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README.md

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-license: mit
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+# 📚 Book Recommendation System using LightFM
+
+This project builds a hybrid book recommendation system using the LightFM library.  
+It leverages collaborative and content-based filtering to suggest books to users based on ratings data.
+
+## 📦 Dataset
+- Source: [Goodbooks-10K Dataset](https://www.kaggle.com/datasets/zygmunt/goodbooks-10k)
+- Files used: `books.csv`, `ratings.csv`
+
+## 🔧 Libraries
+- `pandas`, `numpy`, `matplotlib`
+- `lightfm`
+
+## 🔮 Recommendation Logic
+- Trained a LightFM model using WARP loss function.
+- Built a user-item interaction matrix.
+- Predicted books that the user hasn't rated yet.
+- Displayed top-N recommendations per user.
+
+## 📊 Visualization
+- Bar chart of top 10 most-rated books.
+
+## 🚀 How to Run
+```bash
+pip install -r requirements.txt
+jupyter notebook Book_Recommendation_LightFM.ipynb
+
+✍️ Author
+Hande Çarkcı
+
+
+MİT licance
+eğitim amaclı ve  Streamlit uygulamanda bu modeli kullandım
+

lightfm_book_model.pkl

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