Projects Portfolio

This repository is a collection of independent Python and web projects covering computer vision, machine learning, game development, finance tracking, and data analysis. Each folder is a separate mini-project with its own dependencies and setup instructions, while this root README serves as a central index for the workspace.

Overview

The repository includes:

• Automatic Number Plate Recognition (ANPR): license plate detection and extraction using OpenCV and image processing techniques.
• Chess Game: a Python chess game with a graphical interface and AI opponent.
• Django Finance Tracker: a Django app for tracking transactions, budgets, and goals.
• FaceRecognition_PDC: face detection project with a Streamlit GUI and parallel-processing comparison.
• File Manager (Windows & Linux): a simple cross-platform file management utility.
• Image Classification Model: deep learning image classifier using PyTorch and Flask.
• LexiCraft Pro - NLP: natural language processing notebook/project.
• Stock Trend Prediction: stock market forecasting prototype using Keras and time-series modeling.

Repository Structure

Projects/
├── Automatic Number Plate Recognition/
├── Chess Game/
├── Django Finance Tracker/
├── FaceRecognition_PDC/
├── File Manager (Windows & Linux)/
├── Image Classification Model/
├── LexiCraft_Pro - NLP/
├── Stock-Trend-Prediction/
├── LICENSE
├── README.md
└── .gitignore

Project Summary

Project	Focus	Stack
Automatic Number Plate Recognition	Detect and extract vehicle number plates	Python, OpenCV, Tkinter
Chess Game	Playable chess game with AI	Python, Pygame
Django Finance Tracker	Personal finance dashboard	Django, SQLite
FaceRecognition_PDC	Face recognition and parallel computation demo	Python, OpenCV, Streamlit
File Manager (Windows & Linux)	File navigation and management	Python
Image Classification Model	CNN-based image recognition	Python, PyTorch, Flask
LexiCraft Pro - NLP	NLP research / notebook project	Python, ML / NLP
Stock-Trend-Prediction	Predictive analytics for stock trends	Python, Keras, TensorFlow

Getting Started

Prerequisites

• Python 3.8+
• pip
• A virtual environment is recommended for each project

Recommended workflow

1. Clone the repository.
2. Open the project folder you want to run.
3. Create and activate a virtual environment.
4. Install requirements from that project's requirements.txt.
5. Follow the instructions in that project's own README.

Example:

git clone <repository-url>
cd "Projects"
cd "Automatic Number Plate Recognition"
python -m venv venv
source venv/bin/activate   # Windows: venv\Scripts\activate
pip install -r requirements.txt
python ANPR-App.py

Project-specific Quick Starts

1. Automatic Number Plate Recognition

cd "Automatic Number Plate Recognition"
pip install -r requirements.txt
python ANPR-App.py

2. Chess Game

cd "Chess Game"
pip install -r requirements.txt
python app.py

3. Django Finance Tracker

cd "Django Finance Tracker"
pip install -r requirements.txt
python manage.py migrate
python manage.py runserver

4. FaceRecognition_PDC

cd "FaceRecognition_PDC"
pip install -r requirements.txt
python main.py

5. Image Classification Model

cd "Image Classification Model"
pip install -r requirements.txt
cd web
python serve_model.py

6. Stock-Trend-Prediction

cd "Stock-Trend-Prediction"
pip install -r requirements.txt
python app.py

Notes

• This repo is not a single app; it is a portfolio of separate projects.
• Each subdirectory generally contains its own README, requirements file, and environment-specific setup instructions.
• Some projects are notebooks, prototypes, or coursework assignments; others are complete applications.

License

This repository is licensed under the MIT License. See LICENSE for details.

Contributing

Contributions, improvements, and bug fixes are welcome. If you want to contribute:

1. Fork the repository.
2. Create a feature branch.
3. Make your changes.
4. Open a pull request with a clear description.

Final Note

This workspace is best used as a project archive and learning portfolio. Each project can be explored independently, and many of them are ideal for extension, experimentation, or deployment-based coursework.