Uber uses AI, Data Science to manage their fleet, make predictions of future demand etc. If you want to understand about how Uber use data science, particularly "clustering" techniques to save our time, attend this Live Session.
Uber has 93 million active users. Uber cab processed $26.61 billion worth of bookings in 2020. 1.44 billion rides are completed through Uber every quarter. Uber is the fastest-growing cab company standing at the top of its game. Being able to solve the problems like poor transportation infrastructure in some cities, drivers denying to pick up the customer, bad car experience, and unsatisfactory Uber support. Despite having such a huge number of trips how do you think they manage to get you a cab in a few minutes?
If you might have ever booked an Uber cab, you must have noticed that the process is quite simple –just press a button, set the pickup location, request a car, go for a ride and pay with a click of a button. The process is simple but there is a lot going on behind the scenes.
But how do they do it? And what can we learn from them? As it turns out, there’s a great deal of data being collected, produced, and visualized behind the scenes — all working to create a more efficient company and impact transportation as a whole. Let’s take a closer look.
In this session, we are going to learn how Uber saves your time in reaching your destination.
So, let’s get started!!
Getting a ride from an Uber cab is beautiful in its simplicity: simply open your Uber app, set up the pickup location, request an Uber, get picked up and pay with the tap of a button.
Though Uber has a much more database of drivers, so as soon as you request a car, Uber’s unsupervised learning algorithm goes right to work – in 15 seconds or less and matches you with the driver closest to you. Meanwhile in the background, Uber is storing data for every trip taken — even when the driver has no passengers. All of this data is stored and analyzed to predict supply and demand, as well as setting up future fares.
All of this data is collected, cleaned, analyzed, and used to predict everything from the user’s wait time to recommending where drivers should place themselves in order to take advantage of the best fares and more passengers. All of these features are implemented in real-time.
Unsupervised learning is a machine learning technique in which the users do not need to supervise the model. Instead, it allows the model to work on its own to discover patterns and information that was previously undetected. It mainly deals with the unlabelled data.
There are different types of unsupervised learning. In this session, we’ll talk about these common approaches -
Unsupervised learning is categorized into two groups
The major difference between supervised and unsupervised learning is that the input data is trained using “labeled data” in supervised learning and algorithms are used against the unlabelled data in unsupervised learning.
Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group and dissimilar to the data points in other groups.
In this session we’ll cover these four types of clustering algorithms:
K-means is a distance-based algorithm, where we calculate the distances to assign a point to a cluster. Here, the value of K is to specify the number of clusters to be formed. All the points then are grouped as per the cluster specified.
The k-means clustering example follows a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters) fixed a priori. The main idea is to define k centers, one for each cluster. These centroids should be placed in a smart way because different locations cause different results.
K-means clustering applications include market segmentation, document clustering, image segmentation, and image compression.
That’s an overview of the session. So, what are you waiting for? Register now to book your session.