ML Playground

Welcome to the ML Playground! This project provides an interactive interface for exploring a range of popular machine learning algorithms for regression, classification, and clustering tasks.

Built using Streamlit for the frontend and scikit-learn for model development, this project makes it easy to visualize and understand different ML models.

The project Working has been deployed in:

https://mlplayground-3pgerzuzdwge7jzl5hycud.streamlit.app/

📋 Table of Contents

• Introduction
• Features
• Getting Started
• Installation
• Procedure
• Usage
• Model Types
• Contributing

👋 Introduction

The ML Playground project is an interactive application where users can train and test various machine learning models with customizable datasets. Using Streamlit as the interface, users can choose models, train them, and evaluate results with metrics, visualizations, and model comparisons.

✨ Features

• Supports Regression, Classification, and Clustering models
• Includes 15+ machine learning models, from Linear Regression to Neural Networks
• Streamlit UI for model selection, training, and testing
• Options to choose and upload various datasets for different model types
• Visual representations and evaluation metrics for easy model comparison

🚀 Getting Started

1. Clone the repository:

   git clone https://github.com/22pt16/ML_Playground.git

2. Navigate to the project directory:

   cd ML_Playground

🛠️ Installation

To set up the environment, install the necessary libraries:

pip install streamlit scikit-learn pandas numpy matplotlib seaborn

Verify your installation:

python -c "import streamlit as st; print(st.__version__)"
python -c "import sklearn; print(sklearn.__version__)"
python -c "import pandas as pd; print(pd.__version__)"
python -c "import numpy as np; print(np.__version__)"

▶️ Usage

Run the application from the terminal:

streamlit run streamlit.py

• Select a Model: Choose from regression, classification, and clustering models.
• Load Dataset: Upload or select a dataset, and the data will be preprocessed automatically.
• Evaluate: See metrics, visualizations, and insights for each model.

🧠 Model Types

Regression Models

• Linear Regression
• Multiple Regression
• Gradient Boosting (Regression)
• Decision Tree Regression

Classification Models

• Logistic Regression
• Neural Network
• K-Nearest Neighbors (KNN)
• Naive Bayes
• Support Vector Machine (SVM)
• Decision Tree Classifier

Clustering Models

• K-Means Clustering
• Spectral Clustering
• DBSCAN Clustering
• K-Medoids Clustering
• Mixture of Gaussians
• Principal Component Analysis (PCA)

Thank you for exploring the ML Playground! If you have questions or feedback, feel free to open an issue. Enjoy exploring machine learning! 🧑‍💻