reeba212 / ml-models-implementation

ML Models Implementation

This repository contains implementations of several machine learning models using Python. The goal of this project is to provide a hands-on approach for understanding the inner workings of these models.

The implemented models include:

• Linear Regression
• Decision Trees
• Gradient Descent
• Naive Bayes

Each implementation is provided as a separate Python file in this repository. The implementations use popular libraries such as NumPy and Pandas for data manipulation and visualization.

Getting Started

To run the implementations, you will need to have Python 3 installed on your machine. You will also need to install the following libraries:

• NumPy
• Pandas
• Matplotlib
• Sklearn

You can install these libraries using pip. For example, to install NumPy, you can run the following command:

pip install numpy

Once you have installed the required libraries, you can clone this repository to your local machine using Git. To do this, run the following command:

git clone https://github.com/reeba212/ML-Models-Implementation

To run the notebook, navigate to the project directory in your terminal and run the following command:

jupyter notebook

This will open the Jupyter Notebook interface in your web browser. From here, you can open the notebook and run the cells to train and test the model.

Conclusion

This project provides a comprehensive set of implementations for several popular machine learning models. By studying these implementations, you can gain a deeper understanding of the inner workings of these models and how they can be applied to real-world problems. With this knowledge, you can extend these implementations or use them as a starting point for your own projects.