A Review of Data Analytics Techniques Used to Analyze Patient Satisfaction in Healthcare
Abstract
Advertising plays a crucial role in influencing customer purchasing behaviour and improving business revenue. This study analyses the relationship between advertising expenditure on three media channels—TV, Radio, and Newspaper—and product sales using a supervised machine learning approach. A linear regression model is developed to predict sales performance based on advertising budgets. The dataset consists of 200 observations and four variables. Exploratory data analysis, model training, performance evaluation, and visualization techniques are applied. The results indicate that TV and Radio advertisements have a stronger impact on sales compared to Newspaper advertisements. The proposed model demonstrates good prediction accuracy and provides useful insights for marketing decision-making. It looks at popular data sources, analytical approaches from machine learning and natural language processing to conventional statistical methods, and the major variables affecting patient satisfaction. Along with discussing current issues and potential future research topics, the report also evaluates the effectiveness, benefits, and drawbacks of various analytical methodologies. The results are intended to assist politicians, researchers, and medical professionals in using data-driven approaches to improve patient-centered healthcare delivery.
Authors
Praveen Soneja, Anamika Tiwari, Anand Kumar, Aniket Kumar, Animesh Raj
Institution
Noida Institute of Engineering & Technology (MCA Institute), Greater Noida
Leave A Comment