Welcome to the AI Practice Codes Repository – a comprehensive collection of AI mini-projects, notebooks, and hands-on implementations. This repo serves as a portfolio and reference for anyone learning or revising fundamental and advanced concepts in artificial intelligence, machine learning, and data science.

trahulsingh-ai-practice-codes/
│
├── 8puzzle/                      # 8-puzzle solver using GBFS and state space
├── A STAR/                       # A* search algorithm and Jupyter notebook
├── classifierss/                # Classifier experiments (MLP, Decision Tree)
├── decisiontree/                # Decision Trees on various datasets
├── gamebot tic tac toe/         # AI-powered Tic Tac Toe game
├── hangman game/                # Python-based hangman game
├── Medical Expert System/       # Expert system for diagnosing diseases
├── NLP/                         # Natural Language Processing notebooks
├── nqueens/                     # N-Queens problem solver
├── numpy/                       # NumPy ML demos
├── pandas/                      # Pandas exercises and datasets
├── prolog/                      # Prolog programs for logic-based AI
├── pythonbasics/               # Basic Python & AI lab practice
├── textclassification/         # Text classification example
├── uninformed search/          # BFS & DFS algorithms
├── waterjug code/              # Water jug problem implementations
│
├── README.md
├── requirements.txt

• 🔍 Search Algorithms – BFS, DFS, GBFS, A* implemented from scratch
• 🤖 AI Games – Tic Tac Toe with logic-based AI, Hangman game
• 🧠 Expert Systems – Rule-based system for medical diagnosis
• 🌿 ML Classifiers – Decision Trees, MLP, and SMOTE balancing
• 🧮 Math & Data Tools – NumPy, Pandas, basic ML operations
• 🧾 Prolog Demos – Classic logic programming problems
• 🗣️ NLP Basics – spaCy and text classification notebooks

1. Clone the Repository

git clone https://github.com/your-username/trahulsingh-ai-practice-codes.git
cd trahulsingh-ai-practice-codes

2. Install Dependencies

pip install -r requirements.txt

3. Run any module or Jupyter Notebook

jupyter notebook
# or run .py files as:
python path/to/file.py

ℹ️ Some folders (e.g., Medical Expert System/) have their own requirements.txt — make sure to install them before use.

• AI/ML students & hobbyists
• Academic lab submissions
• Revision and interviews
• Learning foundational AI programming

• 👨‍💻 T RAHUL SINGH

Pull requests and suggestions are welcome!

This project is licensed under the MIT License.

If you found this helpful, leave a ⭐ on GitHub!