scikit-learn tutorial

All notebook material: https://github.com/lesteve/scikit-learn-tutorial/

Follow the tutorial online

Launch an online notebook environment using
Browse the static content online (pre-rendered outputs) using nbviewer

You need an internet connection but you will not have to install any package locally.

Running the tutorial locally

Dependencies

The tutorials will require the following packages:

python>=3.6
jupyter
scikit-learn
pandas
pandas-profiling
matplotlib
seaborn

Local install

We provide both requirements.txt and environment.yml to install packages.

You can install the packages using pip:

$ pip install -r requirements.txt

You can create an scikit-learn-tutorial conda environment executing:

$ conda env create -f environment.yml

and later activate the environment:

$ conda activate scikit-learn-tutorial

You might also only update your current environment using:

$ conda env update --prefix ./env --file environment.yml  --prune

Contributing

To synchronize the notebooks and the Python scripts (based on filestamps, only input cells content is modified in the notebooks):

$ make notebooks

To render all the notebooks (from time to time, slow to run):

$ make

This repo uses Jupytext. In some cases you may need to use a jupytext command directly rather than using the provided Makefile. Here are a few useful jupytext commands:

pair a notebook with a Python script:

$ jupytext --set-formats python_scripts//py:percent,notebooks//ipynb notebooks/your_notebook.ipynb

sync a paired Python script and notebook:

$ jupytext --sync notebooks/your_notebook.ipynb

create an empty notebook from a Python script:

$ jupytext --to ../notebooks//ipynb python_scripts/your_python_script.py