Machine Learning from Scratch by creating own ML models

This repository is dedicated to learning machine learning from scratch with Python by only using native Python with numpy. The goal of this project is to gain a deep understanding of the fundamentals of machine learning algorithms, including how they work, how they are implemented, and how they can be applied to real-world problems.

Before running

To avoid any errors, install the packages from the requirements.txt by running the following commmand in your terminal:

pip install -r requirements.txt

1. K-Nearest Neighbour

• Follow the code in KNN/pre_knn.py by uncommenting the comments and viewing the changes in the terminal to learn about the data.
• Follow the code in KNN/knn.py to learn about the KNN algorithm.
• Follow the code in KNN/knn_test.py to learn about the testing of the KNN algorithm.

The code is commented to make it easier to understand.

2. Linear Regression

• Follow the code in LinearRegression/pre_linear_regression.py by uncommenting the comments and viewing the changes in the terminal to learn about the data.
• Follow the code in LinearRegression/linear_regression.py to learn about the Linear Regression algorithm.
• Follow the code in LinearRegression/linear_regression_test.py to learn about the testing of the Linear Regression algorithm.

Checkout the readme in LinearRegression/README.md for a detailed explanation of the Linear Regression algorithm.