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Financial Environment Segmentation
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diff --git a/Financial Environment Segmentation/Financial Environment Segmentation.ipynb b/Financial Environment Segmentation/Financial Environment Segmentation.ipynb
diff --git a/Financial Environment Segmentation/Financial Insights - Market Segmentation.png b/Financial Environment Segmentation/Financial Insights - Market Segmentation.png
diff --git a/Financial Environment Segmentation/README.md b/Financial Environment Segmentation/README.md
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+# 📈 Financial Environment Segmentation 
+
+## 📚 Table of Contents
+1. [📖 Overview](#-overview)
+2. [🚀 Problem Statement](#-problem-statement)
+3. [💡 Proposed Solution](#-proposed-solution)
+4. [📦 Installation & Usage](#-installation--usage)
+5. [⚙️ Alternatives Considered](#-alternatives-considered)
+6. [📊 Results](#-results)
+7. [🔍 Conclusion](#-conclusion)
+8. [🤝 Acknowledgments](#-acknowledgments)
+9. [📧 Contact](#-contact)
+
+## 📖 Overview
+The **Financial Environment Segmentation** project focuses on identifying and classifying different market regimes using historical stock price data. This approach aids in understanding market dynamics, helping traders and investors make informed decisions.
+
+## 🚀 Problem Statement
+Recognizing distinct market regimes (bull, bear, neutral) is vital for effective investment strategies. Variability in market conditions necessitates a robust framework to identify and respond to these changes promptly.
+
+## 💡 Proposed Solution
+This project employs clustering techniques to segment financial environments, providing insights into market behavior based on historical data.
+
+| Key Components        | Description                                                      |
+|-----------------------|------------------------------------------------------------------|
+| Data Collection       | Historical stock price data gathered from Yahoo Finance.        |
+| Data Preprocessing    | Calculation of daily returns, moving averages, and volatility.   |
+| Feature Engineering    | Normalization and selection of relevant features for analysis.   |
+| Clustering            | K-means clustering to classify market regimes.                   |
+| Analysis & Validation | Evaluation of regimes and their characteristics through backtesting. |
+
+## 📦 Installation & Usage
+To get started, ensure you have the necessary libraries installed:
+
+| Library        | Purpose                                   |
+|----------------|-------------------------------------------|
+| pandas         | Data manipulation and analysis            |
+| numpy          | Numerical computing                        |
+| matplotlib     | Data visualization                        |
+| scikit-learn   | Machine learning algorithms               |
+| yfinance       | Financial data retrieval                  |
+
+### Clone the Repository
+
+1. Clone this repository to your local machine using the following command:
+   ```bash
+   git clone https://github.com/alo7lika/Stock-Price-Prediction.git
+   ```
+2. Navigate to the project directory
+   ```bash
+   cd Stock-Price-Prediction/Financial\ Environment\ Segmentation
+   ```
+3. It is recommended to create a virtual environment to manage dependencies:
+   ```
+   python -m venv env
+   source env/bin/activate  # On Windows use `env\Scripts\activate`
+   ```
+4. Install the necessary libraries using pip:
+   ```
+   pip install -r requirements.txt
+   ```
+
+
+## ⚙️ Alternatives Considered
+Several alternative approaches were evaluated for market regime detection:
+
+| Alternative Approach            | Description                                                          |
+|---------------------------------|----------------------------------------------------------------------|
+| Traditional Machine Learning     | Techniques like SVM and k-NN; effective for smaller datasets.      |
+| Advanced Clustering Algorithms   | Exploring DBSCAN and Hierarchical Clustering for better segmentation.|
+
+## 📊 Results
+The model aims to achieve accurate segmentation of market regimes, facilitating better investment strategies and risk management.
+
+## 🔍 Conclusion
+The project demonstrates the importance of identifying financial market regimes, showcasing how clustering techniques can provide valuable insights for traders and investors.
+
+## 🤝 Acknowledgments
+- **Dataset**: Historical stock price data from Yahoo Finance.
+- **Frameworks**: Built using Python libraries such as Pandas, NumPy, Matplotlib, Scikit-learn, and yfinance.
+
+## 📧 Contact
+For any inquiries or contributions, feel free to reach out:
+
+| Name                | Email                          | GitHub               |
+|---------------------|--------------------------------|----------------------|
+| Alolika Bhowmik     | [email protected]     | [alo7lika](https://github.com/alo7lika) |
diff --git a/Multi-Asset Portfolio Modeling/Multi-Asset Portfolio Modeling.ipynb b/Multi-Asset Portfolio Modeling/Multi-Asset Portfolio Modeling.ipynb
diff --git a/Multi-Asset Portfolio Modeling/README.md b/Multi-Asset Portfolio Modeling/README.md
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+# 📈 Multi-Asset Portfolio Modeling
+
+Welcome to the **Multi-Asset Portfolio Modeling** project! This tool leverages advanced financial models to help optimize portfolio allocation across multiple assets using real-time data, sentiment analysis, and scenario analysis. This README provides an overview of the project, its functionality, and how to get started.
+
+## 📚 Table of Contents
+
+1. [🌟 Features](#-features)
+2. [🚀 Getting Started](#-getting-started)
+   - [Prerequisites](#prerequisites)
+   - [Installation](#installation)
+   - [Set up API Keys](#set-up-your-api-keys-for-real-time-data-integration)
+3. [🛠️ Usage](#-usage)
+4. [🎯 To-Do List](#-to-do-list)
+5. [🤝 Contributing](#-contributing)
+6. [📜 License](#-license)
+7. [💬 Questions?](#-questions)
+
+## 🌟 Features
+
+This project enhances traditional portfolio modeling with several powerful features:
+
+| Feature                         | Description                                                                 |
+|----------------------------------|-----------------------------------------------------------------------------|
+| 📊 **Real-Time Data Integration**  | Automatically adjust portfolios based on real-time market data via APIs      |
+| 📰 **Sentiment Analysis**         | Incorporates news and social media sentiment to provide better decision-making|
+| 🌍 **Scenario Analysis**          | Simulate different economic scenarios (e.g., recessions, booms)              |
+| 🖥️ **Interactive Dashboard**       | User-friendly interface to visualize portfolio performance and results        |
+
+## 🚀 Getting Started
+
+To run the **Multi-Asset Portfolio Modeling** system locally, follow these steps:
+
+### Prerequisites
+Ensure you have the following installed:
+- Python 3.x
+- Pip package manager
+- Virtual environment (optional but recommended)
+
+### Installation
+
+1. **Clone the repository:**
+   ```bash
+   git clone https://github.com/your-username/multi-asset-portfolio-modeling.git
+   cd multi-asset-portfolio-modeling
+   ```
+2. **Install the required dependencies**:
+   ```bash
+   pip install -r requirements.txt
+   ```
+3. **Set up your API keys for real-time data integration**:
+
+Sign up for an API key from Alpha Vantage or Yahoo Finance.
+
+Add your API keys to the environment file or directly to the Python script.
+
+4. **Run the dashboard**:
+```bash
+python app.py
+```
+## 🛠️ Usage
+Once the dashboard is running, you can:
+
+- **Upload your dataset**: Import your asset data in `.csv` format to get started.
+- **Set portfolio parameters**: Adjust risk preferences, asset allocations, and other settings via the dashboard.
+- **Visualize results**: See optimized portfolio weights, performance metrics, and scenario simulations.
+- **Analyze scenarios**: Use the scenario analysis tool to model different market conditions.
+
+## 🎯 To-Do List
+- Incorporate more alternative data sources (e.g., geopolitics, interest rates).
+- Improve sentiment analysis by integrating additional sentiment models.
+- Expand stress testing for various asset classes (bonds, crypto).
+
+## 🤝 Contributing
+Contributions are welcome! To contribute:
+1. Fork the repository.
+2. Create a new branch for your feature or bugfix.
+3. Submit a pull request for review.
+
+Please ensure that your code follows the existing style and includes appropriate tests.
+
+## 📜 License
+This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
+
+## 💬 Questions?
+If you have any questions or feedback, feel free to reach out via [GitHub Issues](https://github.com/alo7lika/multi-asset-portfolio-modeling/issues) or email me at [[email protected]]