Good Features are the backbone of any machine learning model. And good feature creation often needs domain knowledge, creativity, and lots of time. In this post, I am going to talk about: Various methods of feature creation- Both Automated and manual Different Ways to handle categorical features Longitude and Latitude features Some kaggle tricks And some other ideas to think about feature creation.
It is election month in India and a quote by Dr. Rahat Indori sums it up pretty well. “सरहदों पर बहुत तनाव है क्या , पता तो करो चुनाव है क्या !” For English speakers, this means: Is there a lot of tension at the borders? just ask if the elections are on. This election India has talked about a lot of issues. News channels have talked about Patriotism, Socialism, Religion as well as terrorism.
Python has a lot of constructs that are reasonably easy to learn and use in our code. Then there are some constructs which always confuse us when we encounter them in our code. Then are some that even seasoned programmers are not able to understand. *args, **kwargs and decorators are some constructs that fall into this category. I guess a lot of my data science friends have faced them too.
I distinctly remember the time when Seaborn came. I was really so fed up with Matplotlib. To create even simple graphs I had to run through so many StackOverflow threads. The time I could have spent in thinking good ideas for presenting my data was being spent in handling Matplotlib. And it was frustrating. Seaborn is much better than Matplotlib, yet it also demands a lot of code for a simple “good looking” graph.
Parallelization is awesome. We data scientists have got laptops with quad-core, octa-core, turbo-boost. We work with servers with even more cores and computing power. But do we really utilize the raw power we have at hand? Instead, we wait for time taking processes to finish. Sometimes for hours, when urgent deliverables are at hand. Can we do better? Can we get better? In this series of posts named Python Shorts, I will explain some simple constructs provided by Python, some essential tips and some use cases I come up with regularly in my Data Science work.
Python provides us with many styles of coding. In a way, it is pretty inclusive. One can come from any language and start writing Python. However, learning to write a language and writing a language in an optimized way are two different things. In this series of posts named Python Shorts, I will explain some simple but very useful constructs provided by Python, some essential tips and some use cases I come up with regularly in my Data Science work.
Learning a language is easy. Whenever I start with a new language, I focus on a few things in below order, and it is a breeze to get started with writing code in any language. Operators and Data Types: +,-,int,float,str Conditional statements: if,else,case,switch Loops: For, while Data structures: List, Array, Dict, Hashmaps Define Function However, learning to write a language and writing a language in an optimized way are two different things.