ML Algorithms from Scratch

Welcome to the "ML Algorithms from Scratch" project! 🚀

This repository contains a collection of machine learning algorithms implemented from scratch in Python. The goal of this project is to provide an educational resource for understanding the inner workings of various machine learning algorithms by building them from the ground up.

📂 Project Structure

The project is organized into two main folders, each containing subfolders with specific algorithms and their implementations.

1. Supervised Learning

• Regression
  • Linear Regression
  • Polynomial Regression
  • Lasso Regression
  • Ridge Regression
  • Elasticnet Regression
• Classification
  • Logistic Regression
  • Decision Trees
  • Support Vector Machines (SVM)
  • K-Nearest Neighbors (KNN)
  • Naive Bayes
• Ensemble Learning
  • Random Forest
  • AdaBoost

2. Unsupervised Learning

• Clustering
  • K-Means Clustering
  • Hierarchical Clustering
• Dimensionality Reduction
  • Principal Component Analysis (PCA)
  • Independent Component Analysis (ICA)

💻 Getting Started

Prerequisites

Before you begin, ensure you have the following installed on your local machine:

• Python >=3.7
• numpy
• pandas
• matplotlib
• scikit-learn

You can install the required libraries using:

pip install numpy pandas matplotlib scikit-learn

🛠️ Installation

1. Clone the repo to the local machine:

   git clone https://github.com/Puskchan/ML_from_Scratch.git

2. Navigate to the project directory:

   cd ML_from_Scratch

📜 Usage

Each algorithm is implemented as a standalone Python script. To run an algorithm, navigate to its directory and execute the script. For example, to run the Simple Linear Regression:

cd supervised_learning/linear_regression
python simple_linear_regression.py

🤓 Understanding the Code

Each implementation includes a detailed blog with explanations to help you understand the logic behind the algorithm. Additionally, example datasets are provided to test and visualize the algorithms in action.

👾 Link to the blog

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