This repository contains Python implementations of fundamental machine learning algorithms: Linear Regression, K-means, and PCA using Singular Value Decomposition (SVD).
The linear regression implementation is in the Liner Regression/Linear_Regression.ipynb file. It includes a simple linear regression model along with functions for training and making predictions.
The K-Means implementation is in the K-Means/K_means.ipynb file. The code provides a basic K-Means clustering algorithm that can be used for unsupervised learning tasks.
The PCA implementation using Singular Value Decomposition (SVD) is in the PCA/PCA_using_SVD.ipynb file. This script demonstrates how to perform principal component analysis using SVD.
To get started, clone this repository to your local machine or run in google colab:
git clone https://github.com/sm1899/Ml-algos-from-Scratch.git
## Dependencies
Ensure you have the following dependencies installed:
NumPy
Pandas
Scikit-learn
pip install numpy pandas scikit-learn
# Add other dependencies as necessary