We all hear a lot about how AI and ML will transform the future and how the IoT (Internet of Things) and IOET(Internet of everything) are going to make life better for everyone. So what is the one thing that underpins all of these groundbreaking technologies? The response is the data, from social media to IoT tools, all of which produce an immeasurable volume of data.
Remember Uber, the taxi service company. I’m pretty sure all of you used Uber. What you believe makes Uber a multi-billion dollar business worth. Is it the availability of the taxis, or is it their service?
Well, the answer is data. Data makes them really wealthy, but wait, is there enough to make a company grow? It’s not, of course. You ought to know how to use the data to provide practical information and solve problems.
This is where data science comes in. Data science is the method of using data to discover solutions or forecast a problem statement’s results. To better understand the data science, let’s see how it affects our day-to-day activities.
It’s Monday morning, and I’ve got to go to the office before my meeting start. So I quickly open up the Uber app and searching for a taxi, but there’s something odd about the taxi fare. It’s relatively higher at this hour of the day. Why is this happening? Yeah, because Monday mornings are pick hours, and they’re all going to work. The high demand for taxis leads to a rise in taxi fares. We all know this, but how is this done?
Data science is at the core of Uber’s pricing algorithm. The Surge pricing algorithm means that travelers still get a lift when they need one, particularly though it comes to high rates. Uber uses data analytics to determine which neighborhoods will be the busiest to enable search pricing to bring more drivers on the lane. In this way, UBER maximized the number of trips that UBER would offer and get benefits.
Uber surge pricing technology uses data science. Let’s see how the data science method is working. It all starts by knowing the market necessity or the problem you’re trying to solve.
In this case, the market prerequisite is to create a competitive pricing mechanism that takes place when several people in the same region are looking for rides at the same time. A compilation of details accompanies this. Uber gathers data such as temperature, historical data, holidays, time, traffic, pick up and drop venue, and keeps track of all this.
The next move is to clean up the data. Although often needless data is gathered, such data increases the difficulty of the problem. An example is that Uber could collect information about the location of restaurants and cafes in the vicinity. Such data are not required for the study of Uber surge pricing. Therefore such data has to be removed at this step.
Data cleaning is accompanied by data discovery and review. The data discovery stage is like brainstorming data analysis. This is where you can explain the trends in your results. It is accompanied by data processing. The data processing stage involves developing a machine learning model that forecasts the Uber surge at a given time and place.
This model is created by using all the insights and Trends collected in the exploration stage. The model is trained by feeding thousands of customer records to learn to predict the outcome more precisely.
Next is the data validation stage. Now here, the model is tested when new customer books a ride. The new booking data is compared with the historical data to check if there are any anomalies in the search prices or any false predictions.
If any such abnormalities are detected, a notification is immediately sent to the data scientists at Uber, who fix the issue as per the requirements.
This is how Uber forecasts a rise in the price for a given place and time. Deployment and integration is the final step in data science. Thus, after checking the model and enhancing its performance, it is extended to all users. Customer feedback is received at this stage, and if there are any issues, they are fixed here.
So that was the entire data science process. Now, let’s look at a variety of more data science implementations. Data science is being applied to e-commerce sites such as Amazon and Flipkart. It’s also the reasoning behind Netflix’s suggestion framework. Today, consistency data science has experienced dramatic shifts in today’s industry. The technologies range from the monitoring of credit card fraud to self-driving vehicles and virtual assistants such as Siri and Alexa.