Financial Banking Dataset for Supervised Machine Learning Classification

Raicu, Irina

doi:10.12948/issn14531305/23.1.2019.04

Cited by 5 publications

(3 citation statements)

References 21 publications

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“…Nonprofit organizations, government agencies, and universities have archived data [24] for further use, and business organizations and scientific groups use for instance decision making [32,36] from the retrieved information by web scraping. It is commonly used in ecommerce [40,41], banking [42], recruitment agencies [43], and real estate [44] companies for data collection and is applied to their machine learning algorithms. The web-scraping dataset is up-to-date and can be used as a training dataset in a machine learning application for updating a model.…”

Section: Web Data Scrapingmentioning

confidence: 99%

A Ranking Learning Model by K-Means Clustering Technique for Web Scraped Movie Data

et al. 2022

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Business organizations experience cut-throat competition in the e-commerce era, where a smart organization needs to come up with faster innovative ideas to enjoy competitive advantages. A smart user decides from the review information of an online product. Data-driven smart machine learning applications use real data to support immediate decision making. Web scraping technologies support supplying sufficient relevant and up-to-date well-structured data from unstructured data sources like websites. Machine learning applications generate models for in-depth data analysis and decision making. The Internet Movie Database (IMDB) is one of the largest movie databases on the internet. IMDB movie information is applied for statistical analysis, sentiment classification, genre-based clustering, and rating-based clustering with respect to movie release year, budget, etc., for repository dataset. This paper presents a novel clustering model with respect to two different rating systems of IMDB movie data. This work contributes to the three areas: (i) the “grey area” of web scraping to extract data for research purposes; (ii) statistical analysis to correlate required data fields and understanding purposes of implementation machine learning, (iii) k-means clustering is applied for movie critics rank (Metascore) and users’ star rank (Rating). Different python libraries are used for web data scraping, data analysis, data visualization, and k-means clustering application. Only 42.4% of records were accepted from the extracted dataset for research purposes after cleaning. Statistical analysis showed that votes, ratings, Metascore have a linear relationship, while random characteristics are observed for income of the movie. On the other hand, experts’ feedback (Metascore) and customers’ feedback (Rating) are negatively correlated (−0.0384) due to the biasness of additional features like genre, actors, budget, etc. Both rankings have a nonlinear relationship with the income of the movies. Six optimal clusters were selected by elbow technique and the calculated silhouette score is 0.4926 for the proposed k-means clustering model and we found that only one cluster is in the logical relationship of two rankings systems.

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Section: Web Data Scrapingmentioning

confidence: 99%

A Ranking Learning Model by K-Means Clustering Technique for Web Scraped Movie Data

et al. 2022

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“…Using web scraping technique to perform automated and targeted extraction of data, a dataset composed by 2234 of posts from Conso portal (www.conso.ro) from the Romanian financial banking marketplace has been created. The dataset is detailed in [7]. The financial banking products and services are classified into 30 categories as shown in Figure 1.…”

Section: Data Collection and Preprocessingmentioning

confidence: 99%

Multi-Class Text Supervised Classification on Romanian Financial Banking Reviews

Raicu¹,

Bologa²,

Constantinescu³

2019

Proceedings of the 18th International Conference on INFORMATICS in ECONOMY Education, Research and Business Technologies

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Nowadays, with the proliferation of social networks, forum discussions, blogs, and other forms of expression, more and more users have engaged in financial banking communities to gather information about financial banking products or services, to share experiences or to interact with other users. Reviews expressed by users on social media and Internet search activity are a valuable source of information. Text Classification is a growing research area used in social media analysis. The usefulness of Text Classification is manifested in different areas for various needs. For instance, the ability to automatically classify a review into its right financial banking product or service category would be appreciated by users when looking for a specific product or service as well as financial banking institutions to analyze the customers' feedback. Most of the research papers are focusing on exploring Text Classification methods for English-based text. Further, supervised machine learning methods for Text Classification require a huge data volume. In this paper, we focus on implementing supervised machine learning methods for multi-label text classification of Romanian financial banking reviews. We investigate the efficiency of using various supervised machine learning algorithms such as Random Forest, Support Vector Machine, Multinomial Naïve Bayes and Logistic Regression to classify text applied to a limited amount of Romanian financial banking reviews. We analyze the corpus, features and the results data. The baseline features are unigrams and bigrams weighted by TF-IDF. The results illustrate that the SVM classifier outperforms in classification of text into 30 categories of financial banking products and services from Romanian reviews.

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“…It can provide ways and channels to analyze needs and understand how to improve product quality and service, so that financial institutions can tailor personalized needs for customers. This framework uses various machine learning methods and technologies to classify emotions [5]. With fierce market competition, many financial institutions have to take sales as the first premise and use the quickest way to focus on "possible" customers.…”

Section: Introductionmentioning

confidence: 99%

Research on Enterprise Financial Customer Classification Method and Preference Based on Intelligent Algorithm

Zhang

et al. 2021

Wireless Communications and Mobile Computing

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Compared with the past questionnaire survey, this paper applies the intelligent algorithm developed rapidly in recent years to identify the tendency of customers to buy financial products in the market. In addition, for the single state customer classification indicators based on the previous demographic information and action information, it is proposed to combine the action of market activities with demographic information; that is, the static integrated customer classification index is further combined with the improved neural network model to study the classification and preference of enterprise financial customers. Firstly, the enterprise financial customer classification model based on neural network algorithm is studied. Aiming at the shortcomings of easy falling into the local optimal solution of neural network algorithm, slow convergence speed of algorithm, and difficult setting of network structure, combined with the characteristics of genetic algorithm, the concept of adaptive genetic neural network algorithm is proposed. Then, the design of adaptive genetic neural network model is studied. Secondly, combined with the customer data of a financial enterprise and the characteristics of enterprise finance, this paper analyzes the risk influencing factors of enterprise financial customers, analyzes the customer data, evaluates the enterprise financial customers through the adaptive genetic neural network model, and realizes the classification of enterprise financial customers. Through an example, it is proved that the enterprise financial customer classification and preference model based on the adaptive genetic neural network algorithm discussed in this paper has better customer classification accuracy and can provide better method support for enterprise financial customer management.

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Financial Banking Dataset for Supervised Machine Learning Classification

Cited by 5 publications

References 21 publications

A Ranking Learning Model by K-Means Clustering Technique for Web Scraped Movie Data

A Ranking Learning Model by K-Means Clustering Technique for Web Scraped Movie Data

Multi-Class Text Supervised Classification on Romanian Financial Banking Reviews

Research on Enterprise Financial Customer Classification Method and Preference Based on Intelligent Algorithm

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