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Colin Jaffe/2 min read

Implementing User Input for Percentile Calculation in Python

ML Project Workflow

1

Define the Problem

What outcome are you predicting and why?

2

Prepare the Data

Clean, normalize, encode categoricals, split into train/test.

3

Train Models

Start simple — logistic regression baselines often surprise.

4

Evaluate & Iterate

Confusion matrix, ROC, F1 — pick metrics that match the problem.

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Let’s give you a little challenge. Take this age group and execute the block as shown.

Let’s give you a little challenge. Take this age group and execute the block as shown. Now `ages` is available in our Python environment.

This is a random sample of ages. Using that sample, you’ll prompt the user for input. You can use Python’s built-in `input()` function to ask the user for some information.

Of course, we are the user in this case, since we’re running the notebook. But if someone else were reviewing it, they could also interact with it as a user, without being the programmer. We’re going to create an input box. The user will enter a percentile—such as 25,75, or any number—and get back the age below which that percentage of people fall.

If someone enters 90, you should print a string like: “90% of all people are less than 61.” You could also phrase it as “younger than 61, ” if you prefer.

For input 40, you’d display: “40% of all people are less than 27.”

Here’s a hint: user input from `input()` always comes in as a string, and you’ll need to convert it to a number before passing it to `np.percentile()`.

All right—I’ll let you folks take a stab at that, and we’ll look at the solution in a moment.