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Book_Recommadation_System/README.md

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+# 📚 Book Recommendation System Project
+
+## 🎯 Objective
+
+The goal of this project is to build a **Book Recommendation System** for users. Effective recommendation systems are crucial in many industries as they can significantly increase revenue and differentiate a platform from competitors.
+
+---
+
+## 🛠️ Methods Used
+
+- **📊 Descriptive Statistics**
+- **📈 Data Visualization**
+- **🤖 Machine Learning**
+
+## 💻 Technologies
+
+- **Python**
+- **Pandas**
+- **Numpy**
+- **Matplotlib**
+- **Seaborn**
+- **Scikit-learn**
+- **Surprise**
+
+---
+
+## 📂 Dataset
+
+The **Book-Crossing** dataset includes three files:
+
+- **Users**: Contains user information like `User-ID`, `Location`, and `Age`. Some fields may have NULL values if data is unavailable.
+
+- **Books**: Each book is identified by an `ISBN`, with additional details such as `Book-Title`, `Book-Author`, `Year-Of-Publication`, and `Publisher`. Only the first author is listed, and cover images are provided in three sizes: small, medium, and large, linked to Amazon.
+
+- **Ratings**: Contains book rating data (`Book-Rating`), either as explicit ratings (1-10) or implicit ratings (0).
+
+---
+
+## 📋 Project Description
+
+1. **EDA** - Performed exploratory data analysis on numerical and categorical data.
+2. **Data Cleaning** - Handled missing values and outliers.
+3. **Feature Selection** - Used `User-ID`, `ISBN`, and `Book-Rating` for model development.
+4. **Model Development** - Implemented both popularity-based and collaborative filtering models (memory-based and model-based).
+
+---
+
+## ⚙️ Needs of this Project
+
+- Data exploration
+- Data processing/cleaning
+- Recommendation system development
+
+---
+
+## 🚀 Getting Started
+
+1. **Clone this repo**:
+```bash
+git clone https://github.com/your-username/Book-Recommendation-System-Project.git
+```
+
+2. **Dataset Files**:
+   - **Users_data**: Available within the repository.
+   - **Ratings_data**: Available within the repository.
+   - **Books_data**: Available within the repository.
+
+3. **Notebook**:
+   - The complete notebook covering **Data Exploration**, **Processing**, **Transformation**, and **Model Development** is available in the repo.
+
+---
+
+### 📂 Repository Structure
+
+- **Users_data.csv** - User demographics
+- **Ratings_data.csv** - Ratings provided by users
+- **Books_data.csv** - Details of books
+
+---
+
+### 🛤️ Future Enhancements
+
+1. Integrate additional recommendation algorithms.
+2. Implement a web interface for interactive recommendations.
+
+
+### Happy Reading! 📖✨