Data Analysis with R

 Wordcloud2 - Top Three Budget Speech Analysis Wordcloud2 has been generated for top three budget speeches in the recent past three decades starting from 1991-92. It was a historical budget sppech of our recent political history by Dr.Manmohan Singh. Then, came another high profile budget speech in the year 2004-05 by Shri.P.Chidambaram. Then, now the running honourable finance minister Nirmala Seetharaman has made a budget speech 2020-21 for which there was high expectations from the people of India. Wordcloud is too basic a tool for analysing the budget speech. The intent of this blog is demonstration of wordcloud2 generation and budget analysis with wordcloud is only a byproduct.  #------------------------------------------------------------------------- #R Code: #step-by-step approach to wordcloud2 for budget speech in pdf #step1: save budget speech 1991-92, 2004-05, and 2020-21 as pdf in working directory #step2: load NLP, tm, RColorBrewer, SnowballC, wordcloud2, pdftools, stringr

Wordcloud with R Text Mining Made Easy Introduction: Word cloud is a visual representation of text data. Word cloud is also known as text cloud or tag cloud. Word cloud is used by researchers for reporting qualitative data. Marketing makes use of word cloud in order to understand customer feed back on product features and customer pain points. Journalists and social media resort to word cloud to understand the sentiments of the public towards political personalities or on some important social issues. Oxford English Dictionary defines word cloud as "an image composed of words used in a particular text or subject, in which size of each word indicates its frequency or importance". Word cloud is a simple and elegant way of text analysis. In simple terms, word clouds simple, easy to make and fun. It is very useful in understanding a particular subject on which textual data is available. Word cloud is text mining in the most simple form.  Many online word cloud generators exist an

Neural Network – IRIS data Classification Model by Dr. Arunachalam Rajagopal Artificial Neural Network (ANN): A typical artificial neural network is shown in Figure 1. ANN is a sub-domain of artificial intelligence, AI. ANNs attempt to imitate the behaviour of human brain for problem solving by creating an abstract model. ANN models which are basically non-linear in nature could abstract and represent any non-linear function. Activation function: introduces the non-linear characteristics into the neural network. The activation function is selected based on the type of data to be modeled.   The often used activation functions are: Rectified Linear Unit (ReLu) Logistic function Hyperbolic tangent (tanh) Figure 1 Artificial Neural Network Example Demo: Neural Network The iris data set has been made use of for demonstrating the development of neural network classification model. The Figure 2 shows the three flowers in the iris family which are Setosa, V

Technical Analysis using R Dr.Arunachalam Rajagopal Ph.D., Former Professor and HOD 1.1 Fundamental Analysis & Technical Analysis - Introduction Analysts and investors make use of stock analysis for decision making on whether to buy or sell a stock. The investors and traders analyse the past and current data on the price and other performance indices of a firm to take a decision on trading a particular company's stock. Investors and analysts make use of two types of analysis with respect to stock trading:             1. Fundamental analysis             2. Technical analysis Fundamental analysis attempts to evaluate the intrinsic value of a stock. While technical analysis focuses on price movements, trading signals, and other charts for taking decision to buy or sell a stock. 1.2 Technical analysis tools The often used technical tools for buy & sell decisions by investors are: 1. Simple Moving Average (SMA) 2. Exponential Moving Average (EMA) 3