Hello and welcome! In today's lesson, we will explore the critical step of splitting data into training and testing sets, which is foundational for building any robust regression model. By the end of this lesson, you'll be capable of taking a dataset and accurately dividing it into training and testing sets.

When developing a machine learning model, it's essential to test its performance on unseen data. This is because while a model may perform well on the training set, it is the performance on the testing set that determines how well the model generalizes to new, unseen data. Without this split, we risk overestimating the model's capabilities due to its exposure to the training data alone.

By splitting the dataset into training and testing sets, we allow the model to learn on one subset of the data (training set) and evaluate its performance on another subset (testing set). This ensures that the model generalizes well to new data, making it more robust and reliable.

Before we can use the diamonds dataset in our data sets, we need to preprocess it by converting categorical variables into numerical values.

One-hot encoding is a method where each category value is converted into a new binary column. Each column represents a category, and the values are 0 or 1, indicating the absence or presence of the category. This is particularly useful for machine learning algorithms that require numerical input and can benefit from each category being represented as a distinct feature. In Pandas, we use the pd.get_dummies function to achieve one-hot encoding.

Here's how to implement one-hot encoding for our diamonds dataset:

Output:

The drop_first=True parameter in the pd.get_dummies function drops the first category of each categorical variable to avoid multicollinearity, allowing the remaining categories to uniquely represent the data. This ensures that each feature set used in the model is statistically independent and prevents redundancy. Consequently, models can be trained more efficiently without the risk of perfect collinearity.

Note that one-hot encoding is similar but different to the previously mentioned method of categorical encoding, which converts each category to a unique integer code. Categorical encoding is less memory-intensive and can be useful for models that can handle categorical features natively, such as tree-based models. However, for linear models and certain other algorithms, one-hot encoding is often preferred as it avoids the implicit ordinal relationship inferred by categorical encoding.

Now, it’s time to split the dataset into features and labels using train_test_split from scikit-learn. This function will help us create a training set (used to train the model) and a testing set (used to evaluate the model).

First, let's define the features (X) and the target variable (y):

drop is used to remove the target variable column from the feature dataset to ensure that X only contains input features. This prevents the target variable from inadvertently being used as a feature, which would inflate the model's performance metrics and lead to incorrect conclusions. axis=1 is used to indicate that a column will be dropped, rather than a row.

Note that this does affect the original diamonds variable, making it possible to subsequently only extract the prices diamonds['price'].

Now that we have separated the features and the target variable, we can split the dataset into the train and test data set. The train_test_split function takes several parameters, including test_size, which defines the proportion of the dataset to be used as the testing set, and random_state which ensures reproducibility.

It's crucial to check the shapes of the resulting datasets to confirm that the data has been split correctly and the proportions are as expected.

Output:

You'll see that the shapes confirm the data split is correct. Make sure the sum of the lengths of X_train and X_test equals the original dataset length.

If the assertions pass without errors, then the dataset is split correctly.

Congratulations! You've successfully learned how to split a dataset into training and testing sets. This lesson was critical because it ensures that your machine learning models are trained and evaluated on separate data, providing a more realistic measure of model performance. Keep practicing, and soon, splitting datasets will become second nature to you!