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Detection and identification of plant diseases is critical to the success and efficiency of agricultural production. Plant disease outbreaks are becoming more frequent throughout the world, and the presence of these diseases in cultivated plants has a significant impact on productivity. Therefore, researchers are focusing on developing effective and reliable plant disease detection methods. Thus, farmers can take advantage of early detection of this disease to minimize future losses. This article discusses machine learning approaches as well as decision trees, K-nearest neighbors, naive Bayes, support vector machines (SVM), and random forests for detecting coffee leaf diseases using leaf images. The above-mentioned classifications were researched and compared to determine the most suitable plant disease prediction model with the highest accuracy. Compared with other classification algorithms, the SVM algorithm achieves the highest accuracy of 99.75%. All the models trained above will be used by farmers to quickly identify and classify new diseases in images as a prevention strategy. As a preventive measure, farmers can detect and classify new diseases in images early.

This study examines the role of Artificial Intelligence (AI) in marketing strategy through a Systematic Literature Review (SLR) approach. AI has shown a transformative impact by outperforming traditional methods, especially in optimizing big data-based marketing strategies. With the ability to analyze consumer behavior in depth, AI enables businesses to personalize marketing efforts and improve user experiences more efficiently and responsively. However, challenges such as data privacy, high initial investment, and reliance on data quality remain major concerns that must be addressed. This study also evaluates the effectiveness of using AI across marketing channels, which shows significant differences in their impact on business strategy. In addition, the integration of AI-based fitness equipment is considered to have a major contribution in increasing consumer satisfaction while driving online business growth. The results of this study provide valuable insights into the effective implementation of AI, as well as highlight the importance of maintaining data security and implementing AI strategically to provide optimal benefits for consumers and business development.

This study aims to develop an automated pipeline for data cleaning using Pandas and Scikit-learn. The data cleaning process is often performed manually, requiring a long time and prone to errors. This study uses a quantitative experimental method with a dataset of 100,000 rows of e-commerce transaction data. The results show that the automated pipeline reduces missing values by 95.7% and outliers by 91.7%, and accelerates processing time by 35% compared to manual methods. The distribution of data after cleaning becomes more stable, allowing for more accurate analysis. This study contributes to the development of a more efficient and accurate automated data cleaning approach.Keywords: Systematic Literature Review, Artificial Intelligence and Marketing Strategy.