Data-Driven Insights: Applying Machine Learning in Data Analytics - Ayesha Banu (International Journal of Intelligent Systems and Applications in Engineering, 2024)
In recent years, the application of machine learning (ML) in data analytics has garnered significant attention for its potential to revolutionize various domains. This study explores the integration of ML algorithms in data-driven projects, emphasizing a systematic approach to project definition, data collection, preprocessing, model development, evaluation, deployment, and monitoring. The objective is to leverage ML to identify patterns, make predictions, and automate decision-making processes. The research delineates the steps involved in sourcing and cataloging relevant data from diverse origins, ensuring data quality through rigorous preprocessing techniques such as cleaning and transformation. Feature engineering is highlighted as a critical phase to enhance model performance. The study progresses through the selection and training of appropriate ML algorithms, employing methods like cross-validation and hyperparameter tuning to optimize model accuracy and generalizability. Evaluation metrics tailored to specific ML tasks—classification or regression—are utilized to assess model efficacy. The transition from model development to deployment in a production environment is discussed, along with strategies for real-time prediction and analysis. Emphasis is placed on continuous model monitoring and maintenance to adapt to evolving data patterns and ensure sustained performance. The culmination of the study involves generating actionable insights and developing intuitive visualizations to facilitate stakeholder understanding. Detailed documentation of methodologies and model configurations is advocated for transparency and future reference. This comprehensive approach aims to harness the power of ML in data analytics, driving informed decision-making and operational efficiency.