Airline Passenger Satisfaction Analysis And Prediction Using Machine Learning And Deep Learning With Python
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Airline Passenger Satisfaction Analysis And Prediction Using Machine Learning And Deep Learning With Python
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-08-08
Airline Passenger Satisfaction Analysis And Prediction Using Machine Learning And Deep 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 2023-08-08 with Computers categories.
In the project "Airline Passenger Satisfaction Analysis and Prediction Using Machine Learning and Deep Learning with Python," the aim was to analyze and predict passenger satisfaction in the airline industry. The project began with an extensive data exploration phase, wherein the dataset containing various features related to passenger experiences was thoroughly examined. The dataset was then preprocessed, ensuring data cleanliness and preparing it for further analysis. One of the initial steps involved understanding the distribution of categorized features within the dataset. By visualizing the distribution of these features, insights were gained into the prevalence of different categories, providing a preliminary understanding of passenger preferences and experiences. For the prediction aspect, machine learning models were employed, and a Grid Search approach was implemented to fine-tune hyperparameters and optimize model performance. This process allowed the identification of the best-performing model configuration, enhancing the accuracy of passenger satisfaction predictions. The models used are Logistic Regression, Support Vector Machines, K-Nearest Neighbors, Decision Trees, Random Forests, Gradient Boosting, Extreme Gradient Boosting, Light Gradient Boosting. Going beyond traditional machine learning, a Deep Learning approach was introduced using an Artificial Neural Network (ANN). This model, designed to capture intricate patterns and relationships within the data, showcased the potential of deep learning for improving predictive accuracy. The evaluation of both machine learning and deep learning models was centered around key metrics. The accuracy score was a primary indicator of model performance, reflecting the ratio of correctly predicted passenger satisfaction outcomes. Additionally, the Classification Report provided a comprehensive overview of precision, recall, and F1-score for each category, shedding light on the model's ability to classify passenger satisfaction levels accurately. Visualizing the results played a pivotal role in the project. The plotted Training and Validation Accuracy and Loss graphs offered insights into the convergence and generalization capabilities of the models. These visualizations helped in understanding potential overfitting or underfitting issues and guided the fine-tuning process. To assess the models' predictive performance, a Confusion Matrix was constructed. This matrix presented a clear breakdown of correct and incorrect predictions, facilitating an understanding of where the model excelled and where it struggled. Furthermore, scatter plots were utilized to visually compare the predicted values against the actual true values, offering a tangible representation of the models' effectiveness. Throughout the project, rigorous data preprocessing and feature engineering were integral to improving model accuracy. Features were appropriately scaled, and categorical variables were transformed using techniques like one-hot encoding, enabling models to efficiently learn from the data. The project also focused on the interpretability of the models, enabling stakeholders to comprehend the factors influencing passenger satisfaction predictions. This interpretability was essential for making informed business decisions based on the model insights. In conclusion, the project showcased a comprehensive approach to analyzing and predicting airline passenger satisfaction. Through meticulous data exploration, feature distribution analysis, machine learning model selection, hyperparameter tuning, and deep learning implementation, the project provided valuable insights for the airline industry. By utilizing a combination of machine learning and deep learning techniques, the project demonstrated a holistic approach to understanding and enhancing passenger experiences and satisfaction levels.
Analysis And Prediction Projects Using Machine Learning And Deep Learning With Python
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2022-02-17
Analysis And Prediction Projects Using Machine Learning And Deep 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 2022-02-17 with Computers categories.
PROJECT 1: DEFAULT LOAN PREDICTION BASED ON CUSTOMER BEHAVIOR Using Machine Learning and Deep Learning with Python In finance, default is failure to meet the legal obligations (or conditions) of a loan, for example when a home buyer fails to make a mortgage payment, or when a corporation or government fails to pay a bond which has reached maturity. A national or sovereign default is the failure or refusal of a government to repay its national debt. The dataset used in this project belongs to a Hackathon organized by "Univ.AI". All values were provided at the time of the loan application. Following are the features in the dataset: Income, Age, Experience, Married/Single, House_Ownership, Car_Ownership, Profession, CITY, STATE, CURRENT_JOB_YRS, CURRENT_HOUSE_YRS, and Risk_Flag. The Risk_Flag indicates whether there has been a default in the past or not. The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. Finally, you will plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 2: AIRLINE PASSENGER SATISFACTION Analysis and Prediction Using Machine Learning and Deep Learning with Python The dataset used in this project contains an airline passenger satisfaction survey. In this case, you will determine what factors are highly correlated to a satisfied (or dissatisfied) passenger and predict passenger satisfaction. Below are the features in the dataset: Gender: Gender of the passengers (Female, Male); Customer Type: The customer type (Loyal customer, disloyal customer); Age: The actual age of the passengers; Type of Travel: Purpose of the flight of the passengers (Personal Travel, Business Travel); Class: Travel class in the plane of the passengers (Business, Eco, Eco Plus); Flight distance: The flight distance of this journey; Inflight wifi service: Satisfaction level of the inflight wifi service (0:Not Applicable;1-5); Departure/Arrival time convenient: Satisfaction level of Departure/Arrival time convenient; Ease of Online booking: Satisfaction level of online booking; Gate location: Satisfaction level of Gate location; Food and drink: Satisfaction level of Food and drink; Online boarding: Satisfaction level of online boarding; Seat comfort: Satisfaction level of Seat comfort; Inflight entertainment: Satisfaction level of inflight entertainment; On-board service: Satisfaction level of On-board service; Leg room service: Satisfaction level of Leg room service; Baggage handling: Satisfaction level of baggage handling; Check-in service: Satisfaction level of Check-in service; Inflight service: Satisfaction level of inflight service; Cleanliness: Satisfaction level of Cleanliness; Departure Delay in Minutes: Minutes delayed when departure; Arrival Delay in Minutes: Minutes delayed when Arrival; and Satisfaction: Airline satisfaction level (Satisfaction, neutral or dissatisfaction) The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. Finally, you will plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 3: CREDIT CARD CHURNING CUSTOMER ANALYSIS AND PREDICTION USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON The dataset used in this project consists of more than 10,000 customers mentioning their age, salary, marital_status, credit card limit, credit card category, etc. There are 20 features in the dataset. In the dataset, there are only 16.07% of customers who have churned. Thus, it's a bit difficult to train our model to predict churning customers. Following are the features in the dataset: 'Attrition_Flag', 'Customer_Age', 'Gender', 'Dependent_count', 'Education_Level', 'Marital_Status', 'Income_Category', 'Card_Category', 'Months_on_book', 'Total_Relationship_Count', 'Months_Inactive_12_mon', 'Contacts_Count_12_mon', 'Credit_Limit', 'Total_Revolving_Bal', 'Avg_Open_To_Buy', 'Total_Amt_Chng_Q4_Q1', 'Total_Trans_Amt', 'Total_Trans_Ct', 'Total_Ct_Chng_Q4_Q1', and 'Avg_Utilization_Ratio',. The target variable is 'Attrition_Flag'. The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. Finally, you will plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 4: MARKETING ANALYSIS AND PREDICTION USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON This data set was provided to students for their final project in order to test their statistical analysis skills as part of a MSc. in Business Analytics. It can be utilized for EDA, Statistical Analysis, and Visualizations. Following are the features in the dataset: ID = Customer's unique identifier; Year_Birth = Customer's birth year; Education = Customer's education level; Marital_Status = Customer's marital status; Income = Customer's yearly household income; Kidhome = Number of children in customer's household; Teenhome = Number of teenagers in customer's household; Dt_Customer = Date of customer's enrollment with the company; Recency = Number of days since customer's last purchase; MntWines = Amount spent on wine in the last 2 years; MntFruits = Amount spent on fruits in the last 2 years; MntMeatProducts = Amount spent on meat in the last 2 years; MntFishProducts = Amount spent on fish in the last 2 years; MntSweetProducts = Amount spent on sweets in the last 2 years; MntGoldProds = Amount spent on gold in the last 2 years; NumDealsPurchases = Number of purchases made with a discount; NumWebPurchases = Number of purchases made through the company's web site; NumCatalogPurchases = Number of purchases made using a catalogue; NumStorePurchases = Number of purchases made directly in stores; NumWebVisitsMonth = Number of visits to company's web site in the last month; AcceptedCmp3 = 1 if customer accepted the offer in the 3rd campaign, 0 otherwise; AcceptedCmp4 = 1 if customer accepted the offer in the 4th campaign, 0 otherwise; AcceptedCmp5 = 1 if customer accepted the offer in the 5th campaign, 0 otherwise; AcceptedCmp1 = 1 if customer accepted the offer in the 1st campaign, 0 otherwise; AcceptedCmp2 = 1 if customer accepted the offer in the 2nd campaign, 0 otherwise; Response = 1 if customer accepted the offer in the last campaign, 0 otherwise; Complain = 1 if customer complained in the last 2 years, 0 otherwise; and Country = Customer's location. The machine and deep learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. Finally, you will plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 5: METEOROLOGICAL DATA ANALYSIS AND PREDICTION USING MACHINE LEARNING WITH PYTHON Meteorological phenomena are described and quantified by the variables of Earth's atmosphere: temperature, air pressure, water vapour, mass flow, and the variations and interactions of these variables, and how they change over time. Different spatial scales are used to describe and predict weather on local, regional, and global levels. The dataset used in this project consists of meteorological data with 96453 total number of data points and with 11 attributes/columns. Following are the columns in the dataset: Formatted Date; Summary; Precip Type; Temperature (C); Apparent Temperature (C); Humidity; Wind Speed (km/h); Wind Bearing (degrees); Visibility (km); Pressure (millibars); and Daily Summary. The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM classifier, Gradient Boosting, XGB classifier, and MLP classifier. Finally, you will plot boundary decision, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy.
Artificial Intelligence And Machine Learning In The Travel Industry
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Author : Ben Vinod
language : en
Publisher: Springer Nature
Release Date : 2023-05-26
Artificial Intelligence And Machine Learning In The Travel Industry written by Ben Vinod and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-26 with Business & Economics categories.
Over the past decade, Artificial Intelligence has proved invaluable in a range of industry verticals such as automotive and assembly, life sciences, retail, oil and gas, and travel. The leading sectors adopting AI rapidly are Financial Services, Automotive and Assembly, High Tech and Telecommunications. Travel has been slow in adoption, but the opportunity for generating incremental value by leveraging AI to augment traditional analytics driven solutions is extremely high. The contributions in this book, originally published as a special issue for the Journal of Revenue and Pricing Management, showcase the breadth and scope of the technological advances that have the potential to transform the travel experience, as well as the individuals who are already putting them into practice.
Microsoft Azure Essentials Azure Machine Learning
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Author : Jeff Barnes
language : en
Publisher: Microsoft Press
Release Date : 2015-04-25
Microsoft Azure Essentials Azure Machine Learning written by Jeff Barnes and has been published by Microsoft Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-04-25 with Computers categories.
Microsoft Azure Essentials from Microsoft Press is a series of free ebooks designed to help you advance your technical skills with Microsoft Azure. This third ebook in the series introduces Microsoft Azure Machine Learning, a service that a developer can use to build predictive analytics models (using training datasets from a variety of data sources) and then easily deploy those models for consumption as cloud web services. The ebook presents an overview of modern data science theory and principles, the associated workflow, and then covers some of the more common machine learning algorithms in use today. It builds a variety of predictive analytics models using real world data, evaluates several different machine learning algorithms and modeling strategies, and then deploys the finished models as machine learning web services on Azure within a matter of minutes. The ebook also expands on a working Azure Machine Learning predictive model example to explore the types of client and server applications you can create to consume Azure Machine Learning web services. Watch Microsoft Press’s blog and Twitter (@MicrosoftPress) to learn about other free ebooks in the Microsoft Azure Essentials series.
Reinforcement Learning
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Author : Richard S. Sutton
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Reinforcement Learning written by Richard S. Sutton and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
Reinforcement learning is the learning of a mapping from situations to actions so as to maximize a scalar reward or reinforcement signal. The learner is not told which action to take, as in most forms of machine learning, but instead must discover which actions yield the highest reward by trying them. In the most interesting and challenging cases, actions may affect not only the immediate reward, but also the next situation, and through that all subsequent rewards. These two characteristics -- trial-and-error search and delayed reward -- are the most important distinguishing features of reinforcement learning. Reinforcement learning is both a new and a very old topic in AI. The term appears to have been coined by Minsk (1961), and independently in control theory by Walz and Fu (1965). The earliest machine learning research now viewed as directly relevant was Samuel's (1959) checker player, which used temporal-difference learning to manage delayed reward much as it is used today. Of course learning and reinforcement have been studied in psychology for almost a century, and that work has had a very strong impact on the AI/engineering work. One could in fact consider all of reinforcement learning to be simply the reverse engineering of certain psychological learning processes (e.g. operant conditioning and secondary reinforcement). Reinforcement Learning is an edited volume of original research, comprising seven invited contributions by leading researchers.
The Machine Age Of Customer Insight
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Author : Martin Einhorn
language : en
Publisher: Emerald Group Publishing
Release Date : 2021-03-15
The Machine Age Of Customer Insight written by Martin Einhorn and has been published by Emerald Group Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-15 with Business & Economics categories.
The Machine Age of Customer Insight demonstrates the impact of machine learning and data analytics, combining an academic state-of-the-art overview of machine learning with cases from well-known companies. These cases show the opportunities and challenges of the transformation process for business and for customer insights more specifically.
Dive Into Deep Learning
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Author : Joanne Quinn
language : en
Publisher: Corwin Press
Release Date : 2019-07-15
Dive Into Deep Learning written by Joanne Quinn and has been published by Corwin Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-15 with Education categories.
The leading experts in system change and learning, with their school-based partners around the world, have created this essential companion to their runaway best-seller, Deep Learning: Engage the World Change the World. This hands-on guide provides a roadmap for building capacity in teachers, schools, districts, and systems to design deep learning, measure progress, and assess conditions needed to activate and sustain innovation. Dive Into Deep Learning: Tools for Engagement is rich with resources educators need to construct and drive meaningful deep learning experiences in order to develop the kind of mindset and know-how that is crucial to becoming a problem-solving change agent in our global society. Designed in full color, this easy-to-use guide is loaded with tools, tips, protocols, and real-world examples. It includes: • A framework for deep learning that provides a pathway to develop the six global competencies needed to flourish in a complex world — character, citizenship, collaboration, communication, creativity, and critical thinking. • Learning progressions to help educators analyze student work and measure progress. • Learning design rubrics, templates and examples for incorporating the four elements of learning design: learning partnerships, pedagogical practices, learning environments, and leveraging digital. • Conditions rubrics, teacher self-assessment tools, and planning guides to help educators build, mobilize, and sustain deep learning in schools and districts. Learn about, improve, and expand your world of learning. Put the joy back into learning for students and adults alike. Dive into deep learning to create learning experiences that give purpose, unleash student potential, and transform not only learning, but life itself.
Graph Powered Machine Learning
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Author : Alessandro Negro
language : en
Publisher: Simon and Schuster
Release Date : 2021-10-05
Graph Powered Machine Learning written by Alessandro Negro and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-05 with Computers categories.
Upgrade your machine learning models with graph-based algorithms, the perfect structure for complex and interlinked data. Summary In Graph-Powered Machine Learning, you will learn: The lifecycle of a machine learning project Graphs in big data platforms Data source modeling using graphs Graph-based natural language processing, recommendations, and fraud detection techniques Graph algorithms Working with Neo4J Graph-Powered Machine Learning teaches to use graph-based algorithms and data organization strategies to develop superior machine learning applications. You’ll dive into the role of graphs in machine learning and big data platforms, and take an in-depth look at data source modeling, algorithm design, recommendations, and fraud detection. Explore end-to-end projects that illustrate architectures and help you optimize with best design practices. Author Alessandro Negro’s extensive experience shines through in every chapter, as you learn from examples and concrete scenarios based on his work with real clients! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Identifying relationships is the foundation of machine learning. By recognizing and analyzing the connections in your data, graph-centric algorithms like K-nearest neighbor or PageRank radically improve the effectiveness of ML applications. Graph-based machine learning techniques offer a powerful new perspective for machine learning in social networking, fraud detection, natural language processing, and recommendation systems. About the book Graph-Powered Machine Learning teaches you how to exploit the natural relationships in structured and unstructured datasets using graph-oriented machine learning algorithms and tools. In this authoritative book, you’ll master the architectures and design practices of graphs, and avoid common pitfalls. Author Alessandro Negro explores examples from real-world applications that connect GraphML concepts to real world tasks. What's inside Graphs in big data platforms Recommendations, natural language processing, fraud detection Graph algorithms Working with the Neo4J graph database About the reader For readers comfortable with machine learning basics. About the author Alessandro Negro is Chief Scientist at GraphAware. He has been a speaker at many conferences, and holds a PhD in Computer Science. Table of Contents PART 1 INTRODUCTION 1 Machine learning and graphs: An introduction 2 Graph data engineering 3 Graphs in machine learning applications PART 2 RECOMMENDATIONS 4 Content-based recommendations 5 Collaborative filtering 6 Session-based recommendations 7 Context-aware and hybrid recommendations PART 3 FIGHTING FRAUD 8 Basic approaches to graph-powered fraud detection 9 Proximity-based algorithms 10 Social network analysis against fraud PART 4 TAMING TEXT WITH GRAPHS 11 Graph-based natural language processing 12 Knowledge graphs
Personalized Machine Learning
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Author : Julian McAuley
language : en
Publisher: Cambridge University Press
Release Date : 2022-02-03
Personalized Machine Learning written by Julian McAuley and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-03 with Computers categories.
Every day we interact with machine learning systems offering individualized predictions for our entertainment, social connections, purchases, or health. These involve several modalities of data, from sequences of clicks to text, images, and social interactions. This book introduces common principles and methods that underpin the design of personalized predictive models for a variety of settings and modalities. The book begins by revising 'traditional' machine learning models, focusing on adapting them to settings involving user data, then presents techniques based on advanced principles such as matrix factorization, deep learning, and generative modeling, and concludes with a detailed study of the consequences and risks of deploying personalized predictive systems. A series of case studies in domains ranging from e-commerce to health plus hands-on projects and code examples will give readers understanding and experience with large-scale real-world datasets and the ability to design models and systems for a wide range of applications.
Digital Fluency
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Author : Volker Lang
language : en
Publisher:
Release Date : 2021
Digital Fluency written by Volker Lang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
If you are curious about the basics of artificial intelligence, blockchain technology, and quantum computing as key enablers for digital transformation, Digital Fluency is your handy guide. The real-world applications of these cutting-edge technologies are expanding rapidly, and your daily life will continue to be affected by each of them. There is no better time than now to get started and become digitally fluent. You need not have previous knowledge of these technologies, as author Volker Lang will expertly guide you through this digital age. He illustrates key concepts and applications in numerous examples and figures throughout Digital Fluency, and the end of each chapter presents you with a helpful implementation checklist of central lessons before proceeding to the next. This book gets to the heart of digital buzzwords and concepts, and tells you what they truly mean. Breaking down topics such as AI-powered automated driving, blockchain-based cryptocurrencies, quantum optimization of urban traffic, and more is imperative to being ready for what the future of industry holds. Whether your own digital transformation journey takes place within your organization, your studies, or your individual household, Digital Fluency maps out a concrete digital action plan for all of your technology and strategy needs.