Feel free to contact me (radek.svoboda@vsb.cz or office EA404, FEI) if you have any questions or want to discuss any topic from the course 😊
- You can get up to 30 points
- Project are done in groups of 3 students
- 🎯 Put your name into a group in which you want to be in this Google Sheet
- 💡 Click here for the details
The aim of the exercise is to get an overview of the basic capabilities of the Pandas, Matplotlib and Seaborn libraries and be able to setup a Python Virtual Enviroment (venv)
The aim of the exercise is to learn basic techniques for visualization creation and interpretation using Matplotlib and Seaborn libraries.
The aim of the exercise is to learn how to use distance metrics and k-NN classifier.
Goal of the excercise is to learn how to use Scikit-learn library for a regression tasks employing various linear regression models and moreover evaluate the performance of the proposed models.
Goal of the excercise is to learn how to use K-means implementation in the Scikit-learn library to perform clustering and subsequent cluster analysis on a Titanic dataset.
We will learn how to use another clustering algorithm - Hierarchical (or Agglomerative) clustering.
Goal of the excercise is to code selected part of the Decision tree algorithm which is focused on the optimum split part using gini index.
After that the scikit-learn implementation of the Decision tree basic usage will be demonstrated.
Goal of the excercise is to learn how to use Scikit-learn library for a classification tasks and evaluate the performance of the proposed models.
python -m venv venv
- Activate
venvin Windows
.\venv\Scripts\Activate.ps1
- Activate
venvin Linux
source venv/bin/activate
pip install jupyter "jupyterlab>=3" "ipywidgets>=7.6"
pip install pandas matplotlib requests seaborn scipy scikit-learn
jupyter lab