ONLINE RETAIL CLUSTERING AND PREDICTION USING MACHINE LEARNING WITH PYTHON GUI
About this ebook
Using the RFM analysis results, we applied K-means clustering, a popular unsupervised machine learning algorithm, to group customers into distinct clusters based on their RFM values. This clustering approach helped us identify different customer segments within the online retail dataset.
After successfully clustering the customers, we proceeded to predict the clusters for new customer data. To achieve this, we trained various machine learning models, including logistic regression, support vector machines (SVM), K-nearest neighbors (KNN), decision trees, random forests, gradient boosting, naive Bayes, extreme gradient boosting, light gradient boosting, and multi-layer perceptron. These models were trained on the RFM features and the corresponding customer clusters.
To evaluate the performance of the trained models, we employed a range of metrics such as accuracy, recall, precision, and F1 score. Additionally, we generated classification reports to gain a comprehensive understanding of the models' predictive capabilities.
In order to provide a user-friendly and interactive experience, we developed a graphical user interface (GUI) using PyQt. The GUI allowed users to input customer information and obtain real-time predictions of the customer clusters using the trained machine learning models. This made it convenient for users to explore and analyze the clustering results. The GUI incorporated visualizations such as decision boundaries, which provided a clear representation of how the clusters were separated based on the RFM features. These visualizations enhanced the interpretation of the clustering results and facilitated better decision-making.
To ensure the availability of the trained models for future use, we implemented model persistence by saving the trained models using the joblib library. This allowed us to load the models directly from the saved files without the need for retraining, thus saving time and resources. In addition to the real-time predictions, the GUI showcased performance evaluation metrics such as accuracy, recall, precision, and F1 score. This provided users with a comprehensive assessment of the model's performance and helped them gauge the reliability of the predictions.
To delve deeper into the behavior and characteristics of the models, we conducted learning curve analysis, scalability analysis, and performance curve analysis. These analyses shed light on the models' learning capabilities, their performance with varying data sizes, and their overall effectiveness in making accurate predictions. The entire process from dataset exploration to RFM analysis, clustering, model training, GUI development, and real-time predictions was carried out seamlessly, leveraging the power of Python and its machine learning libraries. This approach allowed us to gain valuable insights into customer segmentation and predictive modeling in the online retail domain.
By combining data analysis, clustering, machine learning, and GUI development, we were able to provide a comprehensive solution for online retail businesses seeking to understand their customers better and make data-driven decisions. The developed system offered an intuitive interface and accurate predictions, paving the way for enhanced customer segmentation and targeted marketing strategies. Overall, this project demonstrated the effectiveness of integrating machine learning techniques with graphical user interfaces to provide a user-friendly and interactive platform for analyzing and predicting customer clusters in the online retail industry.
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About the author
Vivian Siahaan is a fast-learner who likes to do new things. She was born, raised in Hinalang Bagasan, Balige, on the banks of Lake Toba, and completed high school education from SMAN 1 Balige. She started herself learning Java, Android, JavaScript, CSS, C ++, Python, R, Visual Basic, Visual C #, MATLAB, Mathematica, PHP, JSP, MySQL, SQL Server, Oracle, Access, and other programming languages. She studied programming from scratch, starting with the most basic syntax and logic, by building several simple and applicable GUI applications. Animation and games are fields of programming that are interests that she always wants to develop. Besides studying mathematical logic and programming, the author also has the pleasure of reading novels. Vivian Siahaan has written dozens of ebooks that have been published on Sparta Publisher: 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; C # .NET and SQL Server for High School / Vocational School and Students. At the ANDI Yogyakarta publisher, Vivian Siahaan also wrote a number of 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; Build From Zero School Database Management System With Java / MySQL.
Rismon Hasiholan Sianipar was born in Pematang Siantar, in 1994. After graduating from SMAN 3 Pematang Siantar 3, the writer traveled to the city of Jogjakarta. In 1998 and 2001 the author completed his Bachelor of Engineering (S.T) and Master of Engineering (M.T) education in the Electrical Engineering of Gadjah Mada University, under the guidance of Prof. Dr. Adhi Soesanto and Prof. Dr. Thomas Sri Widodo, focusing on research on non-stationary signals by analyzing their energy using time-frequency maps. Because of its non-stationary nature, the distribution of signal energy becomes very dynamic on a time-frequency map. By mapping the distribution of energy in the time-frequency field using discrete wavelet transformations, one can design non-linear filters so that they can analyze the pattern of the data contained in it. In 2003, the author received a Monbukagakusho scholarship from the Japanese Government. In 2005 and 2008, he completed his Master of Engineering (M.Eng) and Doctor of Engineering (Dr.Eng) education at Yamaguchi University, under the guidance of Prof. Dr. Hidetoshi Miike. Both the master's thesis and his doctoral thesis, R.H. Sianipar combines SR-FHN (Stochastic Resonance Fitzhugh-Nagumo) filter strength with cryptosystem ECC (elliptic curve cryptography) 4096-bit both to suppress noise in digital images and digital video and maintain its authenticity. The results of this study have been documented in international scientific journals and officially patented in Japan. One of the patents was published in Japan with a registration number 2008-009549. He is active in collaborating with several universities and research institutions in Japan, particularly in the fields of cryptography, cryptanalysis and audio / image / video digital forensics. R.H. Sianipar also has experience in conducting code-breaking methods (cryptanalysis) on a number of intelligence data that are the object of research studies in Japan. R.H. Sianipar has a number of Japanese patents, and has written a number of national / international scientific articles, and dozens of national books. R.H. Sianipar has also participated in a number of workshops related to cryptography, cryptanalysis, digital watermarking, and digital forensics. In a number of workshops, R.H. Sianipar helps Prof. Hidetoshi Miike to create applications related to digital image / video processing, steganography, cryptography, watermarking, non-linear screening, intelligent descriptor-based computer vision, and others, which are used as training materials. Field of interest in the study of R.H. Sianipar is multimedia security, signal processing / digital image / video, cryptography, digital communication, digital forensics, and data compression / coding. Until now, R.H. Sianipar continues to develop applications related to analysis of signal, image, and digital video, both for research purposes and for commercial purposes based on the Python programming language, MATLAB, C ++, C, VB.NET, C # .NET, R, and Java.
