This section contains projects that focus on building recommendation systems
Projects
- MovieLens Recommender+
- FoodieFinders
- Netflix Recommender System
MovieLens Recommender+
The project is a collaborative filtering-based movie recommender system that suggests personalized movie recommendations to users based on their past movie ratings. It uses the k-Nearest Neighbors (k-NN) algorithm in a user-based collaborative filtering approach. The web application allows users to input their user ID and receive top-10 movie recommendations tailored to their movie preferences. Users can explore the recommended movies' titles, genres, and movie posters. The project aims to provide an interactive platform for users to discover new and exciting movies that align with their interests.
FoodieFinders
The project is a content-based restaurant recommender system that suggests restaurants to users based on similarity in features like cuisine type, state, and city. It utilizes a similarity matrix to find and recommend restaurants similar to the user's selection. The system is implemented using Streamlit, allowing users to interact, choose a restaurant, and receive personalized recommendations. Users can also view restaurant details, including location, reviews, and contact information.
Netflix Recommender System
The main objective of this project is to create a recommendation engine
to recommend similar movies to users.
This dataset consists of listings of all the movies and tv shows
available on Netflix, along with details such as - cast, directors,
ratings, release year, duration, etc. as of mid-2021
Click
Here
to interact with deployment site.
Some libraries used in this project includes:
- Pandas
- Numpy
- Scikit-learn
- NeatText
- Streamlit
About Me
Hello, I'm Emuejevoke Eshemitan, a passionate data professional specializing in Machine
Learning Engineering and Data Science. My portfolio showcases projects reflecting my
dedication to leveraging data for insightful and innovative solutions.
As a Machine Learning Engineer, I excel in designing cutting-edge models using TensorFlow and
scikit-learn, building scalable data pipelines, and deploying them in production
environments.
As a Data Scientist, I analyze valuable patterns, develop predictive
models, and communicate insights through impactful visualizations.
Let's collaborate on a data-driven journey! Explore my portfolio for more.