Название: Machine Learning for Business Analytics: Real-Time Data Analysis for Decision-Making
Автор: Hemachandran K., Sayantan Khanra, Raul V. Rodriguez, Juan R. Jaramillo
Издательство: Routledge
Год: 2023
Страниц: 191
Язык: английский
Формат: pdf (true), epub (true)
Размер: 10.2 MB

Machine Learning is an integral tool in a business analyst’s arsenal because the rate at which data is being generated from different sources is increasing and working on complex unstructured data is becoming inevitable. Data collection, data cleaning, and data mining are rapidly becoming more difficult to analyze than just importing information from a primary or secondary source. The Machine Learning model plays a crucial role in predicting the future performance and results of a company. In real-time, data collection and data wrangling are the important steps in deploying the models. Analytics is a tool for visualizing and steering data and statistics. Business analysts can work with different datasets -- choosing an appropriate machine learning model results in accurate analyzing, forecasting the future, and making informed decisions.

The global Machine Learning market was valued at $1.58 billion in 2017 and is expected to reach $20.83 billion in 2024 -- growing at a CAGR of 44.06% between 2017 and 2024. The authors have compiled important knowledge on machine learning real-time applications in business analytics. This book enables readers to get broad knowledge in the field of machine learning models and to carry out their future research work. The future trends of machine learning for business analytics are explained with real case studies.

Machine Learning is increasingly recognized for playing a crucial role in predicting business performances for firms across the world. On the one hand, data collection and data wrangling are the important steps in training Machine Learning models. On the other hand, business analytics includes various tools for visualizing and steering data to deliver insights in management decisions. An expert business analyst may work with different data sets, choose appropriate Machine Learning models, and deliver valuable insights to improve business performances.

Commonly used ML algorithms are given specific attention. The algorithms studied include linear regression, logistic regression, Naive Bayes, kNN, random forest, and others. The basic three types of ML algorithms are as follows.

1) Supervised Learning. This algorithm is made up of a target/result variable (or dependent variable) that must be estimated from a set of predictor variables (independent variables). We create a function that maps inputs to desired outputs using this set of variables. The model is trained until it accomplishes the appropriate level of accuracy on the data set. Regression, decision tree, random forest, KNN, logistic regression, and others are examples of supervised learning.

2) Unsupervised Learning. This algorithm is a type of algorithm that learns patterns from untagged data. The goal is that through mimicry, which is an important mode of learning in people, the machine is forced to build a concise representation of its world and then generate imaginative content from it.

3) Reinforcement Learning. The machine is taught to make certain decisions using this algorithm. It works like this: the machine is deemed to occur where it must constantly train itself through trial and error. This computer adapts from its previous experiences and attempts to gather the most relevant information in order to make appropriate business decisions.

Essentially, this book acts as a guide to all business analysts. The authors blend the basics of data analytics and Machine Learning and extend its application to business analytics. This book acts as a superb introduction and covers the applications and implications of Machine Learning. The authors provide first-hand experience of the applications of Machine Learning for business analytics in the section on real-time analysis. Case studies put the theory into practice so that you may receive hands-on experience with Machine Learning and data analytics. This book is a valuable source for practitioners, industrialists, technologists, and researchers.