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Suppose the experts from step 1 told you that the usual types of reasons for making a claim are either “Medical”, “Travel”, “Phone”, or “Other”. You can also use pany() and fake.address() to generate fake customer addresses and company names. You can use fake.name() to generate n_names names and store them into the names list.
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Implicitly you have also defined the sample size n_names=200,000 in this case. The first five variables you can generate are the customer name, home address, company (for which let’s say the each customer works as an employee), reason for the insurance claim, and the level of data confidentiality attached to the claim. Generate 200,000 random insurance clients and relevant variablesĢ.1 Customer Names, Address, Company Name, Claim Reason, Confidentiality Level Don’t forget to initialize a faker generator as you get set up.Ģ. You will make use of all of these packages as you go along.
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Import relevant Packages: As a first step, you will need to import the relevant Python packages.You can use pandas and numpy to manipulate the data, requests and BeatifulSoup to work with web pages, random and F aker to generate random data.
Random data generator python code#
Let’s now go through the code required to generate 200,000 lines of random insurance claims coming from clients. Once you are done with this first critical step, it is time to use some Python code to come up with your data! Step 2: Generate the data This is probably the most important step in order to derive meaningful generated samples of data, and it surely takes quite some time as you would want to consider all sorts of variables and relationships. The key here is to keep gathering useful information in iterative fashion, so that as you generate the data you can go back to your sources and check your results against them, and adjust your data generation activity accordingly. if 30% of the claims in your market come from Australia and Japan, you would want your data to reflect this) and any other question that is relevant for the problem at hand. In this example, this can mean asking about the geographical split of the data (i.e. The goal of this activity is to get to know as much contextual information for you to generate data in a way that is as close as possible to the reality of the market segment(s) you are trying to represent. Let’s now go through a sample and simple data generation workflow in Python, within which you will mainly make use of the Numpy and Faker packages. In the consulting industry, where such counterparts are your actual clients and gatekeepers to the data, this happens quite often, once the viability of a data solution’s initial exploration is positively assessed. The activity may even convince your counterpart to actually share real-world data with you, which is your ultimate goal. The activity can be vital in order to get initial liftoff for your data project, and get you to a point where you can show the potential of your data solution to either an investor or to your next client.
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You are about to start on your next data project but you immediately run into an obstacle: the data you are looking to use is not easily accessible. Photo by Patrick Fore on Unsplash Introduction