If you don't have Python pre-installed on your machine, you can download it from python.org. After it's installed you need to set up a virtual environment:
python3 -m venv venv
source venv/bin activate
And then you can either install all the packages required with pip
individually, or use the following command:
pip install -r requirements.md
To start using JupyterLab:
jupyter lab
If you want to use a text editor, a recommended tool is VSCode. While most of the R work will be in RStudio, we'll use JupyterLab for a small section, so you need to install the IRKernel. For that follow the official setup instructions.
You can download R from CRAN. After this you should install RStudio from the official website. After this create a new R Project (select use existing directory, use the one where you download this repository) and install the packages individually with install.packages()
.
jupyterlab
(Python and R Kernels)pandas
scikit-learn
yellowbrick
missingno
seaborn
opencv-python
scikit-image
nltk
(download data as well)spaCy
(download model withpython -m spacy download en_core_web_sm
)flask
(optional)mlflow
(optional)
ggplot2
dplyr
prophet
xts
leaflet
shiny
flexdashboard
(optional)shinydashboard
(optional)sdmbench
(optional)
diamonds.csv
(built-in fromggplot2
)boston housing
(built-in fromscikit-learn
)wine
(built in fromscikit-learn
)fires.csv
(from Kaggle)reviews.csv
(from UCSD)image_000001.jpg
(flowers102 from University of Oxford)starwars.csv
(built-in fromdplyr
)temps.csv
(from Machine Learning Mastery Github)quakes
(built-in from RStudio)data_for_ml.csv
(intermediate dataset for case study - processedfires.csv
)