Exercises, problems and solutions, practices related to Artificial Intelligence and Data Science tasks and courses
Problem solving tasks made with Jupyter Notebook or with Python.
Libraries:
1) DataCamp Notebook for eCommerce data analysis; a python based solution for three questions raised by DataCamp. (created 2024.07.27.)
folder: ./DataCamp/eCommerce_Analysis
done: 2024
DataCamp is a website offering tutorials, education in DataScience, Machine learning, ... and offers notebook for data manipulation and immediate (simple) plots. Large variety of python modules are available (for import).
Original notebook may be found here: https://www.datacamp.com/datalab/w/305fa8be-5478-42f9-b113-4937ff4b6879/edit
Data can be found in the folder as * online_retail.zip * and * online_retail.csv *
Solution for platform independent view (especially for those who have no access to any Notebook application): eCommerce_python_Analysis_Notebook_202407.html - readable with any webbrowser eCommerce_python_Analysis_Notebook_202407.pdf - open with any pdf reader
For those who would like to check or modify interactively a notebook file: notebook.ipynb It can be loaded and run by Notebook applications, like DataCamp website (where a profile can be easily set up for free) or Jupyter Notebook (using localhost) or else. Jupyter Notebook 7.0.8 was tested for compatibility.
All above listed files are zipped in: datacamp_workspace.zip
folder: ./JDS_academy
done: 2020
https://data36.com/
International car accident database containing registered car accidents from several countries of Europe (2000-2011).
Seasonality of accidents, costs of accidents, car brands related highlights and further topics are elaborated with Python Pandas as in-built function of Jupyter Notebook.
The notebook can be found as
- accidents.ipynb
- accidents.html (for platform independent reading)
- accidents.pdf (converted html file) Additional analysis of the dataset are in the presentation files: Accidents_table_presentation_final_eng.pptx and its pdf version Accidents_table_presentation_final_eng.pdf
folder: ./DataRepresentations/3Dscatter
done: 2024
Plotting 3 dimensional (X-Y-Z) data using matplotlib 3D scatter plot in a GUI. Rotating and zooming in specified ranges available.
A simple example of dynamic query parameter changing using a Streamlit interactive slider to set limit value(s) in a Spark Notebook.
done: Sept 2024
Another Spark Notebook example of Machine Learning with Snowflake, using Forecast model.
Snowflake folder: ./Snowflake/
done: Sept 2024
See more in the folder.