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3.15 Homework

Dataset

In this homework, we will continue the New York City Airbnb Open Data. You can take it from Kaggle or download from here if you don't want to sign up to Kaggle.

We'll keep working with the 'price' variable, and we'll transform it to a classification task.

Features

For the rest of the homework, you'll need to use the features from the previous homework with additional two 'neighbourhood_group' and 'room_type'. So the whole feature set will be set as follows:

  • 'neighbourhood_group',
  • 'room_type',
  • 'latitude',
  • 'longitude',
  • 'price',
  • 'minimum_nights',
  • 'number_of_reviews',
  • 'reviews_per_month',
  • 'calculated_host_listings_count',
  • 'availability_365'

Select only them and fill in the missing values with 0.

Question 1

What is the most frequent observation (mode) for the column 'neighbourhood_group'?

Split the data

  • Split your data in train/val/test sets, with 60%/20%/20% distribution.
  • Use Scikit-Learn for that (the train_test_split function) and set the seed to 42.
  • Make sure that the target value ('price') is not in your dataframe.

Question 2

  • Create the correlation matrix for the numerical features of your train dataset.
    • In a correlation matrix, you compute the correlation coefficient between every pair of features in the dataset.
  • What are the two features that have the biggest correlation in this dataset?

Example of a correlation matrix for the car price dataset:

Make price binary

  • We need to turn the price variable from numeric into binary.
  • Let's create a variable above_average which is 1 if the price is above (or equal to) 152.

Question 3

  • Calculate the mutual information score with the (binarized) price for the two categorical variables that we have. Use the training set only.
  • Which of these two variables has bigger score?
  • Round it to 2 decimal digits using round(score, 2)

Question 4

  • Now let's train a logistic regression
  • Remember that we have two categorical variables in the data. Include them using one-hot encoding.
  • Fit the model on the training dataset.
    • To make sure the results are reproducible across different versions of Scikit-Learn, fit the model with these parameters:
    • model = LogisticRegression(solver='lbfgs', C=1.0, random_state=42)
  • Calculate the accuracy on the validation dataset and round it to 2 decimal digits.

Question 5

  • We have 9 features: 7 numerical features and 2 categorical.
  • Let's find the least useful one using the feature elimination technique.
  • Train a model with all these features (using the same parameters as in Q4).
  • Now exclude each feature from this set and train a model without it. Record the accuracy for each model.
  • For each feature, calculate the difference between the original accuracy and the accuracy without the feature.
  • Which of following feature has the smallest difference?
    • neighbourhood_group
    • room_type
    • number_of_reviews
    • reviews_per_month

note: the difference doesn't have to be positive

Question 6

  • For this question, we'll see how to use a linear regression model from Scikit-Learn
  • We'll need to use the original column 'price'. Apply the logarithmic transformation to this column.
  • Fit the Ridge regression model on the training data.
  • This model has a parameter alpha. Let's try the following values: [0, 0.01, 0.1, 1, 10]
  • Which of these alphas leads to the best RMSE on the validation set? Round your RMSE scores to 3 decimal digits.

If there are multiple options, select the smallest alpha.

Submit the results

Submit your results here: https://forms.gle/xGpZhoq9Efm9E4RA9

It's possible that your answers won't match exactly. If it's the case, select the closest one.

Deadline

The deadline for submitting is 27 September 2021, 17:00 CET. After that, the form will be closed.

Nagivation