set up

.gitignore

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+########################################################################
+# Python - https://github.com/github/gitignore/blob/master/Python.gitignore
+########################################################################
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# Distribution / packaging
+build/
+dist/
+eggs/
+.eggs/
+*.egg-info/
+*.egg
+.prerelease-version
+
+# Unit test / coverage reports
+.coverage
+.coverage\.*
+.pytest_cache/
+.mypy_cache/
+test-reports
+htmlcov
+.hypothesis
+
+# Test fixtures
+cffi_bin
+
+# Pyenv Stuff
+.python-version
+venv
+
+# Jupyter notebook
+.ipynb_checkpoints
+
+########################################################################
+# OSX - https://github.com/github/gitignore/blob/master/Global/macOS.gitignore
+########################################################################
+.DS_Store
+.DocumentRevisions-V100
+.fseventsd
+.Spotlight-V100
+.TemporaryItems
+.Trashes
+.VolumeIcon.icns
+.com.apple.timemachine.donotpresent
+
+########################################################################
+# JetBrains
+########################################################################
+.idea
+
+########################################################################
+# VSCode
+########################################################################
+.vscode/

README.md

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+# AAAMLP starter
+
+Starter kit for projects from [Approaching Almost Any Machine Learning problem](https://github.com/abhishekkrthakur/approachingalmost) book by [Abhishek Thakur](https://github.com/abhishekkrthakur)
+
+---
+
+## Note 👷
+
+This starter is very much work in progress. I am updating key modules as I am making my way through the book.
+
+## Prereqs
+
+- install [miniconda](https://docs.conda.io/en/latest/miniconda.html)
+- `conda create -n NAME_FOR_YOUR_CONDA_ENV python=3.7.6` (if you are on M1/M2, you might need to run this before `conda config --env --set subdir osx-64`)
+- `conda activate NAME_FOR_YOUR_CONDA_ENV`
+- `conda env create -f environment.yml`
+- OR `conda env create -f environment_osx.yml` on Mac
+- `conda activate ml`
+- download `mnist_train.csv` from [here](https://www.kaggle.com/datasets/abhishek/aaamlp), and save it to `input` directory
+
+## Getting started
+
+This template gets you started with a basic skeleton for your ML app. This includes default MNSIT data set from [Abhishek's Kaggle](https://www.kaggle.com/datasets/abhishek/aaamlp?select=mnist_train.csv)
+
+### Creating folds
+
+- `cd src && python create_folds.py --folds 10` (default is 5 folds)
+
+### Adding/editing available models
+
+`model_dispatcher.py` contains a dictionary with models available in your application
+
+### Training your model
+
+To train your model, use 
+
+`python train.py --fold 0 --model desision_tree_entropy`