A Critical Evaluation of Business Improvement through Machine Learning: Challenges, Opportunities, and Best Practices

This paper presents a critical evaluation of the impact of machine learning (ML) on business improvement, focusing on the challenges, opportunities, and best practices associated with its implementation. The study examines the hurdles faced by businesses while integrating ML, such as data quality, talent acquisition, algorithm bias, interpretability, and privacy concerns. On the other hand, it highlights the advantages of ML, including data-driven decision-making, enhanced customer experience, process optimization, cost reduction, and the potential for new revenue streams. Furthermore, the paper offers best practices to guide businesses in successfully adopting ML solutions, covering data management, talent development, model evaluation, ethics, and regulatory compliance. Through real-world case studies, the study illustrates successful ML applications in different industries. It also addresses the ethical and social implications of ML adoption and discusses emerging trends for future directions. Ultimately, this evaluation provides valuable insights to enable informed decisions and sustainable growth for businesses leveraging machine learning

A Hybrid Deep Learning Model to Predict Business Closure from Reviews and User Attributes Using Sentiment Aligned Topic Model

Business closure is a very good indicator for success or failure of a business. This will help investors and banks as to whether to invest or lend to a particular business for future growth and benefits. Traditional machine learning techniques require extensive manual feature engineering and still do not perform satisfactorily due to significant class imbalance problem and little difference in the attributes for open and closed businesses. We have used historical data besides taking care of the class imbalance problem. Transfer learning also has been used to tackle the issue of having small categorical dalasets. A hybrid deep learning model has been proposed to predict whether a business would be shut down within a specific period of time. Sentiment Aligned Topic Model (SATM) is used to extract aspect-wise sentiment scores from user reviews. Our results show a marked improvement over traditional machine learning techniques. It also shows how the aspect-wise sentiment scores corresponding to each business, computed using SATM, help to give better results

A Hybrid Deep Learning Model to Predict Business Closure from Reviews and User Attributes Using Sentiment Aligned Topic Model

Business closure is a very good indicator for success or failure of a business. This will help investors and banks as to whether to invest or lend to a particular business for future growth and benefits. Traditional machine learning techniques require extensive manual feature engineering and still do not perform satisfactorily due to significant class imbalance problem and little difference in the attributes for open and closed businesses. We have used historical data besides taking care of the class imbalance problem. Transfer learning also has been used to tackle the issue of having small categorical datasets. A hybrid deep learning model has been proposed to predict whether a business would be shut down within a specific period of time. Sentiment Aligned Topic Model (SATM) is used to extract aspect-wise sentiment scores from user reviews. Our results show a marked improvement over traditional machine learning techniques. It also shows how the aspect-wise sentiment scores corresponding to each business, computed using SATM, help to give better results. © 2018 IEEE