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Encryptix-Projects

This repository contains three AI and machine learning projects completed during the internship at Encryptix: Face Detection and Recognition System, Netflix Recommendation System, and Tic-Tac-Toe. Each project includes data preprocessing, model building, and evaluation, using OpenCV, deep learning, collaborative filtering, and game AI algorithms.

#artificial-intelligence#face-detection#machine-learning#netflix-recommendation-system#tic-tac-toe

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Encryptix Internship Projects

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

Face Detection and Recognition System
Netflix Recommendation System
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

Clone the repository:

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

Navigate to the project directory:
```
cd FACE_DETECT/Face_detection
```
Install the required dependencies:
```
pip install -r requirements.txt
```

Usage

Ensure you have a camera connected to your system.
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.

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

Clone the repository:

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

Navigate to the project directory:
```
cd Netflix-Recommenders-System
```
Install the required dependencies

Usage

Prepare your dataset or use the provided sample dataset.
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.

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

Clone the repository:

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

Navigate to the project directory:
```
cd TICTACTOE/Tic-Tac-Toe-AI
```

Usage

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.

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.