How to do KMeans Clustering in Python
KMeans Clustering is a method of clustering that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. This method is known as a “centroid-based” approach because it starts by randomly selecting k initial centroids and then iteratively reassigning observations to the cluster whose centroid is closest. In this article, we will go over the basics of how to do KMeans Clustering in Python.
First, we need to import the necessary libraries such as Numpy and Pandas, which will help us handle our data. Next, we will import the KMeans class from the sklearn.cluster library, which will be used to create our clusters.
Once we have our libraries and classes imported, we can start creating our clusters. To do this, we will first need to load our data into a Pandas dataframe. We can do this by using the read_csv function, which will allow us to read in data from a CSV file.
Once our data is loaded, we can create our model by instantiating the KMeans class. We need to set the number of clusters we want in the model and the initialization method (k-means++ or random) which is the method used to initialize the centroids.
To check the accuracy of our model, we can use different metrics such as silhouette score and calinski harabasz index. The higher the silhouette score and calinski harabasz index, the better our model is at clustering the data.
In conclusion, doing KMeans Clustering in Python is a straightforward process. By using the sklearn.cluster library, we can easily load and cluster our data using the KMeans class. By tuning the number of clusters, initialization method and using metrics, we can optimise the accuracy of our model and make more accurate clusters. One of the major drawback of this algorithm is that it’s sensitive to the initial placement of centroids, also it’s a hard assignment meaning that every data point belongs to exactly one cluster.
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