Movie Recommender is a website that was developed mainly to help users decide on a movie to watch.
TEAM MEMBERS
AL-MUKHTAR JAMILAT LADIDI - FULLSTACK DEVELOPER
Our project was designed for movie lovers who have difficulties deciding what to watch by giving a personalized experience where users can be sure that they can view the top 100 movies of all time knowing that they are good movies, users can also find movies similar to ones that they liked.
As an engineering problem, we wanted to focus on 2 things: Creating an experience that would be both web and mobile-friendly, and also sourcing a reliable and sophisticated database.
I am a movie lover, so when my friend who is a machine learning engineer proposed the idea it was an easy project purpose to agree upon. I love to see a good movie in my leisure time, it helps me unwind, yet one challenge was prevalent, spending a long time trying to find the right movie. Eight months ago when I made this decision to be a software engineer I was desperately looking to find a new purpose so when I was presented with the opportunity to study software engineering at ALX courtesy of the HOLBERTON School, I knew that I had to do all that it took me to learn and give it the attention it required of me.
For the Frontend React was used and for the back-end Express was used.
The MERN stack (MongoDB, Express.js, React, and Node.js) can be a suitable choice for building a movie recommendation system due to its several advantages:
Flexibility: The MERN stack is a flexible and modular framework that allows developers to easily customize and extend the system based on specific requirements. This flexibility can be beneficial when building a recommendation system as it allows for easy integration of different machine learning algorithms and techniques.
Scalability: The MERN stack is built on top of Node.js, which is known for its ability to handle large volumes of traffic and requests. This makes it a suitable choice for building a recommendation system that can handle a large number of users and provide personalized recommendations.
Real-time updates: The MERN stack is designed for real-time applications, which can be important for a recommendation system that requires constant updates and changes based on user feedback and preferences.
Full-stack development: The MERN stack provides developers with a comprehensive set of tools and libraries for both front-end and back-end development, allowing for faster and more efficient development of the system.
Modular design: The MERN stack's modular design allows for easy separation of concerns and better organization of the codebase, making it easier to maintain and update the system.
The website is responsive and compatible with mobile. This means users can access the project from a mobile view!
The biggest challenge was sourcing and finding the right database that had sufficient movies. It was also intensive to come up with the right logic for the recommendation.
This project has helped me become a better problem solver and also additionally piqued my interest in software engineering.
I am a graduate of agriculture, so transitioning and learning something new has been very tasking, however daily, with each project I do or task that I solve, I'm reassured of my interest. I am goal driven and highly committed to ensuring my dreams come to fruition.
https://github.com/Didimukhtar/movie-recommendation-system
https://movierecommender-landingpage.onrender.com/
https://movie-recommender-rn4b.onrender.com/
http://linkedin.com/in/jamilat-al-mukhtar-b0a2551b5
This project would have been impossible without the guidance and input of;
AGBAJE HABEEB - Machine learning Engineer
CHUKWUDE OKECHUKWU - Fullstack Developer