Category: 2. Predictive Modeling

1. Spam Filtering The first and foremost application of Naive Bayes is its ability to classify texts and in particular, spam emails from non-spam ones. It is one of the oldest spam filtering methodology, with the Naive Bayes spam filtering dating back to 1998. Naive Bayes takes two classes – Spam and Ham and classifies…

Naive Bayes is a powerful supervised learning algorithm that is used for classification. The Naive Bayes classifier is an extension of the above discussed standard Bayes Theorem. In a Naive Bayes, we calculate the probability contributed by every factor. Most we use it in textual classification operations like spam filtering. Let us understand how Naive…

Are you planning to become a data scientist? If yes, you must read this extensive article on Bayes’ Theorem for Data Science. No data scientist can work without a complete understanding of conditional probability and Bayesian inference. So, today, we will discuss the same with the help of examples and applications. More importantly, we will discuss…

One of the most popular Machine Learning algorithms is K-means clustering. It is an unsupervised learning algorithm, meaning that it is used for unlabeled datasets. Imagine that you have several points spread over an n-dimensional space. In order to categorize this data on the basis of their similarity, you will use the K-means clustering algorithm. In this…

You must have heard about the Amazon Future Forecast. The way Amazon predicts its business outcomes such as product demand, resources, financial performance, etc results in increasing their profits. Have you ever thought how it becomes so easy for them to predict what is better for their business? You will definitely say data science, right? Yes!…