematics) Involving many linear functions.

Alright then,let’s get started!

• Import required libraries.
```# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd```
• import the data into a dataframe dataset.Slice the data into X and y.

```dataset = pd.read_csv('Polynomial_Regression/Position_Salaries.csv')
X = dataset.iloc[:, 1:2].values
y = dataset.iloc[:, 2].values```
• Import PolynomialFeatures from sklearn.preprocessing module. fit_transform our previously X into variable polyX.
```from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(degree = 4)

polyX = poly_reg.fit_transform(X)
poly_reg.fit(X_poly, y)```
• Let’s create a LinearRegression and fit it with polyX which we previously created and y.
```from sklearn.linear_model import LinearRegression
reg = LinearRegression()
reg.fit(X_poly, y)```
• Predict!
`reg.predict(poly_reg.fit_transform(6.5))`

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

# Importing the dataset
X = dataset.iloc[:, 1:2].values
y = dataset.iloc[:, 2].values

from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(degree = 4)

X_poly = poly_reg.fit_transform(X)
poly_reg.fit(X_poly, y)

from sklearn.linear_model import LinearRegression
reg = LinearRegression()
reg.fit(X_poly, y)

reg.predict(poly_reg.fit_transform(6.5))```

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