The goal of this homework is to train a simple model for predicting the duration of a ride - similar to what we did in this module.
We'll use the same NYC taxi dataset, but instead of "Green Taxi Trip Records", we'll use "Yellow Taxi Trip Records".
Download the data for January and February 2023.
Read the data for January. How many columns are there?
- 16
- 17
- 18
- 19
Now let's compute the duration variable. It should contain the duration of a ride in minutes.
What's the standard deviation of the trips duration in January?
- 32.59
- 42.59
- 52.59
- 62.59
Next, we need to check the distribution of the duration variable. There are some outliers. Let's remove them and keep only the records where the duration was between 1 and 60 minutes (inclusive).
What fraction of the records left after you dropped the outliers?
- 90%
- 92%
- 95%
- 98%
Let's apply one-hot encoding to the pickup and dropoff location IDs. We'll use only these two features for our model.
- Turn the dataframe into a list of dictionaries (remember to re-cast the ids to strings - otherwise it will label encode them)
- Fit a dictionary vectorizer
- Get a feature matrix from it
What's the dimensionality of this matrix (number of columns)?
- 2
- 155
- 345
- 515
- 715
Now let's use the feature matrix from the previous step to train a model.
- Train a plain linear regression model with default parameters, where duration is the response variable
- Calculate the RMSE of the model on the training data
What's the RMSE on train?
- 3.64
- 7.64
- 11.64
- 16.64
Now let's apply this model to the validation dataset (February 2023).
What's the RMSE on validation?
- 3.81
- 7.81
- 11.81
- 16.81
- Submit your results here: https://courses.datatalks.club/mlops-zoomcamp-2024/homework/hw1
- If your answer doesn't match options exactly, select the closest one