Python tutorials

December 12, 2020 Python

Calculating Pearson Correlation Coefficient in Python with Numpy

Introduction This article is an introduction to the Pearson Correlation Coefficient, its manual calculation and its computation via Python’s numpy module. The Pearson correlation coefficient measures the linear association between variables. Its value can be interpreted like so: +1 – Complete positive correlation +0.8 – Strong positive correlation +0.6 – Moderate positive correlation 0 – no correlation whatsoever -0.6 – Moderate negative correlation -0.8 – Strong negative correlation -1 – Complete negative correlation We’ll illustrate how the correlation coefficient varies […]

December 9, 2020 Python

How to Merge Two Dictionaries in Python

Introduction It’s not uncommon to have two dictionaries in Python which you’d like to combine. In this article, we will take a look at various ways on how to merge two dictionaries in Python. Some solutions are not available to all Python versions, so we will examine ways to merge for selected releases too. When merging dictionaries, we have to consider what will happen when the two dictionaries have the same keys. Let’s first define what should happen when we […]

December 6, 2020 Python

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 […]

December 3, 2020 Python

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 […]

December 3, 2020 Python

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 […]

December 3, 2020 Python

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 […]

December 2, 2020 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 […]

December 2, 2020 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 […]

December 2, 2020 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 […]

December 2, 2020 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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