- Lyric Analysis: Predictive Analytics using Machine Learning with R
Use a variety of machine learning (ML) classification algorithms to build models step-by-step that predict the genre of a song and whether it will be successful on the Billboard charts - based entirely on lyrics
This tutorial explains and provides a musical use case for a form of supervised learning, specifically classification, that
is based on the lyrics of a variety of artists (and
a couple of book authors).
In this tutorial, you have built a model to predict the genre of a song based entirely on lyrics. You used supervised machine learning classification algorithms and trained models on a set of five different artists and five different genres. By using the
mlr framework, you created tasks, learners and
resampling strategies to train and then tune a model
(s). Then you ran your model against an unseen test dataset of different artists. You
were able to identify which algorithms work better with the default settings, and eventually, predict the genre of new songs that your model has never seen
https://www.datacamp.com/community/tutorials/predictive-analytics-machine-learning
- Machine Learning vs Predictive Analytics – 7 Useful Differences
Predictive analytics is also a part of machine learning domain
which is limited to predict future outcome from data based on previous patterns.
While predictive analytics has been in use since
more than two decades mainly in banking and finance sector, application of machine learning has taken prominence in recent time with algorithms like object detection from images, text classification, and recommendation systems.
Machine learning internally uses statistics, mathematics, and computer science fundamentals to build logic for algorithms that can do classification, prediction, and optimization in both real times
as well as batch mode.
Classification
we
tend to classify an object based on its various properties into one or more classes.
There are many standard machine learning algorithms
which are used to solve
classification problem. Logistic regression is one such
method, probably most widely used and most well know, also the oldest. Apart from that we also have
some of the most advanced and complicated models ranging from
decision tree to random forest,
AdaBoost, XP
boost, support vector machines, naïve baize and neural network. Since the last couple of years, deep learning is running at the forefront.
Typically neural network and deep learning are used to classify images. If there are hundred thousand images of cats and dog and you want to write a code that can automatically separate images of cats and dog,
you may want to go for deep learning methods like a convolutional neural network.
Regression
Regression is another class of
problem in machine learning where we try to predict a continuous value of a variable instead of a class unlike in
classification problem.
Regression techniques are generally used to predict the share price of a
stock, sale price of a house or car, a demand for a certain item
etc.
Predictive Analytics
There are some areas of overlap between machine learning and predictive analytics. While common techniques like logistic and linear regression come under both machine learning and predictive analytics, advanced algorithms like a decision tree, random forest etc. are
essentially machine learning. Under predictive analytics, the goal of the problems remains
very narrow where the intent is to compute a value of a particular variable at a future point of time. Predictive analytics is heavily statistics loaded while machine learning is more of a blend of statistics, programming, and mathematics.
https://www.educba.com/machine-learning-vs-predictive-analytics/
Big Data has emerged over the last years as a concept to handle data that requires new data modeling concepts, data structures, algorithms and/or large-scale distributed clusters.
OLTP has been around for a long time and focuses on transaction processing. When the concept of OLTP
emerged it has been usually a synonym for
simply using relational databases to store various information related to an application
– most people forgot that
it was related to processing of transactions.
Additionally, it was not about technical database transactions, but business transactions, such as ordering products or receiving money.
Nevertheless, most relational databases secure business transactions via technical transactions by adhering to the ACID criteria.
Today OLTP is relevant given its
numerous implementations in enterprise systems, such as Enterprise Resource Management systems, Customer Relationship Management systems or Supply Chain Management systems
OLAP has been around nearly as long as OLTP, because
most analysis have been done on
historized transactional data.
Due to the
historization and different analysis needs the amount of data is
significant higher than in OLTP systems. However, OLAP has a different access pattern: Less concurrent users, but they are interested in the whole set of data, because they want to generate aggregated statistics for them. Hence, a lot of data
is usually transferred into an OLAP system from different source systems and afterwards it is only read
very often
Going beyond OLTP and OLAP
Aspect 1: Predictive Analytics
Data scientists
employing predictive analytics are using statistic and machine learning techniques to predict how a situation may evolve
in the future.
Some of these techniques exist already since decades, but only since recently they make more sense, because
more data can be processed with Big Data technologies.
However, current Big Data technologies, such as Hadoop, are not transparent to the end user. This is not really an issue with the Big Data technologies themselves, but with the tools used for accessing and processing the data, such as R, Matlab or SAS.
https://jornfranke.
wordpress.com/2015/06/28/big-data-what-is-next-oltp-olap-predictive-analytics-sampling-and-probabilistic-databases/
Advanced Analytics is the autonomous or semi-autonomous examination of data or content using sophisticated techniques and tools, typically beyond those of traditional business intelligence (BI), to discover deeper insights, make predictions, or generate recommendations. Advanced analytic techniques include those such as data/text mining, machine learning, pattern matching, forecasting, visualization, semantic analysis, sentiment analysis, network and cluster analysis, multivariate statistics, graph analysis, simulation, complex event processing, neural networks.
https://www.gartner.com/it-glossary/advanced-analytics/
- What is advanced analytics compared to business intelligence?
Business Intelligence
– traditionally focuses on using a consistent set of metrics to measure past performance and guide business planning. Business Intelligence
consists of querying, reporting, OLAP (online analytical processing), and can answer questions including “what happened,” “how many,” and “how often.”
Advanced Analytics
– goes beyond Business Intelligence by using sophisticated modeling techniques to predict future events or discover patterns which cannot
be detected otherwise. Advanced Analytics can answer questions including “why is this happening,” “what if these trends continue,” “what will happen next” (prediction), “what is the best that can happen” (optimization).
Advanced Analytics vs BI
Where Business Intelligence
is focused on reporting and querying, Advanced Analytics is about optimizing, correlating, and predicting the next best action or the next most likely action
https://rapidminer.com/glossary/advanced-analytics-vs-bi/
- What is Advanced and Predictive Analytics?
Advanced analytics describes data analysis that goes beyond simple mathematical calculations such as sums and averages, or filtering and sorting. Advanced analyses use mathematical and statistical formulas and algorithms to generate new information, to recognize patterns, and also to predict outcomes and their respective probabilities.
Predictive analytics is a sub-division of advanced analytics and focuses on the identification of future events and values with their respective probabilities.
https://bi-survey.com/predictive-analytics
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