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A Case Study on Machine Learning Applications

Megatrends, automation, and technological breakthroughs are now revolutionizing the way we do business. They are also generating enormous data which if rightly used can become huge assets in managing and predicting business trends.

Machine learning is increasingly being used to build predictive models by studying patterns from large databases. Various machine learning techniques are being used to update solutions from changing data trends and optimizing the profits of companies.

The transport industry has evolved to an entirely different level in the past decade and it is purely on the basis of machine learning tools. The rapid rise of Ola, Uber and many other cab hiring companies is an excellent case study of the rising power of Machine learning.

Dynamic Pricing

One such machine learning technique is the use of dynamic pricing by cab hiring companies. It is often frustrating to see a sudden surge in pricing for no obvious reason. But as we delve deep into the mechanics of it we see an excellent case of machine learning use. The various cab hiring companies use machine learning techniques to adjust their prices to changing market conditions. The traditional way would have been to manually map prices to each route but that has its own drawbacks. To attract customers and also boost their revenues, they use dynamic pricing. They vary their prices according to the changing market conditions like time of the day, weather, customer demand, location, etc.

Optimising Routes

Cab hiring companies also rely heavily on machine learning to find the optimal route to move the passengers from their pick-up to their destination point. Overtly it seems a simple application but it is, in fact, a complex web of architectures to optimally place their resources to benefit both the customer and the company.

Estimated Time of Arrival

The estimated time of arrival(ETA) is the first thing that we see when we open a cab hiring app and it is a fine application of Machine learning. Here a number of routing engines are used to produce an ETA using the present data and the available historical data of similar routes in time and space.vThis ETA takes into account hundreds of thousands of similar trips and also compares them with the initial routing engine estimate.

We are indeed living in a golden era of machine learning and the possibilities are endless. Thousands of similar applications have so subtly intruded into our lives that we sometimes don't even realize that it is, in fact, machine learning at play.

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