BANK LOAN STATUS CLASSIFICATION AND PREDICTION USING MACHINE LEARNING WITH PYTHON GUI
About this ebook
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.
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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.
