Data Science - Engineering INA 82

Module 2 – Experiment-Tracking Source https://github.com/DataTalksClub/mlops-zoomcamp/tree/main/02-experiment-tracking Homework Q1. Install MLflow To get started with MLflow you’ll need to install the MLflow Python package. For this we recommend creating a separate Python environment, for example, you can use conda environments, and then install the package there with pip or conda. Once you installed the package, run the command mlflow –version and check the output. What’s the version that you have? import mlflow mlflow . __version__ '2.13.0' Answer of Q1: 2.13.0 Q2. Download and preprocess the data We’ll use the Green Taxi Trip Records dataset to predict the duration of each trip. Download the data for January, February and March 2023 in parquet format from here. Use the script preprocess_data.py located in the folder homework to preprocess the data. The script will: load the data from the folder <TAXI_DATA_FOLDER> (the folder where you have downloaded the data), fit a

   Source https://github.com/DataTalksClub/mlops-zoomcamp/tree/829c51c11962e427e62b0afc63d6c4f7d6e34ac0/01-intro 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, 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 import pandas as pd # Load the data for January 2023 url_january = 'https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2023-01.parquet' df_january = pd . read_parquet(url_january) df_january 3066766 rows × 19 columns Answer Q1 : 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