---
title: ML Model Comparison Tool
emoji: 🔥
colorFrom: red
colorTo: green
sdk: gradio
sdk_version: 6.14.0
python_version: '3.13'
app_file: app.py
pinned: false
---

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

# AI Model Comparison App

A Machine Learning web application built using Python and Gradio.  
This project allows users to compare multiple Machine Learning models on different datasets for both Classification and Regression tasks.

---

# Features

## Classification
Users can choose one of the following datasets:

- Iris Dataset
- Breast Cancer Dataset
- Titanic Dataset

## Regression
Users can choose one of the following datasets:

- California Housing Dataset
- Diabetes Dataset
- Boston Housing Dataset

---

# Machine Learning Models

## Classification Models

- Logistic Regression
- Decision Tree Classifier
- Random Forest Classifier
- K-Nearest Neighbors (KNN)

## Regression Models

- Linear Regression
- Decision Tree Regressor
- Random Forest Regressor
- Support Vector Regressor (SVR)

---

# Technologies Used

- Python
- Scikit-learn
- Pandas
- Gradio
- Hugging Face Spaces

---

# Project Structure

```bash
├── app.py
├── requirements.txt
├── README.md
```

---

# Installation

## Clone the Repository

```bash
git clone https://github.com/your-username/your-repo-name.git
cd your-repo-name
```

## Install Required Libraries

```bash
pip install -r requirements.txt
```

## Run the Application

```bash
python app.py
```

---

# Hugging Face Deployment

## Steps

1. Create a new Space on Hugging Face
2. Choose:
   - SDK: Gradio
3. Upload these files:
   - app.py
   - requirements.txt
   - README.md
4. Wait for automatic deployment

---

# How the Application Works

## Step 1
Select the task type:

- Classification
- Regression

## Step 2
Choose a dataset

## Step 3
Click the button to run the models

## Step 4
The application will:

- Train multiple machine learning models
- Compare their performance
- Display the evaluation results
- Show the best-performing model

---

# Datasets Used

## Classification Datasets

### Iris Dataset
A famous dataset used for flower classification.

### Breast Cancer Dataset
Used to classify tumors as malignant or benign.

### Titanic Dataset
Predicts passenger survival on the Titanic.

---

## Regression Datasets

### California Housing Dataset
Predicts housing prices in California districts.

### Diabetes Dataset
Predicts disease progression measurements.

### Boston Housing Dataset
Predicts house prices using multiple features.

---

# Evaluation Metrics

## Classification

- Accuracy Score

## Regression

- R² Score
- Mean Squared Error (MSE)

---

# Future Improvements

- Add more datasets
- Add XGBoost and LightGBM
- Allow users to upload custom datasets
- Add graphs and visualizations
- Generate downloadable reports
- Deploy with a custom UI design

---

# Example Use Case

A user selects:

- Task Type: Classification
- Dataset: Iris Dataset

The app trains:

- Logistic Regression
- Decision Tree
- Random Forest
- KNN

Then compares their accuracy scores and displays the best model.

---

# Author

## Saja

- Master’s Student in Artificial Intelligence