Category: Python

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Creating Executable Files from Python Scripts with py2exe

Introduction Executing Python scripts requires a lot of prerequisites like having Python installed, having a plethora of modules installed, using the command line, etc. while executing an .exe file is very straightforward. If you want to create a simple application and distribute it to lots of users, writing it as a short Python script is not difficult, but assumes that the users know how to run the script and have Python already installed on their machine. Examples like this show […]

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Simple NLP in Python with TextBlob: N-Grams Detection

Introduction The constant growth of data on the Internet creates a demand for a tool that could process textual information in a faster way with no effort from the ordinary user. Moreover, it’s highly important that this instrument of text analysis could implement solutions for both low and high-level NLP tasks such as counting word frequencies, calculating sentiment analysis of the texts or detecting patterns in relationships between words. TextBlob is a great lightweight library for a wide variety of […]

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Seaborn Bar Plot – Tutorial and Examples

Introduction Seaborn is one of the most widely used data visualization libraries in Python, as an extension to Matplotlib. It offers a simple, intuitive, yet highly customizable API for data visualization. In this tutorial, we’ll take a look at how to plot a Bar Plot in Seaborn. Bar graphs display numerical quantities on one axis and categorical variables on the other, letting you see how many occurrences there are for the different categories. Bar charts can be used for visualizing […]

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Reading and Writing XML Files in Python with Pandas

Introduction XML (Extensible Markup Language) is a markup language used to store structured data. The Pandas data analysis library provides functions to read/write data for most of the file types. For example, it includes read_csv() and to_csv() for interacting with CSV files. However, Pandas does not include any methods to read and write XML files. In this article, we will take a look at how we can use other modules to read data from an XML file, and load it […]

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Analytics Vidhya, Classification, Data Science, Intermediate, Libraries, NLP, Programming, Python, Supervised, Text, Unstructured Data Leave a comment

Step by step guide to building sentiment analysis model using graphlab

I have been using graph lab for quite some time now. The first Kaggle competition I used it for was Click Trough Rate (CTR) and I was amazed to see the speed at which it can crunch such big data. Over last few months, I have realised much broader applications of GraphLab. In this article I will take up the text mining capability of GraphLab and solve one of the Kaggle problems. I will be referring to this problem with […]

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Intermediate, Libraries, Machine Learning, NLP, Programming, Python, Text, Unstructured Data Leave a comment

Natural Language Processing Made Easy – using SpaCy (​in Python)

Introduction Natural Language Processing is one of the principal areas of Artificial Intelligence. NLP plays a critical role in many intelligent applications such as automated chat bots, article summarizers, multi-lingual translation and opinion identification from data. Every industry which exploits NLP to make sense of unstructured text data, not just demands accuracy, but also swiftness in obtaining results. Natural Language Processing is a capacious field, some of the tasks in nlp are – text classification, entity detection, machine translation, question […]

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Intermediate, NLP, Project, Python, Sequence Modeling, Supervised, Text, Unstructured Data Leave a comment

Building a FAQ Chatbot in Python – The Future of Information Searching

Introduction What do we do when we need any information? Simple: “We Ask, and Google Tells”. But if the answer depends on multiple variables, then the existing Ask-Tell model tends to sputter. State of the art search engines usually cannot handle such requests. We would have to search for information available in bits and pieces and then try to filter and assemble relevant parts together. Sounds time consuming, doesn’t it? Source: Inbenta This Ask-Tell model is evolving rapidly with the […]

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Classification, Data Science, Intermediate, Machine Learning, NLP, Project, Python, Supervised, Text, Unstructured Data Leave a comment

A Comprehensive Guide to Understand and Implement Text Classification in Python

Improving Text Classification Models While the above framework can be applied to a number of text classification problems, but to achieve a good accuracy some improvements can be done in the overall framework. For example, following are some tips to improve the performance of text classification models and this framework. 1. Text Cleaning : text cleaning can help to reducue the noise present in text data in the form of stopwords, punctuations marks, suffix variations etc. This article can help to understand how […]

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Advanced, Computer Vision, Data Science, Deep Learning, Github, Listicle, Machine Learning, NLP, Python, Reddit, Reinforcement Learning Leave a comment

Top 5 Machine Learning GitHub Repositories & Reddit Discussions (October 2018)

Introduction “Should I use GitHub for my projects?” – I’m often asked this question by aspiring data scientists. There’s only one answer to this – “Absolutely!”. GitHub is an invaluable platform for data scientists looking to stand out from the crowd. It’s an online resume for displaying your code to recruiters and other fellow professionals. The fact that GitHub hosts open-source projects from the top tech behemoths like Google, Facebook, IBM, NVIDIA, etc. is what adds to the gloss of […]

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Classification, Intermediate, Libraries, NLP, Programming, Python, PyTorch, Supervised, Text, Unstructured Data Leave a comment

Introduction to Flair for NLP: A Simple yet Powerful State-of-the-Art NLP Library

Introduction Last couple of years have been incredible for Natural Language Processing (NLP) as a domain! We have seen multiple breakthroughs – ULMFiT, ELMo, Facebook’s PyText, Google’s BERT, among many others. These have rapidly accelerated the state-of-the-art research in NLP (and language modeling, in particular). We can now predict the next sentence, given a sequence of preceding words. What’s even more important is that machines are now beginning to understand the key element that had eluded them for long. Context! Understanding context […]

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