M&A is a strategic concept of business growth through consolidation, gaining market access, increasing strategic positions, and increasing operational efficiency. To understand the dynamics of M&A, this paper looks at aspects such as targeted firm identification, evaluation, bidding for the target firm, and post-acquisition integration. All forms of M&A, including horizontal, vertical, conglomerate, and acquisitions, are discussed in terms of goals and values, including synergy, cost reduction, competitive advantages, and access to better technology. However, issues such as cultural assimilation, adhesion to regulations, and calculating an inaccurate value are also resolved. The paper then goes deeper to provide insight into how predictive analytics applies to M&A, using ML to improve decision-making with forecasting benefits. Including healthcare, education, and construction industries, the presented predictive models using regression analysis, neural networks, and ensemble techniques help to make decisions. Through time series and real-time data, PDA enables sound M&A strategies, effective risk management and smooth integration.

Over the past few decades, cardiovascular disease and related complications have surpassed all others as the important causes of death on a universal scale. At the moment, they are the important cause of mortality universal, including in India. It is important to know how to find cardiovascular problems early so that patients get better care and prices go down. This project utilizes the UCI Heart Disease Dataset to develop ML and DL models capable of detecting cardiac diseases. Heart illness was categorized using Convolutional Neural Network (CNN) models, which are able to detect intricate patterns in supplied data. A confusion matrix rating, an F1-score, a ROC curve, accuracy, precision, and recall were some of the measures used to grade the model. It did much better than the Neural Network, Deep Neural Network (DNN), and Gradient Boosted Trees (GBT) models, with 91.71% accuracy, 88.88% precision, 82.75% memory, and 85.70% F1-score. Comparative study showed that CNN was the most accurate model. Other models had different balances between accuracy and recall. The experiment results show that the optional CNN model is a decent way to identify cardiovascular disease. This means that it could be used in healthcare systems to find diseases earlier and treat patients better.