Predicting if students will pass

In the previous exercise you calculated the parameters of the logistic regression model that fits the data of hours of study and test outcomes.

With those parameters you can predict the performance of students based on their hours of study. Use model.predict() to get the outcomes based on the logistic regression.

For your convenience, LogisticRegression has been imported from sklearn.linear_model and numpy has been imported as np.

This exercise is part of the course

Foundations of Probability in Python

Exercise instructions

Create an array with the values 10, 11, 12, 13, and 14 to predict the outcomes for a test based on those numbers of hours of study.
Use model.predict() to get the outcomes from the model, and print the outcomes.
Use model.predict_proba() to get the probability of passing the test with 11 hours of study.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Specify values to predict
hours_of_study_test = [[____], [____], [____], [____], [____]]

# Pass values to predict
predicted_outcomes = model.____(____)
print(predicted_outcomes)

# Set value in array
value = np.asarray(11).reshape(-1,1)
# Probability of passing the test with 11 hours of study
print("Probability of passing test ", model.____(value)[:,1])

Edit and Run Code

This exercise is part of the course

Foundations of Probability in Python

IntermediateSkill Level

4.8+

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A coin flip is the classic example of a random experiment. The possible outcomes are heads or tails. This type of experiment, known as a Bernoulli or binomial trial, allows us to study problems with two possible outcomes, like “yes” or “no” and “vote” or “no vote.” This chapter introduces Bernoulli experiments, binomial distributions to model multiple Bernoulli trials, and probability simulations with the scipy library.

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No that you know how to calculate probabilities and important properties of probability distributions, we'll introduce two important results: the law of large numbers and the central limit theorem. This will expand your understanding on how the sample mean converges to the population mean as more data is available and how the sum of random variables behaves under certain conditions. We will also explore connections between linear and logistic regressions as applications of probability and statistics in data science.

Exercise 1: From sample mean to population mean Exercise 2: Generating a sample Exercise 3: Calculating the sample mean Exercise 4: Plotting the sample mean Exercise 5: Adding random variables Exercise 6: Sample means Exercise 7: Sample means follow a normal distribution Exercise 8: Adding dice rolls Exercise 9: Linear regression Exercise 10: Fitting a model Exercise 11: Predicting test scores Exercise 12: Studying residuals Exercise 13: Logistic regression Exercise 14: Fitting a logistic model Exercise 15: Predicting if students will pass

Current Exercise

Exercise 16: Passing two tests Exercise 17: Wrapping up