The Mysql Workshop

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The Mysql Workshop

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Author : Thomas Pettit
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-04-29

The Mysql Workshop written by Thomas Pettit and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-29 with Computers categories.

Learning MySQL just got a whole lot easier, thanks to this hands-on workshop, complete with simple explanations, engaging examples, and realistic exercises that focus on helping you to build and maintain databases effectively Key Features Learn how to set up and maintain a MySQL database Run SQL queries to create, retrieve, and manipulate data Use MySQL effectively with common business applications such as Excel and MS Access Book Description Do you want to learn how to create and maintain databases effectively? Are you looking for simple answers to basic MySQL questions as well as straightforward examples that you can use at work? If so, this workshop is the right choice for you. Designed to build your confidence through hands-on practice, this book uses a simple approach that focuses on the practical, so you can get straight down to business without having to wade through pages and pages of dull, dry theory. As you work through bite-sized exercises and activities, you'll learn how to use different MySQL tools to create a database and manage the data within it. You'll see how to transfer data between a MySQL database and other sources, and use real-world datasets to gain valuable experience of manipulating and gaining insights from data. As you progress, you'll discover how to protect your database by managing user permissions and performing logical backups and restores. If you've already tried to teach yourself SQL, but haven't been able to make the leap from understanding simple queries to working on live projects with a real database management system, The MySQL Workshop will get you on the right track. By the end of this MySQL book, you'll have the knowledge, skills, and confidence to advance your career and tackle your own ambitious projects with MySQL. What you will learn Understand the concepts of relational databases and document stores Use SQL queries, stored procedures, views, functions, and transactions Connect to and manipulate data using MS Access, MS Excel, and Visual Basic for Applications (VBA) Read and write data in the CSV or JSON format using MySQL Manage data while running MySQL Shell in JavaScript mode Use X DevAPI to access a NoSQL interface for MySQL Manage user roles, credentials, and privileges to keep data secure Perform a logical database backup with mysqldump and mysqlpump Who this book is for This book is for anyone who wants to learn how to use MySQL in a productive, efficient way. If you're totally new to MySQL, it'll help you get started or if you've used MySQL before, it'll fill in any gaps, consolidate key concepts, and offer valuable hands-on practice. Prior knowledge of simple SQL or basic programming techniques will help you in quickly grasping the concepts covered, but is not necessary.

The Mysql Workshop

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Author : Thomas Pettit
language : en
Publisher: Packt Publishing
Release Date : 2022-04-29

The Mysql Workshop written by Thomas Pettit and has been published by Packt Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-29 with categories.

Learning MySQL just got a whole lot easier, thanks to this hands-on workshop, complete with simple explanations, engaging examples, and realistic exercises that focus on helping you to build and maintain databases effectively Key Features: Learn how to set up and maintain a MySQL database Run SQL queries to create, retrieve, and manipulate data Use MySQL effectively with common business applications such as Excel and MS Access Book Description: Do you want to learn how to create and maintain databases effectively? Are you looking for simple answers to basic MySQL questions as well as straightforward examples that you can use at work? If so, this workshop is the right choice for you. Designed to build your confidence through hands-on practice, this book uses a simple approach that focuses on the practical, so you can get straight down to business without having to wade through pages and pages of dull, dry theory. As you work through bite-sized exercises and activities, you'll learn how to use different MySQL tools to create a database and manage the data within it. You'll see how to transfer data between a MySQL database and other sources, and use real-world datasets to gain valuable experience of manipulating and gaining insights from data. As you progress, you'll discover how to protect your database by managing user permissions and performing logical backups and restores. If you've already tried to teach yourself SQL, but haven't been able to make the leap from understanding simple queries to working on live projects with a real database management system, The MySQL Workshop will get you on the right track. By the end of this MySQL book, you'll have the knowledge, skills, and confidence to advance your career and tackle your own ambitious projects with MySQL. What You Will Learn: Understand the concepts of relational databases and document stores Use SQL queries, stored procedures, views, functions, and transactions Connect to and manipulate data using MS Access, MS Excel, and Visual Basic for Applications (VBA) Read and write data in the CSV or JSON format using MySQL Manage data while running MySQL Shell in JavaScript mode Use X DevAPI to access a NoSQL interface for MySQL Manage user roles, credentials, and privileges to keep data secure Perform a logical database backup with mysqldump and mysqlpump Who this book is for: This book is for anyone who wants to learn how to use MySQL in a productive, efficient way. If you're totally new to MySQL, it'll help you get started or if you've used MySQL before, it'll fill in any gaps, consolidate key concepts, and offer valuable hands-on practice. Prior knowledge of simple SQL or basic programming techniques will help you in quickly grasping the concepts covered, but is not necessary.

The The Sql Workshop

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Author : Frank Solomon
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-12-30

The The Sql Workshop written by Frank Solomon and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-30 with Computers categories.

Get to grips with SQL fundamentals and learn how to efficiently create, read and update information stored in databases Key FeaturesUnderstand the features and syntax of SQL and use them to query databasesLearn how to create databases and tables and manipulate the data within themCreate advanced queries and apply them on realistic databases with hands-on activitiesBook Description Many software applications are backed by powerful relational database systems, meaning that the skills to be able to maintain a SQL database and reliably retrieve data are in high demand. With its simple syntax and effective data manipulation capabilities, SQL enables you to manage relational databases with ease. The SQL Workshop will help you progress from basic to advanced-level SQL queries in order to create and manage databases successfully. This Workshop begins with an introduction to basic CRUD commands and gives you an overview of the different data types in SQL. You'll use commands for narrowing down the search results within a database and learn about data retrieval from single and multiple tables in a single query. As you advance, you'll use aggregate functions to perform calculations on a set of values, and implement process automation using stored procedures, functions, and triggers. Finally, you'll secure your database against potential threats and use access control to keep your data safe. Throughout this Workshop, you'll use your skills on a realistic database for an online shop, preparing you for solving data problems in the real world. By the end of this book, you'll have built the knowledge, skills and confidence to creatively solve real-world data problems with SQL. What you will learnCreate databases and insert data into themUse SQL queries to create, read, update, and delete dataMaintain data integrity and consistency through normalizationCustomize your basic SQL queries to get the desired outputRefine your database search using the WHERE and HAVING clausesUse joins to fetch data from multiple tables and create custom reportsImprove web application performance by automating processesSecure a database with GRANT and REVOKE privilegesWho this book is for This Workshop is suitable for anyone who wants to learn how to use SQL to work with databases. No prior SQL or database experience is necessary. Whether you're an aspiring software developer, database engineer, data scientist, or systems administrator, this Workshop will quickly get you up and running.

The The Php Workshop

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Author : Alexandru Busuioc
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-10-31

The The Php Workshop written by Alexandru Busuioc and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-31 with Computers categories.

Get to grips with the fundamentals of PHP programming and learn to build dynamic, testable PHP web applications with the help of real-world examples and hands-on projects Key FeaturesStart building modern and testable PHP web applicationsMaster the basic syntax and fundamental features of PHPImplement object-oriented programming to write modular, well-structured codeBook Description Do you want to build your own websites, but have never really been confident enough to turn your ideas into real projects? If your web development skills are a bit rusty, or if you've simply never programmed before, The PHP Workshop will show you how to build dynamic websites using PHP with the help of engaging examples and challenging activities. This PHP tutorial starts with an introduction to PHP, getting you set up with a productive development environment. You will write, execute, and troubleshoot your first PHP script using a built-in templating engine and server. Next, you'll learn about variables and data types, and see how conditions and loops help control the flow of a PHP program. Progressing through the chapters, you'll use HTTP methods to turn your PHP scripts into web apps, persist data by connecting to an external database, handle application errors, and improve functionality by using third-party packages. By the end of this Workshop, you'll be well-versed in web application development, and have the knowledge and skills to creatively tackle your own ambitious projects with PHP. What you will learnSet up a development environment and write your first PHP scriptsUse inheritance, encapsulation, polymorphism and other OOP conceptsUse HTTP and understand the request-response cycle of an applicationPerform file operations and interact with external databasesDeal with application errors and handle exceptionsUse third-party libraries and manage dependenciesConnect your application to web services to allow for data exchangeWho this book is for This book on PHP for beginners will help you if you're just getting started with PHP. Although prior programming experience is not necessary, a basic understanding of HTML, CSS, and JavaScript will help you grasp the concepts covered more easily.

The Applied Data Science Workshop On Medical Datasets Using Machine Learning And Deep Learning With Python Gui

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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2022-01-07

The Applied Data Science Workshop On Medical Datasets Using Machine Learning And Deep 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 2022-01-07 with Computers categories.

Workshop 1: Heart Failure Analysis and Prediction Using Scikit-Learn, Keras, and TensorFlow with Python GUI Cardiovascular diseases (CVDs) are the number 1 cause of death globally taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worldwide. Heart failure is a common event caused by CVDs and this dataset contains 12 features that can be used to predict mortality by heart failure. People with cardiovascular disease or who are at high cardiovascular risk (due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidaemia or already established disease) need early detection and management wherein a machine learning models can be of great help. Dataset used in this project is from Davide Chicco, Giuseppe Jurman. Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC Medical Informatics and Decision Making 20, 16 (2020). Attribute information in the dataset are as follows: age: Age; anaemia: Decrease of red blood cells or hemoglobin (boolean); creatinine_phosphokinase: Level of the CPK enzyme in the blood (mcg/L); diabetes: If the patient has diabetes (boolean); ejection_fraction: Percentage of blood leaving the heart at each contraction (percentage); high_blood_pressure: If the patient has hypertension (boolean); platelets: Platelets in the blood (kiloplatelets/mL); serum_creatinine: Level of serum creatinine in the blood (mg/dL); serum_sodium: Level of serum sodium in the blood (mEq/L); sex: Woman or man (binary); smoking: If the patient smokes or not (boolean); time: Follow-up period (days); and DEATH_EVENT: If the patient deceased during the follow-up period (boolean). The 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 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, learning curve, performace of the model, scalability of the model, training loss, and training accuracy. WORKSHOP 2: Cervical Cancer Classification and Prediction Using Machine Learning and Deep Learning with Python GUI About 11,000 new cases of invasive cervical cancer are diagnosed each year in the U.S. However, the number of new cervical cancer cases has been declining steadily over the past decades. Although it is the most preventable type of cancer, each year cervical cancer kills about 4,000 women in the U.S. and about 300,000 women worldwide. Numerous studies report that high poverty levels are linked with low screening rates. In addition, lack of health insurance, limited transportation, and language difficulties hinder a poor woman’s access to screening services. Human papilloma virus (HPV) is the main risk factor for cervical cancer. In adults, the most important risk factor for HPV is sexual activity with an infected person. Women most at risk for cervical cancer are those with a history of multiple sexual partners, sexual intercourse at age 17 years or younger, or both. A woman who has never been sexually active has a very low risk for developing cervical cancer. Sexual activity with multiple partners increases the likelihood of many other sexually transmitted infections (chlamydia, gonorrhea, syphilis). Studies have found an association between chlamydia and cervical cancer risk, including the possibility that chlamydia may prolong HPV infection. Therefore, early detection of cervical cancer using machine and deep learning models can be of great help. The dataset used in this project is obtained from UCI Repository and kindly acknowledged. This file contains a List of Risk Factors for Cervical Cancer leading to a Biopsy Examination. The 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 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, learning curve, performace of the model, scalability of the model, training loss, and training accuracy. WORKSHOP 3: Chronic Kidney Disease Classification and Prediction Using Machine Learning and Deep Learning with Python GUI Chronic kidney disease is the longstanding disease of the kidneys leading to renal failure. The kidneys filter waste and excess fluid from the blood. As kidneys fail, waste builds up. Symptoms develop slowly and aren't specific to the disease. Some people have no symptoms at all and are diagnosed by a lab test. Medication helps manage symptoms. In later stages, filtering the blood with a machine (dialysis) or a transplant may be required The dataset used in this project was taken over a 2-month period in India with 25 features (eg, red blood cell count, white blood cell count, etc). The target is the 'classification', which is either 'ckd' or 'notckd' - ckd=chronic kidney disease. It contains measures of 24 features for 400 people. Quite a lot of features for just 400 samples. There are 14 categorical features, while 10 are numerical. The dataset needs cleaning: in that it has NaNs and the numeric features need to be forced to floats. Attribute Information: Age(numerical) age in years; Blood Pressure(numerical) bp in mm/Hg; Specific Gravity(categorical) sg - (1.005,1.010,1.015,1.020,1.025); Albumin(categorical) al - (0,1,2,3,4,5); Sugar(categorical) su - (0,1,2,3,4,5); Red Blood Cells(categorical) rbc - (normal,abnormal); Pus Cell (categorical) pc - (normal,abnormal); Pus Cell clumps(categorical) pcc - (present, notpresent); Bacteria(categorical) ba - (present,notpresent); Blood Glucose Random(numerical) bgr in mgs/dl; Blood Urea(numerical) bu in mgs/dl; Serum Creatinine(numerical) sc in mgs/dl; Sodium(numerical) sod in mEq/L; Potassium(numerical) pot in mEq/L; Hemoglobin(numerical) hemo in gms; Packed Cell Volume(numerical); White Blood Cell Count(numerical) wc in cells/cumm; Red Blood Cell Count(numerical) rc in millions/cmm; Hypertension(categorical) htn - (yes,no); Diabetes Mellitus(categorical) dm - (yes,no); Coronary Artery Disease(categorical) cad - (yes,no); Appetite(categorical) appet - (good,poor); Pedal Edema(categorical) pe - (yes,no); Anemia(categorical) ane - (yes,no); and Class (categorical) class - (ckd,notckd). The 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 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, learning curve, performace of the model, scalability of the model, training loss, and training accuracy. WORKSHOP 4: Lung Cancer Classification and Prediction Using Machine Learning and Deep Learning with Python GUI The effectiveness of cancer prediction system helps the people to know their cancer risk with low cost and it also helps the people to take the appropriate decision based on their cancer risk status. The data is collected from the website online lung cancer prediction system. Total number of attributes in the dataset is 16, while number of instances is 309. Following are attribute information of dataset: Gender: M(male), F(female); Age: Age of the patient; Smoking: YES=2 , NO=1; Yellow fingers: YES=2 , NO=1; Anxiety: YES=2 , NO=1; Peer_pressure: YES=2 , NO=1; Chronic Disease: YES=2 , NO=1; Fatigue: YES=2 , NO=1; Allergy: YES=2 , NO=1; Wheezing: YES=2 , NO=1; Alcohol: YES=2 , NO=1; Coughing: YES=2 , NO=1; Shortness of Breath: YES=2 , NO=1; Swallowing Difficulty: YES=2 , NO=1; Chest pain: YES=2 , NO=1; and Lung Cancer: YES , NO. The 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 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, learning curve, performace of the model, scalability of the model, training loss, and training accuracy. WORKSHOP 5: Alzheimer’s Disease Classification and Prediction Using Machine Learning and Deep Learning with Python GUI Alzheimer's is a type of dementia that causes problems with memory, thinking and behavior. Symptoms usually develop slowly and get worse over time, becoming severe enough to interfere with daily tasks. Alzheimer's is not a normal part of aging. The greatest known risk factor is increasing age, and the majority of people with Alzheimer's are 65 and older. But Alzheimer's is not just a disease of old age. Approximately 200,000 Americans under the age of 65 have younger-onset Alzheimer’s disease (also known as early-onset Alzheimer’s). The dataset consists of a longitudinal MRI data of 374 subjects aged 60 to 96. Each subject was scanned at least once. Everyone is right-handed. 206 of the subjects were grouped as 'Nondemented' throughout the study. 107 of the subjects were grouped as 'Demented' at the time of their initial visits and remained so throughout the study. 14 subjects were grouped as 'Nondemented' at the time of their initial visit and were subsequently characterized as 'Demented' at a later visit. These fall under the 'Converted' category. Following are some important features in the dataset: EDUC:Years of Education; SES: Socioeconomic Status; MMSE: Mini Mental State Examination; CDR: Clinical Dementia Rating; eTIV: Estimated Total Intracranial Volume; nWBV: Normalize Whole Brain Volume; and ASF: Atlas Scaling Factor. The 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 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, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. WORKSHOP 6: Parkinson Classification and Prediction Using Machine Learning and Deep Learning with Python GUI The dataset was created by Max Little of the University of Oxford, in collaboration with the National Centre for Voice and Speech, Denver, Colorado, who recorded the speech signals. The original study published the feature extraction methods for general voice disorders. This dataset is composed of a range of biomedical voice measurements from 31 people, 23 with Parkinson's disease (PD). Each column in the table is a particular voice measure, and each row corresponds one of 195 voice recording from these individuals ("name" column). The main aim of the data is to discriminate healthy people from those with PD, according to "status" column which is set to 0 for healthy and 1 for PD. The data is in ASCII CSV format. The rows of the CSV file contain an instance corresponding to one voice recording. There are around six recordings per patient, the name of the patient is identified in the first column. Attribute information of this dataset are as follows: name - ASCII subject name and recording number; MDVP:Fo(Hz) - Average vocal fundamental frequency; MDVP:Fhi(Hz) - Maximum vocal fundamental frequency; MDVP:Flo(Hz) - Minimum vocal fundamental frequency; MDVP:Jitter(%); MDVP:Jitter(Abs); MDVP:RAP; MDVP:PPQ; Jitter:DDP – Several measures of variation in fundamental frequency; MDVP:Shimmer; MDVP:Shimmer(dB); Shimmer:APQ3; Shimmer:APQ5; MDVP:APQ; Shimmer:DDA - Several measures of variation in amplitude; NHR; HNR - Two measures of ratio of noise to tonal components in the voice; status - Health status of the subject (one) - Parkinson's, (zero) – healthy; RPDE,D2 - Two nonlinear dynamical complexity measures; DFA - Signal fractal scaling exponent; and spread1,spread2,PPE - Three nonlinear measures of fundamental frequency variation. The 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 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, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. WORKSHOP 7: Liver Disease Classification and Prediction Using Machine Learning and Deep Learning with Python GUI Patients with Liver disease have been continuously increasing because of excessive consumption of alcohol, inhale of harmful gases, intake of contaminated food, pickles and drugs. This dataset was used to evaluate prediction algorithms in an effort to reduce burden on doctors. This dataset contains 416 liver patient records and 167 non liver patient records collected from North East of Andhra Pradesh, India. The "Dataset" column is a class label used to divide groups into liver patient (liver disease) or not (no disease). This data set contains 441 male patient records and 142 female patient records. Any patient whose age exceeded 89 is listed as being of age "90". Columns in the dataset: Age of the patient; Gender of the patient; Total Bilirubin; Direct Bilirubin; Alkaline Phosphotase; Alamine Aminotransferase; Aspartate Aminotransferase; Total Protiens; Albumin; Albumin and Globulin Ratio; and Dataset: field used to split the data into two sets (patient with liver disease, or no disease). The 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 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, learning curve, performance of the model, scalability of the model, training loss, and training accuracy.

Sams Teach Yourself Mysql In 21 Days

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Author : Anthony Butcher
language : en
Publisher: Sams Publishing
Release Date : 2002

Sams Teach Yourself Mysql In 21 Days written by Anthony Butcher and has been published by Sams Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Computers categories.

This guide teaches readers how to design and implement their an open source database. Topics include designing and creating a database; normalizing data; adding tables, columns and indexes; importing and exporting data; administering, optimizing and troubleshooting My SQL; and locks and keys.

The Applied Data Science Workshop Urinary Biomarkers Based Pancreatic Cancer Classification And Prediction Using Machine Learning With Python Gui

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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-07-23

The Applied Data Science Workshop Urinary Biomarkers Based Pancreatic Cancer Classification 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 2023-07-23 with Computers categories.

The Applied Data Science Workshop on "Urinary Biomarkers-Based Pancreatic Cancer Classification and Prediction Using Machine Learning with Python GUI" embarks on a comprehensive journey, commencing with an in-depth exploration of the dataset. During this initial phase, the structure and size of the dataset are thoroughly examined, and the various features it contains are meticulously studied. The principal objective is to understand the relationship between these features and the target variable, which, in this case, is the diagnosis of pancreatic cancer. The distribution of each feature is analyzed, and potential patterns, trends, or outliers that could significantly impact the model's performance are identified. To ensure the data is in optimal condition for model training, preprocessing steps are undertaken. This involves handling missing values through imputation techniques, such as mean, median, or interpolation, depending on the nature of the data. Additionally, feature engineering is performed to derive new features or transform existing ones, with the aim of enhancing the model's predictive power. In preparation for model building, the dataset is split into training and testing sets. This division is crucial to assess the models' generalization performance on unseen data accurately. To maintain a balanced representation of classes in both sets, stratified sampling is employed, mitigating potential biases in the model evaluation process. The workshop explores an array of machine learning classifiers suitable for pancreatic cancer classification, such as Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forests, Gradient Boosting, Naive Bayes, Adaboost, Extreme Gradient Boosting, Light Gradient Boosting, Naïve Bayes, and Multi-Layer Perceptron (MLP). For each classifier, three different preprocessing techniques are applied to investigate their impact on model performance: raw (unprocessed data), normalization (scaling data to a similar range), and standardization (scaling data to have zero mean and unit variance). To optimize the classifiers' hyperparameters and boost their predictive capabilities, GridSearchCV, a technique for hyperparameter tuning, is employed. GridSearchCV conducts an exhaustive search over a specified hyperparameter grid, evaluating different combinations to identify the optimal settings for each model and preprocessing technique. During the model evaluation phase, multiple performance metrics are utilized to gauge the efficacy of the classifiers. Commonly used metrics include accuracy, recall, precision, and F1-score. By comprehensively assessing these metrics, the strengths and weaknesses of each model are revealed, enabling a deeper understanding of their performance across different classes of pancreatic cancer. Classification reports are generated to present a detailed breakdown of the models' performance, including precision, recall, F1-score, and support for each class. These reports serve as valuable tools for interpreting model outputs and identifying areas for potential improvement. The workshop highlights the significance of graphical user interfaces (GUIs) in facilitating user interactions with machine learning models. By integrating PyQt, a powerful GUI development library for Python, participants create a user-friendly interface that enables users to interact with the models effortlessly. The GUI provides options to select different preprocessing techniques, visualize model outputs such as confusion matrices and decision boundaries, and gain insights into the models' classification capabilities. One of the primary advantages of the graphical user interface is its ability to offer users a seamless and intuitive experience in predicting and classifying pancreatic cancer based on urinary biomarkers. The GUI empowers users to make informed decisions by allowing them to compare the performance of different classifiers under various preprocessing techniques. Throughout the workshop, a strong emphasis is placed on the significance of proper data preprocessing, hyperparameter tuning, and robust model evaluation. These crucial steps contribute to building accurate and reliable machine learning models for pancreatic cancer prediction. By the culmination of the workshop, participants have gained valuable hands-on experience in data exploration, machine learning model building, hyperparameter tuning, and GUI development, all geared towards addressing the specific challenge of pancreatic cancer classification and prediction. In conclusion, the Applied Data Science Workshop on "Urinary Biomarkers-Based Pancreatic Cancer Classification and Prediction Using Machine Learning with Python GUI" embarks on a comprehensive and transformative journey, bringing together data exploration, preprocessing, machine learning model selection, hyperparameter tuning, model evaluation, and GUI development. The project's focus on pancreatic cancer prediction using urinary biomarkers aligns with the pressing need for early detection and treatment of this deadly disease. As participants delve into the intricacies of machine learning and medical research, they contribute to the broader scientific community's ongoing efforts to combat cancer and improve patient outcomes. Through the integration of data science methodologies and powerful visualization tools, the workshop exemplifies the potential of machine learning in revolutionizing medical diagnostics and healthcare practices.

The Applied Data Science Workshop Prostate Cancer Classification And Recognition Using Machine Learning And Deep Learning With Python Gui

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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-07-19

The Applied Data Science Workshop Prostate Cancer Classification And Recognition Using Machine Learning And Deep 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 2023-07-19 with Computers categories.

The Applied Data Science Workshop on Prostate Cancer Classification and Recognition using Machine Learning and Deep Learning with Python GUI involved several steps and components. The project aimed to analyze prostate cancer data, explore the features, develop machine learning models, and create a graphical user interface (GUI) using PyQt5. The project began with data exploration, where the prostate cancer dataset was examined to understand its structure and content. Various statistical techniques were employed to gain insights into the data, such as checking the dimensions, identifying missing values, and examining the distribution of the target variable. The next step involved exploring the distribution of features in the dataset. Visualizations were created to analyze the characteristics and relationships between different features. Histograms, scatter plots, and correlation matrices were used to uncover patterns and identify potential variables that may contribute to the classification of prostate cancer. Machine learning models were then developed to classify prostate cancer based on the available features. Several algorithms, including Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forests, Gradient Boosting, Naive Bayes, Adaboost, Extreme Gradient Boosting, Light Gradient Boosting, and Multi-Layer Perceptron (MLP), were implemented. Each model was trained and evaluated using appropriate techniques such as cross-validation and grid search for hyperparameter tuning. The performance of each machine learning model was assessed using evaluation metrics such as accuracy, precision, recall, and F1-score. These metrics provided insights into the effectiveness of the models in accurately classifying prostate cancer cases. Model comparison and selection were based on their performance and the specific requirements of the project. In addition to the machine learning models, a deep learning model based on an Artificial Neural Network (ANN) was implemented. The ANN architecture consisted of multiple layers, including input, hidden, and output layers. The ANN model was trained using the dataset, and its performance was evaluated using accuracy and loss metrics. To provide a user-friendly interface for the project, a GUI was designed using PyQt, a Python library for creating desktop applications. The GUI allowed users to interact with the machine learning models and perform tasks such as selecting the prediction method, loading data, training models, and displaying results. The GUI included various graphical components such as buttons, combo boxes, input fields, and plot windows. These components were designed to facilitate data loading, model training, and result visualization. Users could choose the prediction method, view accuracy scores, classification reports, and confusion matrices, and explore the predicted values compared to the actual values. The GUI also incorporated interactive features such as real-time updates of prediction results based on user selections and dynamic plot generation for visualizing model performance. Users could switch between different prediction methods, observe changes in accuracy, and examine the history of training loss and accuracy through plotted graphs. Data preprocessing techniques, such as standardization and normalization, were applied to ensure the consistency and reliability of the machine learning and deep learning models. The dataset was divided into training and testing sets to assess model performance on unseen data and detect overfitting or underfitting. Model persistence was implemented to save the trained machine learning and deep learning models to disk, allowing for easy retrieval and future use. The saved models could be loaded and utilized within the GUI for prediction tasks without the need for retraining. Overall, the Applied Data Science Workshop on Prostate Cancer Classification and Recognition provided a comprehensive framework for analyzing prostate cancer data, developing machine learning and deep learning models, and creating an interactive GUI. The project aimed to assist in the accurate classification and recognition of prostate cancer cases, facilitating informed decision-making and potentially contributing to improved patient outcomes.

Yii Framework Application Workshop 2

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Author : มานพ กองอุ่น
language : en
Publisher: Programmer Thailand
Release Date :

Yii Framework Application Workshop 2 written by มานพ กองอุ่น and has been published by Programmer Thailand this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.

เรียนรู้การสร้าง Application ด้วย Yii Framwork กับ 4 Workshop พิเศษ แบบ Step by Step

Yii Framework Application Workshop 1

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Author : มานพ กองอุ่น
language : en
Publisher: Programmer Thailand
Release Date : 2013-12-09

Yii Framework Application Workshop 1 written by มานพ กองอุ่น and has been published by Programmer Thailand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-12-09 with categories.

เรียนรู้การสร้าง Application ด้วย Yii Framwork กับ 4 Workshop พิเศษ แบบ Step by Step