Learn From Scratch Visual Basic Net With Mysql

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Learn From Scratch Visual Basic Net With Mysql

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Author : Rismon Hasiholan Sianipar
language : en
Publisher: Independently Published
Release Date : 2020-11-04

Learn From Scratch Visual Basic Net With Mysql written by Rismon Hasiholan Sianipar and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-04 with categories.

This book will teach you with step-by-step approach to develop from scratch a MySQL-driven desktop application that readers can develop for their own purposes to implement school database project using Visual Basic .NET.In Tutorial 1, you will perform the steps necessary to add 8 tables using phpMyAdmin into School database that you will create. You will build each table and add the associated fields as needed. In this tutorial, you will also build login form and main form.In Tutorial 2, you will build such a form for Parent table. This table has thirteen fields: ParentID, FirstName, LastName, BirthDate, Status, Ethnicity, Nationality, Mobile, Phone, Religion, Gender, PhotoFile, and FingerFile). You need fourteen label controls, two picture boxes, 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 other utilities, one button for searching member's name, one button to upload parent's photo, and button to upload parent's finger. Place these controls on the form.In Tutorial 3, you will build such a form for Student table. This table has fifteen fields: StudentID, ParentID, FirstName, LastName, BirthDate, YearEntry, Status, Ethnicity, Nationality, Mobile, Phone, Religion, Gender, PhotoFile, and FingerFile). You need sixteen label controls, two picture boxes, six text boxes, five comboxes, one check box, two date time pickers, one openfiledialog, and one printpreviewdialog. You also need four buttons for navigation, seven buttons for controlling editing features, one button for searching parent's name, one button to open parent form, one button to upload student's photo, and one button to upload student's finger.In Tutorial 4, you will build a form for Teacher table. This table has fifteen fields: TeacherID, RegNumber, FirstName, LastName, BirthDate, Rank, Status, Ethnicity, Nationality, Mobile, Phone, Religion, Gender, PhotoFile, and FingerFile). You need an input form so that user can edit existing records, delete records, or add new records. The form will also have the capability of navigating from one record to another. You need sixteen label controls, one picture box, seven text boxes, five 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 teacher's name, and one button to upload teacher's photo.In Tutorial 5, you will build a form for Subject table. This table has only three fields: SubjectID, Name, and Description. You need four label controls, four text boxes, one openfiledialog, and one printpreviewdialog. You also need four buttons for navigation, secen buttons for utilities, and one button for searching subject name. Place these controls on the form. You will also build a form for Grade table. This table has seven fields: GradeID, Name, SubjectID, TeacherID, SchoolYear, TimaStart, and TimeFinish. You need to add seven label controls, one text box, four comboxes, and two date time pickers. You also need four buttons for navigation, seven buttons for controlling editing features, one button to open subject form, and one button to open teacher form.In Tutorial 6, you will build a form for Grade_Student table. This table has only three fields: Grade_StudentID, GradeID, and StudentID. You need an input form so that user can edit existing records, delete records, or add new records. The form will also have the capability of navigating from one record to another. You need two label controls and two comboxes. You also need four buttons for navigation, seven buttons for controlling editing features, one button to open grade form, and one button to open student form.

The Fast Tutorial To Learn Database Programming Using Python Gui With Access And Sql Server

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Author : Vivian Siahaan
language : en
Publisher: SPARTA PUBLISHING
Release Date : 2020-01-15

The Fast Tutorial To Learn Database Programming Using Python Gui With Access And Sql Server written by Vivian Siahaan and has been published by SPARTA PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-15 with Computers categories.

This book covers microsoft acces and SQL Server based GUI programming using pyqt. Intentionally designed for various levels of interest and ability of learners, this book is suitable for students, engineers, and even researchers in a variety of disciplines. No advanced programming experience is needed, and only a few school-level programming skill are needed. In the first chapter, you will learn to use several widgets in PyQt5: Display a welcome message; Use the Radio Button widget; Grouping radio buttons; Displays options in the form of a check box; and Display two groups of check boxes. In chapter two, you will learn to use the following topics: Using Signal / Slot Editor; Copy and place text from one Line Edit widget to another; Convert data types and make a simple calculator; Use the Spin Box widget; Use scrollbars and sliders; Using the Widget List; Select a number of list items from one Widget List and display them on another Widget List widget; Add items to the Widget List; Perform operations on the Widget List; Use the Combo Box widget; Displays data selected by the user from the Calendar Widget; Creating a hotel reservation application; and Display tabular data using Table Widgets. In third chapter, you will learn: How to create the initial three tables project in the School database: Teacher, Class, and Subject tables; How to create database configuration files; How to create a Python GUI for inserting and editing tables; How to create a Python GUI to join and query the three tables. In fourth chapter, you will learn how to: Create a main form to connect all forms; Create a project will add three more tables to the school database: Student, Parent, and Tuition tables; Create a Python GUI for inserting and editing tables; Create a Python GUI to join and query over the three tables. In chapter five, you will join the six classes, Teacher, TClass, Subject, Student, Parent, and Tuition and make queries over those tables. In chapter six, you will create dan configure database. In this chapter, you will create Suspect table in crime database. 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. You will also create GUI to display, edit, insert, and delete for this table. In chapter seven, you will create a table with the name Feature_Extraction, which has eight columns: feature_id (primary key), suspect_id (foreign key), feature1, feature2, feature3, feature4, feature5, and feature6. The six fields (except keys) will have VARBINARY(MAX) data type. You will also create GUI to display, edit, insert, and delete for this table. In chapter eight, you will create two tables, Police and Investigator. The Police table has six columns: police_id (primary key), province, city, address, telephone, and photo. The Investigator table has eight columns: investigator_id (primary key), investigator_name, rank, birth_date, gender, address, telephone, and photo. You will also create GUI to display, edit, insert, and delete for both tables. In the last chapter, you will create two tables, Victim and Case_File. The Victim table has nine columns: victim_id (primary key), victim_name, crime_type, birth_date, crime_date, gender, address, telephone, and photo. The Case_File table has seven columns: case_file_id (primary key), suspect_id (foreign key), police_id (foreign key), investigator_id (foreign key), victim_id (foreign key), status, and description. You will create GUI to display, edit, insert, and delete for both tables as well.

Cryptocurrency Price Analysis Prediction And Forecasting Using Machine Learning With Python

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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-07-21

Cryptocurrency Price Analysis Prediction And Forecasting Using Machine Learning With Python written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-21 with Computers categories.

In this project, we will be conducting a comprehensive analysis, prediction, and forecasting of cryptocurrency prices using machine learning with Python. The dataset we will be working with contains historical cryptocurrency price data, and our main objective is to build models that can accurately predict future price movements and daily returns. The first step of the project involves exploring the dataset to gain insights into the structure and contents of the data. We will examine the columns, data types, and any missing values present. After that, we will preprocess the data, handling any missing values and converting data types as needed. This will ensure that our data is clean and ready for analysis. Next, we will proceed with visualizing the dataset to understand the trends and patterns in cryptocurrency prices over time. We will create line plots, box plot, violin plot, and other visualizations to study price movements, trading volumes, and volatility across different cryptocurrencies. These visualizations will help us identify any apparent trends or seasonality in the data. To gain a deeper understanding of the time-series nature of the data, we will conduct time-series analysis year-wise and month-wise. This analysis will involve decomposing the time-series into its individual components like trend, seasonality, and noise. Additionally, we will look for patterns in price movements during specific months to identify any recurring seasonal effects. To enhance our predictions, we will also incorporate technical indicators into our analysis. Technical indicators, such as moving averages, Relative Strength Index (RSI), and Moving Average Convergence Divergence (MACD), provide valuable information about price momentum and market trends. These indicators can be used as additional features in our machine learning models. With a strong foundation of data exploration, visualization, and time-series analysis, we will now move on to building machine learning models for forecasting the closing price of cryptocurrencies. We will utilize algorithms like Linear Regression, Support Vector Regression, Random Forest Regression, Decision Tree Regression, K-Nearest Neighbors Regression, Adaboost Regression, Gradient Boosting Regression, Extreme Gradient Boosting Regression, Light Gradient Boosting Regression, Catboost Regression, Multi-Layer Perceptron Regression, Lasso Regression, and Ridge Regression to make forecasting. By training our models on historical data, they will learn to recognize patterns and make predictions for future price movements. As part of our machine learning efforts, we will also develop models for predicting daily returns of cryptocurrencies. Daily returns are essential indicators for investors and traders, as they reflect the percentage change in price from one day to the next. By using historical price data and technical indicators as input features, we can build models that forecast daily returns accurately. Throughout the project, we will perform extensive hyperparameter tuning using techniques like Grid Search and Random Search. This will help us identify the best combinations of hyperparameters for each model, optimizing their performance. To validate the accuracy and robustness of our models, we will use various evaluation metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared. These metrics will provide insights into the model's ability to predict cryptocurrency prices accurately. In conclusion, this project on cryptocurrency price analysis, prediction, and forecasting is a comprehensive exploration of using machine learning with Python to analyze and predict cryptocurrency price movements. By leveraging data visualization, time-series analysis, technical indicators, and machine learning algorithms, we aim to build accurate and reliable models for predicting future price movements and daily returns. The project's outcomes will be valuable for investors, traders, and analysts looking to make informed decisions in the highly volatile and dynamic world of cryptocurrencies. Through rigorous evaluation and validation, we strive to create robust models that can contribute to a better understanding of cryptocurrency market dynamics and support data-driven decision-making.

Company Bankruptcy Analysis And Prediction Using Machine Learning With Python Gui

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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-08-25

Company Bankruptcy 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 2023-08-25 with Computers categories.

In this comprehensive project titled "Company Bankruptcy Analysis and Prediction Using Machine Learning with Python GUI," we embarked on a journey to explore, analyze, and predict the bankruptcy status of companies. Our project began with an exploration of the dataset, which involved importing it using Pandas and refining it by removing leading spaces and replacing spaces with underscores in column names to ensure consistency. To grasp the dataset's characteristics, we delved into categorized features' distributions, allowing us to understand the underlying patterns within the data. This step helped us gain insights into the distribution of attributes across different classes, aiding in feature selection and engineering. Moving on to the heart of our project, the prediction of company bankruptcy, we employed various machine learning models. Utilizing grid search, we performed hyperparameter tuning to optimize model performance. Our model arsenal included Logistic Regression, K-Nearest Neighbors, Support Vector, Decision Trees, Random Forests, Gradient Boosting, AdaBoost, Extreme Gradient Boosting, Light Gradient Boosting, and Multi-Layer Perceptron (MLP), which were evaluated using accuracy, precision, recall, and F1-score. Transitioning to deep learning, we implemented an Artificial Neural Network (ANN) model. This involved constructing a feed-forward neural network with hidden layers, dropouts, and activation functions. We evaluated the ANN using accuracy, precision, recall, and F1-score, gaining a comprehensive understanding of its classification performance. Our journey into deep learning continued with the implementation of Long Short-Term Memory (LSTM) networks, which are well-suited for sequence data. We structured the LSTM model with multiple layers and dropouts, evaluating its performance using metrics like accuracy, precision, recall, and F1-score. This marked a pivotal step in predicting company bankruptcy. Furthermore, we explored Feed-Forward Neural Networks (FNN) for prediction. Constructing a multi-layered architecture with varied dropouts and activation functions, we assessed its classification capabilities using metrics similar to previous models. Incorporating Recurrent Neural Networks (RNN) added another dimension to our analysis. Building an RNN model with sequential data, we examined its accuracy, precision, recall, and F1-score, highlighting its ability to capture sequential patterns in bankruptcy data. To comprehensively evaluate our models, we employed a range of metrics including precision, recall, F1-score, and accuracy. These metrics enabled us to gauge not only the overall model performance but also its capability to correctly predict bankrupt and non-bankrupt cases. Our project also extended into creating a Python GUI using PyQt. This graphical interface facilitated user interaction, allowing them to input data for prediction and view the outcomes through an intuitive interface. This GUI enhanced accessibility and usability, making it easier for users to engage with our models. In conclusion, our journey through the "Company Bankruptcy Analysis and Prediction Using Machine Learning with Python GUI" project encompassed data exploration, categorized features distribution analysis, model selection, performance evaluation using diverse metrics, and the creation of an interactive GUI. This endeavor combined analytical rigor, machine learning expertise, and user-centric design to provide a comprehensive solution for predicting company bankruptcy.

Time Series Sales Forecasting And Prediction Using Machine Learning With Tkinter

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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-09-23

Time Series Sales Forecasting And Prediction Using Machine Learning With Tkinter 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 2023-09-23 with Computers categories.

This project leverages the power of data visualization and exploration to provide a comprehensive understanding of sales trends over time. Through an intuitive GUI built with Tkinter, users can seamlessly navigate through various aspects of their sales data. The journey begins with a detailed visualization of the dataset. This critical step allows users to grasp the overall structure, identify trends, and spot outliers. The application provides a user-friendly interface to interact with the data, offering an informative visual representation of the sales records. Moving forward, users can delve into the distribution of features within the dataset. This feature distribution analysis provides valuable insights into the characteristics of the sales data. It enables users to identify patterns, anomalies, and correlations among different attributes, paving the way for more accurate forecasting and prediction. One of the central functionalities of this application lies in its ability to perform sales forecasting using machine learning regressors. By employing powerful regression models, such as Random Forest Regressor, KNN regressor, Support Vector Regressor, AdaBoost regressor, Gradient Boosting Regressor, MLP regressor, Lasso regressor, and Ridge regressor, the application assists users in predicting future sales based on historical data. This empowers businesses to make informed decisions and plan for upcoming periods with greater precision. The application takes sales forecasting a step further by allowing users to fine-tune their models using Grid Search. This powerful optimization technique systematically explores different combinations of hyperparameters to find the optimal configuration for the machine learning models. This ensures that the models are fine-tuned for maximum accuracy in sales predictions. In addition to sales forecasting, the application addresses the critical issue of customer churn prediction. It identifies customers who are likely to churn based on a combination of features and behaviors. By employing a selection of machine learning models and Grid Search such as Random Forest Classifier, Support Vector Classifier, and K-Nearest Neighbors Classifier, Linear Regression Classifier, AdaBoost Classifier, Support Vector Classifier, Gradient Boosting Classifier, Extreme Gradient Boosting Classifier, and Multi-Layer Perceptron Classifier, the application provides a robust framework for accurately predicting which customers are at risk of leaving. The project doesn't just stop at prediction; it also includes functionalities for evaluating model performance. Users can assess the accuracy, precision, recall, and F1-score of their models, allowing them to gauge the effectiveness of their forecasting and customer churn predictions. Furthermore, the application incorporates an intuitive user interface with widgets such as menus, buttons, listboxes, and comboboxes. These elements facilitate seamless interaction and navigation within the application, ensuring a user-friendly experience. To enhance user convenience, the application also supports data loading from external sources. It enables users to import their sales datasets directly into the application, streamlining the analysis process. The project is built on a foundation of modular and organized code. Each functionality is encapsulated within separate classes, promoting code reusability and maintainability. This ensures that the application is robust and can be easily extended or modified to accommodate future enhancements. You can download the dataset from: http://viviansiahaan.blogspot.com/2023/09/time-series-sales-forecasting-and.html.

Time Series Analysis Forecasting Stock Price Using Machine Learning With Python Gui

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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-07-02

Time Series Analysis Forecasting Stock Price 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 2023-07-02 with Computers categories.

Stock trading and financial instrument markets offer significant opportunities for wealth creation. The ability to predict stock price movements has long intrigued researchers and investors alike. While some theories, like the Efficient Market Hypothesis, suggest that consistently beating the market is nearly impossible, others contest this viewpoint. Stock price prediction involves forecasting the future value of a given stock. In this project, we focus on the S&P 500 Index, which consists of 500 stocks from various sectors of the US economy and serves as a key indicator of US equities. To tackle this task, we utilize the Yahoo stock price history dataset, which contains 1825 rows and 7 columns including Date, High, Low, Open, Close, Volume, and Adj Close. To enhance our predictions, we incorporate technical indicators such as daily returns, Moving Average Convergence-Divergence (MACD), Relative Strength Index (RSI), Simple Moving Average (SMA), lower and upper bands, and standard deviation. In this book, for the forecasting task, we employ various regression algorithms including Linear Regression, Random Forest Regression, Decision Tree Regression, Support Vector Regression, Naïve Bayes Regression, K-Nearest Neighbor Regression, Adaboost Regression, Gradient Boosting Regression, Extreme Gradient Boosting Regression, Light Gradient Boosting Regression, Catboost Regression, MLP Regression, Lasso Regression, and Ridge Regression. These models aim to predict the future Adj Close price of the stock based on historical data. In addition to stock price prediction, we also delve into predicting stock daily returns using machine learning models. We utilize K-Nearest Neighbor Classifier, Random Forest Classifier, Naive Bayes Classifier, Logistic Regression Classifier, Decision Tree Classifier, Support Vector Machine Classifier, LGBM Classifier, Gradient Boosting Classifier, XGB Classifier, MLP Classifier, and Extra Trees Classifier. These models are trained to predict the direction of daily stock returns (positive or negative) based on various features and technical indicators. To assess the performance of these machine learning models, we evaluate several important metrics. Accuracy measures the overall correctness of the predictions, while recall quantifies the ability to correctly identify positive cases (upward daily returns). Precision evaluates the precision of positive predictions, and the F1 score provides a balanced measure of precision and recall. Additionally, we consider macro average, which calculates the average metric value across all classes, and weighted average, which provides a balanced representation considering class imbalances. To enhance the user experience and facilitate data exploration, we develop a graphical user interface (GUI). The GUI is built using PyQt and offers an interactive platform for users to visualize and interact with the data. It provides features such as plotting boundary decisions, visualizing feature distributions and importance, comparing predicted values with true values, displaying confusion matrices, learning curves, model performance, and scalability analysis. The GUI allows users to customize the analysis by selecting different models, time periods, or variables of interest, making it accessible and user-friendly for individuals without extensive programming knowledge. The combination of exploring the dataset, forecasting stock prices, predicting daily returns, and developing a GUI creates a comprehensive framework for analyzing and understanding stock market trends. By leveraging machine learning algorithms and evaluating performance metrics, we gain valuable insights into the accuracy and effectiveness of our predictions. The GUI further enhances the accessibility and usability of the analysis, enabling users to make data-driven decisions and explore the stock market with ease.

Bank Loan Status Classification And Prediction Using Machine Learning With Python Gui

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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-08-02

Bank Loan Status 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 2023-08-02 with Computers categories.

The project "Bank Loan Status Classification and Prediction Using Machine Learning with Python GUI" begins with data exploration, where the dataset containing information about bank loan applicants is analyzed. The data is examined to understand its structure, check for missing values, and gain insights into the distribution of features. Exploratory data analysis techniques are used to visualize the distribution of loan statuses, such as approved and rejected loans, and the distribution of various features like credit score, number of open accounts, and annual income. After data exploration, the preprocessing stage begins, where data cleaning and feature engineering techniques are applied. Missing values are imputed or removed, and categorical variables are encoded to numerical form for model compatibility. The dataset is split into training and testing sets to prepare for the machine learning model's training and evaluation process. Three preprocessing methods are used: raw data, normalization, and standardization. The machine learning process involves training several classifiers on the preprocessed data. Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, Random Forest, Gradient Boosting, Naive Bayes, Adaboost, XGBoost, and LightGBM classifiers are considered. Each classifier is trained using the training data and evaluated using performance metrics such as accuracy, precision, recall, and F1-score on the testing data. To enhance model performance, hyperparameter tuning is performed using Grid Search with cross-validation. Grid Search explores different combinations of hyperparameters for each model, seeking the optimal configuration that yields the best performance. This step helps to find the most suitable hyperparameters for each classifier, improving their predictive capabilities. The implementation of a graphical user interface (GUI) using PyQt comes next. The GUI allows users to interact with the trained machine learning models easily. Users can select their preferred preprocessing method and classifier from the available options. The GUI provides visualizations of the models' performance, including confusion matrices, real vs. predicted value plots, learning curves, scalability curves, and performance curves. Users can examine the decision boundaries of the classifiers for different features to gain insights into their behavior. The application of the GUI is intuitive and user-friendly. Users can visualize the results of different models, compare their performance, and choose the most suitable classifier based on their preferences and requirements. The GUI allows users to assess the performance of each classifier on the test dataset, providing a clear understanding of their strengths and weaknesses. The project fosters transparency and reproducibility by saving the trained machine learning models using joblib's pickle functionality. This enables users to load and use pre-trained models in the future without retraining, saving time and resources. Throughout the project, the team pays close attention to data handling and model evaluation, ensuring that no data leakage occurs and the models are well-evaluated using appropriate evaluation metrics. The GUI is designed to present results in a visually appealing and informative manner, making it accessible to both technical and non-technical users. The project's effectiveness is validated by its ability to accurately predict the loan status of bank applicants based on various features. It demonstrates how machine learning techniques can aid in decision-making processes, such as loan approval or rejection, in financial institutions. Overall, the "Bank Loan Status Classification and Prediction Using Machine Learning with Python GUI" project combines data exploration, feature preprocessing, model training, hyperparameter tuning, and GUI implementation to create a user-friendly application for loan status prediction. The project empowers users with valuable insights into the loan application process, supporting banks and financial institutions in making informed decisions and improving customer experience.

Data Visualization Time Series Forecasting And Prediction Using Machine Learning With Tkinter

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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-09-06

Data Visualization Time Series Forecasting And Prediction Using Machine Learning With Tkinter 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 2023-09-06 with Computers categories.

This "Data Visualization, Time-Series Forecasting, and Prediction using Machine Learning with Tkinter" project is a comprehensive and multifaceted application that leverages data visualization, time-series forecasting, and machine learning techniques to gain insights into bitcoin data and make predictions. This project serves as a valuable tool for financial analysts, traders, and investors seeking to make informed decisions in the stock market. The project begins with data visualization, where historical bitcoin market data is visually represented using various plots and charts. This provides users with an intuitive understanding of the data's trends, patterns, and fluctuations. Features distribution analysis is conducted to assess the statistical properties of the dataset, helping users identify key characteristics that may impact forecasting and prediction. One of the project's core functionalities is time-series forecasting. Through a user-friendly interface built with Tkinter, users can select a stock symbol and specify the time horizon for forecasting. The project supports multiple machine learning regressors, such as Linear Regression, Decision Trees, Random Forests, Gradient Boosting, Extreme Gradient Boosting, Multi-Layer Perceptron, Lasso, Ridge, AdaBoost, and KNN, allowing users to choose the most suitable algorithm for their forecasting needs. Time-series forecasting is crucial for making predictions about stock prices, which is essential for investment strategies. The project employs various machine learning regressors to predict the adjusted closing price of bitcoin stock. By training these models on historical data, users can obtain predictions for future adjusted closing prices. This information is invaluable for traders and investors looking to make buy or sell decisions. The project also incorporates hyperparameter tuning and cross-validation to enhance the accuracy of these predictions. These models employ metrics such as Mean Absolute Error (MAE), which quantifies the average absolute discrepancy between predicted values and actual values. Lower MAE values signify superior model performance. Additionally, Mean Squared Error (MSE) is used to calculate the average squared differences between predicted and actual values, with lower MSE values indicating better model performance. Root Mean Squared Error (RMSE), derived from MSE, provides insights in the same units as the target variable and is valued for its lower values, denoting superior performance. Lastly, R-squared (R2) evaluates the fraction of variance in the target variable that can be predicted from independent variables, with higher values signifying better model fit. An R2 of 1 implies a perfect model fit. In addition to close price forecasting, the project extends its capabilities to predict daily returns. By implementing grid search, users can fine-tune the hyperparameters of machine learning models such as Random Forests, Gradient Boosting, Support Vector, Decision Tree, Gradient Boosting, Extreme Gradient Boosting, Multi-Layer Perceptron, and AdaBoost Classifiers. This optimization process aims to maximize the predictive accuracy of daily returns. Accurate daily return predictions are essential for assessing risk and formulating effective trading strategies. Key metrics in these classifiers encompass Accuracy, which represents the ratio of correctly predicted instances to the total number of instances, Precision, which measures the proportion of true positive predictions among all positive predictions, and Recall (also known as Sensitivity or True Positive Rate), which assesses the proportion of true positive predictions among all actual positive instances. The F1-Score serves as the harmonic mean of Precision and Recall, offering a balanced evaluation, especially when considering the trade-off between false positives and false negatives. The ROC Curve illustrates the trade-off between Recall and False Positive Rate, while the Area Under the ROC Curve (AUC-ROC) summarizes this trade-off. The Confusion Matrix provides a comprehensive view of classifier performance by detailing true positives, true negatives, false positives, and false negatives, facilitating the computation of various metrics like accuracy, precision, and recall. The selection of these metrics hinges on the project's specific objectives and the characteristics of the dataset, ensuring alignment with the intended goals and the ramifications of false positives and false negatives, which hold particular significance in financial contexts where decisions can have profound consequences. Overall, the "Data Visualization, Time-Series Forecasting, and Prediction using Machine Learning with Tkinter" project serves as a powerful and user-friendly platform for financial data analysis and decision-making. It bridges the gap between complex machine learning techniques and accessible user interfaces, making financial analysis and prediction more accessible to a broader audience. With its comprehensive features, this project empowers users to gain insights from historical data, make informed investment decisions, and develop effective trading strategies in the dynamic world of finance. You can download the dataset from: http://viviansiahaan.blogspot.com/2023/09/data-visualization-time-series.html.

Learning Mariadb

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Author : Vivian Siahaan
language : en
Publisher: SPARTA PUBLISHING
Release Date : 2019-09-03

Learning Mariadb written by Vivian Siahaan and has been published by SPARTA PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-03 with Computers categories.

In this book, you will learn how to build from scratch a criminal records management database system using MariaDB Connector/J. As you know, MariaDB server is a community developed fork of MySQL server. Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. In the first chapter, you will be taught how to create Crime database and its tables. In the second chapter, you will create Suspect table. 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 the third chapter, 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 the fourth chapter, 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 the fifth chapter, 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. Here, you will also design a Java GUI to display, edit, fill, and delete data in both tables.

Learning Sql Server

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Author : Vivian Siahaan
language : en
Publisher: SPARTA PUBLISHING
Release Date : 2019-10-07

Learning Sql Server written by Vivian Siahaan and has been published by SPARTA PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-07 with Computers categories.

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 a SQL Server database management system using Java. In designing a GUI and as an IDE, you will make use of the NetBeans tool. Gradually and step by step, you will be taught how to use SQL Server in Java. In chapter one, you will learn: How to create SQL Server database and six tables. In the 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 the last chapter, you will study how to query the six tables. Finally, this book is hopefully useful and can improve database programming skills for every Java/SQL Server programmer.