Encryptix Internship Projects

This repository contains three distinct projects demonstrating various applications of AI and machine learning techniques:

1. Face Detection and Recognition System
2. Netflix Recommendation System
3. Tic-Tac-Toe AI

Each project includes its own set of files, dependencies, and instructions for running the code. Detailed explanations and instructions for each project are provided below.

Project 1: Face Detection and Recognition System

Description

Implemented a face detection and recognition system utilizing OpenCV and deep learning techniques for accurate and efficient facial recognition.

Features

• Face detection using OpenCV.
• Face recognition using deep learning models.
• Real-time processing capability.

Installation

1. Clone the repository:

   https://github.com/Shreyaprasad21/Encryptix-Projects.git

2. Navigate to the project directory:

   cd FACE_DETECT/Face_detection

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

1. Ensure you have a camera connected to your system.
2. Run the face detection and recognition script:

   python -m streamlit run Streamlit_cam.py

Dependencies

• OpenCV
• TensorFlow/Keras
• NumPy

Files

• Streamlit_cam.py: Main script for face detection and recognition.
• requirements.txt: List of required Python packages.

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Project 2: Netflix Recommendation System

Description

Built a Netflix recommendation system using collaborative filtering techniques to suggest movies based on user preferences.

Features

• Collaborative filtering for personalized movie recommendations.
• Data preprocessing and feature extraction.
• Evaluation of recommendation accuracy.

Installation

1. Clone the repository:

   https://github.com/Shreyaprasad21/Encryptix-Projects.git

2. Navigate to the project directory:

   cd Netflix-Recommenders-System

3. Install the required dependencies

Usage

1. Prepare your dataset or use the provided sample dataset.
2. Run the recommendation system script:

   Recommender-system-project.ipynb

Dependencies

• pandas
• NumPy
• scikit-learn
• surprise

Files

• Recommender-system-project.ipynb: Main script for generating movie recommendations.
• requirements.txt: List of required Python packages.
• dataset: Directory for storing datasets.

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Project 3: Tic-Tac-Toe AI

Description

Developed an AI-driven Tic-Tac-Toe game leveraging alpha-beta pruning and minimax algorithms, achieving up to 50% faster decision-making.

Features

• Intelligent AI opponent using minimax algorithm with alpha-beta pruning.
• Command-line interface for gameplay.
• Efficient and optimized decision-making process.

Installation

1. Clone the repository:

   https://github.com/Shreyaprasad21/Encryptix-Projects.git

2. Navigate to the project directory:

   cd TICTACTOE/Tic-Tac-Toe-AI

Usage

1. Run the Tic-Tac-Toe game script:

   Tic-Tac-Toe-AI.py

Dependencies

• None (standard Python library)

Files

• Tic-Tac-Toe-AI.py: Main script for the Tic-Tac-Toe game.

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Contributing

Contributions are welcome! Please fork the repository and submit a pull request for any enhancements or bug fixes.

License

This repository is licensed under the MIT License. See the LICENSE file for more information.