README.md

metadata

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.

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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

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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)

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Technologies Used

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

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Project Structure

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

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Installation

Clone the Repository

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

Install Required Libraries

pip install -r requirements.txt

Run the Application

python app.py

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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

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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

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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.

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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.

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Evaluation Metrics

Classification

• Accuracy Score

Regression

• R² Score
• Mean Squared Error (MSE)

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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

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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.

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Author

Saja

• Master’s Student in Artificial Intelligence