## Homework

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.


## Q1. Downloading the data

We'll use [the same NYC taxi dataset](https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page),
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


## Q2. Computing duration

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


## Q3. Dropping outliers

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%


## Q4. One-hot encoding

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


## Q5. Training a model

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


## Q6. Evaluating the model

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 the results

* Submit your results here: https://courses.datatalks.club/mlops-zoomcamp-2025/homework/hw1
* If your answer doesn't match options exactly, select the closest one