A novel Machine learning technique for fake smart watches advertisement detection

Zaheer, Anum; Tahir, Sidra; Humayun, Mamoona; Almufareh, Maram Fahhad; Jhanjhi, N. Z.

doi:10.1109/macs56771.2022.10023151

Cited by 10 publications

(1 citation statement)

References 14 publications

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“…While machine learning and data mining share some similarities, their primary differences lie in their objectives and methodologies. Machine learning is more concerned with building predictive models and decision-making systems (Ma et al, 2023;Razaque et al, 2023), whereas data mining is focused on knowledge discovery and pattern identification within existing data (Tsui et al, 2023;Zaheer et al, 2022). In many cases, data mining can be considered a precursor to machine learning, as the insights gained from data mining can inform the feature selection, preprocessing, and model building stages of a machine learning project.…”

Section: A Definition Of Machine Learning and Data Miningmentioning

confidence: 99%

Analysing Influential Factors in Student Academic Achievement: Prediction Modelling and Insight

Ananna,

Nowreen,

Al Jahwari

et al. 2023

IJEMD-CSAI

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The fascination with understanding student academic performance has drawn widespread attention from various stakeholders, including parents, policymakers, and businesses. The 'Students Performance in Exams' dataset, available on platforms like Kaggle, stands as a treasure trove. It extends beyond test scores, encompassing diverse student attributes like ethnicity, gender, parental education, test preparation, and even lunch type. In our tech-driven age, predicting academic success has become a compelling pursuit. This study aims to delve deep into this dataset, utilizing data mining methods and robust classification algorithms like Logistic Regression and Random Forest in a Jupyter Notebook environment. Rigorous model training, testing, and fine-tuning strive for the utmost predictive accuracy. Data cleaning and preprocessing play a crucial role in establishing a reliable dataset for accurate predictions. Beyond numbers, the project emphasizes data visualization's impact, transforming raw data into comprehensible insights for effective communication. The Logistic Regression Model exhibits an impressive 87.6% accuracy, highlighting its potential in predicting academic performance. Moreover, the Random Forest Model excels with a remarkable 100% accuracy in forecasting student grades, showcasing its effectiveness in this domain.

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