General Introduction:
Welcome to my GitHub repository. To summarise, this repository contains machine learning models that I have explored and used throughout my research work.

----- Machine Learning Models -----
In this repository, I have included the following machine learning algorithms. The algorithms are located in ML-Algo folder

Classification Folder
1) Artificial Neural Network (classified as neuralnetwork.py)
2) Decision Tree (classified as decision_tree_classification.py)
3) Naive Bayes (classified as naive_bayes.py)
4) Random Forest (classified as randomforest.py)
5) Support Vector Machine (classified as svm_classification.py)
Sidenote: When running decision tree and random forest, you will notice a few images being generated. These images are to show how the trees came about with the solution / reading.


Regression Folder
1) Linear Regression (classified as linear_regression.py)
2) Multiple Regression (classified as multipleregression.py)
3) Polynomial Regression (classified as polynomialregression.py)

---- Folder Architecture ----
The repository is divided into the following folders
1) CGKS Algorithms - My Masters work involving machine learning
2) ML-Algo -  Machine learning models that I have explored, it is divided to the following:
- Regression (Regression algorithms)
- Classification (Classification algorithms)
- Clustering (Clustering algorithms)
- Deep Learning (AI + Reinforcement learning)
3) Plot Files - Python Plot Files

---- How to Run the Machine Learning Models  ----
1) Select a machine learning model
2) Right click and run the file.
3) If needed, update the sample data to your own. 

Additional Notes:
1) Python must be installed in your local machine (Python 2.XX or Python 3.XX, preferably the latter). Installing Conda is optional
2) Python configuration is required for your IDE.
3) Python libraries require installation through terminal. Installation guide:
Windows - To install the python libraries, type pip install <library name>