Movie-Recommender

Introduction

In this project, we developed a powerful movie recommender system that combines content-based and collaborative filtering techniques. Recommender systems are crucial in today’s digital world, enabling users to discover personalized and relevant content based on their preferences and history. Leading platforms like Netflix, Amazon Prime, and Disney utilize sophisticated algorithms to recommend movies to their users, enhancing the user experience.

Objective

Our primary goal in this project was to create a recommendation algorithm capable of accurately predicting how users would rate movies they haven’t seen yet, based on their historical preferences. To achieve this, we harnessed the strength of content-based filtering, which suggests items similar to those users have enjoyed previously, and collaborative filtering, which recommends items based on the preferences of similar users.

My Role

I had the privilege of leading a dedicated team of six members through this project, where we built a comprehensive movie recommender system. My contributions to this project include:

• Coordinated project management efforts to ensure a smooth workflow.
• Conducted in-depth and business-relevant exploratory data analysis (EDA) to gain valuable insights.
• Provided expert support in model building and evaluation.
• Guided and contributed to the creation of a user-friendly Streamlit app.
• Successfully managed cloud deployment via AWS for efficient system performance.

Project Demo

Here is a video demonstration showcasing our project in action.

Conclusion

Our movie recommender system project has successfully harnessed the power of content-based and collaborative filtering techniques to deliver accurate movie recommendations. Through extensive exploratory data analysis, model building, and cloud deployment, our team has achieved a top-three ranking on the leaderboard. This project not only showcases the potential of data-driven recommendations but also underscores the capacity of recommender systems to drive business growth, enhance profits, and boost customer satisfaction. Our efforts in creating this recommendation algorithm serve as a testament to the impact of data science in shaping modern user experiences.