NumPy with machine learning libraries - Mini Project: Build & Apply

Using NumPy with Machine Learning Libraries

📖 Scenario: You are working on a simple machine learning task where you need to prepare data using NumPy arrays before feeding it into a machine learning library.Imagine you have collected data about students' study hours and their exam scores. You want to organize this data and calculate the average score for students who studied more than a certain number of hours.

🎯 Goal: Build a small program that creates a NumPy array with students' study hours and scores, sets a threshold for study hours, filters the students who studied more than the threshold, and calculates the average score of those students.

📋 What You'll Learn

Create a NumPy array with exact data for study hours and scores

Create a variable for the study hours threshold

Use NumPy to filter the array based on the threshold

Calculate the average score of filtered students

Print the average score

💡 Why This Matters

🌍 Real World

Data scientists often use NumPy to prepare and filter data before applying machine learning models. Filtering data based on conditions is a common step in data cleaning and feature selection.

💼 Career

Understanding how to manipulate data with NumPy and integrate it with machine learning workflows is essential for roles like data analyst, data scientist, and machine learning engineer.

1

Create the NumPy array with study hours and scores

Import NumPy as np and create a NumPy array called data with these exact rows: [2, 50], [5, 80], [1, 40], [7, 90], [3, 60]. Each row represents [study_hours, score].

NumPy

import numpy as np
# Create the NumPy array called data with the exact rows
# Your code here

Need a hint?

Use np.array() to create the array with the exact rows inside a list.

2

Set the study hours threshold

Create a variable called threshold and set it to the integer 4. This will be the minimum study hours to filter students.

NumPy

import numpy as np

data = np.array([[2, 50], [5, 80], [1, 40], [7, 90], [3, 60]])
# Create the threshold variable and set it to 4
# Your code here

Need a hint?

Just assign the number 4 to the variable threshold.

3

Filter students who studied more than the threshold

Use NumPy boolean indexing to create a new array called filtered_scores that contains only the scores of students whose study hours are greater than threshold. Use data[:, 0] to access study hours and data[:, 1] to access scores.

NumPy

import numpy as np

data = np.array([[2, 50], [5, 80], [1, 40], [7, 90], [3, 60]])

threshold = 4
# Create filtered_scores with scores where study hours > threshold
# Your code here

Need a hint?

Use data[:, 0] > threshold to get a boolean mask, then select scores with data[mask, 1].

4

Calculate and print the average score

Calculate the average of filtered_scores using np.mean() and store it in average_score. Then print average_score.

NumPy

import numpy as np

data = np.array([[2, 50], [5, 80], [1, 40], [7, 90], [3, 60]])

threshold = 4

filtered_scores = data[data[:, 0] > threshold, 1]
# Calculate average_score and print it
# Your code here

Need a hint?

Use np.mean(filtered_scores) to get the average, then print it.