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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
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