Handwritten Digit Recognition

Handwritten Digit Recognition/README.md

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+# 🖊️ Handwritten Digit Recognition Project
+
+<div align="center">
+  <img src="https://raw.githubusercontent.com/alo7lika/ML-Nexus/refs/heads/main/Handwritten%20Digit%20Recognition/DigitVision%20-%20Handwritten%20Digit%20Recognition%20(1).png" alt="CNN Model Architecture" width="600"/>
+</div>
+
+
+## 📚 Table of Contents
+1. [📖 Overview](#-overview)
+2. [🚀Problem Statement](#-problem-statement)
+3. [💡 Proposed Solution](#-proposed-solution)
+4. [⚙️ Alternatives Considered](#-alternatives-considered)
+5. [📊 Results](#-results)
+6. [🔍 Conclusion](#-conclusion)
+7. [📦 Installation & Usage](#-installation--usage)
+8. [🤝 Acknowledgments](#-acknowledgments)
+9. [📧 Contact](#-contact)
+
+## 📖 Overview
+The Handwritten Digit Recognition project aims to accurately identify and classify handwritten digits. This challenge is crucial for various applications, such as automated data entry and postal services. Leveraging the **MNIST dataset**, this project develops a robust Convolutional Neural Network (CNN) model using **TensorFlow** and **Keras**.
+
+## 🚀 Problem Statement
+Accurately recognizing handwritten digits is essential for many automated systems. The variability in handwriting styles presents a significant challenge for traditional recognition methods, leading to decreased accuracy. This project seeks to create a model that effectively learns to distinguish between different handwritten digits.
+
+## 💡 Proposed Solution
+### Key Components
+
+| Component            | Description                                                                                     |
+|----------------------|-------------------------------------------------------------------------------------------------|
+| **Data Preprocessing** | Normalize pixel values and reshape input data for CNN requirements.                           |
+| **CNN Architecture** | Utilize convolutional, pooling, and fully connected layers for feature extraction and classification. |
+| **Training Process** | Employ appropriate loss functions and optimization algorithms to enhance model performance.     |
+| **Evaluation Metrics**| Assess accuracy and loss on training and validation datasets for generalization.               |
+
+### 🏗️ CNN Architecture
+- **Convolutional Layers**: Extract features from images.
+- **Pooling Layers**: Reduce dimensionality while retaining important features.
+- **Fully Connected Layers**: Perform final classification.
+
+## ⚙️ Alternatives Considered
+Several alternative approaches were evaluated:
+
+| Alternative Approach           | Description                                                                             |
+|--------------------------------|-----------------------------------------------------------------------------------------|
+| **Traditional Machine Learning**| Algorithms like SVM and k-NN were considered; effective for smaller datasets but struggle with complexity. |
+
+## 📊 Results
+The model aims to achieve high accuracy in recognizing handwritten digits, providing an efficient tool for applications requiring digit recognition.
+
+## 🔍 Conclusion
+This project showcases the effectiveness of deep learning in image classification. The structured approach demonstrates the potential of neural networks in automating digit recognition across various industries.
+
+## 📦 Installation & Usage
+To get started, ensure you have Python and the necessary libraries installed:
+
+```bash
+pip install tensorflow numpy matplotlib
+```
+## 🤝 Acknowledgments
+Dataset: MNIST Database
+Frameworks: TensorFlow and Keras
+
+## 📧 Contact
+For any inquiries or contributions, feel free to reach out:
+
+Name: Alolika Bhowmik
+Email: [email protected]
+GitHub: alo7lika

Neural Networks/Handwritten Digit Recognition/README.md

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+# 🖊️ Handwritten Digit Recognition Project
+
+<div align="center">
+  <img src="https://raw.githubusercontent.com/alo7lika/ML-Nexus/refs/heads/main/Neural%20Networks/Handwritten%20Digit%20Recognition/DigitVision%20-%20Handwritten%20Digit%20Recognition%20(1).png" alt="CNN Model Architecture" width="600"/>
+</div>
+
+
+## 📚 Table of Contents
+1. [📖 Overview](#-overview)
+2. [🚀Problem Statement](#-problem-statement)
+3. [💡 Proposed Solution](#-proposed-solution)
+4. [⚙️ Alternatives Considered](#-alternatives-considered)
+5. [📊 Results](#-results)
+6. [🔍 Conclusion](#-conclusion)
+7. [📦 Installation & Usage](#-installation--usage)
+8. [🤝 Acknowledgments](#-acknowledgments)
+9. [📧 Contact](#-contact)
+
+## 📖 Overview
+The Handwritten Digit Recognition project aims to accurately identify and classify handwritten digits. This challenge is crucial for various applications, such as automated data entry and postal services. Leveraging the **MNIST dataset**, this project develops a robust Convolutional Neural Network (CNN) model using **TensorFlow** and **Keras**.
+
+## 🚀 Problem Statement
+Accurately recognizing handwritten digits is essential for many automated systems. The variability in handwriting styles presents a significant challenge for traditional recognition methods, leading to decreased accuracy. This project seeks to create a model that effectively learns to distinguish between different handwritten digits.
+
+## 💡 Proposed Solution
+### Key Components
+
+| Component            | Description                                                                                     |
+|----------------------|-------------------------------------------------------------------------------------------------|
+| **Data Preprocessing** | Normalize pixel values and reshape input data for CNN requirements.                           |
+| **CNN Architecture** | Utilize convolutional, pooling, and fully connected layers for feature extraction and classification. |
+| **Training Process** | Employ appropriate loss functions and optimization algorithms to enhance model performance.     |
+| **Evaluation Metrics**| Assess accuracy and loss on training and validation datasets for generalization.               |
+
+### 🏗️ CNN Architecture
+- **Convolutional Layers**: Extract features from images.
+- **Pooling Layers**: Reduce dimensionality while retaining important features.
+- **Fully Connected Layers**: Perform final classification.
+
+## ⚙️ Alternatives Considered
+Several alternative approaches were evaluated:
+
+| Alternative Approach           | Description                                                                             |
+|--------------------------------|-----------------------------------------------------------------------------------------|
+| **Traditional Machine Learning**| Algorithms like SVM and k-NN were considered; effective for smaller datasets but struggle with complexity. |
+
+## 📊 Results
+The model aims to achieve high accuracy in recognizing handwritten digits, providing an efficient tool for applications requiring digit recognition.
+
+## 🔍 Conclusion
+This project showcases the effectiveness of deep learning in image classification. The structured approach demonstrates the potential of neural networks in automating digit recognition across various industries.
+
+## 📦 Installation & Usage
+To get started, ensure you have Python and the necessary libraries installed:
+
+```bash
+pip install tensorflow numpy matplotlib
+```
+## 🤝 Acknowledgments
+Dataset: MNIST Database
+Frameworks: TensorFlow and Keras
+
+## 📧 Contact
+For any inquiries or contributions, feel free to reach out:
+
+Name: Alolika Bhowmik
+Email: [email protected]
+GitHub: alo7lika
+

Stock Price Prediction Project/README.md

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+<div align="center">
+
+##  📈 Stock Price Prediction Project
+
+![Stock Prediction Model](https://raw.githubusercontent.com/alo7lika/ML-Nexus/refs/heads/main/Stock%20Price%20Prediction%20Project/InvestWise%20-%20Stock%20Prediction%20Model.png)
+
+</div>
+
+# 📚 Table of Contents
+
+1. [Overview](#-overview)
+2. [Dataset](#-dataset)
+3. [Methodology](#-methodology)
+4. [Algorithms Used](#-algorithms-used)
+5. [Evaluation Metrics](#-evaluation-metrics)
+6. [Visualization](#-visualization)
+7. [Documentation](#-documentation)
+8. [Conclusion](#-conclusion)
+
+## 🚀 Overview
+This project aims to develop a **Stock Price Prediction Model** that assists investors in making informed decisions based on historical stock data. By leveraging machine learning techniques, this model provides insights into potential future trends, helping users identify buying or selling opportunities in the stock market.
+
+## 📊 Dataset
+The dataset used in this project contains historical stock prices and includes the following features:
+
+| Column Name | Description                                                  |
+|-------------|--------------------------------------------------------------|
+| **Ticker**  | Stock ticker symbol (e.g., AAPL for Apple)                  |
+| **Date**    | Date of the stock prices                                     |
+| **Open**    | Opening price on that date                                   |
+| **High**    | Highest price during the trading day                         |
+| **Low**     | Lowest price during the trading day                          |
+| **Close**   | Closing price at the end of the day                          |
+| **Adj Close**| Adjusted closing price (accounting for dividends and splits)|
+| **Volume**  | Number of shares traded                                      |
+
+## 🔍 Methodology
+The project follows a structured approach to stock price prediction:
+
+1. **Data Acquisition**: Historical stock prices are gathered from reliable sources.
+2. **Data Preprocessing**: The data is cleaned and organized, ensuring consistency and handling any missing values.
+3. **Feature Engineering**: Additional features, such as moving averages and technical indicators, are created to enhance the dataset.
+4. **Model Training**: Various regression models (e.g., Linear Regression, Decision Trees, Random Forest) are implemented and evaluated.
+5. **Model Evaluation**: The performance of each model is assessed using metrics like Mean Absolute Error (MAE).
+6. **Visualization**: Results are visualized to compare predicted vs. actual stock prices.
+7. **Deployment**: A user-friendly web application is developed for real-time predictions.
+
+## ⚙️ Algorithms Used
+| Algorithm                   | Description                                                  |
+|-----------------------------|--------------------------------------------------------------|
+| **Linear Regression**       | A basic regression technique to model the relationship between features and stock prices. |
+| **Decision Trees**          | A tree-like model used for making predictions based on feature values. |
+| **Random Forest**           | An ensemble method that combines multiple decision trees to improve accuracy. |
+
+## 📈 Evaluation Metrics
+The model's performance is evaluated using the following metrics:
+
+| Metric                     | Description                                                   |
+|----------------------------|---------------------------------------------------------------|
+| **Mean Absolute Error (MAE)** | Measures the average magnitude of errors in predictions. Lower values indicate better performance. |
+
+## 🎨 Visualization
+Visualizations are created to illustrate the model's predictions against actual stock prices, providing clear insights into performance and trends.
+
+## 📖 Documentation
+A comprehensive documentation section is provided, including:
+- Project Overview
+- Data Description
+- Modeling Approach
+- Evaluation Metrics
+- Results
+- Installation and Usage Instructions
+
+## 🌟 Conclusion
+This project addresses the challenges investors face in predicting stock prices by using a blend of data science and machine learning techniques. The resulting model not only showcases analytical skills but also offers a practical solution for investment decisions.
+
+## 📧 Contact
+For any inquiries or contributions, feel free to reach out:
+
+Name: Alolika Bhowmik
+Email: [email protected]
+GitHub: alo7lika
+
+---