Today, we’re gonna build a model to predict how much a customer is willing to pay for a product. This is called dynamic pricing.

We’ll start by:

1. Reviewing the dataset
2. We’ll then build a Machine Learning model for predicting willingness to pay.
3. And instantly see how we can make predictions and share them with others on the team.

So let’s get started.

In this dataset, every single row is a customer that has taken a taxi ride. And every column tells us something about that ride. Like city, number of passengers, time and location of pickup, etc. At the end, we have a column here called fare amount. This tells us how much a customer in that row has paid for that ride.

This is a historical dataset. And we now wanna use it to predict if a new customer is likely to pay for a given ride. We want to show them a price they’re most likely to pay. No more, No less.

Number 2: Building the machine learning model

To build a model, we start by uploading this CSV.  We can upload spreadsheets,

Connect to apps & services like Salesforce and Dropbox and even databases like MySQL and BigQuery. Once uploaded, all you have to do is select your prediction column.  In this case, it’s the Fare Amount column.

From here, Obviously, AI will automatically build a machine learning model to make predictions, in less than 1 minute.

Number 3: Make predictions and share with your team

Once your model is built, it’s ready to be used right away. No deploying, no maintenance. You can see it’s accuracy and performance details here. 

Seems like we’re good to go!

We start by heading to Personas to make the first prediction.

Let’s say we have a new ride. We’ve randomly chosen a latitude and longitude for their pickup and dropoff locations. Now, let’s say their passenger count is 3. As I change these details, I can see the prediction for this new ride come up right here. The ideal cost for this ride is $6.43 give or take $1.5.

Now, let’s head to export predictions. We can upload a list of new customers and get a prediction on each one. An API can also be used to automate these predictions and embed them in your own app or website.

You will also have the option to get a shareable link to send to anyone on your team. It will enable them to use the model you built to start making their own predictions.

So, what would you like to predict today?