Applied Data Science Coding with Python: Regression with KNN Algorithm

Regression with the K-Nearest Neighbors (KNN) algorithm is a method for solving regression problems in machine learning. It is based on the idea that similar data points tend to have similar target variable values.

The KNN algorithm for regression starts by finding the K number of data points in the training set that are closest to a new data point. The target variable value of the new data point is then determined by taking the average of the target variable values of the K nearest neighbors.

In order to use the KNN algorithm for regression in Python, you need to have a dataset that includes both the input data and the target variable values. You also need to decide on the value of K, which is the number of nearest neighbors to consider. The larger the value of K, the more smooth the predictions will be, and the less complex the model will be.

There are several libraries available in Python to implement the KNN algorithm for regression, such as scikit-learn, NumPy, and Pandas. These libraries provide pre-built functions and methods to build, train, and evaluate a KNN model for regression.

It is important to note that the performance of KNN algorithm for regression is highly dependent on the choice of K and the distance metric used. Therefore, it’s important to test different values of K and distance metrics to find the best one for the dataset. Also, KNN algorithm might be sensitive to the scale of the features, so it’s important to scale the features before using the algorithm.

In summary, Regression with the K-Nearest Neighbors (KNN) algorithm is a method for solving regression problems in machine learning.

In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in Python programming: How to apply KNN Algorithm in regression problems.