You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
This project focuses on building end-to-end machine learning pipeline using AWS SageMaker to predict the price range of mobile phones based on their specifications, enhancing consumer decision-making and streamlining the development process.
A deployed machine learning model that has the capability to automatically classify the incoming disaster messages into related 36 categories. Project developed as a part of Udacity's Data Science Nanodegree program.
Collaborative team machine learning project classifying reviews scraped from the IMDB website as either positive or negative using sentiment classification. Tools used: BeautifulSoup and Splinter to scrape reviews, Pyspark, SQLAlchemy and Heroku.
In this project, I developed a completed Vertex and Kubeflow pipelines SDK to build and deploy an AutoML / BigQuery ML regression model for online predictions. Using this ML Pipeline, I was able to develop, deploy, and manage the production ML lifecycle efficiently and reliably.
Big data application of Machine Learning concepts for sentiment classification of US Airlines tweets. The focus is on the usage of pyspark libraries (ml-lib) on big data to solve a problem using Machine Learning algorithms and not about the choice of algorithm used in the ML model creation. It also involves data pre-processing using NLP techniqu…
This Project is a part of Data Science Nanodegree Program by Udacity in collaboration with Figure Eight. The initial dataset contains pre-labelled tweet and messages from real-life disasters. The aim of this project is to build a Natural Language Processing tool that categorize messages.