An exploratory data analysis approach for analyzing financial accounting data using machine learning

Chakri, Potta; Pratap, Saurabh; Lakshay,; Gouda, Sanjeeb Kumar

doi:10.1016/j.dajour.2023.100212

Cited by 10 publications

(5 citation statements)

References 43 publications

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“…The missing value is data that is not available or not recorded in a variable. There are several ways to detect outliers, one of which is using the IQR method [27]. IQR stands for Inter Quartile Range.…”

Section: Methodsmentioning

confidence: 99%

Optimization of the Random Forest Method Using Principal Component Analysis to Predict House Prices

Elmuna,

Chamidy,

Nugroho

2023

International Journal of Advances in Data and Information Syste

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Investment is an interesting thing, especially property investment. The developer must also be careful in determining the price of the property. It should be noted that every year, both short-term and long-term, property prices increase and rarely go down. In determining the price, it is often also based on the features of the house such as the concept, location, bedrooms, etc. To predict house prices based on their features, the random forest has a good performance for predicting house prices. However, the random forest method has the disadvantage that if you use too many variables, the training process will take longer and feature selection tends to select features that are not informative. One way to reduce features without removing other features is to use Principal Component Analysis. In this research, the method used is Principal Component Analysis (PCA) and Random Forest. From the results of model training, it can be concluded that the use of model evaluation results using PCA has a smaller error rate and more consistent values, with an average of 0.018. While the results of the evaluation without PCA and using only Random Forest have a higher error value with an average of 0.03125. The training time using the PCA model has a faster time, with an average of 7918 milliseconds, while those using only random forest without PCA have an average time of 8975 milliseconds.

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Section: Methodsmentioning

confidence: 99%

Optimization of the Random Forest Method Using Principal Component Analysis to Predict House Prices

Elmuna,

Chamidy,

Nugroho

2023

International Journal of Advances in Data and Information Syste

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“…H2: ROE has an effect on stock prices. c. Net Profit Margin Net Profit Margin refers to financial ratios to measure a company's efficiency in generating profits on the sale of goods and services (Chakri et al, 2023). Top financial ratiosnet profit margin This, indirectly can also affect the stock price of a company, so that the 3rd hypothesis built in this study is as follows:…”

Section: A Return On Assets (Roa)mentioning

confidence: 98%

The Influence ROA, ROE, NPM, EPS and PER to Market Price on Public Listed Manufacture of Food Products 2018-2022

Sholihah,

Tjaraka

2024

Ecombis. Rev. J. Ilm. Eco. and Bussines.

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This study aims to provide empirical evidence in financial ratios which include ROA, ROE, NPM, EPS and PER for significance in predicting market prices on public listed food manufacture of food products in the 2018-2022 to minimize information asymmetry, increase transparency and accuracy between management and stakeholders that occur in a stock market. The research samples in this study are public food manufacturing companies in Indonesia in 2018-2022. This study uses panel data with cross-sectional data for 45 companies and time series data from 2018-2022. The total sample in this study is 201 samples. The results of this study indicate that H1 ROA has no significant effect on market price. Meanwhile, H2 ROE, H3 NPM, H4 EPS and H5 PER have a significant effect on market price

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“…Exploratory data analysis (EDA) [31] is a method used to understand relationships in data. This statistical method is widely used in various fields including medicine, finance, social media, and fighting crime, to reduce the complexity of data and reveal underlying patterns [32][33][34][35]. EDA is an essential tool for understanding the structure of underlying data and enhancing model interpretability.…”

Section: Overview Of Exploratory Data Analysismentioning

confidence: 99%

Prediction and Factor Analysis of Liquefaction Ground Subsidence Based on Machine-Learning Techniques

Karimai,

Liu,

Maruyama

2024

Applied Sciences

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Liquefaction is a significant challenge in the fields of earthquake risk assessment and soil dynamics, as it has the potential to cause extensive damage to buildings and infrastructure through ground failure. During the 2011 Great East Japan Earthquake, Urayasu City in the Chiba Prefecture experienced severe soil liquefaction, leading to evacuation losses due to the effect of the liquefaction on roads. Therefore, developing quantitative predictions of ground subsidence caused by liquefaction and understanding its contributing factors are imperative in preparing for potential future mega-earthquakes. This research is novel because previous research primarily focused on developing predictive models for determining the presence or absence of liquefaction, and there are few examples available of quantitative liquefaction magnitude after liquefaction has occurred. This research study extracts features from existing datasets and builds a predictive model, supplemented by factor analysis. Using the Cabinet Office of Japan’s Nankai Trough Megathrust Earthquake model, liquefaction-induced ground subsidence was designated as the dependent variable. A gradient-boosted decision-tree (GDBT) prediction model was then developed. Additionally, the Shapley additive explanations (SHAP) method was employed to analyze the contribution of each feature to the prediction results. The study found that the XGBoost model outperformed the LightGBM model in terms of predictive accuracy, with the predicted values closely aligned with the actual measurements, thereby proving its effectiveness in predicting ground subsidence due to liquefaction. Furthermore, it was demonstrated that liquefaction assessments, which were previously challenging, can now be interpreted using SHAP factors. This enables accountable wide-area prediction of liquefaction-induced ground subsidence.

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An exploratory data analysis approach for analyzing financial accounting data using machine learning

Cited by 10 publications

References 43 publications

Optimization of the Random Forest Method Using Principal Component Analysis to Predict House Prices

Optimization of the Random Forest Method Using Principal Component Analysis to Predict House Prices

The Influence ROA, ROE, NPM, EPS and PER to Market Price on Public Listed Manufacture of Food Products 2018-2022

Prediction and Factor Analysis of Liquefaction Ground Subsidence Based on Machine-Learning Techniques

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