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Machine Learning for Business Analytics
Real-Time Data Analysis for Decision-Making
Hemachandran K, Sayantan Khanra, Raul V. Rodriguez, Juan Jaramillo, Hemachandran K, Sayantan Khanra, Raul V. Rodriguez, Juan Jaramillo
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eBook - ePub
Machine Learning for Business Analytics
Real-Time Data Analysis for Decision-Making
Hemachandran K, Sayantan Khanra, Raul V. Rodriguez, Juan Jaramillo, Hemachandran K, Sayantan Khanra, Raul V. Rodriguez, Juan Jaramillo
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About This Book
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.
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.
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Chapter 1 Introduction to Machine Learning for Data Analytics
DOI: 10.4324/9781003206316-1
Contents
1.1 Introduction
1.2 Basics of Data
1.2.1 What Is Data?
1.2.2 What Is Data Analysis?
1.2.3 Why Is Data Analysis Required?
1.2.3.1 Types of Data Analysis
1.2.3.1.1 Descriptive Analysis
1.2.3.1.2 Diagnostic Analysis
1.2.3.1.3 Predictive Analysis
1.2.3.1.4 Prescriptive Analysis
1.2.4 Base of Data Mining
1.2.4.1 Data Processing
1.2.4.1.1 Types of Data Processing
1.2.4.1.2 Data Pre-processing
1.2.4.2 Data Cleaning
1.2.4.2.1 What Is Data Cleaning?
1.2.4.2.2 Comparison of Data Transformation and Data Cleaning
1.2.4.2.3 Method to Clean Data
1.2.4.2.4 Essential Elements for Quality Data
1.2.4.2.5 Uses of Data Cleaning
1.2.4.3 Data Exploratory
1.2.4.3.1 Requirement of Exploratory Data Analysis
1.2.4.4 Data Visualization
1.2.4.4.1 Essential of Data Visualization
1.2.5 Introduction to Machine Learning
1.2.5.1 Necessity of Machine Learning
1.2.5.2 Applications of Machine Learning
1.2.5.2.1 Machine Learning Usage in Industries
1.2.5.2 Relationship between Machine Learning and Data Analysis
1.2.5.3 Necessity of Probability for Machine Learning
1.2.5.4 Three Types of Machine Learning Algorithm
1.2.5.4.1 Supervised Learning
1.2.5.4.2 Unsupervised Learning
1.2.5.4.3 Reinforcement Learning
1.3 Conclusion
1.1 Introduction
In the 1960s, computer science became an academic discipline. Many basic computer science subjects like computer architecture, operating systems, and computer networks, underpins them and were all highlighted. The study of algorithms was added as an important component of theory in the 1970s. The goal was to make computers more useful. Today, a fundamental shift is taking place, with the emphasis shifting to a wide range of applications. There are a variety of explanations behind this shift. Computing and communications have become increasingly intertwined. In the natural sciences, commerce, and other sectors, the increased ability to monitor, acquire, and store data necessitates a shift in our understanding of data and how to handle it in the modern era. The rise of the internet and social media as important components of daily life brings theoretical opportunities as well as challenges.
Whereas traditional areas of computer science will continue to be important, future researchers will be more concerned with using computers to understand and extract usable data from multiple data generated by applications, rather than just how to make computers helpful for specific well-defined problems.
Digital data is frequently presented with a large number of components in domains as varied as cognition, retrieval, and machine learning (ML). The topic model is more than just a way to keep track of multiple fields in a record.
One of the most surprising developments in computer science in the last two decades is that some domain-independent methods have proven to be quite effective in solving problems from a variety of fields. A good example is ML.
Analysts can derive insights from data through statistical analysis. Data is analyzed using both statistics and ML approaches. Big Data is employed in the development of statistical models that show data trends. These models can then be used to create predictions and inform decision-making using new data. This procedure requires statistical programming languages like R or Python (with pandas). Advanced analysis is also possible because to open source libraries and packages like Tensor Flow.
The process of analyzing, cleaning, manipulating, and modeling data with the objective of identifying usable information, informing conclusions, and assisting decision-making is known as data analysis. Data analysis has several dimensions and approaches, including a wide range of techniques under various titles and being applied in a variety of business, science, and social science sectors. Data analysis is important in todayâs business environment since it helps businesses make more scientific decisions and run more efficiently.
Chapter 1 gives an overall view of data analysis, data processing, data cleaning, data visualizing, requirement of ML, probability of ML, and basic algorithm of ML.
1.2 Basics of Data
1.2.1 What Is Data?
Data in computing knowledge has been converted into a format that is easy to transfer or process. Data is information translated into binary digital form, as it relates to todayâs computers and transmission media. It is allowed to use data as either a solitary or plural subject. The term âraw dataâ refers to data in its most basic digital version.
The terms âdata processingâ and âelectronic data processing,â although for a time were used interchangeably to refer to the entire range of what was then recognized as digital technologies, indicated that data analysis is important in computer-supported collaborative. In the history of corporate computing, specialization has occurred, and a distinct data profession has emerged in line only with the development of enterprise data handling.
Computers represent data like video, images, audio, and text as binary values, which are made up of simply two numbers: 1 and 0. A bit is the simplest data unit, with a single value. Eight binary digits make up a byte. Megabytes and gigabytes are capacity and storage units.
As the amount of data collected and stored expands, so do the units of data measurement. For example, the phrase âbrontobyteâ refers to data storage equal to 10 to the 27th power of bytes.
Information will be stored in file types, identical to just how mainframe systems employ ISAM and VSAM. Some other data format for data storage, transmission, and analysis is comma-separated values. Further specialization occurred as a database, a database management system, and then relational database technologies appeared to organize information.
Over the last decade, the rise of the internet and smartphones has resulted in a boom in digital data production. Text, audio, and video data, as well as register and online activity records, are now included in the data. Unstructured data makes up a large portion of this.
Data of the petabyte or larger range has been referred to as âBig Data.â The 3Vsâvolume, variety, and velocityâdescribe large data in a simplified way. Big Dataâdriven business models have evolved as web-based e-commerce has increased in popularity, recognizing data as an important commodity for its own sake.
Outside of its use in data-processing computing applications, data has a value. In electrical component connectivity and network communication, the term âdataâ is often distinguished from âcontrol information,â âcontrol bits,â and related expressions to describe the core substance of a transmission unit. Furthermore, in science, the term âdataâ refers to a collection of facts. This can be seen in finance, marketing, demographics, and healthcare, to name a few.
1.2.2 What Is Data Analysis?
Working with data to extract relevant information that can subsequently be utilized to make informed decisions is known as data analysis.
This concept is the foundation of data analysis. We are better able to make decisions when we can extract meaning from facts. And we live in an era where we have access to more data than ever before.
1.2.3 Why Is Data Analysis Required?
In the business world, data analysis is critical for understanding challenges and exploring data in meaningful ways. Data is nothing more than numbers and facts. Data analysis is the process of organizing, interpreting, structuring, and presenting data into valuable information.
Everyone realizes that the goal of data analysis is to help you make data-driven business choices, otherwise why would you let it take so long that the results are obsolete by the time you get them? Web data integration automates all processes of web data analysis, allowing you to gain insights from data as soon as it is collected. You can use real-time data insights instead of obsolete insights as a foundation for your company decisions.
1.2.3.1 Types of Data Analysis
Data can be utilized in a variety of ways to answer questions and assist choices. These types of analyses can be categorized into four groups that are regularly employed in the field. Weâll go through each of these data analysis techniques, as well as an example of how they could be used in the real world.
1.2.3.1.1 Descriptive Analysis
Big Data and data science have become popular terms in recent years. They tend to be well-researched, which necessitates careful processing and analysis of the data. Descriptive analysis is one of the approaches used to analyze this data. What transpired is revealed through descriptive analysis. This sort of analysis uses statistics to describe or summarize quantitative data. Statistical analysis, for example, might reveal the distribution of sales among a group of students as well as the average marks per student. Explanation of âWhat Happenedâ is referred as descriptive analysis.
1.2.3.1.2 Diagnostic Analysis
The âwhatâ is determined by descriptive analysis, whereas the âwhyâ is determined by diagnostic analysis. Now, let us imagine a descriptive analysis reveals that a hospital is experiencing an extraordinary influx of patients. If you go deeper into the data, you might find that many of these individuals have the same virus symptoms. This diagnostic study can help you figure out if the inflow of patients was caused by an infectious pathogenâthe âwhy.â Explanation of âWhy It Happenedâ is referred as diagnostic analysis.
1.2.3.1.3 Predictive Analysis
As of now, weâve examined the methods of analysis that look at the past and draw conclusions. Predictive analytics makes predictions about the future based on data. You might notice that a particular product has had its strongest sales during the months of September and October each year, leading you to predict a similar high point during the future year using predictive analysis. Explanation of âFuture Status (What May Happen?)â is referred as predictive analysis.
1.2.3.1.4 Prescriptive Analysis
Prescriptive analysis combines the findings of the preceding three forms of analysis to make ideas for how a corporation should proceed. Applying our analogy, this form of analysis might recommend a business strategy to capitalize on the accomplishment of the high-sale months while also identifying fresh growth prospects during the weaker months. Explanation of âWhat Is the Reactionâ is referred as diagnostic analysis. It will support th...