Financial Data Analysis and Application Based on Big Data Mining Technology

Abstract

We provide a brief overview of the connotation and characteristics of data mining technology in the era of big data, analyze the feasibility of data mining technology in business management from the economic and technical perspectives, and propose specific application suggestions according to the content and requirements of business management. This paper describes in detail the principles and steps of using the weighted plain Bayesian algorithm and the decision tree algorithm to analyze students’ performance; firstly, we need to obtain the plain Bayesian analysis model of college students’ learning literacy in physical education and the C4.5 graduation literacy analysis model, and then use certain rules to combine the weighted plain Bayesian algorithm and the decision tree algorithm to obtain the WNB-C4.5 college students’ learning literacy analysis model. In addition, in the prediction of financial risks, the classification scheme can be used in the judgment of violation of regulations, but the most used classification scheme is the decision tree. Experiments show that the effectiveness of this scheme in data mining for financial companies is increased by 2% compared to the benchmark method.

Construction and Optimization of Financial Transformation Decision Model Based on Data Mining

“…Finding directions, seizing market chances, and maximizing profits are all made possible with the help of data mining [7]. The financial sector requires extensive data collection and processing due to its complexity [8]. Due to the complexity of these transactions, information asymmetry, and the volume of people conducting related business on a daily basis, Financial services are provided by most financial banks and financial institutions like loan, personal deposit, credit card, investment business.…”

Construction of Enterprise Financial Data Visualization Analysis System Based on Data Mining Technology

“…[2] In view of this, this paper believes that enterprises should re-examine the changes and innovations brought about by digital transformation and upgrading, adhere to innovation-driven, actively explore effective ways to deal with massive financial data information under big data technology, and promote the transformation of enterprise financial management mode to digitalization and intelligence. [3] The visual analysis system of enterprise financial data based on data mining technology can organically combine data mining technology with financial analysis methods. Through data collection, data cleaning, data storage, model construction, result expression and other steps, it can help enterprise users to obtain enterprise financial status and operation status in a timely, accurate and intuitive manner, and then make scientific and reasonable management development decisions, and make useful attempts to improve the effectiveness of enterprise internal management.…”

“…import pandas as pd from sklearn.linear_model import LogisticRegression X = df.iloc[:,[1,2,3]]. values y = df.iloc[:, 0].values X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0) sc_X = StandardScaler() X_train = sc_X.fit_transform(X_train) X_test = sc_X.transform(X_test) classifier = LogisticRegression(random_state = 0) classifier.fit(X_train, y_train)…”