Preprocess Data

K-Nearest Neighbors (KNN):

KNN is a non-parametric algorithm, meaning it doesn’t make any underlying assumptions about the distribution of data. It’s an instance-based or memory-based learning algorithm, which means it memorizes the entire training dataset and uses it to make predictions. The fundamental idea behind KNN is to classify a new data point by considering the majority class among its K-nearest neighbors....

Tidymodels

Tidymodels is a powerful and user-friendly ecosystem for modeling and machine learning in R. It provides a structured workflow for creating, tuning, and evaluating models. Before we proceed, make sure you have tidymodels and the necessary packages installed. You can install them using:...

Pre-Requisites

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Load Required Libraries and Data

Before moving forward make sure you have Caret and ggplot packages installed....

Preprocess Data

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Create and Train the k-NN Model

We’ll start by loading the necessary libraries and a dataset. For this tutorial, we’ll use the classic Iris dataset, which contains three different species of iris flowers (setosa, versicolor, and virginica)....

Make Predictions

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Evaluate the Model (Optional)

Data preprocessing is crucial for building a robust model. In this step, we’ll create a recipe to preprocess the data. In our case, we don’t need any preprocessing since the Iris dataset is well-structured and doesn’t have any missing values....

Performing KNN on MTCars Dataset

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Conclusion

Now, it’s time to create and train our k-NN model. We’ll use the `nearest_neighbor()` function from the `parsnip` package, which is part of tidymodels....