Volume 3, Issue 1

June 2025 · 4 published papers

IJACIS

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Data Preprocessing: Case Study On Australia Weather Dataset

Moubarak Mohamed Misbahou

The weather remains one of the challenges in a country's development. It remains a mystery that meteorologists are still discovering new insights to understand it and make it use to our advantage to develop our country. Developing a predictive analysis to predict the next-day rain was suggested to overcome this problem. This paper will provide a detailed explanation of the dataset's pre-processing, with a focus on data reduction, cleaning, and transformation. We'll also talk about handling class imbalance problems and their associated data transformation.

July 25, 2025 · Volume 3, Issue 1 · 10.66917/ijacis.a000018

IJACIS

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Comparative Study On Australia Weather Classification

Moubarak Mohamed Misbahou

Classification is a key data analysis technique for developing predictive models from labeled data. This study compares the performance of four classification algorithms such as Decision Tree, Neural Network, Naïve Bayes, and Support Vector Machine, applied to the Australian weather dataset. The objective is to predict the next day’s rainfall based on the target variable RainTomorrow. Each model is trained and evaluated using a consistent preprocessing pipeline and evaluated with various performance metrics including accuracy, precision, recall, and F1-score. Our analysis reveals Support Vector Machine (SVM) as the top-performing classifier, offering practical insights for weather-based predictive modeling tasks.

July 27, 2025 · Volume 3, Issue 1 · 10.66917/ijacis.a000019

IJACIS

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A Smart One-Stop Business Solution Using Flutter

Tanjim Khan Nokib, Md. Fokhray Hossain

In this paper, a unique one-stop smart application that integrates note-taking, selling, and purchasing features into a single platform is introduced. Developed with Flutter, MongoDB, NodeJS, and Express, the application provides an easy-to-use interface for smooth participation in e-commerce operations. Its unique feature is that it combines the features of a buyer, seller, and guest mode, making it easier for users to conduct transactions and effectively manage their accounts. The note-taking feature of the app, which is available in all other modes, makes it much easier for users to take notes while managing their purchases or shopping. The software protects user data by using SQL-Lite for local data storage, which lowers the possibility of unwanted access or data breaches. The app's architecture also permits scalability and future growth, with the intention of introducing a delivery portion that will generate revenue for authorized staff members. This development supports the app's objective of providing a complete solution for buyers and sellers, doing away with the need for numerous platforms for various tasks. Compared to cloud-based solutions, the app's usage of local data storage reduces its carbon footprint in terms of environmental impact. This emphasis on environmental sustainability is consistent with current developments in technology, where eco-friendly methods are becoming more and more important. Overall, this initiative offers a forward-thinking method of approaching digital commerce by providing a safe, adaptable platform that takes into account the sustainability of the environment and the changing demands of users.

August 06, 2025 · Volume 3, Issue 1 · 10.66917/ijacis.a000017

IJACIS

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Attrition Rate Prediction Using A Frequency-Recency-Monetization-Based Smoteen-Boosted Approach

Paul Onoma, Joy Agboi, Eferhire Valentine Ugbotu, Tabitha Chukwudi Aghaunor, Chris Chukwufunaya Odiakaose, Arnold Adimabua Ojugo, Andrew Okonji Eboka, Amaka Pat Binitie, Peace Oguguo Ezzeh, Patrick Ogholuwarami Ejeh, Chris Chukwudi Onochie, Victor Ochuko Geteloma, Frances Uche Emordi, Anderson Ise Orobor, Chris Obaro Obruche

Businesses today seek to generate more revenue for their products and services. However, attrition or churn for products and services over the digital market space has continued to plague businesses to proffer consumers with various alternates and choices to services and products for competitive money’s worth. Today’s businesses must thus, seek to yield better value while meeting consumer needs cum demands through a retention strategy. Thus, our study utilizes the recency-frequency-monetization as retention model that explores SMOTEEN-based AdaBoost ensemble to study the impact of imbalanced dataset to identify consumer churn. With chi-square feature selection, our proposed model yields F1 0.9819, Accuracy 0.9882, Recall 0.9848, Precision 0.9886, and Specificity 0.9824 respectively with 9632-correctly identified and 15-incorrectly classified cases. Conversely, other scheme yields ranges of F1 [0.6305–0.8875], Accuracy [0.6432-0.8879], Precision [0.6320–0.8805], Recall [0.6128-0.8876] and Specificity [06370-0.8894] respectively. This shows that model yields enhanced performance, and that the explored RFM-retention strategy was found to curb churn and improve monetization.

August 06, 2025 · Volume 3, Issue 1 · 10.66917/ijacis.a000020