Feature selection is widely used in nearly all data science pipelines. Hence I have created functions that do a form of backward stepwise selection based on the XGBoost classifier feature importance and a set of other input values with the goal to return the number of features to keep in regard to a prefered AUC-score.

A code-first way to define Ploomber pipelines

🌀 #11. "Machine Learning Operations (MLOps) - NLP"

Machine Learning Operations - Disaster Tweets Classification

apply machine learning backup

In this project I'm using machine learning Pipeline which is then made into a Flask Application which is then dockerized using docker and then the docker image is deployed on Amazon-Web-Services, Elastic Beanstalk.

My personal notes, code and projects of the Udacity Data Science Nanodegree.

Machine Learning pipelines are deployed to accomplish the objective of credit risk analysis.

data fetched by wafers (thin slices of semiconductors) is to be passed through the machine learning pipeline and it is to be determined whether the wafer at hand is faulty or not. Wafers are predominantly used to manufacture solar cells and are located at remote locations in bulk and they themselves consist of few hundreds of sensors.

This repository contains project files for a Flask app that classifies disaster messages into relevant categories.

Implementation of Various Machine Learning(Supervised and Unsupervised) Algorithms

Work with a set of Tweets about US airlines and examine their sentiment polarity.The aim is to learn to classify Tweets as either “positive”, “neutral”, or “negative” by using two classifiers and pipelines for pre-processing and model building.

During disaster events, sending messages to appropriate disaster relief agencies on a timely manner is critical. Using natural language processing and machine learning, I built a model for an API that classifies disaster messages and also a webapp for emergency works.

This Project is in collaboration with Figure Eight. The dataset contains pre-labelled tweets and messages from real-life disaster events. The project aim is to build a Natural Language Processing (NLP) model to categorize messages on a real time basis.

ML AutoTrainer Engine, developed using Streamlit, is an advanced app designed to automate the machine learning workflow. It provides a user-friendly platform for data processing, model training, and prediction, enabling a seamless, code-free interaction for machine learning tasks.

Data Engineering Project of Udacity Data Scientist Nanodegree

A NLP/Machine Learning Pipeline to detect text messages from people of need

Although PdM revolves around detecting anomalies and possible defects before they happen, here, however, based on befitting predictors and machine failures, this project attempts to predict Air Temperature that in actuality is generated using random walk process, via a regression algorithm.