Practices

Intro to ML Practice Exercises

Part 1: Classification

You are given a dataset containing information about students' exam scores and whether they passed or failed the final exam. Your task is to build a classification model to predict whether a student will pass or fail based on their exam scores.

Dataset: Student Performance

Questions:

1. What is the target variable in this dataset?
   • Exam 1 Score
   • Exam 2 Score
   • Passed
2. Which type of machine learning problem is this?
   • Clustering
   • Regression
   • Classification
3. What is the purpose of the classification model in this scenario?
4. Choose the correct statement:
   • Classification predicts numerical values.
   • Classification predicts categorical outcomes.
   • Classification predicts time series data.

Part 2: Regression

You are given a dataset containing information about houses, including their sizes (in square feet) and corresponding prices. Your task is to build a regression model to predict the price of a house based on its size.

Dataset: House Prices

Questions:

1. What is the target variable in this regression dataset?
   • Size (sq ft)
   • Price ($)
2. Which type of machine learning problem is this?
   • Clustering
   • Classification
   • Regression
3. What is the purpose of the regression model in this scenario?
4. Choose the correct statement:
   • Regression predicts numerical values.
   • Regression predicts categorical outcomes.
   • Regression predicts time series data.

Submission

You are required to submit documentation for practice exercises over the course of the term. Each one will count for 1/10 of your practice grade, or 2% of your overall grade.

• Practice exercises will be graded for completion not perfect correctness.
• You have to document that you did the work, but we won't be checking if you got it right.
• You MUST upload your analysis/visuals as a single file to Practices - ML on Gradescope after the exercise to get the grade for this exercise.

Your log will count for credit as long as:

• It is accessible to your instructor, and
• It shows your own work.