Tech Scouter

Introduction Python is a really powerful language and any piece of software like web application, windows application, ML model etc  can be built using Python. Prior to Pandas, Python was majorly used for data munging and preparation. It had a very little contribution towards data analysis. Pandas solved this problem. Using Pandas, we can accomplish five typical steps in the processing and analysis of data, regardless of the origin of data — load, prepare, manipulate, model, and analyze. Python with Pandas is used in a wide range of fields including academic and commercial domains including finance, economics, Statistics, analytics, etc. This blog provides code for basic usage of Series Data Type in Pandas. Data types in Pandas Series : 1D,labeled homogeneous array, size immutable. Data Frames : General 2D labeled, size-mutable tabular structure with potentially heterogeneously typed columns Panel : General 3D labeled, size-mutable array. Seri

This one took long due to the Analysis work I was doing for this post.There is a lot of work going on in the subject of Sentiment analysis so I decided to compare the accuracy of the products. Let's start with some basics... NLP: Natural Language Processing Natural Language Processing is a very interesting topic and a subject of debate when it comes to accuracy of the NLP. Natural Language is very ambiguous as same sentences can have different meanings like "I saw a man on a hill with a telescope. " It seems like a simple statement until you begin to unpack the many alternate meanings: There’s a man on a hill, and I’m watching him with my telescope. There’s a man on a hill, who I’m seeing, and he has a telescope. There’s a man, and he’s on a hill that also has a telescope on it. I’m on a hill, and I saw a man using a telescope. There’s a man on a hill, and I’m seeing him with a telescope. Sarcasm is that component of the language that is diffi