Linear Regression with Python

1. Simple Linear Regression

Let's start with the simplest Linear Regression model! You will learn the idea behind Linear Regression and how to make predictions in Python.

What is Linear Regression

Finding the Parameters

Building Linear Regression Using NumPy

Building Linear Regression Using Statsmodels

Predict House Prices

2. Multiple Linear Regression

Most real-world prediction tasks involve more than one feature. You will learn how to handle Linear Regression with multiple features.

Linear Regression with Two Features

Linear Regression with n Features

Building Multiple Linear Regression

Choosing the Features

Predict Prices Using Two Features

3. Polynomial Regression

A straight line does not always describe the data well. Let's learn how to build a more complex model for prediction! That's what the Polynomial Regression is suited for.

Quadratic Regression

Polynomial Regression

Building Polynomial Regression

Interpolation vs Extrapolation

Evaluate the Model

4. Choosing The Best Model

Now that you know how to build many Linear Regression models, you need a way to choose the best one. This is achievable using metrics. This section explains the most used ones and the difficulties you can face using them.

Metrics

Overfitting

R-squared

Predict Prices Using Polynomial Regression

For this challenge, you will build the same Polynomial Regression of degree 2 as in the previous challenge. However, you will need to split the set into a training set and a test set to calculate RMSE for both those sets. This is required to judge whether the model overfits/underfits or not.
Here is the reminder of the train_test_split() function you'll want to use.

And also reminder of the mean_squared_error() function needed to calculate RMSE:

Now let's move to coding!

Tarefa

Assign the DataFrame with a single column 'age' of df to the X variable.
Preprocess the X using the PolynomialFeatures class.
Split the dataset using the appropriate function from sklearn.
Build and train a model on the training set.
Predict the targets of both training and test set.
Calculate the RMSE for both training and test set.
Print the summary table.

When you complete the task, you will notice that the test RMSE is even lower than the training RMSE. Usually, models do not show better results on unseen instances. Here, the difference is tiny and caused by chance. Our dataset is relatively small, and while splitting, the test set received a bit better(easier to predict) data points.