A collection of various object-oriented machine learning models built from scratch in Python.
These models have not been optimized and are meant for educational purposes rather than maximum performance.
These instructions will get a copy of the project up and running on your local machine.
Instructions for installing these software are listed in the next section: Installing. These are the software packages needed to run:
- Python 2.7
These Python packages are also needed:
- numpy
- pandas
- matplotlib
- scikit-learn
If your computer does not already have Python 2.7 installed, download it here.
By default, Python should come with pip (a package manager). Use it to install the following dependencies by opening the Terminal/command line and entering the commands as follows, each line as a separate command (make sure to capitalize Tkinter):
pip install numpy pandas matplotlib scikit-learn
To use this module, save the "ml_models.py" file in the same directory as the project file you are working with. Then, add the following line:
from ml_models import LinearRegression, LogisticRegression,...
where import contains the models you would like to import. Currently supported models are:
LinearRegression: Used for numerical regression.
LogisticRegression: Used for binary classification.
NeuralNetwork: Used for multi-class classification.
For all models, it is assumed that the model receives well-prepared and cleaned input data X and targets T. Any feature engineering should be done prior to creating a model.
- Python - A programming language used here to create exploratory data graphs
- Numpy - Python library for mathematical and matrix operations
- Pandas - Python library for data manipulation
- Matplotlib - Python library for graphing data
- Eric Yates - Github Profile
This project is licensed under the MIT License - see the LICENSE.md file for details.
- LazyProgrammer: For his courses on machine learning