A model of this decision process would allow a program to make recommendations to a customer and motivate product purchases.
E-Commerce businesses such as Amazon has this capability.
Use cases of ML are making near perfect diagnoses, recommend best medicines, predict readmissions and identify high-risk patients.
These predictions are based on the dataset of anonymized patient records and symptoms exhibited by a patient.
Businesses have a huge amount of marketing relevant data from various sources such as email campaign, website visitors and lead data.
Using data mining and machine learning, an accurate prediction for individual marketing offers and incentives can be achieved.Whereas predictive maintenance minimizes the risk of unexpected failures and reduces the amount of unnecessary preventive maintenance activities.For predictive maintenance, ML architecture can be built which consists of historical device data, flexible analysis environment, workflow visualization tool and operations feedback loop.ML programs use the discovered data to improve the process as more calculations are made.Thus machines can learn to perform time-intensive documentation and data entry tasks.Adoption of ML is happening at a rapid pace despite many hurdles, which can be overcome by practitioners and consultants who know the legal, technical, and medical obstacles.Customer segmentation, churn prediction and customer lifetime value (LTV) prediction are the main challenges faced by any marketer.Given a purchase history for a customer and a large inventory of products, ML models can identify those products in which that customer will be interested and likely to purchase.The algorithm identifies hidden pattern among items and focuses on grouping similar products into clusters.The SU LIS takes no responsibility for the content published within this journal, and disclaim all liability arising out of the use of or inability to use the information contained herein.We assume no responsibility, and shall not be liable for any breaches of agreement with other publishers/hosts.