Merge pull request #1296 from alo7lika/main

Market Trend Classification Model

Author: sanjay-kv

Classification Models/Market Trend Classification Model/Market Trend Classification Model.ipynb

@@ -0,0 +1,69 @@
+# 📈 Market Trend Classification Model
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/alo7lika/machine-learning-repos/refs/heads/main/Classification%20Models/Market%20Trend%20Classification%20Model/MarketTrend%20Analytics%20-%20Classification%20Model.png" alt="Market Trend Classification Model" width="600"/>
+</p>
+
+## 📖 Overview
+The Market Trend Classification Model aims to identify different market conditions by analyzing historical stock price data. This project classifies market regimes by utilizing advanced data analysis techniques 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 traders' and investors' decision-making processes.
+
+## 🤝 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) |

Classification Models/Market Trend Classification Model/MarketTrend Analytics - Classification Model.png

@@ -0,0 +1,201 @@
+CustomerID,Genre,Age,Annual Income (k$),Spending Score (1-100)
+0001,Male,19,15,39
+0002,Male,21,15,81
+0003,Female,20,16,6
+0004,Female,23,16,77
+0005,Female,31,17,40
+0006,Female,22,17,76
+0007,Female,35,18,6
+0008,Female,23,18,94
+0009,Male,64,19,3
+0010,Female,30,19,72
+0011,Male,67,19,14
+0012,Female,35,19,99
+0013,Female,58,20,15
+0014,Female,24,20,77
+0015,Male,37,20,13
+0016,Male,22,20,79
+0017,Female,35,21,35
+0018,Male,20,21,66
+0019,Male,52,23,29
+0020,Female,35,23,98
+0021,Male,35,24,35
+0022,Male,25,24,73
+0023,Female,46,25,5
+0024,Male,31,25,73
+0025,Female,54,28,14
+0026,Male,29,28,82
+0027,Female,45,28,32
+0028,Male,35,28,61
+0029,Female,40,29,31
+0030,Female,23,29,87
+0031,Male,60,30,4
+0032,Female,21,30,73
+0033,Male,53,33,4
+0034,Male,18,33,92
+0035,Female,49,33,14
+0036,Female,21,33,81
+0037,Female,42,34,17
+0038,Female,30,34,73
+0039,Female,36,37,26
+0040,Female,20,37,75
+0041,Female,65,38,35
+0042,Male,24,38,92
+0043,Male,48,39,36
+0044,Female,31,39,61
+0045,Female,49,39,28
+0046,Female,24,39,65
+0047,Female,50,40,55
+0048,Female,27,40,47
+0049,Female,29,40,42
+0050,Female,31,40,42
+0051,Female,49,42,52
+0052,Male,33,42,60
+0053,Female,31,43,54
+0054,Male,59,43,60
+0055,Female,50,43,45
+0056,Male,47,43,41
+0057,Female,51,44,50
+0058,Male,69,44,46
+0059,Female,27,46,51
+0060,Male,53,46,46
+0061,Male,70,46,56
+0062,Male,19,46,55
+0063,Female,67,47,52
+0064,Female,54,47,59
+0065,Male,63,48,51
+0066,Male,18,48,59
+0067,Female,43,48,50
+0068,Female,68,48,48
+0069,Male,19,48,59
+0070,Female,32,48,47
+0071,Male,70,49,55
+0072,Female,47,49,42
+0073,Female,60,50,49
+0074,Female,60,50,56
+0075,Male,59,54,47
+0076,Male,26,54,54
+0077,Female,45,54,53
+0078,Male,40,54,48
+0079,Female,23,54,52
+0080,Female,49,54,42
+0081,Male,57,54,51
+0082,Male,38,54,55
+0083,Male,67,54,41
+0084,Female,46,54,44
+0085,Female,21,54,57
+0086,Male,48,54,46
+0087,Female,55,57,58
+0088,Female,22,57,55
+0089,Female,34,58,60
+0090,Female,50,58,46
+0091,Female,68,59,55
+0092,Male,18,59,41
+0093,Male,48,60,49
+0094,Female,40,60,40
+0095,Female,32,60,42
+0096,Male,24,60,52
+0097,Female,47,60,47
+0098,Female,27,60,50
+0099,Male,48,61,42
+0100,Male,20,61,49
+0101,Female,23,62,41
+0102,Female,49,62,48
+0103,Male,67,62,59
+0104,Male,26,62,55
+0105,Male,49,62,56
+0106,Female,21,62,42
+0107,Female,66,63,50
+0108,Male,54,63,46
+0109,Male,68,63,43
+0110,Male,66,63,48
+0111,Male,65,63,52
+0112,Female,19,63,54
+0113,Female,38,64,42
+0114,Male,19,64,46
+0115,Female,18,65,48
+0116,Female,19,65,50
+0117,Female,63,65,43
+0118,Female,49,65,59
+0119,Female,51,67,43
+0120,Female,50,67,57
+0121,Male,27,67,56
+0122,Female,38,67,40
+0123,Female,40,69,58
+0124,Male,39,69,91
+0125,Female,23,70,29
+0126,Female,31,70,77
+0127,Male,43,71,35
+0128,Male,40,71,95
+0129,Male,59,71,11
+0130,Male,38,71,75
+0131,Male,47,71,9
+0132,Male,39,71,75
+0133,Female,25,72,34
+0134,Female,31,72,71
+0135,Male,20,73,5
+0136,Female,29,73,88
+0137,Female,44,73,7
+0138,Male,32,73,73
+0139,Male,19,74,10
+0140,Female,35,74,72
+0141,Female,57,75,5
+0142,Male,32,75,93
+0143,Female,28,76,40
+0144,Female,32,76,87
+0145,Male,25,77,12
+0146,Male,28,77,97
+0147,Male,48,77,36
+0148,Female,32,77,74
+0149,Female,34,78,22
+0150,Male,34,78,90
+0151,Male,43,78,17
+0152,Male,39,78,88
+0153,Female,44,78,20
+0154,Female,38,78,76
+0155,Female,47,78,16
+0156,Female,27,78,89
+0157,Male,37,78,1
+0158,Female,30,78,78
+0159,Male,34,78,1
+0160,Female,30,78,73
+0161,Female,56,79,35
+0162,Female,29,79,83
+0163,Male,19,81,5
+0164,Female,31,81,93
+0165,Male,50,85,26
+0166,Female,36,85,75
+0167,Male,42,86,20
+0168,Female,33,86,95
+0169,Female,36,87,27
+0170,Male,32,87,63
+0171,Male,40,87,13
+0172,Male,28,87,75
+0173,Male,36,87,10
+0174,Male,36,87,92
+0175,Female,52,88,13
+0176,Female,30,88,86
+0177,Male,58,88,15
+0178,Male,27,88,69
+0179,Male,59,93,14
+0180,Male,35,93,90
+0181,Female,37,97,32
+0182,Female,32,97,86
+0183,Male,46,98,15
+0184,Female,29,98,88
+0185,Female,41,99,39
+0186,Male,30,99,97
+0187,Female,54,101,24
+0188,Male,28,101,68
+0189,Female,41,103,17
+0190,Female,36,103,85
+0191,Female,34,103,23
+0192,Female,32,103,69
+0193,Male,33,113,8
+0194,Female,38,113,91
+0195,Female,47,120,16
+0196,Female,35,120,79
+0197,Female,45,126,28
+0198,Male,32,126,74
+0199,Male,32,137,18
+0200,Male,30,137,83

Classification Models/Market Trend Classification Model/README.md

@@ -0,0 +1,108 @@
+# 🛍️ ClusterLogic Model
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/alo7lika/machine-learning-repos/refs/heads/main/Prediction%20Models/ClusterLogic%20Model/ClusterLogic%20Model.png" alt="Customer Clust Segmentation Tool" width="600"/>
+</p>
+
+
+## 📚 Table of Contents
+1. [Overview](#-overview)
+2. [Features](#-features)
+3. [How It Works](#-how-it-works)
+4. [Tech Stack](#-tech-stack)
+5. [Installation](#-installation)
+6. [Usage](#-usage)
+7. [Visualizations](#-visualizations)
+8. [Machine Learning Models](#-machine-learning-models)
+9. [Goals](#-goals)
+10. [License](#-license)
+11. [Contact](#-contact)
+
+
+## 📋 Overview
+ClusterLogic Model is a powerful customer segmentation tool designed to categorize customers based on their purchasing behavior, preferences, and demographic characteristics. By leveraging advanced data analytics and machine learning techniques, this tool helps businesses:
+
+- 📈 Enhance marketing strategies
+- 🧠 Improve customer understanding
+- ⚙️ Optimize resource allocation
+- 🚀 Drive business growth
+- 💡 Foster a data-driven culture
+
+## 🔍 Features
+- **Segmentation**: Classifies customers into distinct groups for targeted marketing.
+- **Behavioral Insights**: Provides valuable insights into customer preferences and purchasing habits.
+- **Visualization**: Interactive charts and graphs for easy interpretation of customer segments.
+- **Advanced Metrics**: Incorporates KPIs to measure the impact of different segments on business growth.
+
+## 🧑‍💻 How It Works
+1. **Data Collection**: Input customer purchase history, preferences, and demographic data.
+2. **Data Preprocessing**: Clean and preprocess the data for machine learning models.
+3. **Modeling**: Apply clustering algorithms like K-Means or Hierarchical Clustering to identify customer groups.
+4. **Evaluation**: Analyze the results using metrics like silhouette score or within-cluster sum of squares (WCSS).
+5. **Visualization**: Visualize the segmentation results using intuitive dashboards.
+
+## 🛠️ Tech Stack
+- **Languages**: Python 🐍
+- **Libraries**: 
+  - pandas 📊
+  - numpy 🔢
+  - scikit-learn 📚
+  - matplotlib 📉
+  - seaborn 📈
+
+## 🚀 Getting Started
+
+### Prerequisites
+- Python 3.8+
+- Jupyter Notebook
+- Required libraries in `requirements.txt`
+
+### Installation
+Clone this repository:
+```bash
+git clone https://github.com/yourusername/ClusterLogic.git
+cd ClusterLogic
+```
+Install the necessary dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+### Usage
+Run the Jupyter notebook to explore the data and generate customer segments:
+
+```bash
+jupyter notebook notebooks/ClusterLogic_Model.ipynb
+```
+To run the segmentation pipeline as a script:
+
+```bash
+python scripts/segment_customers.py
+```
+
+## 📊 Visualizations
+The tool provides insightful visualizations to help you understand customer clusters and trends, such as:
+
+- 📉 **Purchase trends over time**
+- 🧩 **Segmented customer behavior**
+- 🗺️ **Demographic distribution maps**
+- 🎯 **Targeted marketing groupings**
+
+## 🧠 Machine Learning Models
+ClusterLogic Model uses unsupervised learning techniques, primarily focusing on:
+
+- **K-Means Clustering**: For grouping customers into meaningful clusters.
+- **Hierarchical Clustering**: To provide more granular segmentation if needed.
+
+## 🏆 Goals
+- Improve customer retention and acquisition.
+- Maximize marketing campaign efficiency.
+- Tailor product recommendations to specific customer segments.
+
+## 🛡️ License
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+
+## 💬 Contact
+For more information or queries, feel free to contact the project maintainers at: [[email protected]]
+
+