Logistic Regression with Spark : Learn Data Science

Logistic Regression with Spark

Logistic regression with Spark is achieved using MLlib. Logistic regression returns binary class labels that is “0” or “1”. In this example, we consider a data set that consists only one variable “study hours” and class label is whether the student passed (1) or not passed (0). from pyspark import SparkContext from pyspark import SparkContext […]

k-Means Clustering Spark Tutorial : Learn Data Science

k-Means Clustering Spark

k-Means clustering with Spark is easy to understand. MLlib comes bundled with k-Means implementation (KMeans) which can be imported from pyspark.mllib.clustering package. Here is a very simple example of clustering data with height and weight attributes. Arguments to KMeans.train: k is the number of desired clusters maxIterations is the maximum number of iterations to run. […]

Data Mining : Intuitive Partitioning of Data or 3-4-5 Rule

Intuitive Partitioning

Intuitive partitioning or natural partitioning is used in data discretization. Data discretization is the process of converting continuous values of an attribute into categorical data or partitions or intervals. Discretization helps reducing data size by reducing number of possible values. Instead of storing every observation we can only store partition range in which each observation […]

k-means Clustering Algorithm with Python : Learn Data Science

k-Means Clustering Spark

k-means clustering algorithm is used to group samples (items) in k clusters; k is specified by the user. The method works by calculating mean distance between cluster centroids and samples, hence the name k-means clustering. Euclidean distance is used as distance measure. See references for more information on the algorithm. This is a article describes k-means […]