Pinned Repositories
Movie-Recommender-System
Movie Recommender System leverages a content-based approach, suggesting films to users based on the attributes of movies they have previously enjoyed. By analyzing movie metadata such as genre, cast, director, keywords, etc., this project offers personalized recommendations aligned with users' cinematic tastes.
ns-nexus
This repository offers a glimpse into my interests, skills, and everything in between.
Rain-Prediction-in-Australia
We are going to use the different classification algorithms to create a model to predict rain in Australia. This project was done as a part of the Honors portion of the IBM Machine Learning Course on Coursera.
Sentiment-Analysis-on-Amazon-Reviews
This project aims to analyze sentiment from Amazon reviews using natural language processing (NLP) techniques. By leveraging machine learning algorithms, we classify reviews as positive, negative, or neutral, providing insights into customer satisfaction and product feedback.
ns-nexus's Repositories
ns-nexus/Movie-Recommender-System
Movie Recommender System leverages a content-based approach, suggesting films to users based on the attributes of movies they have previously enjoyed. By analyzing movie metadata such as genre, cast, director, keywords, etc., this project offers personalized recommendations aligned with users' cinematic tastes.
ns-nexus/ns-nexus
This repository offers a glimpse into my interests, skills, and everything in between.
ns-nexus/Rain-Prediction-in-Australia
We are going to use the different classification algorithms to create a model to predict rain in Australia. This project was done as a part of the Honors portion of the IBM Machine Learning Course on Coursera.
ns-nexus/Sentiment-Analysis-on-Amazon-Reviews
This project aims to analyze sentiment from Amazon reviews using natural language processing (NLP) techniques. By leveraging machine learning algorithms, we classify reviews as positive, negative, or neutral, providing insights into customer satisfaction and product feedback.