Demo ML repository, using Algorithmia Model Deployment Github Action, to auto deploy on an Algorithmia algorithm backed by Github

Example repository demonstrating a CI workflow between Github and Algorithmia

Deployment of ML projects using Streamlit

Breast Cancer prediction app using KNN and SVC algorithm.

In this project we are building a system that can predict weather a person has diabetes or not with the help of Machine Learning.

Create, deploy and interact with a Machine Learning model in real time. Project involves training a Logistics Regression model for a binary classification task and deploying the trained model in a webserver to obtain real-time predictions via rest API using a simple web page

Tech Challenge of the Postgraduate in Data Analytics, from FIAP, analyzing Brent Oil price data, in comparison with historical, economic and societal data, integrating correlation and causality analyzes of items with prices, as well as developing a model forecast and an importance analysis through information gain from a forest model (XGBoost)

Hello World Project in Real-Time NLP deployments.

Exploratory data analysis, data modelling, model building and interpretation, machine learning production, quality assurance

I did exploratory data analysis on scraped job listings from Glassdoor, and built a salary prediction model deployed as a client-facing forecast tool.

Predict the genre of a given movie description.

This is SMS spam detection app deployed in Heroku

Successfully established a machine learning model which can predict whether any given customer currently utilizing the products and services offered by a company will churn at anytime in the future or not, depending upon a set of unique features/characteristics pertaining to that specific individual, to a great level of accuracy.

The aim of this project is to classify people’s emotions based on their face images

ML model deployment using IRIS dataset

Model deployment project using Heroku and FastAPI for the Udacity MLOps Nanodegree

E-Commerce Customer Churn Prediction using Machine Learning

An ML monitoring framework, applied to an attrition risk assessment system.

How to train and deploy a Weka model.