Applied Data Science Coding with Python - KNN Algorithm смотреть онлайн

Data Science is a field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured. Applied Data Science is the practice of using these techniques to solve real-world problems. One way to do this is by using coding with Python, a powerful programming language that is widely used in the field of Data Science.

One specific algorithm that is often used in Applied Data Science is called the k-Nearest Neighbors (KNN) algorithm. The KNN algorithm is a type of supervised machine learning algorithm that is used for classification and regression problems. It is called a lazy learning algorithm because it does not require a model to be built during training.

The KNN algorithm works by finding the k-nearest points to a new data point and then predicting the class or value of the new point based on the majority class or value of the k-nearest points. The number of nearest points, k, is a user-specified parameter. The algorithm uses a distance metric, such as Euclidean distance, to determine the similarity between points.

The KNN algorithm can be implemented in Python using the scikit-learn library. The library provides a KNeighborsClassifier class for classification problems and a KNeighborsRegressor class for regression problems. These classes provide a number of parameters that can be used to control the behavior of the algorithm, such as the number of nearest neighbors, k, and the distance metric used.

Once the model is trained, it can be used to make predictions about the dependent variable for new observations. The model can also be used to understand the relationship between the data by examining the k-nearest points to a specific data point.

In summary, the KNN algorithm is a powerful tool for Applied Data Science problems. It is a lazy learning algorithm that can be used for classification and regression problems. It is simple to implement and understand, as it doesn't require a model to be built during training. It can be implemented in Python using the scikit-learn library which provides an easy-to-use interface for training and using the model. It is a useful algorithm when you have a small dataset and you want to find similar points to a new data point.

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