Practice

Car specification

Imagine you're working with a dataset containing information about cars and their specifications. Here's a sample dataset:


Car ID:     [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
Brand:      ['Toyota', 'Ford', 'Honda', 'Chevrolet', 'Nissan', 'Toyota', 'Ford', 'Honda', 'Nissan', 'Chevrolet']
Mileage:    [25000, 35000, 15000, 45000, 30000, 20000, 40000, 28000, 32000, 38000]
Horsepower: [150, 200, 120, 180, 160, 140, 210, 130, 170, 190]
Fuel Type:  ['Gasoline', 'Diesel', 'Gasoline', 'Diesel', 'Gasoline', 'Hybrid', 'Diesel', 'Gasoline', 'Hybrid', 'Gasoline']
Price ($):  [20000, 25000, 18000, 22000, 23000, 26000, 24000, 21000, 27000, 23000]
  1. Scaling: Use Standardization to scale the "Mileage" and "Horsepower" features. Calculate the scaled values for each feature.
  2. Binning: Bin the "Horsepower" feature into three bins: 'Low', 'Medium', and 'High'. Assign each data point to the appropriate bin based on its horsepower.
  3. Label Encoding: Convert the "Brand" feature using label encoding. Assign unique integer labels to each brand.
  4. One-Hot Encoding: Perform one-hot encoding on the "Fuel Type" feature. Create new binary columns for each fuel type, where 1 indicates the presence of that fuel type and 0 indicates absence.
  5. Evaluation: Discuss the trade-offs between label encoding and one-hot encoding for categorical features. Also, mention the potential benefits of adding polynomial features.

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 Practice - Feature Engineering 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.