Update README.md

README.md

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 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