# Applied Data Science Coding with Python: Regression with KNN Algorithm

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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.

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What is KNN Algorithm?

In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a non-parametric method used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space. The output depends on whether k-NN is used for classification or regression:

In k-NN classification, the output is a class membership. An object is classified by a plurality vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor.

In k-NN regression, the output is the property value for the object. This value is the average of the values of k nearest neighbors.

k-NN is a type of instance-based learning, or lazy learning, where the function is only approximated locally and all computation is deferred until classification.

Both for classification and regression, a useful technique can be to assign weights to the contributions of the neighbors, so that the nearer neighbors contribute more to the average than the more distant ones. For example, a common weighting scheme consists in giving each neighbor a weight of 1/d, where d is the distance to the neighbor.

The neighbors are taken from a set of objects for which the class (for k-NN classification) or the object property value (for k-NN regression) is known. This can be thought of as the training set for the algorithm, though no explicit training step is required.

A peculiarity of the k-NN algorithm is that it is sensitive to the local structure of the data.

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