This repository outlines a framework for building an anomaly detection algorithm and deploying into a web app

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.

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

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.

A basic classification model based on Random Forest Classifier predicting the Titanic Disaster Survival for a set of test data. Data Structure provided by Kaggle.

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

Machine Learning application of a of predicting housing values using regression task utilizing the SciKit-Learn extensions; the pipeline has various algorithms such as Linear Regression, Decision Trees, and Random Forests.

Implementation of Various Machine Learning(Supervised and Unsupervised) Algorithms

This repository shows the implementation of machine learning algorithms, data pipelines and data visualization with scikit-learn and python.

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.

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.

Final Project Submission: Build a Machine Learning Pipeline for Airfoil Noise Prediction

Creating a Machine Learning Pipeline to build and evaluate multiple models, using Python3

Machine Learning Tool to categorize messages that have been send after a disaster