Mysql With Visual Basic And Visual C

DOWNLOAD

Download Mysql With Visual Basic And Visual C PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Mysql With Visual Basic And Visual C book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page

Visual C Net With Mysql

DOWNLOAD
Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2020-09-13

Visual C Net With Mysql 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 2020-09-13 with Computers categories.

In chapter one, you will learn to know the properties and events of each control in a Windows Visual C# application. You need to learn and know in order to be more familiar when applying them to some applications in this book. In chapter two, you will go through step by step to build a SALES database using MySQL. You will build each table and add associated data fields (along with the necessary keys and indexes). The first field in the Client table is ClientID. Enter the clien ID in the Name Field and select AutoNumber in the Data Type. You define primary key and other indexes which are useful for quick searching. ClientID is a primary field. You will define FamilyName as an index. You then will create Ordering table with three fields: OrderID, ClientID, and OrderDate. You then will create Purchase table with three fields: OrderID, ProductID, and Quantity. And you will create Product table with four fields: ProductID, Description, Price, and QtySold. Before designing Visual C# interface, you will build the relationships between four tables. The interface will be used to enter new orders into the database. The order form will be used to enter the following information into the database: order ID, order date, client ID, client’s first name and family name, client’s address, product information ordered. The form will have the ability to add new orders, find clients, add new clients. The completed order invoice will be provided in a printed report. In chapter three, you will build a database management system where you can store information about valuables in your warehouse. The table will have seven fields: Item (description of the item), Location (where the item was placed), Shop (where the item was purchased), DatePurchased (when the item was purchased), Cost (how much the item cost), SerialNumber (serial number of the item), PhotoFile (path of the photo file of the item), and Fragile (indicates whether a particular item is fragile or not). The development of this Warehouse Inventory Project will be performed, as usual, in a step-by-step manner. You will first create the database. Furthermore, the interface will be built so that the user can view, edit, add, or add data records from the database. Finally, you add code to create a printable list of information from the database. In chapter four, you will build an application that can be used to track daily high and low pollutant PM2.5 and air quality level. The steps that need to be taken in building Siantar Air Quality Index (SAQI) database project are: Build and test a Visual C# interface; Create an empty database using code; and Report database. The designed interface will allow the user to enter max pollutant, min pollutant, and air quality for any date that the user chooses in a particular year. This information will be stored in a database. Graphical result of the data will be provided, along with summary information relating to the maximum value, minimum value, and mean value. You will use a tab control as the main component of the interface. The control has three tabs: one for viewing and editing data, one for viewing graph of pollutant data, and another for viewing graph of air quality data. Each tab on this control operates like a Visual C# control panel. In chapter five, you will perform the steps necessary to build a MySQL book inventory database that contains 4 tables. You will build each table and add the associated fields as needed. You will have four tables in the database and define the relationship between the primary key and foreign key. You will associate AuthorID (foreign key) field in the Title_Author table with AuthorID (primary key) in the Author table. Then, you want to associate the ISBN (foreign key) field in Title_Author table with ISBN (primary key) in the Title table.

Visual Basic Net And Database Practical Tutorials

DOWNLOAD
Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2020-10-31

Visual Basic Net And Database Practical Tutorials 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 2020-10-31 with Computers categories.

This book aims to develop a MySQL-driven desktop application that readers can develop for their own purposes to implement library project using Visual Basic .NET. In Tutorial 1, you will build a Visual Basic interface for the database. This interface will used as the main terminal in accessing other forms. This tutorial will also discuss how to create login form and login table. You will create login form. Place on the form one picture box, two labels, one combo box, one text box, and two buttons. In Tutorial 2, you will build a school inventory project where you can store information about valuables in school. The table will have nine fields: Item (description of the item), Quantity, Location (where the item was placed), Shop (where the item was purchased), DatePurchased (when the item was purchased), Cost (how much the item cost), SerialNumber (serial number of the item), PhotoFile (path of the photo file of the item), and Fragile (indicates whether a particular item is fragile or not). In Tutorial 3, you will perform the steps necessary to add 5 new tables using phpMyAdmin into Academy database. You will build each table and add the associated fields as needed. Every table in the database will need input form. In this tutorial, you will build such a form for Author table. Although this table is quite simple (only four fields: AuthorID, Name, BirthDate, and PhotoFile), it provides a basis for illustrating the many steps in interface design. SQL statement is required by the Command object to read fields (sorted by Name). Then, you will build an interface so that the user can maintain the Publisher table in the database (Academy). The Publisher table interface is more or less the same as Author table interface. This Publisher table interface only requires more input fields. So you will use the interface for the Author table and modify it for the Publisher table. In Tutorial 4, you will perform the steps necessary to design and implement title form, library member form, and book borrowal form. You start by designing and testing the basic entry form for book titles. The Title table has nine fields: BookTitle, PublishYear, ISBN, PublisherID, AuthorID, Description, Note, Subject, and Comment. Then, you will build such a form for Member table. This table has twelve fields: MemberID, FirstName, LastName, BirthDate, Status, Ethnicity, Nationality, Mobile, Phone, Religion, Gender, and PhotoFile). You need thirteen label controls, one picture box, six text boxes, four comboxes, one check box, one date time picker, one openfiledialog, and one printpreviewdialog. You also need four buttons for navigation, six buttons for controlling editing features, one button for searching member’s name, and one button to upload member’s photo. Finally, you will build such a form for Borrow table. This table has seven fields: BorrowID, MemberID, BorrowCode, ISBN, BorrowDate, ReturnDate, and Penalty. In this form, you need fourteen label controls, seven text boxes, two comboxes, two date time pickers, and one printpreviewdialog. You also need four buttons for navigation, seven buttons for other utilities, one button to generate borrowal code, and one button to return book.

Visual Basic Net For Students

DOWNLOAD
Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2020-08-03

Visual Basic Net For Students 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 2020-08-03 with Computers categories.

In chapter one, you will get to know the properties and events of each control in a Windows Visual Basic application. You need to learn and know in order to be more familiar when applying them to some desktop applications in this book. In Tutorial 1.1, you will build a dual-mode stopwatch. The stopwatch can be started and stopped whenever desired. Two time traces: the running time when the stopwatch is active (running time) and the total time since the first stopwatch was activated. Two label controls are used to display the time (two more labels to display title information). Two button controls are used to start/stop and reset the application, one more button to exit the application. The timer control is used to periodically (every second) update the displayed time. In Tutorial 1.2, you will build a project so that children can practice basic skills in addition, subtraction, multiplication, and division operations. This Math Game project can be used to choose the types of questions and what factor you want to use. This project has three timing options. In Tutorial 1.3, you will build Bank Code game. The storage box is locked and can only be opened if you enter the correct digit combination. Combinations can be 2 to 4 non-repetitive digits (range of digits from 1 to 9). After a guess is given, you will be notified of how many digits are right and how many digits are in the right position. Based on this information, you will give another guess. You continue to guess until you get the right combination or until you stop the game. In Tutorial 1.4, you will build Horse Racing game. This is a simple game. Up to 10 horses will race to the finish line. You guessed two horses that you thought could win the race. By clicking on the Start button, the race will start. All horses will race speed to get to the finish line. In chapter two, you will learn the basic concepts of classes and objects. Next, it will demonstrate how to define class and type of enumeration, which shows how both are used in the application. In Tutorial 2.1, you will create a two-level application that uses a form to pass input user to the People class. The form class is the level of representation and the People class is the middle level. You will add controls to the form so people can enter ID, last name, and their height. When the user clicks the Save button, the code will assign input values ​​to the People class properties. Finally, you will display the People object on a label. Figure below shows the form after the user clicks the Save button. In Tutorial 2.2, you will add a parameterized constructor to the People class. The application will ask the user to enter values, which will then be passed to the People constructor. Then, the application will display the values ​​stored on the People object. In Tutorial 2.3, you will create an application that utilizes enumeration type. The user will choose one type of account that is listed in a ListBox control and what he chooses is then displayed in a Label control. In Tutorial 2.4, you will create a simple Bank application. This application has one class, BankAcc, and a startup form. In Tutorial 2.5, you will improve the simple Bank application, by implementing the following two properties in the BankAcc class: TotalDeposit- Total money saved in current account; TotalWithdraw- Total funds that have been withdrawn from current account. In Tutorial 2.6, you will create an application to calculate the time needed for a particular aircraft to reach takeoff speed. You will also calculate how long the runway will be required. For each type of aircraft, you are given (1) the name of the aircraft, (2) the required take-off speed (feet/sec), and (3) how fast the plane accelerates (feet/sec2). In Tutorial 2.7, you will provide a number of programming training for those who want to improve their programming skills. Your task here is to write an object-oriented application so that training manager can display and edit the training services offered. There are several training categories: (1) Application Development, (2) Database, (3) Networking, and (4) System Administration. The training itself consists of: (1) title, (2) training days, (3) category, and (4) cost. Create a class named Training that contains this information, along with its properties and a ToString() method. In chapter three, several tutorials will be presented to build more complex projects. You will build them gradually and step by step. In Tutorial 3.1, you will build Catching Ball game. The bird flew and dropped ball from the sky. User is challenged to position man under the fallen ball to catch it. In Tutorial 3.2, you will build Smart Tic Tac Toe game. The aim of this game is to win the game on a 3 x 3 grid with the victory of three identical symbols (X or O) on horizontal, diagonal, or vertical lines. The players will play alternately. In this game given two game options: player 1 against player 2 or human player against computer. A smart but simple strategy will be developed for computer logic to be a formidable opponent for human. In Tutorial 3.3, you will build a Matching Images game. Ten pairs of images hidden on the game board. The object of the game is to find image pairs. In Two Players mode, players will get turns in turn. In One Player mode, there are two options to choose from: Playing Alone or Against Computer. When Play Alone option is selected, the player will play alone without an opponent. If Against Computer option is selected, then the level of computer intelligence is given with several levels according to the level of difficulty of the game. In Tutorial 3.4, you will build Throwing Fire program. This program can be played by two human players or human player versus computer. In chapter four, tutorials will be presented to build two advanced projects. You will build them gradually and step by step. In Tutorial 4.1, you will build Roasted Duck Delivery simulation. In this simulation, a number of decisions are needed. The basic idea is to read the order by incoming telephone and tell the delivery scooter to go to the location of the order. You also need to make sure that you always provide a roasted duck ready to be transported by the delivery scooter. The delivery area is a 20 by 20 square grid. The more roasted duck is sold, the more profit it gets. In Tutorial 4.2, you will build a Drone Simulation. In this simulation, you control both vertical and horizontal thrusters to maneuver the ride to the landing pad. You will adjust the landing speed so that it is slow enough so that no accident occurs.

Supermarket Sales Analysis And Prediction Using Machine Learning With Python Gui

DOWNLOAD
Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2022-04-15

Supermarket Sales Analysis And Prediction Using Machine Learning With Python Gui 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 2022-04-15 with Computers categories.

The dataset used in this project consists of the growth of supermarkets with high market competitions in most populated cities. The dataset is one of the historical sales of supermarket company which has recorded in 3 different branches for 3 months data. Predictive data analytics methods are easy to apply with this dataset. Attribute information in the dataset are as follows: Invoice id: Computer generated sales slip invoice identification number; Branch: Branch of supercenter (3 branches are available identified by A, B and C); City: Location of supercenters; Customer type: Type of customers, recorded by Members for customers using member card and Normal for without member card; Gender: Gender type of customer; Product line: General item categorization groups - Electronic accessories, Fashion accessories, Food and beverages, Health and beauty, Home and lifestyle, Sports and travel; Unit price: Price of each product in $; Quantity: Number of products purchased by customer; Tax: 5% tax fee for customer buying; Total: Total price including tax; Date: Date of purchase (Record available from January 2019 to March 2019); Time: Purchase time (10am to 9pm); Payment: Payment used by customer for purchase (3 methods are available – Cash, Credit card and Ewallet); COGS: Cost of goods sold; Gross margin percentage: Gross margin percentage; Gross income: Gross income; and Rating: Customer stratification rating on their overall shopping experience (On a scale of 1 to 10). In this project, you will perform predicting rating using machine learning. The machine learning models used in this project 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.

Data Science For Programmer A Project Based Approach With Python Gui

DOWNLOAD
Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2021-08-19

Data Science For Programmer A Project Based Approach With Python Gui 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 2021-08-19 with Computers categories.

Book 1: Practical Data Science Programming for Medical Datasets Analysis and Prediction with Python GUI In this book, you will implement two data science projects using Scikit-Learn, Scipy, and other libraries with Python GUI. In Project 1, you will learn how to use Scikit-Learn, NumPy, Pandas, Seaborn, and other libraries to perform how to predict early stage diabetes using Early Stage Diabetes Risk Prediction Dataset provided by Kaggle. This dataset contains the sign and symptpom data of newly diabetic or would be diabetic patient. This has been collected using direct questionnaires from the patients of Sylhet Diabetes Hospital in Sylhet, Bangladesh and approved by a doctor. You will develop a GUI using PyQt5 to plot distribution of features, feature importance, cross validation score, and prediced values versus true values. The machine learning models used in this project are Adaboost, Random Forest, Gradient Boosting, Logistic Regression, and Support Vector Machine. In Project 2, you will learn how to use Scikit-Learn, NumPy, Pandas, and other libraries to perform how to analyze and predict breast cancer using Breast Cancer Prediction Dataset provided by Kaggle. Worldwide, breast cancer is the most common type of cancer in women and the second highest in terms of mortality rates.Diagnosis of breast cancer is performed when an abnormal lump is found (from self-examination or x-ray) or a tiny speck of calcium is seen (on an x-ray). After a suspicious lump is found, the doctor will conduct a diagnosis to determine whether it is cancerous and, if so, whether it has spread to other parts of the body. This breast cancer dataset was obtained from the University of Wisconsin Hospitals, Madison from Dr. William H. Wolberg. You will develop a GUI using PyQt5 to plot distribution of features, pairwise relationship, test scores, prediced values versus true values, confusion matrix, and decision boundary. The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, and Support Vector Machine. Book 2: Step by Step Tutorials For Data Science With Python GUI: Traffic And Heart Attack Analysis And Prediction In this book, you will implement two data science projects using Scikit-Learn, Scipy, and other libraries with Python GUI. In Chapter 1, you will learn how to use Scikit-Learn, Scipy, and other libraries to perform how to predict traffic (number of vehicles) in four different junctions using Traffic Prediction Dataset provided by Kaggle. This dataset contains 48.1k (48120) observations of the number of vehicles each hour in four different junctions: 1) DateTime; 2) Juction; 3) Vehicles; and 4) ID. In Chapter 2, you will learn how to use Scikit-Learn, NumPy, Pandas, and other libraries to perform how to analyze and predict heart attack using Heart Attack Analysis & Prediction Dataset provided by Kaggle. Book 3: BRAIN TUMOR: Analysis, Classification, and Detection Using Machine Learning and Deep Learning with Python GUI In this project, you will learn how to use Scikit-Learn, TensorFlow, Keras, NumPy, Pandas, Seaborn, and other libraries to implement brain tumor classification and detection with machine learning using Brain Tumor dataset provided by Kaggle. This dataset contains five first order features: Mean (the contribution of individual pixel intensity for the entire image), Variance (used to find how each pixel varies from the neighboring pixel 0, Standard Deviation (the deviation of measured Values or the data from its mean), Skewness (measures of symmetry), and Kurtosis (describes the peak of e.g. a frequency distribution). It also contains eight second order features: Contrast, Energy, ASM (Angular second moment), Entropy, Homogeneity, Dissimilarity, Correlation, and Coarseness. The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, and Support Vector Machine. The deep learning models used in this project are MobileNet and ResNet50. In this project, you will develop a GUI using PyQt5 to plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, training loss, and training accuracy.

Full Source Code Sqlite For Students And Programmers With Python Gui

DOWNLOAD
Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2022-07-26

Full Source Code Sqlite For Students And Programmers With Python Gui 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 2022-07-26 with Computers categories.

In this project, we provide you with a SQLITE version of an Oracle sample database named OT which is based on a global fictitious company that sells computer hardware including storage, motherboard, RAM, video card, and CPU. You can find the detailed structures of the database: https://www.oracletutorial.com/getting-started/oracle-sample-database/. The company maintains the product information such as name, description standard cost, list price, and product line. It also tracks the inventory information for all products including warehouses where products are available. Because the company operates globally, it has warehouses in various locations around the world. The company records all customer information including name, address, and website. Each customer has at least one contact person with detailed information including name, email, and phone. The company also places a credit limit on each customer to limit the amount that customer can owe. Whenever a customer issues a purchase order, a sales order is created in the database with the pending status. When the company ships the order, the order status becomes shipped. In case the customer cancels an order, the order status becomes canceled. In addition to the sales information, the employee data is recorded with some basic information such as name, email, phone, job title, manager, and hire date. In this project, you will write Python script to create every table and insert rows of data into each of them. You will develop GUI with PyQt5 to each table in the database. You will also create GUI to plot: case distribution of order date by year, quarter, month, week, and day; the distribution of amount by year, quarter, month, week, day, and hour; the distribution of bottom 10 sales by product, top 10 sales by product, bottom 10 sales by customer, top 10 sales by customer, bottom 10 sales by category, top 10 sales by category, bottom 10 sales by status, top 10 sales by status, bottom 10 sales by customer city, top 10 sales by customer city, bottom 10 sales by customer state, top 10 sales by customer state, average amount by month with mean and EWM, average amount by every month, amount feature over June 2016, amount feature over 2017, and amount payment in all years.

Full Source Code Practical Data Science With Sqlite And Python Gui

DOWNLOAD
Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2022-08-08

Full Source Code Practical Data Science With Sqlite And Python Gui 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 2022-08-08 with Computers categories.

In this project, we provide you with the SQLite sample database named chinook. The chinook sample database is a good database for practicing with SQL, especially SQLite. The detailed description of the database can be found on: https://www.sqlitetutorial.net/sqlite-sample-database/. There are 11 tables in the chinook sample database:The employee table stores employees data such as employee id, last name, first name, etc. It also has a field named ReportsTo to specify who reports to whom; customers table stores customers data; invoices & invoice_items tables: these two tables store invoice data. The invoice table stores invoice header data and the invoice_items table stores the invoice line items data; The artist table stores artists data. It is a simple table that contains only the artist id and name; The album table stores data about a list of tracks. Each album belongs to one artist. However, one artist may have multiple albums; The media_type table stores media types such as MPEG audio and AAC audio files; genre table stores music types such as rock, jazz, metal, etc; The track table stores the data of songs. Each track belongs to one album; playlist & playlist_track tables: The playlist table store data about playlists. Each playlist contains a list of tracks. Each track may belong to multiple playlists. The relationship between the playlist table and track table is many-to-many. The playlist_track table is used to reflect this relationship. In this project, you will write Python script to create every table and insert rows of data into each of them. You will develop GUI with PyQt5 to each table in the database. You will also create GUI to plot: case distribution of order date by year, quarter, month, week, and day; the distribution of amount by year, quarter, month, week, day, and hour; the bottom/top 10 sales by employee, the bottom/top 10 sales by customer, the bottom/top 10 sales by customer, the bottom/top 10 sales by artist, the bottom/top 10 sales by genre, the bottom/top 10 sales by play list, the bottom/top 10 sales by customer city, the bottom/top 10 sales by customer city, the bottom/top 10 sales by customer city, the payment amount by month with mean and EWM, the average payment amount by every month, and amount payment in all years.

Full Source Code Postgresql And Data Science For Programmers With Python Gui

DOWNLOAD
Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2022-09-19

Full Source Code Postgresql And Data Science For Programmers With Python Gui 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 2022-09-19 with Computers categories.

This project uses the PostgreSQL version of MySQL-based Sakila sample database which is a fictitious database designed to represent a DVD rental store. The tables of the database include film, film_category, actor, film_actor, customer, rental, payment and inventory among others. You can download the database from https://dev.mysql.com/doc/sakila/en/. In this project, you will write Python script to create every table and insert rows of data into each of them. You will develop GUI with PyQt5 to each table in the database. You will also create GUI to plot case distribution of film release year, film rating, rental duration, and categorize film length; plot rating variable against rental_duration variable in stacked bar plots; plot length variable against rental_duration variable in stacked bar plots; read payment table; plot case distribution of Year, Day, Month, Week, and Quarter of payment; plot which year, month, week, days of week, and quarter have most payment amount; read film list by joining five tables: category, film_category, film_actor, film, and actor; plot case distribution of top 10 and bottom 10 actors; plot which film title have least and most sales; plot which actor have least and most sales; plot which film category have least and most sales; plot case distribution of top 10 and bottom 10 overdue costumers; plot which store have most sales; plot average payment amount by month with mean and EWM; and plot payment amount over June 2005.

Full Source Code Postgresql For Data Analytics And Visualization With Python Gui

DOWNLOAD
Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2022-09-13

Full Source Code Postgresql For Data Analytics And Visualization With Python Gui 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 2022-09-13 with Computers categories.

In this project, we provide you with a PostgreSQL version of an Oracle sample database named OT which is based on a global fictitious company that sells computer hardware including storage, motherboard, RAM, video card, and CPU. The company maintains the product information such as name, description standard cost, list price, and product line. It also tracks the inventory information for all products including warehouses where products are available. Because the company operates globally, it has warehouses in various locations around the world. The company records all customer information including name, address, and website. Each customer has at least one contact person with detailed information including name, email, and phone. The company also places a credit limit on each customer to limit the amount that customer can owe. Whenever a customer issues a purchase order, a sales order is created in the database with the pending status. When the company ships the order, the order status becomes shipped. In case the customer cancels an order, the order status becomes canceled. In addition to the sales information, the employee data is recorded with some basic information such as name, email, phone, job title, manager, and hire date. In this project, you will write Python script to create every table and insert rows of data into each of them. You will develop GUI with PyQt5 to each table in the database. You will also create GUI to plot: case distribution of order date by year, quarter, month, week, and day; the distribution of amount by year, quarter, month, week, day, and hour; the distribution of bottom 10 sales by product, top 10 sales by product, bottom 10 sales by customer, top 10 sales by customer, bottom 10 sales by category, top 10 sales by category, bottom 10 sales by status, top 10 sales by status, bottom 10 sales by customer city, top 10 sales by customer city, bottom 10 sales by customer state, top 10 sales by customer state, average amount by month with mean and EWM, average amount by every month, amount feature over June 2016, amount feature over 2017, and amount payment in all years.

In Depth Tutorials Deep Learning Using Scikit Learn Keras And Tensorflow With Python Gui

DOWNLOAD
Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2021-06-05

In Depth Tutorials Deep Learning Using Scikit Learn Keras And Tensorflow With Python Gui 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 2021-06-05 with Computers categories.

BOOK 1: LEARN FROM SCRATCH MACHINE LEARNING WITH PYTHON GUI In this book, you will learn how to use NumPy, Pandas, OpenCV, Scikit-Learn and other libraries to how to plot graph and to process digital image. Then, you will learn how to classify features using Perceptron, Adaline, Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN) models. You will also learn how to extract features using Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Kernel Principal Component Analysis (KPCA) algorithms and use them in machine learning. In Chapter 1, you will learn: Tutorial Steps To Create A Simple GUI Application, Tutorial Steps to Use Radio Button, Tutorial Steps to Group Radio Buttons, Tutorial Steps to Use CheckBox Widget, Tutorial Steps to Use Two CheckBox Groups, Tutorial Steps to Understand Signals and Slots, Tutorial Steps to Convert Data Types, Tutorial Steps to Use Spin Box Widget, Tutorial Steps to Use ScrollBar and Slider, Tutorial Steps to Use List Widget, Tutorial Steps to Select Multiple List Items in One List Widget and Display It in Another List Widget, Tutorial Steps to Insert Item into List Widget, Tutorial Steps to Use Operations on Widget List, Tutorial Steps to Use Combo Box, Tutorial Steps to Use Calendar Widget and Date Edit, and Tutorial Steps to Use Table Widget. In Chapter 2, you will learn: Tutorial Steps To Create A Simple Line Graph, Tutorial Steps To Create A Simple Line Graph in Python GUI, Tutorial Steps To Create A Simple Line Graph in Python GUI: Part 2, Tutorial Steps To Create Two or More Graphs in the Same Axis, Tutorial Steps To Create Two Axes in One Canvas, Tutorial Steps To Use Two Widgets, Tutorial Steps To Use Two Widgets, Each of Which Has Two Axes, Tutorial Steps To Use Axes With Certain Opacity Levels, Tutorial Steps To Choose Line Color From Combo Box, Tutorial Steps To Calculate Fast Fourier Transform, Tutorial Steps To Create GUI For FFT, Tutorial Steps To Create GUI For FFT With Some Other Input Signals, Tutorial Steps To Create GUI For Noisy Signal, Tutorial Steps To Create GUI For Noisy Signal Filtering, and Tutorial Steps To Create GUI For Wav Signal Filtering. In Chapter 3, you will learn: Tutorial Steps To Convert RGB Image Into Grayscale, Tutorial Steps To Convert RGB Image Into YUV Image, Tutorial Steps To Convert RGB Image Into HSV Image, Tutorial Steps To Filter Image, Tutorial Steps To Display Image Histogram, Tutorial Steps To Display Filtered Image Histogram, Tutorial Steps To Filter Image With CheckBoxes, Tutorial Steps To Implement Image Thresholding, and Tutorial Steps To Implement Adaptive Image Thresholding. You will also learn: Tutorial Steps To Generate And Display Noisy Image, Tutorial Steps To Implement Edge Detection On Image, Tutorial Steps To Implement Image Segmentation Using Multiple Thresholding and K-Means Algorithm, Tutorial Steps To Implement Image Denoising, Tutorial Steps To Detect Face, Eye, and Mouth Using Haar Cascades, Tutorial Steps To Detect Face Using Haar Cascades with PyQt, Tutorial Steps To Detect Eye, and Mouth Using Haar Cascades with PyQt, Tutorial Steps To Extract Detected Objects, Tutorial Steps To Detect Image Features Using Harris Corner Detection, Tutorial Steps To Detect Image Features Using Shi-Tomasi Corner Detection, Tutorial Steps To Detect Features Using Scale-Invariant Feature Transform (SIFT), and Tutorial Steps To Detect Features Using Features from Accelerated Segment Test (FAST). In Chapter 4, In this tutorial, you will learn how to use Pandas, NumPy and other libraries to perform simple classification using perceptron and Adaline (adaptive linear neuron). The dataset used is Iris dataset directly from the UCI Machine Learning Repository. You will learn: Tutorial Steps To Implement Perceptron, Tutorial Steps To Implement Perceptron with PyQt, Tutorial Steps To Implement Adaline (ADAptive LInear NEuron), and Tutorial Steps To Implement Adaline with PyQt. In Chapter 5, you will learn how to use the scikit-learn machine learning library, which provides a wide variety of machine learning algorithms via a user-friendly Python API and to perform classification using perceptron, Adaline (adaptive linear neuron), and other models. The dataset used is Iris dataset directly from the UCI Machine Learning Repository. You will learn: Tutorial Steps To Implement Perceptron Using Scikit-Learn, Tutorial Steps To Implement Perceptron Using Scikit-Learn with PyQt, Tutorial Steps To Implement Logistic Regression Model, Tutorial Steps To Implement Logistic Regression Model with PyQt, Tutorial Steps To Implement Logistic Regression Model Using Scikit-Learn with PyQt, Tutorial Steps To Implement Support Vector Machine (SVM) Using Scikit-Learn, Tutorial Steps To Implement Decision Tree (DT) Using Scikit-Learn, Tutorial Steps To Implement Random Forest (RF) Using Scikit-Learn, and Tutorial Steps To Implement K-Nearest Neighbor (KNN) Using Scikit-Learn. In Chapter 6, you will learn how to use Pandas, NumPy, Scikit-Learn, and other libraries to implement different approaches for reducing the dimensionality of a dataset using different feature selection techniques. You will learn about three fundamental techniques that will help us to summarize the information content of a dataset by transforming it onto a new feature subspace of lower dimensionality than the original one. Data compression is an important topic in machine learning, and it helps us to store and analyze the increasing amounts of data that are produced and collected in the modern age of technology. You will learn the following topics: Principal Component Analysis (PCA) for unsupervised data compression, Linear Discriminant Analysis (LDA) as a supervised dimensionality reduction technique for maximizing class separability, Nonlinear dimensionality reduction via Kernel Principal Component Analysis (KPCA). You will learn: Tutorial Steps To Implement Principal Component Analysis (PCA), Tutorial Steps To Implement Principal Component Analysis (PCA) Using Scikit-Learn, Tutorial Steps To Implement Principal Component Analysis (PCA) Using Scikit-Learn with PyQt, Tutorial Steps To Implement Linear Discriminant Analysis (LDA), Tutorial Steps To Implement Linear Discriminant Analysis (LDA) with Scikit-Learn, Tutorial Steps To Implement Linear Discriminant Analysis (LDA) Using Scikit-Learn with PyQt, Tutorial Steps To Implement Kernel Principal Component Analysis (KPCA) Using Scikit-Learn, and Tutorial Steps To Implement Kernel Principal Component Analysis (KPCA) Using Scikit-Learn with PyQt. In Chapter 7, you will learn how to use Keras, Scikit-Learn, Pandas, NumPy and other libraries to perform prediction on handwritten digits using MNIST dataset. You will learn: Tutorial Steps To Load MNIST Dataset, Tutorial Steps To Load MNIST Dataset with PyQt, Tutorial Steps To Implement Perceptron With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Perceptron With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Perceptron With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Logistic Regression (LR) Model With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Logistic Regression (LR) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Logistic Regression (LR) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement , Tutorial Steps To Implement Support Vector Machine (SVM) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Support Vector Machine (SVM) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Decision Tree (DT) Model With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Decision Tree (DT) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Decision Tree (DT) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Random Forest (RF) Model With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Random Forest (RF) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Random Forest (RF) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement K-Nearest Neighbor (KNN) Model With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement K-Nearest Neighbor (KNN) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, and Tutorial Steps To Implement K-Nearest Neighbor (KNN) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt. BOOK 2: THE PRACTICAL GUIDES ON DEEP LEARNING USING SCIKIT-LEARN, KERAS, AND TENSORFLOW WITH PYTHON GUI In this book, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to implement deep learning on recognizing traffic signs using GTSRB dataset, detecting brain tumor using Brain Image MRI dataset, classifying gender, and recognizing facial expression using FER2013 dataset In Chapter 1, you will learn to create GUI applications to display line graph using PyQt. You will also learn how to display image and its histogram. In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, Pandas, NumPy and other libraries to perform prediction on handwritten digits using MNIST dataset with PyQt. You will build a GUI application for this purpose. In Chapter 3, you will learn how to perform recognizing traffic signs using GTSRB dataset from Kaggle. There are several different types of traffic signs like speed limits, no entry, traffic signals, turn left or right, children crossing, no passing of heavy vehicles, etc. Traffic signs classification is the process of identifying which class a traffic sign belongs to. In this Python project, you will build a deep neural network model that can classify traffic signs in image into different categories. With this model, you will be able to read and understand traffic signs which are a very important task for all autonomous vehicles. You will build a GUI application for this purpose. In Chapter 4, you will learn how to perform detecting brain tumor using Brain Image MRI dataset provided by Kaggle (https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection) using CNN model. You will build a GUI application for this purpose. In Chapter 5, you will learn how to perform classifying gender using dataset provided by Kaggle (https://www.kaggle.com/cashutosh/gender-classification-dataset) using MobileNetV2 and CNN models. You will build a GUI application for this purpose. In Chapter 6, you will learn how to perform recognizing facial expression using FER2013 dataset provided by Kaggle (https://www.kaggle.com/nicolejyt/facialexpressionrecognition) using CNN model. You will also build a GUI application for this purpose. BOOK 3: STEP BY STEP TUTORIALS ON DEEP LEARNING USING SCIKIT-LEARN, KERAS, AND TENSORFLOW WITH PYTHON GUI In this book, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to implement deep learning on classifying fruits, classifying cats/dogs, detecting furnitures, and classifying fashion. In Chapter 1, you will learn to create GUI applications to display line graph using PyQt. You will also learn how to display image and its histogram. Then, you will learn how to use OpenCV, NumPy, and other libraries to perform feature extraction with Python GUI (PyQt). The feature detection techniques used in this chapter are Harris Corner Detection, Shi-Tomasi Corner Detector, and Scale-Invariant Feature Transform (SIFT). In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform classifying fruits using Fruits 360 dataset provided by Kaggle (https://www.kaggle.com/moltean/fruits/code) using Transfer Learning and CNN models. You will build a GUI application for this purpose. In Chapter 3, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform classifying cats/dogs using dataset provided by Kaggle (https://www.kaggle.com/chetankv/dogs-cats-images) using Using CNN with Data Generator. You will build a GUI application for this purpose. In Chapter 4, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform detecting furnitures using Furniture Detector dataset provided by Kaggle (https://www.kaggle.com/akkithetechie/furniture-detector) using VGG16 model. You will build a GUI application for this purpose. In Chapter 5, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform classifying fashion using Fashion MNIST dataset provided by Kaggle (https://www.kaggle.com/zalando-research/fashionmnist/code) using CNN model. You will build a GUI application for this purpose. BOOK 4: Project-Based Approach On DEEP LEARNING Using Scikit-Learn, Keras, And TensorFlow with Python GUI In this book, implement deep learning on detecting vehicle license plates, recognizing sign language, and detecting surface crack using TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries. In Chapter 1, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform detecting vehicle license plates using Car License Plate Detection dataset provided by Kaggle (https://www.kaggle.com/andrewmvd/car-plate-detection/download). In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform sign language recognition using Sign Language Digits Dataset provided by Kaggle (https://www.kaggle.com/ardamavi/sign-language-digits-dataset/download). In Chapter 3, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform detecting surface crack using Surface Crack Detection provided by Kaggle (https://www.kaggle.com/arunrk7/surface-crack-detection/download). BOOK 5: Hands-On Guide To IMAGE CLASSIFICATION Using Scikit-Learn, Keras, And TensorFlow with PYTHON GUI In this book, implement deep learning-based image classification on detecting face mask, classifying weather, and recognizing flower using TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries. In Chapter 1, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform detecting face mask using Face Mask Detection Dataset provided by Kaggle (https://www.kaggle.com/omkargurav/face-mask-dataset/download). In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to classify weather using Multi-class Weather Dataset provided by Kaggle (https://www.kaggle.com/pratik2901/multiclass-weather-dataset/download). In Chapter 3, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to recognize flower using Flowers Recognition dataset provided by Kaggle (https://www.kaggle.com/alxmamaev/flowers-recognition/download). BOOK 6: Step by Step Tutorial IMAGE CLASSIFICATION Using Scikit-Learn, Keras, And TensorFlow with PYTHON GUI In this book, implement deep learning-based image classification on classifying monkey species, recognizing rock, paper, and scissor, and classify airplane, car, and ship using TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries. In Chapter 1, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to classify monkey species using 10 Monkey Species dataset provided by Kaggle (https://www.kaggle.com/slothkong/10-monkey-species/download). In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to recognize rock, paper, and scissor using 10 Monkey Species dataset provided by Kaggle (https://www.kaggle.com/sanikamal/rock-paper-scissors-dataset/download). In Chapter 3, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to classify airplane, car, and ship using Multiclass-image-dataset-airplane-car-ship dataset provided by Kaggle (https://www.kaggle.com/abtabm/multiclassimagedatasetairplanecar).