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# ⚔️ Game of Thrones Character Similarity Explorer |
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Explore semantic relationships between **Game of Thrones characters** using state-of-the-art embedding techniques. This project leverages **SBERT** and **TF-IDF embeddings** to map characters into high-dimensional semantic spaces, enabling similarity analysis, visualization, and recommendations. |
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## 🚀 Features |
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- **Semantic Similarity Search:** Find the most similar characters using **cosine similarity** on embeddings. |
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- **Hybrid Embeddings:** Combine SBERT (semantic) and TF-IDF (frequency-based) embeddings for improved results. |
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- **Dimensionality Reduction Visualizations:** |
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- **2D & 3D t-SNE:** Non-linear reduction highlighting local neighborhoods and clusters. |
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- **2D & 3D PCA:** Linear reduction emphasizing global variance and main directions. |
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- **Interactive Plots:** Explore embeddings with **Plotly 2D/3D scatter plots**, hoverable labels, and downloadable plots. |
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- **Downloadable Results:** Export similarity results as **CSV** for further analysis. |
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## 🧠 How It Works |
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### Data Preprocessing |
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Character names are normalized, and embeddings are precomputed for SBERT and TF-IDF. |
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### Similarity Computation |
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- **SBERT embeddings** capture contextual and semantic relationships. |
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- **TF-IDF embeddings** capture frequency-based textual features. |
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- **Hybrid similarity** is a weighted combination of both embeddings. |
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### Recommendation |
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The app computes **top-N similar characters** based on the chosen embedding model and similarity type. |
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### Visualization |
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- Reduce embeddings to **2D or 3D space** with t-SNE or PCA. |
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- Interactive plots allow exploration of latent structures and clusters. |
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## 🛠️ Technologies Used |
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- **Python** & **Streamlit** for the web interface |
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- **Pandas**, **NumPy**, **Joblib** for data processing |
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- **Scikit-learn** for embeddings, similarity, t-SNE, and PCA |
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- **Plotly** for interactive 2D/3D visualizations |
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- **Pillow** for image handling |
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- **SBERT & TF-IDF** for semantic and frequency-based embeddings |
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## ⚙️ How to Use |
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1. Select a **character** from the sidebar. |
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2. Choose an **embedding model**: SBERT, TF-IDF, or Hybrid. |
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3. Adjust the number of **similar characters** to display. |
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4. Click **Find Similar Characters** to get results with images and similarity scores. |
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5. Explore **Dimensionality Reduction** to visualize embeddings in 2D/3D space. |
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6. Download **CSV results** or **plots** for offline use. |
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## 📊 Applications |
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- Character similarity analysis for literature and fandom studies |
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- Semantic search engines |
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- Recommendation systems |
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- Knowledge graph construction and clustering of entities |
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## 🔗 Live Demo |
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[Game of Thrones Character Similarity Explorer](https://huggingface.co/spaces/Daksh0505/Game-Of-Thrones-Character-Similarity) |
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## 📄 License |
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MIT License – feel free to use and adapt for research or educational purposes. |
