satbilla Goto Github PK

adjusted-r-squared

The adjusted R-squared increases only if the new term improves the model more than would be expected by chance.

anomaly-detection-using-time-series

Anomaly Detection using Time Series

aws-glue-samples

AWS Glue code samples

content-github-actions-deep-dive-lesson

Lesson repository for Github Actions Deep Dive (1074)

employee-over-time-prediction

A manufacturing unit is consistently facing project over-time to finish. This is usually a sign for poor project planning or resource allocation. Now the company has decided smartly allocate the resource based on ML models. One indicator they have about project going out-of-budget is number of employees working overtime. If a good predictive model can help them predict which employees are expected to do overtime, then they can balance the resource allocation accordingly.

genetic-algorithm---portfolio-allocation

Calculate stock returns from closing value of each stock for every month

hate-speech-classification

Text Multi Label Classification

hello-world

hello world

hello-world-1

intro-to-cloudformation_ac

An Introduction to CloudFormation taking students through constructing templates and the use of advanced functionality

power-bi-course-resources

simple-devops-project

time-series

Predict the future price of a product using Time Series.

titanic

The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships. One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class. In this challenge, we ask you to complete the analysis of what sorts of people were likely to survive. In particular, we ask you to apply the tools of machine learning to predict which passengers survived the tragedy.