This post will go through the process of gathering and cleaning this data followed by an exploratory analysis examining price trends and the impact of events on prices using data from the IEX API and scraped events from financial news sites.
The Pattern library is a multipurpose library capable of handling the following tasks:
- Natural Language Processing: Performing tasks such as tokenization, stemming, POS tagging, sentiment analysis, etc.
- Data Mining: It contains APIs to mine data from sites like Twitter, Facebook, Wikipedia, etc.
- Machine Learning: Contains machine learning models such as SVM, KNN, and perceptron, which can be used for classification, regression, and clustering tasks.
In this article, we will see the first two applications of the Pattern library from the above list. We will explore the use of the Pattern Library for NLP by performing tasks such as tokenization, stemming and sentiment analysis. We will also see how the Pattern library can be used for web mining.
As many of us prepare to go to PyCon, we wanted to share a sampling of how Python is used at Netflix. We use Python through the full content lifecycle, from deciding which content to fund all the way to operating the CDN that serves the final video to 148 million members. We use and contribute to many open-source Python packages, some of which are mentioned below. If any of this interests you, check out the jobs site or find us at PyCon. We have donated a few Netflix Originals posters to the PyLadies Auction and look forward to seeing you all there.
Topic Model: In a nutshell, it is a type of statistical model used for tagging abstract “topics” that occur in a collection of documents that best represents the information in them.
Many techniques are used to obtain topic models. This post aims to demonstrate the implementation of LDA: a widely used topic modeling technique.
“Buy the dip” — it’s a frustratingly simple piece of advice. Like most pieces of advice, it’s easier said than done and the giver of such advice has probably not attempted to practice what they preach. It induces FOMO, which leads to the “hope trade”, when the “hope trade” goes awry you’re stuck as the “long term investor” who “really believes in the company’s mission”.
The bot is written in Python and relies on two core libraries for the majority of its functionality: robin-stocks and ta. robin-stocks is a library that interacts with the Robinhood API and allows one to execute buy and sell orders, get real time ticker information, and more. ta is a technical analysis library that also incorporates the Python Pandas library to generate indicators from stock data.
Cropping, Resizing, Rotating, Thresholding, Blurring, Drawing & Writing on an image, Face Detection & Contouring to detect objects. All Explained.
A tutorial on how to build a GitHub App that predicts and applies issue labels using Tensorflow and public datasets.
A curated list of awesome resources for practicing data science using Python, including not only libraries, but also links to tutorials, code snippets, blog posts and talks. So. Much. Python.
The first 2 tutorials will cover getting started with the de facto approach to sentiment analysis: recurrent neural networks (RNNs). The third notebook covers the FastText model and the final covers a convolutional neural network (CNN) model.
String manipulations are an essential part of Data Science. The latest release of Vaex adds incredibly fast and memory efficient support for all common string manipulations. Compared to Pandas, the most popular DataFrame library in the Python ecosystem, string operations are up to ~30–100x faster on your quadcore laptop, and up to a 1000 times faster on a 32 core machine.
The very basic idea of anomalies is really centered around two values – extremely high values and extremely low values. Then why are they given importance? In this article, we will try to investigate questions like this. We will see how they are created/generated, why they are important to consider while developing machine learning models, how they can be detected.
In this article, we will explore TextBlob, which is another extremely powerful NLP library for Python. TextBlob is built upon NLTK and provides an easy to use interface to the NLTK library. We will see how TextBlob can be used to perform a variety of NLP tasks ranging from parts-of-speech tagging to sentiment analysis, and language translation to text classification.
Pyodide is an experimental project from Mozilla to create a full Python data science stack that runs entirely in the browser.