We can build the K-Means in python using the ‘KMeans’ algorithm provided by the scikit-learn package. Now we have all the required components to build the K-Means model. Let’s import all the primary packages into our python environment. Our primary packages include pandas for working on the data, NumPy for working with the arrays, matplotlib & seaborn for visualization, mplot3d for three-dimensional visualization, and finally scikit-learn for building the K-Means model. Without further ado, let’s dive into the coding part! Importing the PackagesĮvery task must begin with importing the required packages into the respective environment (python in our case). Analyzing and visualizing the built K-Means model.Building the model using the K-Means algorithm.Analyzing the data and find some useful information.Importing the customer data into the python environment.Now let’s use the K-Means algorithm to segment customers based on characteristics provided in the data with python. Another group might include customers from non-profit organizations. A business task is to retain those customers. For example, one group might contain customers who are high-profit and low-risk, that is, more likely to purchase products, or subscribe for a service. It is a significant strategy as a business can target these specific groups of customers and effectively allocate marketing resources. Customer segmentation is the practice of partitioning a customer base into groups of individuals that have similar characteristics. Imagine that you have a customer dataset, and you need to apply customer segmentation on this historical data. Now let’s see a little bit about the case we are going to solve. In this article, we are going to tackle a clustering problem which is customer segmentation (dividing customers into groups based on similar characteristics) using the K-means algorithm. K-Means clustering works really well with medium and large-sized data.ĭespite the algorithm’s simplicity, K-Means is still powerful for clustering cases in data science. The values which are within a cluster are very similar to each other but, the values across different clusters vary enormously. The K-Means divides the data into non-overlapping subsets without any cluster-internal structure. The K-Means is an unsupervised learning algorithm and one of the simplest algorithm used for clustering tasks. The K-Means clustering beams at partitioning the ‘n’ number of observations into a mentioned number of ‘k’ clusters (produces sphere-like clusters).
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