amirsoahil101/AI_Model_Arena_Rankings

🏆 AI Model Arena Rank Predictor

An interactive Machine Learning Web Application built with Streamlit that predicts the leaderboard rank of AI models based on evaluation metrics like Rating, Variance, Vote Count, and Year.

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🖼️ Application Preview & UI

Enter numerical model metrics in the interactive Streamlit dashboard to instantly estimate leaderboard rank.

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🔗 Live Application

You can access and test the deployed application directly here: 👉 Click Here to Launch Live Demo

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📌 Project Architecture & Model Evolution

1. Initial Approach: Deep Learning (Sequential Neural Network)

Initially, a multi-layer Deep Neural Network (Sequential) was trained using Keras, incorporating Dense, BatchNormalization, and Dropout layers. However, due to a relatively small dataset size, the deep learning approach suffered from lower performance and overfitting compared to traditional Machine Learning algorithms.

2. Optimized Approach: Tree-Based Machine Learning

To achieve higher accuracy and better generalization, the pipeline was transitioned to Machine Learning algorithms. Ensemble methods—specifically Extra Trees Regressor ($R^2 \approx 0.9858$) and Random Forest Regressor ($R^2 \approx 0.9794$)—outperformed all other models by a significant margin.

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📊 Model Evaluation & Benchmarks

Here is the comprehensive performance metric comparison across all evaluated Machine Learning models:

Model Name	$R^2$ Score	MSE	MAE	RMSE
Extra Trees Regressor 🏆	0.985858	114.65	5.04	10.71
Random Forest Regressor	0.979452	166.58	6.00	12.91
Decision Tree Regressor	0.962737	302.08	6.48	17.38
K-Neighbors Regressor	0.949303	410.98	9.82	20.27
Gradient Boosting	0.708248	2365.16	34.72	48.63
Linear Regression	0.527966	3826.66	49.34	61.86
Ridge Regression	0.527965	3826.67	49.34	61.86
Lasso Regression	0.527865	3827.48	49.37	61.87
ElasticNet	0.516176	3922.24	50.52	62.63

Key Insight: Extra Trees achieved the best performance with an $R^2$ score of ~98.58% and a Low Mean Absolute Error (MAE) of 5.04.

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🛠️ Tech Stack & Dependencies

• Frontend / UI: Streamlit
• Machine Learning: Scikit-Learn
• Deep Learning (Experimental): Keras / TensorFlow
• Data Manipulation: Pandas, NumPy
• Model Serialization: Joblib

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📁 Repository Structure

├── app.py                      # Main Streamlit Web Application
├── requirements.txt            # Python Dependencies
├── ai_model_arena_rankings/    # Project Directory
├── .gitignore                  # Git Ignore File
├── .gitattributes              # Git Attributes File
├── UI.png                      # Streamlit UI Screenshot
├── ML_model.pkl                # Best Trained ML Model (Extra Trees / Random Forest)
├── scaler.pkl                  # Fitted StandardScaler Object
├── columns.pkl                 # Feature Column Definitions
├── notebook.ipynb              # Model Training & EDA Notebook
├── requirements.txt            # Python Dependencies
└── README.md                   # Project Documentation

git clone https://github.com/amirsohail100/AI-Model-Arena-Rank-Predictor.git

cd AI-Model-Arena-Rank-Predictor

streamlit run app.py

pip install -r requirements.txt

📄 License

This project is licensed under the MIT License.

📝 Author

👤 Amir Sohail

AI Model Arena Rank Predictor is a Streamlit web application designed to forecast model ranks based on ratings, variance, vote counts, year, and text descriptions. Built with Python, Scikit-Learn, Keras, and Joblib, it provides seamless real-time predictions using pickled scalers, tokenizers, and ML/DL models.