This repository is under construction
In this repository I will work through some aspects, good practices and elements used in creation of a machine learning model. In this notebook we will cover the diamonds dataset and do linear regression (for price) and classification (for cut) with Decision Tree models, but the following methods can be applied to any supervised machine learning model.
In the main directory there are notebooks that contain the essential concepts for supervised machine learning:
- Data Preprocessing with Sci-kit learn and Numpy
- Training and test data - how to split like a pro
- How to optimize and create a model
- Evaluating a regression and classification models
In the directiories Linear_Regression and Classification different models can be found, with already created syntax and statistical explanation of these methods.
In the directory Unsipervised you might find basic syntax for clustering and dimentional reduction as well as some theory regarding the unsupervised machine learning.