Customer Personality Exploratory Data Analysis
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Customer Personality Exploratory Data Analysis
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Author : Snehi Nainesh Pachchigar
language : en
Publisher:
Release Date : 2022
Customer Personality Exploratory Data Analysis written by Snehi Nainesh Pachchigar and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Businesses these days rely largely on targeted marketing to have a better possibility of expanding as well as retaining their customer base. Tech giants like Google, Facebook have grown their business model surrounding targeted ads which help businesses to grow. Customer Personality Analysis is a detailed analysis of a company's ideal set of customers. Here, a dataset of customer characteristics is used to understand and analyze the customer habits and preferences. The dataset includes customer attributes such as birth year, education, marital status, having children or not, income, the amount spent on various products. The thesis represents how the data is cleaned and preprocessed along with reducing dimensionality. The meat of the thesis lies in clustering of data and discovering and predicting their choices. To enable the same the data went through iteration of data preprocessing to enable getting the best results. For clustering, the agglomerative algorithm and k-means algorithm are being used which will help bucket customers into segments per their choices. The analysis aided with visualization will help to target customers based on their interests. The model and techniques used here can further be replicated to adjust to the different datasets and hence gain valuable insights.
Customer Personality Analysis And Prediction Using Machine Learning With Python
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-07-01
Customer Personality Analysis And Prediction Using Machine Learning With Python written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-01 with Computers categories.
In this book, we embark on an exciting journey through the world of machine learning, where we explore the intricacies of working with datasets, visualizing their distributions, performing regression analysis, and predicting clusters. This book serves as a comprehensive guide for both beginners and experienced practitioners who are eager to delve into the realm of machine learning and discover the power of predictive analytics. Chapter 1 and Chapter 2 sets the stage by introducing the importance of data exploration. We learn how to understand the structure of a dataset, identify its features, and gain insights into the underlying patterns. Through various visualization techniques, we uncover the distribution of variables, detect outliers, and discover the relationships between different attributes. These exploratory analyses lay the foundation for the subsequent chapters, where we dive deeper into the realms of regression and cluster prediction. Chapter 3 focuses on regression analysis on number of total purchases, where we aim to predict continuous numerical values. By applying popular regression algorithms such as linear regression, random forest, naïve bayes, KNN, decision trees, support vector, Ada boost, gradient boosting, extreme gradient boosting, and light gradient boosting, we unlock the potential to forecast future trends and make data-driven decisions. Through real-world examples and practical exercises, we demonstrate the step-by-step process of model training, evaluation, and interpretation. We also discuss techniques to handle missing data, feature selection, and model optimization to ensure robust and accurate predictions. Chapter 4 sets our exploration of clustering customers, we embarked on a captivating journey that allowed us to uncover hidden patterns and gain valuable insights from our datasets. We began by understanding the importance of data exploration and visualization, which provided us with a comprehensive understanding of the distribution and relationships within the data. Moving forward, we delved into the realm of clustering, where our objective was to group similar data points together and identify underlying structures. By applying K-means clustering algorithm, we were able to unveil intricate patterns and extract meaningful insights. These techniques enabled us to tackle various real-world challenges, including customer personality analysis. Building upon the foundation of regression and cluster prediction, Chapter 5 delves into advanced topics, using machine learning models to predict cluster. We explore the power of logistic regression, random forest, naïve bayes, KNN, decision trees, support vector, Ada boost, gradient boosting, extreme gradient boosting, and light gradient boosting models to predict the clusters. Throughout the book, we emphasize a hands-on approach, providing practical code examples and interactive exercises to reinforce the concepts covered. By utilizing popular programming languages and libraries such as Python and scikit-learn, we ensure that readers gain valuable coding skills while exploring the diverse landscape of machine learning. Whether you are a data enthusiast, a business professional seeking insights from your data, or a student eager to enter the world of machine learning, this book equips you with the necessary tools and knowledge to embark on your own data-driven adventures. By the end of this journey, you will possess the skills and confidence to tackle real-world challenges, make informed decisions, and unlock the hidden potential of your data. So, let us embark on this exhilarating voyage through the intricacies of machine learning. Together, we will unravel the mysteries of datasets, harness the power of predictive analytics, and unlock a world of endless possibilities. Get ready to dive deep into the realm of machine learning and unleash the potential of your data. Welcome to the exciting world of predictive analytics!
Exploratory Data Analysis With Python Cookbook
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Author : Ayodele Oluleye
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-06-30
Exploratory Data Analysis With Python Cookbook written by Ayodele Oluleye and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-30 with Computers categories.
Extract valuable insights from data by leveraging various analysis and visualization techniques with this comprehensive guide Purchase of the print or Kindle book includes a free PDF eBook Key Features Gain practical experience in conducting EDA on a single variable of interest in Python Learn the different techniques for analyzing and exploring tabular, time series, and textual data in Python Get well versed in data visualization using leading Python libraries like Matplotlib and seaborn Book DescriptionIn today's data-centric world, the ability to extract meaningful insights from vast amounts of data has become a valuable skill across industries. Exploratory Data Analysis (EDA) lies at the heart of this process, enabling us to comprehend, visualize, and derive valuable insights from various forms of data. This book is a comprehensive guide to Exploratory Data Analysis using the Python programming language. It provides practical steps needed to effectively explore, analyze, and visualize structured and unstructured data. It offers hands-on guidance and code for concepts such as generating summary statistics, analyzing single and multiple variables, visualizing data, analyzing text data, handling outliers, handling missing values and automating the EDA process. It is suited for data scientists, data analysts, researchers or curious learners looking to gain essential knowledge and practical steps for analyzing vast amounts of data to uncover insights. Python is an open-source general purpose programming language which is used widely for data science and data analysis given its simplicity and versatility. It offers several libraries which can be used to clean, analyze, and visualize data. In this book, we will explore popular Python libraries such as Pandas, Matplotlib, and Seaborn and provide workable code for analyzing data in Python using these libraries. By the end of this book, you will have gained comprehensive knowledge about EDA and mastered the powerful set of EDA techniques and tools required for analyzing both structured and unstructured data to derive valuable insights.What you will learn Perform EDA with leading python data visualization libraries Execute univariate, bivariate and multivariate analysis on tabular data Uncover patterns and relationships within time series data Identify hidden patterns within textual data Learn different techniques to prepare data for analysis Overcome challenge of outliers and missing values during data analysis Leverage automated EDA for fast and efficient analysis Who this book is forWhether you are a data analyst, data scientist, researcher or a curious learner looking to analyze structured and unstructured data, this book will appeal to you. It aims to empower you with essential knowledge and practical skills for analyzing and visualizing data to uncover insights. It covers several EDA concepts and provides hands-on instructions on how these can be applied using various Python libraries. Familiarity with basic statistical concepts and foundational knowledge of python programming will help you understand the content better and maximize your learning experience.
Applied Data Analysis
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Author : Seohee Park
language : en
Publisher: 서희아카데미
Release Date : 2020-09-01
Applied Data Analysis written by Seohee Park and has been published by 서희아카데미 this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-01 with Business & Economics categories.
This book will help you gain a master of business administration (MBA) degree. Think you’ve got what it takes to become a future leader? An MBA could help you achieve those goals. Intensive, competitive and highly respected, the Master of Business Administration (MBA) is an elite professional qualification. This book provides best reports with good grades. Reading the papers, you can get a sense of how to write a good paper to get good grades. This is a book that tells you how to get good grades on MBA courses in the U.S. For the MBA course, students have to take a total of 36 credits. Each class is worth 3 credits and the students should take 12 classes. It's a series of 12 books, one book for each subject. This book is a collection of best answers for the "Applied Data Analysis" subject.
Data Mining And Exploration
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Author : Chong Ho Alex Yu
language : en
Publisher: CRC Press
Release Date : 2022-10-27
Data Mining And Exploration written by Chong Ho Alex Yu and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-27 with Business & Economics categories.
This book introduces both conceptual and procedural aspects of cutting-edge data science methods, such as dynamic data visualization, artificial neural networks, ensemble methods, and text mining. There are at least two unique elements that can set the book apart from its rivals. First, most students in social sciences, engineering, and business took at least one class in introductory statistics before learning data science. However, usually these courses do not discuss the similarities and differences between traditional statistics and modern data science; as a result learners are disoriented by this seemingly drastic paradigm shift. In reaction, some traditionalists reject data science altogether while some beginning data analysts employ data mining tools as a “black box”, without a comprehensive view of the foundational differences between traditional and modern methods (e.g., dichotomous thinking vs. pattern recognition, confirmation vs. exploration, single method vs. triangulation, single sample vs. cross-validation etc.). This book delineates the transition between classical methods and data science (e.g. from p value to Log Worth, from resampling to ensemble methods, from content analysis to text mining etc.). Second, this book aims to widen the learner's horizon by covering a plethora of software tools. When a technician has a hammer, every problem seems to be a nail. By the same token, many textbooks focus on a single software package only, and consequently the learner tends to fit the problem with the tool, but not the other way around. To rectify the situation, a competent analyst should be equipped with a tool set, rather than a single tool. For example, when the analyst works with crucial data in a highly regulated industry, such as pharmaceutical and banking, commercial software modules (e.g., SAS) are indispensable. For a mid-size and small company, open-source packages such as Python would come in handy. If the research goal is to create an executive summary quickly, the logical choice is rapid model comparison. If the analyst would like to explore the data by asking what-if questions, then dynamic graphing in JMP Pro is a better option. This book uses concrete examples to explain the pros and cons of various software applications.
Business Analytics
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Author : Arul Mishra
language : en
Publisher: SAGE Publications
Release Date : 2024-01-25
Business Analytics written by Arul Mishra and has been published by SAGE Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-25 with Business & Economics categories.
Businesses typically encounter problems first and then seek out analytical methods to help in decision making. Business Analytics: Solving Business Problems with R by Arul Mishra and Himanshu Mishra offers practical, data-driven solutions for today′s dynamic business environment. This text helps students see the real-world potential of analytical methods to help meet their business challenges by demonstrating the application of crucial methods. These methods are cutting edge, including neural nets, natural language processing, and boosted decision trees. Applications throughout the book, including pricing models, social sentiment analysis, and branding show students how to use these analytical methods in real business settings, including Frito-Lay, Netflix, and Zappos. Step-by-step R code with commentary gives readers the tools to adapt each method to their business settings. The book offers comprehensive coverage across diverse business domains, including finance, marketing, human resources, operations, and accounting. Finally, an entire chapter explores equity and fairness in analytical methods, as well as the techniques that can be used to mitigate biases and enhance equity in the results. Included with this title: LMS Cartridge: Import this title’s instructor resources into your school’s learning management system (LMS) and save time. Don’t use an LMS? You can still access all of the same online resources for this title via the password-protected Instructor Resource Site.
Soft Computing And Signal Processing
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Author : Vustikayala Sivakumar Reddy
language : en
Publisher: Springer Nature
Release Date : 2024-04-15
Soft Computing And Signal Processing written by Vustikayala Sivakumar Reddy and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-15 with Computers categories.
This book presents selected research papers on current developments in the fields of soft computing and signal processing from the Sixth International Conference on Soft Computing and Signal Processing (ICSCSP 2023). The book covers topics such as soft sets, rough sets, fuzzy logic, neural networks, genetic algorithms and machine learning and discusses various aspects of these topics, e.g., technological considerations, product implementation and application issues.
Encyclopedia Of Measurement And Statistics
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Author : Neil J. Salkind
language : en
Publisher: SAGE
Release Date : 2007
Encyclopedia Of Measurement And Statistics written by Neil J. Salkind and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Psychology categories.
Publisher Description
Handbook Of Psychology Research Methods In Psychology
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Author : John A. Schinka
language : en
Publisher: John Wiley & Sons
Release Date : 2003-03-19
Handbook Of Psychology Research Methods In Psychology written by John A. Schinka and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-03-19 with Psychology categories.
Includes established theories and cutting-edge developments. Presents the work of an international group of experts. Presents the nature, origin, implications, an future course of major unresolved issues in the area.
Cognitive Science Computational Intelligence And Data Analytics
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Author : Vikas Khare
language : en
Publisher: Elsevier
Release Date : 2024-06-06
Cognitive Science Computational Intelligence And Data Analytics written by Vikas Khare and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-06 with Computers categories.
Cognitive Science, Computational Intelligence, and Data Analytics: Methods and Applications with Python introduces readers to the foundational concepts of data analysis, cognitive science, and computational intelligence, including AI and Machine Learning. The book's focus is on fundamental ideas, procedures, and computational intelligence tools that can be applied to a wide range of data analysis approaches, with applications that include mathematical programming, evolutionary simulation, machine learning, and logic-based models. It offers readers the fundamental and practical aspects of cognitive science and data analysis, exploring data analytics in terms of description, evolution, and applicability in real-life problems.The authors cover the history and evolution of cognitive analytics, methodological concerns in philosophy, syntax and semantics, understanding of generative linguistics, theory of memory and processing theory, structured and unstructured data, qualitative and quantitative data, measurement of variables, nominal, ordinals, intervals, and ratio scale data. The content in this book is tailored to the reader's needs in terms of both type and fundamentals, including coverage of multivariate analysis, CRISP methodology and SEMMA methodology. Each chapter provides practical, hands-on learning with real-world applications, including case studies and Python programs related to the key concepts being presented. - Demystifies the theory of data analytics using a step-by-step approach - Covers the intersection of cognitive science, computational intelligence, and data analytics by providing examples and case studies with applied algorithms, mathematics, and Python programming code - Introduces foundational data analytics techniques such as CRISP-DM, SEMMA, and Object Detection Models in the context of computational intelligence methods and tools - Covers key concepts of multivariate and cognitive data analytics such as factor analytics, principal component analytics, linear regression analysis, logistic regression analysis, and value chain applications