Give me a dozen healthy infants, well formed, and my own specified world to bring them up in and I’ll guarantee to take any one at random and train him to become any type of specialist I might select—doctor, lawyer, artist, merchant-chief and yes, even beggar-man thief, regardless of his talents, penchants, tendencies, abilities, vocations, and race of his ancestors.

This was John Watson, one of the founders of Behaviorism, writing around 1925. He believed that human behavior is completely malleable and that it can be shaped into anything given the right environment. While I don’t harbor any grand objectives or sinister experiments like Watson did, I do hope to be able to teach my kids good habits using controlled environments. For instance, my two year old kids started developing the habit of getting too close to the TV. I didn’t want to use force or impose restrictions on them, so I thought I could use technology to discourage them from getting too close to TV.

In 2nd Century AD, Hellenic Cartographer Ptolemy was beset with an arbitrary choice of whether his maps should have north on the top or any other direction. Based in Alexandria, he reasoned that all population centers and places of importance lie to the north and would be convenient for study if they were in the upper right corner of the map. This arbitrary choice had long, unintended repercussions for mankind such as Australia being considered “Down under” or even our solar system to be perceived as rotating in counter-clockwise direction. Who would have thought that the stroke of a cartographer carried celestial importance!

Security is a huge, complex, rapidly changing field. Advancements in infrastructue hosting, development methodologies has had the most impact on this domain. Thanks to automation, instances are spawned and deleted in a matter of second. Continuous development/Continuous Integration means that an average lifetime of a block of code is ever decreasing. Code review and vulnerability assessments based on static code and IP are hardly affordable at current rate of change. At the same time, the rate of proliferation of technology has seen comparable increase in risk vectors, vulnerabilities and attack methodologies. To keep up with this pace, automation in security operations has become more important than ever.

In the previous post we saw how to scrape raw data from a content rich webpage. In this post, we will explore how to process that raw data and use Machine Learning tools to predict the playing role of a cricket player just based on his career statistics.

In this project, we will apply Machine Learning techniques to predict whether a particular cricket player is a batsman or bowler based on his career stats. First we will use Deep Neural Networks (DNN) model and later compare the results with a simpler classifier algorithm such as Random Forest Classifier.