Tag Archives: pandas

BudapestPy Workshops 101 (2019-09-04)

The workshop was the first since we started the cooperation with the budapest.py meetup group, and it was the 15th we’ve done since January, when we started to study together.

First Dóri talked about Anaconda and Jupyter notebooks, then showed us some basic pandas code for EDA (Exploratory Data Analysis) on a dataset about Pokemons. She walked through us how to get insights from our data, how to slice it, make new entries and lots more.
After some basic visualization and aggregation we found out which Pokémon is the strongest based on our simple analysis. The second part was Berci’s and his Pandas tutorial notebook, which holds over a hundred different pandas functions and examples. He gave a short tour of the notebook. We aim to create some tutorial notebooks to help us focus on understanding the current dataset and spend less time looking up functions. These notebooks are going to help newcomers catch up with returning participants.

All of our material is avaliable on GitHub: https://github.com/DatasRev/budapest.py_workshops

This workshop was about getting and inspecting the data. Next time we show how to build a basic machine learning model using another sample dataset.

You can join us on our meetup page:
https://www.meetup.com/budapest-py/

The Team: Balogh Balázs, Rónai Bertalan, Szabó Dóra, Doma Miklós, Hackl Krisztián and Zsarnowszky Lóránt (last name, first name order)

Titanic: Machine Learning

Berci asked me to upload my version of kaggle’s Titanic competition. Together on our workshop we achieved around 78%, which was a good starting point.

Speaking about the workshop: in January 2019 a Data Science group formed on Facebook, called Data Revolution:
https://www.facebook.com/groups/DatasRev/
Feel free to join.

Solving this task at first I started with the standard Decision Tree, without any tuning. Then I get into GridSearchCV and RandomizedSearchCV for the best parameters. But after tweaking the model with these validations, I still couldn’t get higher than 79%. RandomForest didn’t help either.

That’s when I found XGBoost, a powerful model, getting more and more attention in machine learning. With it, I could go over 80%.

If you have any questions, or tips, you can find me on LinkedIn:
https://www.linkedin.com/in/baloghbalazs88/

You can find the notebook on:
https://anaconda.org/bbalazs88/titanic/notebook