- On Render
- On PythonAnywhere
Welcome to our website, Learning Gamified where we offer interactive games for students of all ages. Our website is divided into two sections : one for school children and another for college students. Our games are interactive and designed to make learning fun and engaging. They are accessible to students of all levels, from beginners to advanced learners. Our website is user-friendly and also very easy to navigate, making it easy for students to find the games they need to enhance their learning experience. We hope you enjoy using our website and learning through our interactive games.
- HTML: For the structure of the website
- CSS: For the styling of the website
- JavaScript: For the interactive elements of the website
- Python & Flask: For the backend of the website involving routing and machine learning model training and prediction
- Git & GitHub: For version control and collaboration via pull requests
- PythonAnywhere: For hosting the website
The school children's section includes fun and engaging games such as Simon game, card-flip game and a math game. Each of these games are specially designed to help children improve their cognitive skills memory and problem solving skills while having fun at the same time. The Simon game is a memory game where the player must repeat a pattern of colored lights and sounds. The card flip game involves flipping cards and matching pairs to improve memory skills. The math game is a fun and interactive way for children to improve their math skills, including addition, subtraction, multiplication, and division.
In the college section, we offer more advanced things like RSA encryption, Morse code encryption and machine learning. These games are aimed at college students who want to learn and improve their technical skills. Encryption is a section that teaches students how to encrypt and decrypt messages using the RSA and the Morse code algorithm. Machine learning involve training algorithms to recognize patterns and make predictions based on data. We use our custom-trained ML Image Classification Model to predict the name of a wild cat based on an image of the cat. The model is trained on a dataset of 10 different wild cat species. The model is integrated into the website and the user can upload an image of a wild cat and get the name of the cat as the output.