While exploring the area of human activity recognition out of research interest, I came across several publications, research-articles and blogs. The researchers have done phenomenal work in this area and achieved state-of-the-art (SOTA) results by using some sophisticated machine learning algorithms. But most of these papers/blogs that I’ve read are either using already-engineered features or fail to provide detailed explanation on how to extract features from raw time-series data.
In this article, we will be exploring different techniques to transform the raw time-series data and extract new features from it.
The techniques that we are going to see in this…
Bollinger Bands® are a technical analysis tool created by John Bollinger in the early 1980s for generating oversold or overbought signals. They arose from the need for adaptive trading bands and the observation that volatility was dynamic, not static as was widely believed at the time.
Bollinger Bands® can be applied in all the financial markets including equities, commodities, forex, and futures. Bollinger Bands® can be used in most time frames, from very short-term periods, to hourly, daily, weekly or monthly. Since their introduction 30 years ago they have become one of the most widely used technical indicators worldwide.
Last weekend, I participated in an NLP hackathon titled — “Topic Modeling for Research Articles 2.0”. This hackathon was hosted by Analytics Vidhya Platform as a part of their HackLive initiative. The participants were guided by experts in a 2 hour live-session and later on were given a week’s time to compete and climb the leaderboard.
Given the abstracts for a set of research articles, the task is to predict the tags for each article included in the test set.
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Disclaimer — The trading strategies and related information in this article is for the educational purpose only. All investments and trading in the stock market involve risk. Any decisions related to buying/selling of stocks or other financial instruments should only be made after a thorough research and seeking a professional assistance if required.
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Indicators such as Moving averages(MAs), Bollinger bands, Relative Strength Index(RSI) are mathematical technical analysis tools…
Coronavirus or COVID-19 needs no introduction. It has already been declared as a pandemic by WHO and in past couple of weeks it’s impact has been deleterious from both health perspective and an economic one. Plenty has been written about it, especially statistical reports on its exponential growth and the importance of “flattening the curve”.
As of now, most of us are staying and working from home to avoid the spread of corona virus. I decided to utilize the surplus time to write a Python Script that pulls the latest Statewise data of COVID-19 cases from the official website of…
With an increase in the penetration of analytics into numerous facets of our lives, finance is definitely one of the earliest to catch onto this trend. In this article, I have attempted to showcase how data analytics and visualization techniques can be incorporated in the world of finance.
For this analysis, I have used 2 years of historical data from around mid-Feb 2018 to Feb 2020 of the below stocks listed on National Stock Exchange(NSE)—
The reason why I selected these is…
In my previous article, we saw how to derive insights from the data by performing univariate data analysis. In this article, we’ll perform full-fledged exploratory data analysis and visualization on Haberman’s Survival Dataset using Python.
Exploratory Data Analysis (EDA) is the practice of describing the data by means of statistical and visualization techniques to bring important aspects of that data into focus for further analysis. This involves looking at the dataset from many angles, describing and summarizing it without making any assumptions about its contents.
Exploratory data analysis is an attitude, a state of flexibility, a willingness to look for…
A public company is a corporation whose ownership is distributed amongst general public shareholders via the free trade of shares on the stock exchanges like the New York Stock Exchange(NYSE), Bombay Stock Exchange(BSE), etc.
A private company is different from a publicly-traded company in that its stock is not traded on stock exchanges. Instead, all the shares of privately-owned companies rest only in the hands of a few people and are traded privately among the willing investors. Most of the shareholders in a private limited company will consist of very close groups of relatives, friends or big investors.
Exploratory data analysis (EDA) is a process of analyzing data by using simple concepts from statistics & probability and presenting the results in easy-to-understand pictorial format.
In one sentence — Being Sherlock Holmes of data!
The Iris flower data set consists of 50 samples from each of three species of Iris Flowers — Iris Setosa, Iris Virginica and Iris Versicolor . The Iris flower data set was introduced by the British statistician and biologist Ronald Fisher in his 1936 paper “The use of multiple measurements in taxonomic problems”.
Iris data is a multivariate data set. Four features measured from each…