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Homework

Set up the environment

You need to install Python, NumPy, Pandas, Matplotlib and Seaborn. For that, you can the instructions from 06-environment.md.

Question 1

What's the version of Pandas that you installed?

You can get the version information using the __version__ field:

pd.__version__

Getting the data

For this homework, we'll use the California Housing Prices dataset. Download it from here.

You can do it with wget:

wget https://raw.githubusercontent.com/alexeygrigorev/datasets/master/housing.csv

Or just open it with your browser and click "Save as...".

Now read it with Pandas.

Question 2

How many columns are in the dataset?

  • 10
  • 6560
  • 10989
  • 20640

Question 3

Which columns in the dataset have missing values?

  • total_rooms
  • total_bedrooms
  • both of the above
  • no empty columns in the dataset

Question 4

How many unique values does the ocean_proximity column have?

  • 3
  • 5
  • 7
  • 9

Question 5

What's the average value of the median_house_value for the houses located near the bay?

  • 49433
  • 124805
  • 259212
  • 380440

Question 6

  1. Calculate the average of total_bedrooms column in the dataset.
  2. Use the fillna method to fill the missing values in total_bedrooms with the mean value from the previous step.
  3. Now, calculate the average of total_bedrooms again.
  4. Has it changed?

Has it changed?

Hint: take into account only 3 digits after the decimal point.

  • Yes
  • No

Question 7

  1. Select all the options located on islands.
  2. Select only columns housing_median_age, total_rooms, total_bedrooms.
  3. Get the underlying NumPy array. Let's call it X.
  4. Compute matrix-matrix multiplication between the transpose of X and X. To get the transpose, use X.T. Let's call the result XTX.
  5. Compute the inverse of XTX.
  6. Create an array y with values [950, 1300, 800, 1000, 1300].
  7. Multiply the inverse of XTX with the transpose of X, and then multiply the result by y. Call the result w.
  8. What's the value of the last element of w?

Note: You just implemented linear regression. We'll talk about it in the next lesson.

  • -1.4812
  • 0.001
  • 5.6992
  • 23.1233

Submit the results

  • Submit your results here: https://forms.gle/jneGM91mzDZ23i8HA
  • You can submit your solution multiple times. In this case, only the last submission will be used
  • If your answer doesn't match options exactly, select the closest one

Deadline

The deadline for submitting is 18 September 2023 (Monday), 23:00 CEST (Berlin time).

After that, the form will be closed.