Java Homework Projects Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Java Homework Projects book. This book definitely worth reading, it is an incredibly well-written.
Java Homework Projects by Philip Conrod,Lou Tylee Pdf
AVA HOMEWORK PROJECTS teaches Java GUI (Graphical User Interface) Swing programming concepts and provides detailed step-by-step instructions in building many fun and useful projects. Students learn about project design, the Java Swing controls, many elements of the Java language, and how to distribute finished projects.
Java Programming Projects by John Sestak,CEP, Inc Pdf
Supplement any Java text with Java Programming Projects. With over 50 projects, 30 applications exercises, and 11 critical thinking projects this workbook provides the extra reinforcement needed to learn important Java concepts and skills.
Learn how to build scalable, resilient, and effective applications in Java that suit your software requirements. Key Features Explore advanced technologies that Java 11 delivers such as web programming and parallel computing Discover modern programming paradigms such as microservices, cloud computing and enterprise structures Build highly responsive applications with this practical introduction to Reactive programming Book Description Java is one of the most commonly used software languages by programmers and developers. In this book, you’ll learn the new features of Java 11 quickly and experience a simple and powerful approach to software development. You’ll see how to use the Java runtime tools, understand the Java environment, and create a simple namesorting Java application. Further on, you'll learn about advanced technologies that Java delivers, such as web programming and parallel computing, and will develop a mastermind game. Moving on, we provide more simple examples, to build a foundation before diving into some complex data structure problems that will solidify your Java 11 skills. With a special focus on the features of new projects: Project Valhalla, Project Panama, Project Amber, and Project Loom, this book will help you get employed as a top-notch Java developer. By the end of the book, you’ll have a firm foundation to continue your journey toward becoming a professional Java developer. What you will learn Compile, package, and run a program using a build management tool Get to know the principles of test-driven development Separate the wiring of multiple modules from application logic Use Java annotations for configuration Master the scripting API built into the Java language Understand static versus dynamic implementation of code Who this book is for This book is for anyone who wants to learn the Java programming language. No programming experience required. If you have prior experience, it will help you through the book more easily.
The java projects book enables you to develop java applications using an easy and simple approac.The book is designed for the readers,who are familiar with java programming.The book provides numerous listings and figures for an affective understanding of java concepts.The book consists of a CD that includes source code for all the java applications. Table of contents: Chapter 1 Creating a calculator applications Chapter 2 Creating analog clock applications Chapter 3 Creating a 9-box puzzle game Chapter 4 Student information management system Chapter 5 Creating a text editor applications Chapter 6 Creating an online test applications Chapter 7 Creating a shopping cart applications Chapter 8 Share trading application Chapter 9 Online banking applications
It’s easy to write correct Ruby code, but to gain the fluency needed to write great Ruby code, you must go beyond syntax and absorb the “Ruby way” of thinking and problem solving. In Eloquent Ruby, Russ Olsen helps you write Ruby like true Rubyists do–so you can leverage its immense, surprising power. Olsen draws on years of experience internalizing the Ruby culture and teaching Ruby to other programmers. He guides you to the “Ah Ha!” moments when it suddenly becomes clear why Ruby works the way it does, and how you can take advantage of this language’s elegance and expressiveness. Eloquent Ruby starts small, answering tactical questions focused on a single statement, method, test, or bug. You’ll learn how to write code that actually looks like Ruby (not Java or C#); why Ruby has so many control structures; how to use strings, expressions, and symbols; and what dynamic typing is really good for. Next, the book addresses bigger questions related to building methods and classes. You’ll discover why Ruby classes contain so many tiny methods, when to use operator overloading, and when to avoid it. Olsen explains how to write Ruby code that writes its own code–and why you’ll want to. He concludes with powerful project-level features and techniques ranging from gems to Domain Specific Languages. A part of the renowned Addison-Wesley Professional Ruby Series, Eloquent Ruby will help you “put on your Ruby-colored glasses” and get results that make you a true believer.
Java is the world’s most popular programming language, but it’s known for having a steep learning curve. Learn Java the Easy Way takes the chore out of learning Java with hands-on projects that will get you building real, functioning apps right away. You’ll start by familiarizing yourself with JShell, Java’s interactive command line shell that allows programmers to run single lines of code and get immediate feedback. Then, you’ll create a guessing game, a secret message encoder, and a multitouch bubble-drawing app for both desktop and mobile devices using Eclipse, an industry-standard IDE, and Android Studio, the development environment for making Android apps. As you build these apps, you’ll learn how to: -Perform calculations, manipulate text strings, and generate random colors -Use conditions, loops, and methods to make your programs responsive and concise -Create functions to reuse code and save time -Build graphical user interface (GUI) elements, including buttons, menus, pop-ups, and sliders -Take advantage of Eclipse and Android Studio features to debug your code and find, fix, and prevent common mistakes If you’ve been thinking about learning Java, Learn Java the Easy Way will bring you up to speed in no time.
There are many good Java programming books on the market, but it's not easy to find one fit for a beginner. This book simplifies the complexity of Java programming and guides you through the journey to effectively work under the hood. You'll start with the fundamentals of Java programming and review how it integrates with basic mathematical concepts through many practical examples. You'll witness firsthand how Java can be a powerful tool or framework in your experimentation work. Learn Java with Math reveals how a strong math foundation is key to learning programming design. Using this as your motivation, you'll be programming in Java in no time. What You'll Learn Explore Java basicsProgram with Java using fun math-inspired examplesWork with Java variables and algorithmsReview I/O, loops, and control structuresUse projects such as the Wright brothers coin flip gameWho This Book Is For Those new to programming and Java but have some background in mathematics and are at least comfortable with using a computer.
Fundamentals of Java Programming by Mitsunori Ogihara Pdf
Making extensive use of examples, this textbook on Java programming teaches the fundamental skills for getting started in a command-line environment. Meant to be used for a one-semester course to build solid foundations in Java, Fundamentals of Java Programming eschews second-semester content to concentrate on over 180 code examples and 250 exercises. Key object classes (String, Scanner, PrintStream, Arrays, and File) are included to get started in Java programming. The programs are explained with almost line-by-line descriptions, also with chapter-by-chapter coding exercises. Teaching resources include solutions to the exercises, as well as digital lecture slides.
Teach Yourself Java for Macintosh in 21 Days by Laura Lemay,Charles L. Perkins,Tim Webster Pdf
Takes a tutorial approach towards developing and serving Java applets, offering step-by-step instruction on such areas as motion pictures, animation, applet interactivity, file transfers, sound, and type. Original. (Intermediate).
Learning Java by Patrick Niemeyer,Jonathan Knudsen Pdf
This updated edition introduces the basics of Java and everything necessary to get up to speed on the new 1.4 version quickly. CD contains the Java 2 SDK for Windows, Linux and Solaris.
Java for Kids - a Computer Programming Tutorial by Philip Conrod,Lou Tylee Pdf
JAVA FOR KIDS is a beginning programming tutorial consisting of 10 chapters explaining (in simple, easy-to-follow terms) how to build a Java application. Students learn about project design, object-oriented programming, console applications, graphics applications and many elements of the Java language. Numerous examples are used to demonstrate every step in the building process. The tutorial also includes several detailed computer projects for students to build and try. These projects include a number guessing game, a card game, an allowance calculator, a state capitals game, Tic-Tac-Toe, a simple drawing program, and even a basic video game. JAVA FOR KIDS is presented using a combination of over 400 pages of FULL-COLOR notes and actual Java examples. This teacher or parent facilitated material should be understandable to kids aged 10 and up. No programming experience is necessary, but familiarity with doing common tasks using a computer operating system (simple editing, file maintenance, understanding directory structures, working on the Internet) is expected. JAVA FOR KIDS requires Windows XP-SP2, Vista or Windows 7. You will also need JCreator 5.0 SE and Version 7 of the Java Development Kit. The Java source code and all needed multimedia files are available for download from the publisher's website (www.KidwareSoftware.com) after book registration.
Practical Database Programming with Java by Ying Bai Pdf
Covers fundamental and advanced Java database programming techniques for beginning and experienced readers This book covers the practical considerations and applications in database programming using Java NetBeans IDE, JavaServer Pages, JavaServer Faces, and Java Beans, and comes complete with authentic examples and detailed explanations. Two data-action methods are developed and presented in this important resource. With Java Persistence API and plug-in Tools, readers are directed step by step through the entire database programming development process and will be able to design and build professional data-action projects with a few lines of code in mere minutes. The second method, runtime object, allows readers to design and build more sophisticated and practical Java database applications. Advanced and updated Java database programming techniques such as Java Enterprise Edition development kits, Enterprise Java Beans, JavaServer Pages, JavaServer Faces, Java RowSet Object, and Java Updatable ResultSet are also discussed and implemented with numerous example projects. Ideal for classroom and professional training use, this text also features: A detailed introduction to NetBeans Integrated Development Environment Java web-based database programming techniques (web applications and web services) More than thirty detailed, real-life sample projects analyzed via line-by-line illustrations Problems and solutions for each chapter A wealth of supplemental material available for download from the book's ftp site, including PowerPoint slides, solution manual, JSP pages, sample image files, and sample databases Coverage of two popular database systems: SQL Server 2008 and Oracle This book provides undergraduate and graduate students as well as database programmers and software engineers with the necessary tools to handle the database programming issues in the Java NetBeans environment. To obtain instructor materials please send an email to: [email protected]
This illustrated book teaches kids to write computer programs. Kids will learn basics of programming while creating such computer games as Tic-Tac-Toe, Ping-Pong and others. This book can be useful for three categories of people: kids from 10 to 18 years old, school computer teachers, parents who want to teach their kids programming.
Learn Java in One Week by Vivian Siahaan,Rismon Hasiholan Sianipar Pdf
This book is the ultimate beginners’ crash course to Java GUI programming, as it will help you learn enough about the language in as little as 1 week. Complex concepts in developing MS Access and SQLite driven projects are broken down into easy steps to ensure that you can easily master the Java language even if you have never coded before. The best way to learn Java is by doing it. The lessons in this book are a highly organized and well-indexed set of tutorials meant for students and programmers. Netbeans, a specific IDE (Integrated Development Environment) is used to create GUI (Graphical User Interface applications).The finished product is the reward, but the readers are fully engaged and enriched by the process. This kind of learning is often the focus of training. In this book, you will learn how to build from scratch two access database management systems using Java. In designing a GUI and as an IDE, you will make use of the NetBeans tool. In chapter one, you will create School database and six tables. In chapter two, you will study: Creating the initial three table projects in the school database: Teacher table, TClass table, and Subject table; Creating database configuration files; Creating a Java GUI for viewing and navigating the contents of each table; Creating a Java GUI for inserting and editing tables; and Creating a Java GUI to join and query the three tables. In chapter three, you will learn: Creating the main form to connect all forms; Creating a project will add three more tables to the school database: the Student table, the Parent table, and Tuition table; Creating a Java GUI to view and navigate the contents of each table; Creating a Java GUI for editing, inserting, and deleting records in each table; Creating a Java GUI to join and query the three tables and all six. In chapter four, you will study how to query the six tables. In chapter five, you will create Crime database and its six tables. In chapter six, you will be taught how to extract image features, utilizing BufferedImage class, in Java GUI. In chapter seven, you will be taught to create Java GUI to view, edit, insert, and delete Suspect table data. This table has eleven columns: suspect_id (primary key), suspect_name, birth_date, case_date, report_date, suspect_ status, arrest_date, mother_name, address, telephone, and photo. In chapter eight, you will be taught to create Java GUI to view, edit, insert, and delete Feature_Extraction table data. This table has eight columns: feature_id (primary key), suspect_id (foreign key), feature1, feature2, feature3, feature4, feature5, and feature6. All six fields (except keys) will have a BLOB data type, so that the image of the feature will be directly saved into this table. In chapter nine, you will add two tables: Police_Station and Investigator. These two tables will later be joined to Suspect table through another table, File_Case, which will be built in the seventh chapter. The Police_Station has six columns: police_station_id (primary key), location, city, province, telephone, and photo. The Investigator has eight columns: investigator_id (primary key), investigator_name, rank, birth_date, gender, address, telephone, and photo. Here, you will design a Java GUI to display, edit, fill, and delete data in both tables. In chapter ten, you will add two tables: Victim and File_Case. The File_Case table will connect four other tables: Suspect, Police_Station, Investigator and Victim. The Victim table has nine columns: victim_id (primary key), victim_name, crime_type, birth_date, crime_date, gender, address, telephone, and photo. The File_Case has seven columns: file_case_id (primary key), suspect_id (foreign key), police_station_id (foreign key), investigator_id (foreign key), victim_id (foreign key), status, and description.
REGRESSION, SEGMENTATION, CLUSTERING, AND PREDICTION PROJECTS WITH PYTHON by Vivian Siahaan,Rismon Hasiholan Sianipar Pdf
PROJECT 1: TIME-SERIES WEATHER: FORECASTING AND PREDICTION WITH PYTHON Weather data are described and quantified by the variables of Earth's atmosphere: temperature, air pressure, humidity, 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 contains weather data for New Delhi, India. This data was taken out from wunderground. It contains various features such as temperature, pressure, humidity, rain, precipitation, etc. The main target is to develop a prediction model accurate enough for forecasting temperature and predicting target variable (condition). Time-series weather forecasting will be done using ARIMA models. The machine learning models used in this project to predict target variable (condition) 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. PROJECT 2: HOUSE PRICE: ANALYSIS AND PREDICTION USING MACHINE LEARNING WITH PYTHON The dataset used in this project is taken from the second chapter of Aurélien Géron's recent book 'Hands-On Machine learning with Scikit-Learn and TensorFlow'. It serves as an excellent introduction to implementing machine learning algorithms because it requires rudimentary data cleaning, has an easily understandable list of variables and sits at an optimal size between being to toyish and too cumbersome. The data contains information from the 1990 California census. Although it may not help you with predicting current housing prices like the Zillow Zestimate dataset, it does provide an accessible introductory dataset for teaching people about the basics of machine learning. The data pertains to the houses found in a given California district and some summary stats about them based on the 1990 census data. Be warned the data aren't cleaned so there are some preprocessing steps required! The columns are as follows: longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income, median_house_value, and ocean_proximity. The machine learning models used in this project used to perform regression on median_house_value and to predict it as target variable 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. PROJECT 3: CUSTOMER PERSONALITY ANALYSIS AND PREDICTION USING MACHINE LEARNING WITH PYTHON Customer Personality Analysis is a detailed analysis of a company’s ideal customers. It helps a business to better understand its customers and makes it easier for them to modify products according to the specific needs, behaviors and concerns of different types of customers. Customer personality analysis helps a business to modify its product based on its target customers from different types of customer segments. For example, instead of spending money to market a new product to every customer in the company’s database, a company can analyze which customer segment is most likely to buy the product and then market the product only on that particular segment. 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; and Complain = 1 if customer complained in the last 2 years, 0 otherwise. The target in this project is to perform clustering and predicting to summarize customer segments. In this project, you will perform clustering using KMeans to get 4 clusters. The machine learning models used in this project to perform regression on total number of purchase and to predict clusters as target variable are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM, Gradient Boosting, XGB, and MLP. 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. PROJECT 4: CUSTOMER SEGMENTATION, CLUSTERING, AND PREDICTION WITH PYTHON In this project, you will develop a customer segmentation, clustering, and prediction to define marketing strategy. The sample dataset summarizes the usage behavior of about 9000 active credit card holders during the last 6 months. The file is at a customer level with 18 behavioral variables. Following is the Data Dictionary for Credit Card dataset: CUSTID: Identification of Credit Card holder (Categorical); BALANCE: Balance amount left in their account to make purchases; BALANCEFREQUENCY: How frequently the Balance is updated, score between 0 and 1 (1 = frequently updated, 0 = not frequently updated); PURCHASES: Amount of purchases made from account; ONEOFFPURCHASES: Maximum purchase amount done in one-go; INSTALLMENTSPURCHASES: Amount of purchase done in installment; CASHADVANCE: Cash in advance given by the user; PURCHASESFREQUENCY: How frequently the Purchases are being made, score between 0 and 1 (1 = frequently purchased, 0 = not frequently purchased); ONEOFFPURCHASESFREQUENCY: How frequently Purchases are happening in one-go (1 = frequently purchased, 0 = not frequently purchased); PURCHASESINSTALLMENTSFREQUENCY: How frequently purchases in installments are being done (1 = frequently done, 0 = not frequently done); CASHADVANCEFREQUENCY: How frequently the cash in advance being paid; CASHADVANCETRX: Number of Transactions made with "Cash in Advanced"; PURCHASESTRX: Number of purchase transactions made; CREDITLIMIT: Limit of Credit Card for user; PAYMENTS: Amount of Payment done by user; MINIMUM_PAYMENTS: Minimum amount of payments made by user; PRCFULLPAYMENT: Percent of full payment paid by user; and TENURE: Tenure of credit card service for user. In this project, you will perform clustering using KMeans to get 5 clusters. The machine learning models used in this project to perform regression on total number of purchase and to predict clusters as target variable are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM, Gradient Boosting, XGB, and MLP. 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.
