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

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

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

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

Hint

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

Practice

1. Why do machine learning libraries often use NumPy arrays for data input?

easy

A. Because NumPy arrays can only store text data

B. Because NumPy arrays are fast and easy to manipulate for numerical data

C. Because NumPy arrays automatically train machine learning models

D. Because NumPy arrays are the only data type in Python

Solution

1. Step 1: Understand NumPy array properties

   NumPy arrays store numerical data efficiently and support fast operations.

2. Step 2: Connect to machine learning needs

   Machine learning needs fast numerical data processing, which NumPy arrays provide.

3. Final Answer:

   Because NumPy arrays are fast and easy to manipulate for numerical data -> Option B

4. Quick Check:

   NumPy arrays speed up numerical data handling = A [OK]

Hint: Remember: ML needs fast number crunching, NumPy fits well [OK]

Common Mistakes:

• Thinking NumPy arrays store only text
• Believing NumPy trains models automatically
• Assuming Python has only one data type

2. Which of the following is the correct way to convert a Python list [1, 2, 3] into a NumPy array?

easy

A. np.toarray([1, 2, 3])

B. np.list([1, 2, 3])

C. np.convert([1, 2, 3])

D. np.array([1, 2, 3])

Solution

1. Step 1: Recall NumPy array creation syntax

   The function to create arrays from lists is np.array().

2. Step 2: Check each option

   Only np.array([1, 2, 3]) is valid syntax; others are not NumPy functions.

3. Final Answer:

   np.array([1, 2, 3]) -> Option D

4. Quick Check:

   Use np.array() to convert lists = A [OK]

Hint: Use np.array() to convert lists to arrays [OK]

Common Mistakes:

• Using non-existent functions like np.list()
• Confusing array creation with conversion functions
• Forgetting to import NumPy as np

3. What will be the output of this code snippet?

import numpy as np
from sklearn.preprocessing import StandardScaler

X = np.array([[1, 2], [3, 4], [5, 6]])
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
print(np.round(X_scaled, 2))

medium

A. [[-1.22 -1.22]
[ 0. 0. ]
[ 1.22 1.22]]

B. [[1 2]
[3 4]
[5 6]]

C. [[0. 0.]
[0. 0.]
[0. 0.]]

D. Error: StandardScaler not imported

Solution

1. Step 1: Understand StandardScaler effect

   StandardScaler centers data to mean 0 and scales to unit variance.

2. Step 2: Calculate scaled values

   For each column, mean is 3 and 4; std dev approx 1.63. Values transform to approx -1.22, 0, 1.22.

3. Final Answer:

   [[-1.22 -1.22] [ 0. 0. ] [ 1.22 1.22]] -> Option A

4. Quick Check:

   StandardScaler centers and scales data = D [OK]

Hint: StandardScaler centers data to mean 0 and scales variance 1 [OK]

Common Mistakes:

• Expecting original data output
• Confusing StandardScaler with MinMaxScaler
• Missing import causing runtime error

4. Identify the error in this code that uses NumPy with scikit-learn:

import numpy as np
from sklearn.linear_model import LinearRegression

X = [[1, 2], [3, 4], [5, 6]]
y = [1, 2, 3]
model = LinearRegression()
model.fit(X, y)
print(model.coef_)

medium

A. X should be a NumPy array, not a list

B. y should be a 2D array, not a list

C. LinearRegression requires data scaling before fitting

D. model.fit() is missing required parameters

Solution

1. Step 1: Check data types for model.fit()

   scikit-learn expects NumPy arrays or similar, not plain Python lists.

2. Step 2: Identify the issue in X

   X is a list of lists; converting to np.array is needed for proper fitting.

3. Final Answer:

   X should be a NumPy array, not a list -> Option A

4. Quick Check:

   Use NumPy arrays for input data = B [OK]

Hint: Always convert input data to NumPy arrays before fitting models [OK]

Common Mistakes:

• Thinking y must be 2D array
• Believing scaling is mandatory before fitting
• Assuming fit() needs extra parameters

5. You have a dataset as a Python list of lists representing features. You want to prepare it for a machine learning model that requires normalized NumPy arrays. Which steps should you perform?

hard

A. Convert the list to a Python tuple, then normalize manually with loops

B. Directly pass the list to the model without conversion or scaling

C. Convert the list to a NumPy array, then use a scaler like MinMaxScaler to normalize

D. Use pandas DataFrame without converting to NumPy arrays

Solution

1. Step 1: Convert list to NumPy array

   Machine learning models expect NumPy arrays for efficient computation.

2. Step 2: Normalize data using scaler

   Use MinMaxScaler or similar to scale features between 0 and 1 for better model performance.

3. Final Answer:

   Convert the list to a NumPy array, then use a scaler like MinMaxScaler to normalize -> Option C

4. Quick Check:

   Convert then scale data before modeling = C [OK]

Hint: Always convert data to NumPy arrays and scale before modeling [OK]

Common Mistakes:

• Passing raw lists directly to models
• Using tuples which are not efficient for ML
• Skipping normalization hurting model accuracy