Data Analytics and Reporting API – A Reliable Tool for Data Visualization and Predictive Analysis

Ignatius, Joe Louis Paul; Selvakumar, Sasirekha; JSN, Spandana; Govindarajan, Subasri

doi:10.5755/j01.itc.51.1.29467

Cited by 2 publications

(2 citation statements)

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“…Most businesses have refocused their efforts and are investing heavily in data analytics. Data analytics refers to the process of using prior data to gain insights into data [17].…”

Section: Introductionmentioning

confidence: 99%

Big Mart Sales Data Visualization and Correlation

Arista,

Theresiawati,

Seta

2024

JOIV : Int. J. Inform. Visualization

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The amount of unprocessed data available every day is growing. This massive amount of data needs to be effectively assessed to give results that are extremely useful. In the present day, it is crucial for inventory management and demand forecasting to collect sales data for commodities or things, together with all their numerous dependent or independent parts. In a Big Mart Company, the use of sales forecasting is to estimate numerous goods that are readily available and supplied at multiple retailers in different towns. As the number of products and outlets increased drastically, it became increasingly difficult to forecast them manually. As a result, it is necessary to see to what extent the relationship between several variables, including price, popularity, time of day, outlet type, outlet location, etc., affects the appeal of a product. In this research, a data cleaning process was carried out, and data visualization using scatter plots, as well as finding Pearson correlations. The raw processing the data with study of a case big mart sales data is taken from the Kaggle website [https://www.kaggle.com/datasets/sandeepgauti/bigmart-sales]. The Pearson correlation test determines a lack of connection between the two Item_Weight and Item_Outlet_Sales variables. There is a strong but negative correlation where if Item_Visibility decreases, Item_Outlet_Sales also decreases. Positive relationships exist between the two Item_MRP and Item_Outlet_Sales variables. In addition to the correlation test, descriptive statistical analysis is also performed here. With this simple data processing, the raw data will be better organized and easier to analyze, read, and use.

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“…Most businesses have refocused their efforts and are investing heavily in data analytics. Data analytics refers to the process of using prior data to gain insights into data [17].…”

Section: Introductionmentioning

confidence: 99%

Big Mart Sales Data Visualization and Correlation

Arista,

Theresiawati,

Seta

2024

JOIV : Int. J. Inform. Visualization

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“…In the paper [24], the authors introduce the Data Analytics and Reporting API (DARAPI), which offers Analytics as a Service in the form of an API. DARAPI simplifies the data analysis process for users, regardless of their expertise in the field.…”

Section: Introductionmentioning

confidence: 99%

Improvement Technologies for Data Imputation in Bioinformatics

Mochurad,

Horun

2023

Technologies

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Using existing software technologies for imputing missing genetic data (GD), such as Beagle, HPImpute, Impute, MACH, AlphaPlantImpute, MissForest, and LinkImputeR, has its advantages and disadvantages. The wide range of input parameters and their nonlinear dependence on the target results require a lot of time and effort to find optimal values in each specific case. Thus, optimizing resources for GD imputation and improving its quality is an important current issue for the quality analysis of digitized deoxyribonucleic acid (DNA) samples. This work provides a critical analysis of existing methods and approaches for obtaining high-quality imputed GD. We observed that most of them do not investigate the problem of time and resource costs, which play a significant role in a mass approach. It is also worth noting that the considered articles are often characterized by high development complexity and, at times, unclear (or missing) descriptions of the input parameters for the methods, algorithms, or models under consideration. As a result, two algorithms were developed in this work. The first one aims to optimize the imputation time, allowing for real-time solutions, while the second one aims to improve imputation accuracy by selecting the best results at each iteration. The success of the first algorithm in improving imputation speed ranges from 47% (for small files) to 87% of the time (for medium and larger files), depending on the available resources. For the second algorithm, the accuracy has been improved by about 0.1%. This, in turn, encourages continued research on the latest version of Beagle software, particularly in the selection of optimal input parameters and possibly other models with similar or higher imputation accuracy.

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Data Analytics and Reporting API – A Reliable Tool for Data Visualization and Predictive Analysis

Cited by 2 publications

References 0 publications

Big Mart Sales Data Visualization and Correlation

Big Mart Sales Data Visualization and Correlation

Improvement Technologies for Data Imputation in Bioinformatics

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