Why Polynomial Regression:
Using polynomial regression:
They are mainly used to define or describe a nonlinear phenomenon such as:
The main purpose of the regression analysis is modeling the expected value of the dependent variable y in terms of the value of the independent variable x. In simple regression, we used the following equation —
y = a + bx + e
Here y — dependent variable, a — intersection y, b — slope, and e — error rate.
In many cases, this linear model will not work. For example, if we analyze the production of a chemical synthesis in terms of the temperature at which the synthesis occurs, then we use a quadratic model.
y = a + b1x + b2 ^ 2 + e
Here y — dependent variable of x, a — intercept y, and e — error rate.
In general, we can simulate it for the nth value.
y = a + b1x + b2x ^ 2 + ... . + bnx ^ n
Since the regression function is linear in terms of unknown variables, therefore, these models are linear in terms of estimation.
Therefore, using the least squares method, let`s calculate the answer value which is y.
Polynomial Regression in Python:
To get the dataset used for polynomial regression analysis, press here .
Step 1: Importing libraries and datasets
Import important libraries and dataset that we use to perform polynomial regression.

Step 2: Split the dataset into 2 components
Split the dataset into two components, i.e. X and yX will contain a column between 1 and 2.y will contain column 2.

Step 3: Fitting the linear regression to the dataset
Fitting the linear regression model in two components.

Step 4: Fitting a polynomial regression to a dataset
Fitting a polynomial regression model to two components X and y.

Step 5: In this step, we visualize the linear regression results using scatter plot.

Step 6: Render the polynomial regression results using a scatter plot.

Step 7: Predicting a new result with linear and polynomial regression.
# Predict a new result using linear regression
lin.predict (
110.0
)

Benefits of using polynomial regression:
Disadvantages polynomial regression
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