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# How and when to use polynomial regression?

# How and when to use polynomial regression?

This recipe explains how and when to use polynomial regression

Polynomial Regression is a form of linear regression in which the relationship between the independent variable x and dependent variable y is modeled as an nth degree polynomial.

So this recipe is a short example on How and when to use polynomial regression. Let's get started.

```
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_boston
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
```

Let's pause and look at these imports. We have exported train_test_split which helps in randomly breaking the datset in two parts. Here sklearn.dataset is used to import one classification based model dataset. Also, we have exported LinearRegression and PolynomialFeatures to build the model.

```
X,y=load_boston(return_X_y=True)
poly = PolynomialFeatures(degree = 2)
X = poly.fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)
```

Here, we have used load_boston function to import our dataset in two list form (X and y) and therefore kept return_X_y to be True. Thereby, we have introduced polnomial features in our dataset for degree upto 2. Further with have broken down the dataset into 2 parts, train and test with ratio 3:4.

Now our dataset is ready.

```
model = LinearRegression()
```

We have simply built a regressor model with LinearRegression (our data already has polnomial features and linear regreession simply means predicting coefficient) with default values.

```
model.fit(X_train, y_train)
expected_y = y_test
predicted_y = model.predict(X_test)
```

Here we have simply fit used fit function to fit our model on X_train and y_train. Now, we are predicting the values of X_test using our built model.

```
print(model.score(X_train,y_train))
print(model.score(X_test,y_test))
```

Here we have calculating accuracy score of our trained set and also, on the unknown dataset (X_test and y_test)

Once we run the above code snippet, we will see:

0.9371457121192392 0.7328994385490928

The model has low accuracy score on unknown datset and hence might not be that efficient.

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