Bogazici University - MIS 49Y: Applied Machine Learning Course

Istanbul sea transportation system analysis for September 2023

The dataset contains information about transitions related to the Istanbul transportation system for September 2023. It includes details such as date, time, line, transfer type, number of passages, and number of passengers.

Note: Id’s are not consistent, because we extracted the data as just one road type (which is sea transportation).

Data Source: https://data.ibb.gov.tr/dataset/hourly-public-transport-data-set/resource/b3f67274-4ebc-4453-8f6b-1dcc1b1bf008?inner_span=True

There are two main part on this assignment.

• First is related to Car Rental, Car Sharing or Mobility business.
• Second is related to geospatial data visualization.

1. Data Visualization (Car Rental, Car Sharing or Mobility business)

Find a dataset related to Car Rental, Car Sharing or Mobility business. You may use the Matplotlib, Seaborn etc. python libraries to create your visualization(s). a. Describe your dataset b. Select 5 columns (attributes / features) from your dataset, use different and rich statistics functions to give info about important attributes of your dataset. c. Use different charts for visualization.

2. Geospatial Data Visualization for Mobility

GeoPandas (geopandas.org) is an open source project to make working with geospatial data in python easier. GeoPandas extends the datatypes used by pandas to allow spatial operations on geometric types. Another library Folium makes it easy to visualize data that’s been manipulated in Python on an interactive leaflet map.

a. Find a mobility geospatial dataset and describe your dataset using relevant python geospatial data and map libraries.

b. Plot geospatial data ( car rental density, geometry etc. ) using different functions for visualization.

c. Read https://medium.com/@ns_geoai/70-geospatial-python-libraries-54604d815a7b and repeat “b” using different python packages.