My stash

For almost three years I am curious about tracking errors in my daily work, there are lot of tools like Data dog, Splunk, Dynatrace etc... available as observability tools. it would have been easy to use those, rather I though why not build one. There is an advantage for projects that are small to rely on tools that are built around them. Standards set by industries are important, what if certain configuration don't align with general market standards. I think some developers agree with this approach. The idea of tracking error is not new, but the way we categorise them is unique to different environments. the nature of these categories depends on infrastructure used, network topology, development strategy. A tool designed to handle them all might be bit over engineered for the purpose, because I think the value it creates by reducing our efforts in identifying problems is not more valuable than the product itself. Again this is my opinion on projects that are small but needs quali...

K-means classification K-means, a simple Machine learning algorithm used for classification purposes. Machine learning is an awesome field, full of interesting topics and scenarios to study. While starting in this track one of the basics we study is classifiers, these things can be complex as hell and also easy as watching Tom chasing Jerry on television. I have implemented the K-means with a simple NumPy library of python 3. It’s a good start for someone who is started to learn or teaching this classifier. No Oop used, just some function call which intern makes some more function calls. View the full code in my GitHub repository follow this link K-means notebook