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Author: alo7lika

Classification Models/Market Trend Classification Model/README.md

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+# 📈 Market Trend Classification Model
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/alo7lika/ML-Nexus/refs/heads/main/Time%20Series%20Analysis/Market%20Regime%20Detection/Market%20Regime%20Detection%20Project%20-%20Analysis%20Dashboard.png" alt="Market Regime Detection Dashboard" width="600"/>
+</p>
+
+
+## 📖 Overview
+The Market Trend Classification Model aims to identify different market conditions by analyzing historical stock price data. By utilizing advanced data analysis techniques, this project classifies market regimes to aid in informed trading decisions and strategy development.
+
+## 📚 Table of Contents
+- [🚀 Problem Statement](#-problem-statement)
+- [💡 Proposed Solution](#-proposed-solution)
+  - [Key Components](#key-components)
+- [📦 Installation & Usage](#-installation--usage)
+- [⚙️ Alternatives Considered](#-alternatives-considered)
+- [📊 Results](#-results)
+- [🔍 Conclusion](#-conclusion)
+- [🤝 Acknowledgments](#-acknowledgments)
+- [📧 Contact](#-contact)
+
+## 🚀 Problem Statement
+Accurate Market Trend Classification Model is crucial for investors and traders. Identifying whether the market is in a bull, bear, or neutral phase can significantly influence trading strategies and risk management.
+
+## 💡 Proposed Solution
+This project employs clustering algorithms to categorize market regimes based on features derived from stock price movements.
+
+### Key Components
+| Component               | Description                                                  |
+|-------------------------|--------------------------------------------------------------|
+| **Data Collection**     | Historical stock data is sourced from Yahoo Finance.       |
+| **Data Preprocessing**  | Calculating daily returns, moving averages, and volatility. |
+| **Feature Engineering** | Normalizing data for effective clustering.                  |
+| **Clustering**          | K-Means clustering is used to classify market regimes.      |
+| **Model Validation**    | Evaluating the effectiveness of detected regimes.           |
+
+## 📦 Installation & Usage
+To get started, ensure you have Python and the following libraries installed:
+
+| Library          | Installation Command                     |
+|------------------|------------------------------------------|
+| **Pandas**       | `pip install pandas`                     |
+| **NumPy**        | `pip install numpy`                      |
+| **Matplotlib**   | `pip install matplotlib`                 |
+| **Scikit-learn** | `pip install scikit-learn`               |
+| **yfinance**     | `pip install yfinance`                   |
+
+## ⚙️ Alternatives Considered
+Several alternative approaches were evaluated, including:
+
+| Alternative Approach       | Description                                      |
+|----------------------------|--------------------------------------------------|
+| **Traditional Machine Learning** | Algorithms like SVM and k-NN were considered; effective for smaller datasets but struggled with complexity. |
+
+## 📊 Results
+The model aims to classify market regimes accurately, providing valuable insights for trading strategies.
+
+## 🔍 Conclusion
+This project demonstrates the significance of time series analysis and clustering techniques in financial market analysis. The identified regimes can enhance decision-making processes for traders and investors.
+
+## 🤝 Acknowledgments
+- **Dataset:** Yahoo Finance
+- **Frameworks:** Pandas, NumPy, Matplotlib, Scikit-learn, yfinance
+
+## 📧 Contact
+For any inquiries or contributions, feel free to reach out:
+
+| Name               | Email                       | GitHub             |
+|--------------------|-----------------------------|---------------------|
+| Alolika Bhowmik    | [email protected]   | [alo7lika](https://github.com/alo7lika) |