ML Implementations

A collection of different ML Algorithms implemented by me.

Table of Contents

• Algorithms
• Getting Started
• License

Algorithms

• Linear Regression using Gradient Descent

• Multiple Linear Regression using Gradient Descent with Z Score Normalisation

  • 1. Linear Regression using Gradient Descent

    • Dataset used- "Salary Dataset: Simple linear regression"
    • View this notebook for the implementation.
    • Visualisation

    

  • 2. Multiple Linear Regression using Gradient Descent with Z Score Normalisation

    • Dataset used- "Student Performance"
    • View this notebook for the implementation.
    • Unlike the previous implementation, this time I divided my dataset into testing and training data and found my R^2 Score using the testing data while also comparing it with SkiKit.
    • Learning Curve for the 2 different alphas I took:

    

    • Actual vs Predicted Values Graph (for alpha=0.03):

    

Getting Started

1. Clone the repository:

   git clone https://github.com/IndoRexian/ml-implementations.git
   cd ml-implementations

2. Install dependencies:

   pip install -r requirements.txt

3. You can now play with various notebooks listed here.

License

This project is licensed under the MIT License.