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Author: Vivian Siahaan
Publisher: BALIGE PUBLISHING
ISBN:
Category : Computers
Languages : en
Pages : 394
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Book Description
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.
Author: Vivian Siahaan
Publisher: BALIGE PUBLISHING
ISBN:
Category : Computers
Languages : en
Pages : 394
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Book Description
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.
Author: Vivian Siahaan
Publisher: BALIGE PUBLISHING
ISBN:
Category : Computers
Languages : en
Pages : 1165
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Book Description
Book 1: BANK LOAN STATUS CLASSIFICATION AND PREDICTION USING MACHINE LEARNING WITH PYTHON GUI The dataset used in this project consists of more than 100,000 customers mentioning their loan status, current loan amount, monthly debt, etc. There are 19 features in the dataset. The dataset attributes are as follows: Loan ID, Customer ID, Loan Status, Current Loan Amount, Term, Credit Score, Annual Income, Years in current job, Home Ownership, Purpose, Monthly Debt, Years of Credit History, Months since last delinquent, Number of Open Accounts, Number of Credit Problems, Current Credit Balance, Maximum Open Credit, Bankruptcies, and Tax Liens. The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, and XGB classifier. Three feature scaling used in machine learning are raw, minmax scaler, and standard scaler. Finally, you will develop a GUI using PyQt5 to plot cross validation score, predicted values versus true values, confusion matrix, learning curve, decision boundaries, performance of the model, scalability of the model, training loss, and training accuracy. Book 2: OPINION MINING AND PREDICTION USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON GUI Opinion mining (sometimes known as sentiment analysis or emotion AI) refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. This dataset was created for the Paper 'From Group to Individual Labels using Deep Features', Kotzias et. al,. KDD 2015. It contains sentences labelled with a positive or negative sentiment. Score is either 1 (for positive) or 0 (for negative). The sentences come from three different websites/fields: imdb.com, amazon.com, and yelp.com. For each website, there exist 500 positive and 500 negative sentences. Those were selected randomly for larger datasets of reviews. Amazon: contains reviews and scores for products sold on amazon.com in the cell phones and accessories category, and is part of the dataset collected by McAuley and Leskovec. Scores are on an integer scale from 1 to 5. Reviews considered with a score of 4 and 5 to be positive, and scores of 1 and 2 to be negative. The data is randomly partitioned into two halves of 50%, one for training and one for testing, with 35,000 documents in each set. IMDb: refers to the IMDb movie review sentiment dataset originally introduced by Maas et al. as a benchmark for sentiment analysis. This dataset contains a total of 100,000 movie reviews posted on imdb.com. There are 50,000 unlabeled reviews and the remaining 50,000 are divided into a set of 25,000 reviews for training and 25,000 reviews for testing. Each of the labeled reviews has a binary sentiment label, either positive or negative. Yelp: refers to the dataset from the Yelp dataset challenge from which we extracted the restaurant reviews. Scores are on an integer scale from 1 to 5. Reviews considered with scores 4 and 5 to be positive, and 1 and 2 to be negative. The data is randomly generated a 50-50 training and testing split, which led to approximately 300,000 documents for each set. Sentences: for each of the datasets above, labels are extracted and manually 1000 sentences are manually labeled from the test set, with 50% positive sentiment and 50% negative sentiment. These sentences are only used to evaluate our instance-level classifier for each dataset3. They are not used for model training, to maintain consistency with our overall goal of learning at a group level and predicting at the instance level. The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, and XGB classifier. Three feature scaling used in machine learning are raw, minmax scaler, and standard scaler. Finally, you will develop a GUI using PyQt5 to plot cross validation score, predicted values versus true values, confusion matrix, learning curve, decision boundaries, performance of the model, scalability of the model, training loss, and training accuracy. Book 3: EMOTION PREDICTION FROM TEXT USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON GUI In the dataset used in this project, there are two columns, Text and Emotion. Quite self-explanatory. The Emotion column has various categories ranging from happiness to sadness to love and fear. You will build and implement machine learning and deep learning models which can identify what words denote what emotion. The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, and XGB classifier. Three feature scaling used in machine learning are raw, minmax scaler, and standard scaler. Finally, you will develop a GUI using PyQt5 to plot cross validation score, predicted values versus true values, confusion matrix, learning curve, decision boundaries, performance of the model, scalability of the model, training loss, and training accuracy. Book 4: HATE SPEECH DETECTION AND SENTIMENT ANALYSIS USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON GUI The objective of this task is to detect hate speech in tweets. For the sake of simplicity, a tweet contains hate speech if it has a racist or sexist sentiment associated with it. So, the task is to classify racist or sexist tweets from other tweets. Formally, given a training sample of tweets and labels, where label '1' denotes the tweet is racist/sexist and label '0' denotes the tweet is not racist/sexist, the objective is to predict the labels on the test dataset. The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, XGB classifier, LSTM, and CNN. Three feature scaling used in machine learning are raw, minmax scaler, and standard scaler. Finally, you will develop a GUI using PyQt5 to plot cross validation score, predicted values versus true values, confusion matrix, learning curve, decision boundaries, performance of the model, scalability of the model, training loss, and training accuracy. Book 5: TRAVEL REVIEW RATING CLASSIFICATION AND PREDICTION USING MACHINE LEARNING WITH PYTHON GUI The dataset used in this project has been sourced from the Machine Learning Repository of University of California, Irvine (UC Irvine): Travel Review Ratings Data Set. This dataset is populated by capturing user ratings from Google reviews. Reviews on attractions from 24 categories across Europe are considered. Google user rating ranges from 1 to 5 and average user rating per category is calculated. The attributes in the dataset are as follows: Attribute 1 : Unique user id; Attribute 2 : Average ratings on churches; Attribute 3 : Average ratings on resorts; Attribute 4 : Average ratings on beaches; Attribute 5 : Average ratings on parks; Attribute 6 : Average ratings on theatres; Attribute 7 : Average ratings on museums; Attribute 8 : Average ratings on malls; Attribute 9 : Average ratings on zoo; Attribute 10 : Average ratings on restaurants; Attribute 11 : Average ratings on pubs/bars; Attribute 12 : Average ratings on local services; Attribute 13 : Average ratings on burger/pizza shops; Attribute 14 : Average ratings on hotels/other lodgings; Attribute 15 : Average ratings on juice bars; Attribute 16 : Average ratings on art galleries; Attribute 17 : Average ratings on dance clubs; Attribute 18 : Average ratings on swimming pools; Attribute 19 : Average ratings on gyms; Attribute 20 : Average ratings on bakeries; Attribute 21 : Average ratings on beauty & spas; Attribute 22 : Average ratings on cafes; Attribute 23 : Average ratings on view points; Attribute 24 : Average ratings on monuments; and Attribute 25 : Average ratings on gardens. The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, XGB classifier, and MLP classifier. Three feature scaling used in machine learning are raw, minmax scaler, and standard scaler. Finally, you will develop a GUI using PyQt5 to plot cross validation score, predicted values versus true values, confusion matrix, learning curve, decision boundaries, performance of the model, scalability of the model, training loss, and training accuracy. Book 6: ONLINE RETAIL CLUSTERING AND PREDICTION USING MACHINE LEARNING WITH PYTHON GUI The dataset used in this project is a transnational dataset which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail. The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers. You will be using the online retail transnational dataset to build a RFM clustering and choose the best set of customers which the company should target. In this project, you will perform Cohort analysis and RFM analysis. You will also perform clustering using K-Means to get 5 clusters. The machine learning models used in this project to predict clusters as target variable are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM, Gradient Boosting, XGB, and MLP. Finally, you will plot boundary decision, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy.
Author: Dirk P. Kroese
Publisher: CRC Press
ISBN: 1000730778
Category : Business & Economics
Languages : en
Pages : 538
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Book Description
Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code
Author: Srikanta Patnaik
Publisher: Springer Nature
ISBN: 9811552436
Category : Technology & Engineering
Languages : en
Pages : 853
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Book Description
This book gathers selected high-quality papers presented at the International Conference on Machine Learning and Computational Intelligence (ICMLCI-2019), jointly organized by Kunming University of Science and Technology and the Interscience Research Network, Bhubaneswar, India, from April 6 to 7, 2019. Addressing virtually all aspects of intelligent systems, soft computing and machine learning, the topics covered include: prediction; data mining; information retrieval; game playing; robotics; learning methods; pattern visualization; automated knowledge acquisition; fuzzy, stochastic and probabilistic computing; neural computing; big data; social networks and applications of soft computing in various areas.
Author: Serg Masís
Publisher: Packt Publishing Ltd
ISBN: 1800206577
Category : Computers
Languages : en
Pages : 737
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Book Description
A deep and detailed dive into the key aspects and challenges of machine learning interpretability, complete with the know-how on how to overcome and leverage them to build fairer, safer, and more reliable models Key Features Learn how to extract easy-to-understand insights from any machine learning model Become well-versed with interpretability techniques to build fairer, safer, and more reliable models Mitigate risks in AI systems before they have broader implications by learning how to debug black-box models Book DescriptionDo you want to gain a deeper understanding of your models and better mitigate poor prediction risks associated with machine learning interpretation? If so, then Interpretable Machine Learning with Python deserves a place on your bookshelf. We’ll be starting off with the fundamentals of interpretability, its relevance in business, and exploring its key aspects and challenges. As you progress through the chapters, you'll then focus on how white-box models work, compare them to black-box and glass-box models, and examine their trade-off. You’ll also get you up to speed with a vast array of interpretation methods, also known as Explainable AI (XAI) methods, and how to apply them to different use cases, be it for classification or regression, for tabular, time-series, image or text. In addition to the step-by-step code, this book will also help you interpret model outcomes using examples. You’ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods you’ll explore here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining. By the end of this book, you'll be able to understand ML models better and enhance them through interpretability tuning. What you will learn Recognize the importance of interpretability in business Study models that are intrinsically interpretable such as linear models, decision trees, and Naïve Bayes Become well-versed in interpreting models with model-agnostic methods Visualize how an image classifier works and what it learns Understand how to mitigate the influence of bias in datasets Discover how to make models more reliable with adversarial robustness Use monotonic constraints to make fairer and safer models Who this book is for This book is primarily written for data scientists, machine learning developers, and data stewards who find themselves under increasing pressures to explain the workings of AI systems, their impacts on decision making, and how they identify and manage bias. It’s also a useful resource for self-taught ML enthusiasts and beginners who want to go deeper into the subject matter, though a solid grasp on the Python programming language and ML fundamentals is needed to follow along.
Author:
Publisher:
ISBN: 9781642954760
Category :
Languages : en
Pages : 168
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Book Description
Machine learning is a branch of artificial intelligence (AI) that develops algorithms that allow computers to learn from examples without being explicitly programmed. Machine learning identifies patterns in the data and models the results. These descriptive models enable a better understanding of the underlying insights the data offers. Machine learning is a powerful tool with many applications, from real-time fraud detection, the Internet of Things (IoT), recommender systems, and smart cars. It will not be long before some form of machine learning is integrated into all machines, augmenting the user experience and automatically running many processes intelligently. SAS offers many different solutions to use machine learning to model and predict your data. The papers included in this special collection demonstrate how cutting-edge machine learning techniques can benefit your data analysis. Also available free as a PDF from sas.com/books.
Author: Brett Lantz
Publisher: Packt Publishing Ltd
ISBN: 1782162151
Category : Computers
Languages : en
Pages : 587
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Book Description
Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.Intended for those who want to learn how to use R's machine learning capabilities and gain insight from your data. Perhaps you already know a bit about machine learning, but have never used R; or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required.
Author: Dipanjan Sarkar
Publisher: Apress
ISBN: 1484223888
Category : Computers
Languages : en
Pages : 397
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Book Description
Derive useful insights from your data using Python. You will learn both basic and advanced concepts, including text and language syntax, structure, and semantics. You will focus on algorithms and techniques, such as text classification, clustering, topic modeling, and text summarization. Text Analytics with Python teaches you the techniques related to natural language processing and text analytics, and you will gain the skills to know which technique is best suited to solve a particular problem. You will look at each technique and algorithm with both a bird's eye view to understand how it can be used as well as with a microscopic view to understand the mathematical concepts and to implement them to solve your own problems. What You Will Learn: Understand the major concepts and techniques of natural language processing (NLP) and text analytics, including syntax and structure Build a text classification system to categorize news articles, analyze app or game reviews using topic modeling and text summarization, and cluster popular movie synopses and analyze the sentiment of movie reviews Implement Python and popular open source libraries in NLP and text analytics, such as the natural language toolkit (nltk), gensim, scikit-learn, spaCy and Pattern Who This Book Is For : IT professionals, analysts, developers, linguistic experts, data scientists, and anyone with a keen interest in linguistics, analytics, and generating insights from textual data
Author: Prateek Joshi
Publisher: Packt Publishing Ltd
ISBN: 1786469677
Category : Computers
Languages : en
Pages : 437
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Book Description
Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application.
Author: Hele-Mai Haav
Publisher: Springer Science & Business Media
ISBN: 9781402010385
Category : Computers
Languages : en
Pages : 350
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Book Description
Databases and database systems in particular, are considered as kerneIs of any Information System (IS). The rapid growth of the web on the Internet has dramatically increased the use of semi-structured data and the need to store and retrieve such data in a database. The database community quickly reacted to these new requirements by providing models for semi-structured data and by integrating database research to XML web services and mobile computing. On the other hand, IS community who never than before faces problems of IS development is seeking for new approaches to IS design. Ontology based approaches are gaining popularity, because of a need for shared conceptualisation by different stakeholders of IS development teams. Many web-based IS would fail without domain ontologies to capture meaning of terms in their web interfaces. This volume contains revised versions of 24 best papers presented at the th 5 International Baltic Conference on Databases and Information Systems (BalticDB&IS'2002). The conference papers present original research results in the novel fields of IS and databases such as web IS, XML and databases, data mining and knowledge management, mobile agents and databases, and UML based IS development methodologies. The book's intended readers are researchers and practitioners who are interested in advanced topics on databases and IS.
