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Assignment 1 Analyzing Taxi Trips in Chicago

Completi di Data Science and Security for Mobility per il corso di Mobility Engineering presso Politecnico di Milano. Materiale proveniente dall’archivio storico Studwiz e classificato per la consultazione online.

Data Science and Security for MobilityCompleti

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Completi di Data Science and Security for Mobility per il corso di Mobility Engineering presso Politecnico di Milano. Materiale proveniente dall’archivio storico Studwiz e classificato per la consultazione online.

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Data Science & Security for Mobility Assignment 1: Analyzing Taxi Trips in Chicago (Mon 26/10/2020) In this assignment you will analyze taxi trip data from Chicago for 2020: • The assignment consists of building a Jupyter notebook that analyses the data as described below. • The assignment is to be completed in groups. Each group should have a minimum of 3 and maximum of 4 students. (Ideally groups should consist of students from the same lecture group, but that is not a requirement.) • The due date for the assignment is midnight on Tuesday the 17th of November (via BEEP). On Wednesday the 18th or Thursday the 19th of November, your group will need to present your notebook during the practical session. The assignment makes use of publicly available data from the City of Chicago and the National Oceanic and Atmospheric Administration (NOAA): 1. Anonymised taxi trip information for over 3 million trips performed in 2020 was been downloaded from: https://data.cityofchicago.org/Transportation/Taxi-Trips-2020/r2u4-wwk3 Note that full dataset for 2020 is 1.5 GB uncompressed, so we have sampled 10% of the data and provided it on BEEP. If you wish to use the full dataset instead, you can. (Just download it by clicking on the export button). Note also the description of the various fields in the data provided on the website. 2. Daily weather data for Chicago O’Hare airport has been sourced from NOAA: https://www.ncdc.noaa.gov/cdo-web/search The assignment requires you to visualize various aspects of the data and build predictive models with it. The tasks listed below are suggestions for some basic analysis that your group could perform. You are strongly encouraged to investigate further the data and present all your insights in your notebook. 1) Data Visualization Show some…

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