Predicting Loan Defaults Using Machine Learning Techniques
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Default Loan Prediction Based On Customer Behavior Using Machine Learning And Deep Learning With Python
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-07-13
Default Loan Prediction Based On Customer Behavior Using Machine Learning And Deep 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 2023-07-13 with Computers categories.
In this project, we aim to predict the risk of defaulting on a loan based on customer behavior using machine learning and deep learning techniques. We start by exploring the dataset and understanding its structure and contents. The dataset contains various features related to customer behavior, such as credit history, income, employment status, loan amount, and more. We analyze the distribution of these features to gain insights into their characteristics and potential impact on loan default. Next, we preprocess the data by handling missing values, encoding categorical variables, and normalizing numerical features. This ensures that the data is in a suitable format for training machine learning models. To predict the risk flag for loan default, we apply various machine learning models. We start with logistic regression, which models the relationship between the input features and the probability of loan default. We evaluate the model's performance using metrics such as accuracy, precision, recall, and F1-score. Next, we employ decision tree-based algorithms, such as random forest and gradient boosting, which can capture non-linear relationships and interactions among features. These models provide better predictive power and help identify important features that contribute to loan default. Additionally, we explore support vector machines (SVM), which aim to find an optimal hyperplane that separates the loan default and non-default instances in a high-dimensional feature space. SVMs can handle complex data distributions and can be tuned to optimize the classification performance. After evaluating the performance of these machine learning models, we turn our attention to deep learning techniques. We design and train an Artificial Neural Network (ANN) to predict the risk flag for loan default. The ANN consists of multiple layers of interconnected neurons that learn hierarchical representations of the input features. We configure the ANN with several hidden layers, each containing a varying number of neurons. We use the ReLU activation function to introduce non-linearity and ensure the model's ability to capture complex relationships. Dropout layers are incorporated to prevent overfitting and improve generalization. We compile the ANN using the Adam optimizer and the binary cross-entropy loss function. We train the model using the preprocessed dataset, splitting it into training and validation sets. The model is trained for a specific number of epochs, with a defined batch size. Throughout the training process, we monitor the model's performance using metrics such as loss and accuracy on both the training and validation sets. We make use of early stopping to prevent overfitting and save the best model based on the validation performance. Once the ANN is trained, we evaluate its performance on a separate test set. We calculate metrics such as accuracy, precision, recall, and F1-score to assess the model's predictive capabilities in identifying loan default risk. In conclusion, this project involves the exploration of a loan dataset, preprocessing of the data, and the application of various machine learning models and a deep learning ANN to predict the risk flag for loan default. The machine learning models, including logistic regression, decision trees, SVM, and ensemble methods, provide insights into feature importance and achieve reasonable predictive performance. The deep learning ANN, with its ability to capture complex relationships, offers the potential for improved accuracy in predicting loan default risk. By combining these approaches, we can assist financial institutions in making informed decisions and managing loan default risks more effectively.
Predicting Loan Defaults Using Machine Learning Techniques
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Author : Abhishek Bhagat
language : en
Publisher:
Release Date : 2018
Predicting Loan Defaults Using Machine Learning Techniques written by Abhishek Bhagat and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
In today’s world, obtaining loans from financial institutions has become a very common phenomenon. Every day people apply for loans, for a variety of purposes. But not all the applicants are reliable, and not everyone can be approved. Every year, there are cases where people do not repay the bulk of the loan amount to the bank which results in huge financial loss. The risk associated with making a decision on a loan approval is immense. Hence, the idea of the project is to gather loan data from the lending club website and use machine learning techniques on this data to extract important information and predict if a customer would be able to repay the loan or not. In other words, the goal is to predict if the customer would be a defaulter or not.
Algorithms In Advanced Artificial Intelligence
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Author : R. N. V. Jagan Mohan
language : en
Publisher: CRC Press
Release Date : 2024-07-08
Algorithms In Advanced Artificial Intelligence written by R. N. V. Jagan Mohan and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-08 with Computers categories.
The most common form of severe dementia, Alzheimer’s disease (AD), is a cumulative neurological disorder because of the degradation and death of nerve cells in the brain tissue, intelligence steadily declines and most of its activities are compromised in AD. Before diving into the level of AD diagnosis, it is essential to highlight the fundamental differences between conventional machine learning (ML) and deep learning (DL). This work covers a number of photo-preprocessing approaches that aid in learning because image processing is essential for the diagnosis of AD. The most crucial kind of neural network for computer vision used in medical image processing is called a Convolutional Neural Network (CNN). The proposed study will consider facial characteristics, including expressions and eye movements using the diffusion model, as part of CNN’s meticulous approach to Alzheimer’s diagnosis. Convolutional neural networks were used in an effort to sense Alzheimer’s disease in its early stages using a big collection of pictures of facial expressions.
Proceedings Of The Future Technologies Conference Ftc 2022 Volume 1
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Author : Kohei Arai
language : en
Publisher: Springer Nature
Release Date : 2022-10-12
Proceedings Of The Future Technologies Conference Ftc 2022 Volume 1 written by Kohei Arai and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-12 with Technology & Engineering categories.
The seventh Future Technologies Conference 2022 was organized in a hybrid mode. It received a total of 511 submissions from learned scholars, academicians, engineers, scientists and students across many countries. The papers included the wide arena of studies like Computing, Artificial Intelligence, Machine Vision, Ambient Intelligence and Security and their jaw- breaking application to the real world. After a double-blind peer review process 177 submissions have been selected to be included in these proceedings. One of the prominent contributions of this conference is the confluence of distinguished researchers who not only enthralled us by their priceless studies but also paved way for future area of research. The papers provide amicable solutions to many vexing problems across diverse fields. They also are a window to the future world which is completely governed by technology and its multiple applications. We hope that the readers find this volume interesting and inspiring and render their enthusiastic support towards it.
Proceedings Of The 2nd International Conference On Big Data Iot And Machine Learning
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Author : Mohammad Shamsul Arefin
language : en
Publisher: Springer Nature
Release Date : 2024-03-29
Proceedings Of The 2nd International Conference On Big Data Iot And Machine Learning written by Mohammad Shamsul Arefin and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-29 with Computers categories.
This book gathers a collection of high-quality peer-reviewed research papers presented at the International Conference on Big Data, IoT and Machine Learning (BIM 2023), organised by Jahangirnagar University, Bangladesh, and Daffodil International University, Bangladesh, held in Dhaka, Bangladesh, during 6–8 September 2023. The book covers research papers in the field of big data, IoT and machine learning. The book is helpful for active researchers and practitioners in the field.
Bdeim 2022
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Author : Paulo Batista
language : en
Publisher: European Alliance for Innovation
Release Date : 2023-06-14
Bdeim 2022 written by Paulo Batista and has been published by European Alliance for Innovation this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-14 with Computers categories.
BDEIM 2022 created an academic platform for academic communication and scientific innovation, brought together experts, scholars, and scientists in the fields of big data economy and information management from all over the world to present their research results and to exchange information, promoted the industrial cooperation of academic achievements, and facilitated the collaboration in the future among all the participants. The scope of the conference covered all areas of research in big data economy and information management, including Big Data Mining, Economic Statistics under Big Data, Sensor Network and Internet of Things, Computer Science and Internet, Network and Information Security, Database Technology, etc. The conference brought together about 150 participants, primarily from China, but also from USA, France, Portugal, and other countries. This volume contains the papers presented at the 3rd International Conference on Big Data Economy and Information Management (BDEIM 2022), held during December 2nd-3rd, 2023 in Zhengzhou, China.
Algorithms And Computational Theory For Engineering Applications
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Author : Sripada Rama Sree
language : en
Publisher: Springer Nature
Release Date : 2025-01-24
Algorithms And Computational Theory For Engineering Applications written by Sripada Rama Sree and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-24 with Computers categories.
This book goes deeply into the world of algorithms and computational theory and its astounding influence on numerous engineering areas. The book's carefully chosen content highlights the most recent studies, approaches, and real-world applications that are revolutionising engineering. The book is structured into distinct sections, each of which examines an important topic in computational theory and algorithms. The authors propose cutting-edge optimisation methods that revolutionise the way engineers approach engineering problems by allowing them to solve complicated issues quickly and effectively. The book illustrates the techniques and equipment used in the fields of data science and big data analytics to glean insightful information from enormous databases. Data visualisation, predictive modelling, clustering, and anomaly detection are a few examples of how algorithms are used to find patterns and trends that help engineers make well-informed decisions. Before being physically implemented, complex systems are built, tested, and optimised in the virtual environment thanks to computational modelling and simulation. The book examines numerical techniques, finite element analysis, computational fluid dynamics, and other simulation techniques to highlight how algorithms are changing engineering system design and performance optimisation. The book also delves into the intriguing field of robotics and control systems. The book's readers will learn about the algorithms that advance sensor fusion, intelligent control, path planning, and real-time systems, paving the way for innovations in autonomous driving, industrial automation, and smart cities. Readers will learn more about how algorithms and computational theory are modifying engineering environments, opening up new opportunities, and changing industries by examining the book's chapters. This book is a must-have for anyone looking to keep on top of the intersection of algorithms, computational theory, and engineering applications because of its concentration on practical applications and theoretical breakthroughs.
Evolutionary Artificial Intelligence
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Author : David Asirvatham
language : en
Publisher: Springer Nature
Release Date : 2024-03-13
Evolutionary Artificial Intelligence written by David Asirvatham and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-13 with Computers categories.
This book gathers a collection of selected works and new research results of scholars and graduate students presented at International Conference on Evolutionary Artificial Intelligence (ICEAI 2023) held in Malaysia during 13-14 September 2023. The focus of the book is interdisciplinary in nature and includes research on all aspects of evolutionary computation to find effective solutions to a wide range of computationally difficult problems. The book covers topics such as particle swarm optimization, evolutionary programming, genetic programming, hybrid evolutionary algorithms, ant colony optimization, evolutionary neural networks, evolutionary reinforcement learning, genetic algorithms, memetic algorithms, novel bio-inspired algorithms, evolving multi-agent systems, agent-based evolutionary approaches, and evolutionary game theory.
Proceedings Of International Conference On Generative Ai Cryptography And Predictive Analytics
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Author : Deepali Virmani
language : en
Publisher: Springer Nature
Release Date : 2025-03-08
Proceedings Of International Conference On Generative Ai Cryptography And Predictive Analytics written by Deepali Virmani and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-08 with Computers categories.
The book presents the proceedings of the International Conference on Generative AI, Cryptography and Predictive Analytics (ICGCPA 2024), held at VIPS-TC, School of Engineering and Technology, Pitampura, Delhi, India, during June 28 – 29, 2024. It covers Generative AI's role in problem-solving, examining applications in image synthesis, content creation, healthcare, and optimization challenges. This book is a valuable resource for postgraduate students in various engineering disciplines.
R Unleash Machine Learning Techniques
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Author : Raghav Bali
language : en
Publisher: Packt Publishing Ltd
Release Date : 2016-10-24
R Unleash Machine Learning Techniques written by Raghav Bali 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 2016-10-24 with Computers categories.
Find out how to build smarter machine learning systems with R. Follow this three module course to become a more fluent machine learning practitioner. About This Book Build your confidence with R and find out how to solve a huge range of data-related problems Get to grips with some of the most important machine learning techniques being used by data scientists and analysts across industries today Don't just learn – apply your knowledge by following featured practical projects covering everything from financial modeling to social media analysis Who This Book Is For Aimed for intermediate-to-advanced people (especially data scientist) who are already into the field of data science What You Will Learn Get to grips with R techniques to clean and prepare your data for analysis, and visualize your results Implement R machine learning algorithms from scratch and be amazed to see the algorithms in action Solve interesting real-world problems using machine learning and R as the journey unfolds Write reusable code and build complete machine learning systems from the ground up Learn specialized machine learning techniques for text mining, social network data, big data, and more Discover the different types of machine learning models and learn which is best to meet your data needs and solve your analysis problems Evaluate and improve the performance of machine learning models Learn specialized machine learning techniques for text mining, social network data, big data, and more In Detail R is the established language of data analysts and statisticians around the world. And you shouldn't be afraid to use it... This Learning Path will take you through the fundamentals of R and demonstrate how to use the language to solve a diverse range of challenges through machine learning. Accessible yet comprehensive, it provides you with everything you need to become more a more fluent data professional, and more confident with R. In the first module you'll get to grips with the fundamentals of R. This means you'll be taking a look at some of the details of how the language works, before seeing how to put your knowledge into practice to build some simple machine learning projects that could prove useful for a range of real world problems. For the following two modules we'll begin to investigate machine learning algorithms in more detail. To build upon the basics, you'll get to work on three different projects that will test your skills. Covering some of the most important algorithms and featuring some of the most popular R packages, they're all focused on solving real problems in different areas, ranging from finance to social media. This Learning Path has been curated from three Packt products: R Machine Learning By Example By Raghav Bali, Dipanjan Sarkar Machine Learning with R Learning - Second Edition By Brett Lantz Mastering Machine Learning with R By Cory Lesmeister Style and approach This is an enticing learning path that starts from the very basics to gradually pick up pace as the story unfolds. Each concept is first defined in the larger context of things succinctly, followed by a detailed explanation of their application. Each topic is explained with the help of a project that solves a real-world problem involving hands-on work thus giving you a deep insight into the world of machine learning.