A glance into Machine Learning in Mobile Apps

Nowadays we can see that Machine learning and Artificial Intelligence continue to transform each field by helping one to think broader and come up with innovative solutions for the business.

Machine learning has also transformed mobile app development by helping developers write powerful algorithms to create intelligent apps that can understand human behavior, assist users, and entertain them. For example, we can see that some of the items are shown in the “recommended” and “you may also like” in the shopping sites. This solution is provided based on mobile application content analysis, customer behavior, and purchase patterns of the customers.

Few areas where Machine Learning is in place to help

  • Data Mining Mobile applications
  • Mobile financial apps
  • E-commerce apps
  • Healthcare mobile market
  • Transportation mobile applications

Data Mining Mobile applications

Data mining is the analysis of big data and the discovery of useful, non-obvious patterns and connections within significant sets of data. It involves the process of data storage, maintenance, and analysis of data. It’s simply impossible for a human to analyze all the possible variations and find obscure behavior patterns. Machine Learning provides a set of tools and learning algorithms necessary to find all possible connections within the data sets. A good example is the travel app. The algorithm helps collect client data and categorize it with Gender, age, social media profiles, and place preferences which are considered as variables to design custom applications and services. Then operators receive business intelligence to modify tours and schedules listed in-app based on the customer preference

Mobile financial apps

The finance market is most concerned about security, earnings, investment, and lending. We can use such mobile applications, powered to receive insights into your personal finances. In most cases, such apps are developed by banks to provide their clients with additional value. By utilizing machine learning algorithms, the app analyzes your transaction history and comes up with your expenditure predictions, track spending habits, and gives financial advice. This can help to build your portfolio and investment plans.

E-commerce apps

Online retail mobile applications can use machine learning algorithms in several ways. For example, such algorithms are handy in providing the buyer with more relevant product recommendations based on purchase history. Shopping apps use Machine Learning to suggest products for their clients. All this helps to provide the best-personalized experience to the user.

Healthcare mobile market

Numerous healthcare mobile applications help users to keep track of heart illnesses, diabetes, epilepsy, and migraines. Machine learning algorithms help such apps analyze user input, predict the possibility of one or other medical conditions, and notify the doctor about the patient’s current condition for early treatment. Some tracking applications can measure our daily water intake or a number of activities, and use the data from thousands of people with diabetes to learn and provide them with valuable trending data. Another area is fitness tracking where ML analyzes your daily activities, steps, jogging rhythm, and much more. This can help you in achieving your goals and how to change your diet/activities to achieve them faster.

Transportation mobile applications

The mobile applications used in transportation such as cab booking apps need to provide the driver with up-to-date information about traffic conditions. Based on current situations, such apps optimize routes to travel to avoid traffic jams, reach the destination on time, and avoid extra fuel consumption. To receive such traffic information, developers integrate traffic prediction software with machine learning algorithms into road optimization mobile apps. The algorithm analyses historical data about traffic conditions in the specific route and predicts the traffic patterns for a particular day in real-time.

Leave a Reply

Your email address will not be published. Required fields are marked *

20 − 17 =