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The K-nearest Neighbors (KNN) algorithm is a type of supervised machine learning algorithm used for classification, regression as well as outlier detection. It is extremely easy to implement in its most basic form but can perform fairly complex tasks. It is a lazy learning algorithm since it doesn't have a...
Cássia Sampaio
K-means clustering is an unsupervised learning algorithm that groups data based on each point euclidean distance to a central point called centroid. The centroids are defined by the means of all points that are in the same cluster. The algorithm first chooses random points as centroids and then iterates adjusting...
K-Means clustering is one of the most widely used unsupervised machine learning algorithms that form clusters of data based on the similarity between data instances. In this guide, we will first take a look at a simple example to understand how the K-Means algorithm works before implementing it using Scikit-Learn....
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Scikit-Learn offers several datasets to play around with - most of them being toy datasets to learn from and test things out. Some beginners find the comfort of a tabular Pandas DataFrame format more intuitive than NumPy arrays. Thankfully, you can import a dataset as a Bunch object containing a...
David Landup
K-Means is one of the most popular clustering algorithms. By having central points to a cluster, it groups other points based on their distance to that central point. A downside of K-Means is having to choose the number of clusters, K, prior to running the algorithm that groups points. If...
Models are more often than not trained to be deployed to production and to give meaningful predictions for new input. To move them outside of your training environment - you'll want to save a trained model and load it in a different one. XGBoost is a great, flexible and blazingly...
Agglomerative Hierarchical Clustering is an unsupervised learning algorithm that links data points based on distance to form a cluster, and then links those already clustered points into another cluster, creating a structure of clusters with sub-clusters. It is easily implemented using Scikit-Learn which already has single, average, complete and ward...
In this guide, we will focus on implementing the Hierarchical Clustering Algorithm with Scikit-Learn to solve a marketing problem. After reading the guide, you will understand: When to apply Hierarchical Clustering How to visualize the dataset to understand if it is fit for clustering How to pre-process features and engineer...
Regression is a technique in statistics and machine learning, in which the value of an independent variable is predicted by its relationship with other variables. Frameworks like Scikit-Learn make it easier than ever to perform regression with a wide variety of models - one of the strongest ones being built...
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