This chatbot utilizes natural language processing to engage in meaningful conversations with users. It can respond to various queries, provide information, and assist users in finding the answers they need.
- User-friendly interface for interaction.
- Ability to understand and respond to a variety of questions.
- Continuous learning to improve responses over time.
- Python
- Natural Language Processing libraries (e.g., NLTK, SpaCy)
- Clone the repository.
- Install required libraries using
pip install -r requirements.txt. - Run the chatbot script.
This project is a console-based implementation of the classic Tic-Tac-Toe game, allowing a human player to compete against an AI opponent using the Minimax algorithm.
- Play against an AI that uses the Minimax algorithm for optimal moves.
- Simple and clear console interface.
- Game outcomes: Win, Lose, or Draw.
- Python
- Clone the repository.
- Run the Tic-Tac-Toe script in a Python environment.
- Follow the on-screen instructions to play.
This movie recommendation system uses collaborative filtering to suggest movies to users based on their preferences and ratings. It fetches data from The Movie Database (TMDb) API to provide up-to-date movie information.
- Fetch popular movies from TMDb API.
- Recommend movies based on user ratings and preferences.
- Display detailed information about recommended movies.
- Python
- Pandas for data manipulation
- Scikit-learn for implementing the recommendation algorithm
- Requests for API calls
- Clone the repository.
- Install required libraries using
pip install -r requirements.txt. - Obtain a TMDb API key and replace it in the script.
- Run the movie recommendation script and follow the prompts.
Each project demonstrates fundamental programming concepts and provides practical implementations of algorithms and data manipulation techniques. Explore each project to learn more about their functionalities and technologies!