SDK and API are the two terms that are often heard when integrating different systems. In general, developers tend to overthink and spend time on deciding whether to use an SDK or API directly. This often leads to comparing them, in reality they overlap and there is no clear solution/steps to make us understand situations where we can clearly decide on one.
API is the acronym for Application Programming Interface. An API is a set of functions and procedures that allows users to to interact with the data or the functionality of an existing application. …
‘Uni’ is ‘one’, when you perform analysis on one variable. The main purpose of this is to describe the data and find inherent patterns with in that. It doesn’t deal with relationships w.r.t other data.
Questions usually asked during analysis phase:
I will be using “tips” dataset (publicly) present in Seaborn library for the next steps. Complete code for this article is here.
df = sns.load_dataset('tips')
Missing values using pandas dataframe can be…
Logging is one of the most complicated parts of micro-services. Often, there will the millions of requests coming to services and they go through complex pipelines like having an threads. async executables etc.;. It is hard for someone to correlate what actions are performed to cater a single web request just by looking at logs. This makes it difficult to diagnose if there is an issue with a web request.
A good logging system should record entire system activity, along with users activity and how system responded to every user interaction. It should provide holistic view on end to end…
Sudoku is a logic based number placement puzzle. The term sudoku is originated from Japanese, where Su means ‘Number’ and Doku means ‘Single’. The objective of the problem is to fill 9x9 grid with the following rules
Wiki definition: Backtracking can be defined as a general algorithmic technique that considers searching every possible combination in order to solve a computational problem.
Spoiler alert: My answer to get 1 is stupid
In this post, I am going to explain my journey of improving the score from 0.72 to 0.83 (top-3 percent at the time of submission) and then from 0.83 to 1 the Titanic machine learning from disaster.
Good thing about Titanic problem it is an never ending contest. So, if you want to understand where you stand as Data scientist this problem will be of great use.
Always, keep in mind this quote
If you torture data long enough, it will confess the truth
-Ronald H. Coase