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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.

Q1. Pandas version

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 Laptops Price dataset. Download it from here.

You can do it with wget:

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

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

Now read it with Pandas.

Q2. Records count

How many records are in the dataset?

  • 12
  • 1000
  • 2160
  • 12160

Q3. Laptop brands

How many laptop brands are presented in the dataset?

  • 12
  • 27
  • 28
  • 2160

Q4. Missing values

How many columns in the dataset have missing values?

  • 0
  • 1
  • 2
  • 3

Q5. Maximum final price

What's the maximum final price of Dell notebooks in the dataset?

  • 869
  • 3691
  • 3849
  • 3936

Q6. Median value of Screen

  1. Find the median value of Screen column in the dataset.
  2. Next, calculate the most frequent value of the same Screen column.
  3. Use fillna method to fill the missing values in Screen column with the most frequent value from the previous step.
  4. Now, calculate the median value of Screen once again.

Has it changed?

Hint: refer to existing mode and median functions to complete the task.

  • Yes
  • No

Q7. Sum of weights

  1. Select all the "Innjoo" laptops from the dataset.
  2. Select only columns RAM, Storage, Screen.
  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 [1100, 1300, 800, 900, 1000, 1100].
  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 sum of all the elements of the result?

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

  • 0.43
  • 45.29
  • 45.58
  • 91.30

Submit the results