TIME-SERIES SALES FORECASTING AND PREDICTION USING MACHINE LEARNING WITH TKINTER
About this ebook
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.
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About the author
Vivian Siahaan is a highly motivated individual with a passion for continuous learning and exploring new areas. Born and raised in Hinalang Bagasan, Balige, situated on the picturesque banks of Lake Toba, she completed her high school education at SMAN 1 Balige. Vivian's journey into the world of programming began with a deep dive into various languages such as Java, Android, JavaScript, CSS, C++, Python, R, Visual Basic, Visual C#, MATLAB, Mathematica, PHP, JSP, MySQL, SQL Server, Oracle, Access, and more. Starting from scratch, Vivian diligently studied programming, focusing on mastering the fundamental syntax and logic. She honed her skills by creating practical GUI applications, gradually building her expertise. One particular area of interest for Vivian is animation and game development, where she aspires to make significant contributions. Alongside her programming and mathematical pursuits, she also finds joy in indulging in novels, nurturing her love for literature. Vivian Siahaan's passion for programming and her extensive knowledge are reflected in the numerous ebooks she has authored. Her works, published by Sparta Publisher, cover a wide range of topics, including "Data Structure with Java," "Java Programming: Cookbook," "C++ Programming: Cookbook," "C Programming For High Schools/Vocational Schools and Students," "Java Programming for SMA/SMK," "Java Tutorial: GUI, Graphics and Animation," "Visual Basic Programming: From A to Z," "Java Programming for Animation and Games," "C# Programming for SMA/SMK and Students," "MATLAB For Students and Researchers," "Graphics in JavaScript: Quick Learning Series," "JavaScript Image Processing Methods: From A to Z," "Java GUI Case Study: AWT & Swing," "Basic CSS and JavaScript," "PHP/MySQL Programming: Cookbook," "Visual Basic: Cookbook," "C++ Programming for High Schools/Vocational Schools and Students," "Concepts and Practices of C++," "PHP/MySQL For Students," "C# Programming: From A to Z," "Visual Basic for SMA/SMK and Students," and "C# .NET and SQL Server for High School/Vocational School and Students." Furthermore, at the ANDI Yogyakarta publisher, Vivian Siahaan has contributed to several notable books, including "Python Programming Theory and Practice," "Python GUI Programming," "Python GUI and Database," "Build From Zero School Database Management System In Python/MySQL," "Database Management System in Python/MySQL," "Python/MySQL For Management Systems of Criminal Track Record Database," "Java/MySQL For Management Systems of Criminal Track Records Database," "Database and Cryptography Using Java/MySQL," and "Build From Zero School Database Management System With Java/MySQL." Vivian's diverse range of expertise in programming languages, combined with her passion for exploring new horizons, makes her a dynamic and versatile individual in the field of technology. Her dedication to learning, coupled with her strong analytical and problem-solving skills, positions her as a valuable asset in any programming endeavor. Vivian Siahaan's contributions to the world of programming and literature continue to inspire and empower aspiring programmers and readers alike.
Rismon Hasiholan Sianipar, born in Pematang Siantar in 1994, is a distinguished researcher and expert in the field of electrical engineering. After completing his education at SMAN 3 Pematang Siantar, Rismon ventured to the city of Jogjakarta to pursue his academic journey. He obtained his Bachelor of Engineering (S.T) and Master of Engineering (M.T) degrees in Electrical Engineering from Gadjah Mada University in 1998 and 2001, respectively, under the guidance of esteemed professors, Dr. Adhi Soesanto and Dr. Thomas Sri Widodo. During his studies, Rismon focused on researching non-stationary signals and their energy analysis using time-frequency maps. He explored the dynamic nature of signal energy distribution on time-frequency maps and developed innovative techniques using discrete wavelet transformations to design non-linear filters for data pattern analysis. His research showcased the application of these techniques in various fields. In recognition of his academic prowess, Rismon was awarded the prestigious Monbukagakusho scholarship by the Japanese Government in 2003. He went on to pursue his Master of Engineering (M.Eng) and Doctor of Engineering (Dr.Eng) degrees at Yamaguchi University, supervised by Prof. Dr. Hidetoshi Miike. Rismon's master's and doctoral theses revolved around combining the SR-FHN (Stochastic Resonance Fitzhugh-Nagumo) filter strength with the cryptosystem ECC (elliptic curve cryptography) 4096-bit. This innovative approach effectively suppressed noise in digital images and videos while ensuring their authenticity. Rismon's research findings have been published in renowned international scientific journals, and his patents have been officially registered in Japan. Notably, one of his patents, with registration number 2008-009549, gained recognition. He actively collaborates with several universities and research institutions in Japan, specializing in cryptography, cryptanalysis, and digital forensics, particularly in the areas of audio, image, and video analysis. With a passion for knowledge sharing, Rismon has authored numerous national and international scientific articles and authored several national books. He has also actively participated in workshops related to cryptography, cryptanalysis, digital watermarking, and digital forensics. During these workshops, Rismon has assisted Prof. Hidetoshi Miike in developing applications related to digital image and video processing, steganography, cryptography, watermarking, and more, which serve as valuable training materials. Rismon's field of interest encompasses multimedia security, signal processing, digital image and video analysis, cryptography, digital communication, digital forensics, and data compression. He continues to advance his research by developing applications using programming languages such as Python, MATLAB, C++, C, VB.NET, C#.NET, R, and Java. These applications serve both research and commercial purposes, further contributing to the advancement of signal and image analysis. Rismon Hasiholan Sianipar is a dedicated researcher and expert in the field of electrical engineering, particularly in the areas of signal processing, cryptography, and digital forensics. His academic achievements, patented inventions, and extensive publications demonstrate his commitment to advancing knowledge in these fields. Rismon's contributions to academia and his collaborations with prestigious institutions in Japan have solidified his position as a respected figure in the scientific community. Through his ongoing research and development of innovative applications, Rismon continues to make significant contributions to the field of electrical engineering.
