The analysis of datasets using different regression and classification algorithms with different complexity is covered by this repository. The analysis is done using Ipython Notebooks in Python (.ipynb files).
Regression is a mathematical measure that seeks to assess the intensity of the relationship between one dependent variable (usually denoted by Y) and a variety of other changing variables in economics, investment and other fields (known as independent variables).
A Classification Algorithm is a method for choosing a class from a set of alternatives that fits a set of observations better. An example will be to determine whether a consumer would purchase a specific product or not use the consumers' different buying patterns or apply a disease diagnosis to a particular patient as defined by the patient's observed characteristics (gender, blood pressure, presence or absence of certain symptoms, etc.).
This repository requires Python 3.6 and the following Python libraries installed:
- NumPy
- Pandas
- matplotlib
- scikit-learn
Download the required notebooks. In a terminal or command window, navigate to the top-level project directory(that contains this README) and run one of the following commands:
ipython notebook notebookname.ipynb
or
jupyter notebook notebookname.ipynb
This will open the Jupyter Notebook in your browser.
The data files may required to be downloaded through the source links provided in the notebook and saved in the same Directory as the notebook file