This project contains jupyter notebook files, that were shown during the workshop and that can be used as a good starting point for own projects.
| Topic | Notebookfile |
|---|---|
| Python - Introduction | Basic Python Concepts |
| Introduction to Numpys | Numpys |
| Introduction to Pandas and Graphical Representation | Pandas and Graphical Representation |
| Hints on Data Preprocessing | Data Preprocessing |
| Markup | An overview of markup commands to format text in notebooks can be found here: |
| Algorithm | Notebookfile |
|---|---|
| Simple Linear Rgression | Simple Linear Rgression |
| Multiple Linear Regression | Multiple Linear Regression |
| Logistic Regression | Logistic Regression |
| K Nearest Neighbours | K Nearest Neighbours |
| Support Vector Machine | Support Vector Machine |
| Decission Trees | Decission Trees |
| Random Forest | Random Forest |
| Gradient Boosting (LightGBM) | Gradient Boosting (LightGBM) |
| K-Means Clustering | K-Means Clustering |
| Gaussian Mixture Model | Gaussian Mixture Model |
To run the notebooks the following tools and libs need to be installed:
conda install -c conda-forge cufflinks-py
pip install plotly==3.10.0
pip install python-highcharts