# SciPy SciPy is an open-source Python library providing algorithms and data structures for scientific computing. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## PySurvival: An Open Source Python Package for Survival Analysis Modeling DevFeed: [PySurvival: An Open Source Python Package for Survival Analysis Modeling]() Original publisher: [Read original article]() Author: Stephane Fotso Published: 2019-04-12T21:06:21Z Content type: article Language: en Sources: [Square Corner Blog](), [Square Corner Blog RSS Feed]() Topics: [Python](), [Open Source](), [NumPy](), [PyTorch](), [SciPy](), [Data analysis]() Tags: [analysis](), [data-analysis](), [data-science](), [machine-learning](), [open-source](), [python](), [pytorch](), [validation]() ### AI overview The article introduces PySurvival, an open source Python package for survival analysis modeling. It describes supported models, theory summaries, API examples, tutorials, performance metrics, and model loading and saving. ### Source excerpt PySurvival is an open source python package for Survival Analysis modeling. ## Introducing PySurvival DevFeed: [Introducing PySurvival]() Original publisher: [Read original article]() Author: sfotso Published: 2019-04-12T21:06:21Z Content type: article Language: en Sources: [Square Corner Blog]() Topics: [Python](), [Data analysis](), [NumPy](), [PyTorch](), [SciPy](), [Open Source]() Tags: [api](), [article](), [data-analysis](), [data-science](), [deep-learning](), [machine-learning](), [open-source](), [performance-metrics](), [python](), [pytorch](), [survival-analysis](), [tutorials]() ### AI overview Square introduces PySurvival, an open source Python package for survival analysis modeling. The article describes its models, theory summaries, API documentation, examples, tutorials, performance metrics, and model loading and saving features. ### Source excerpt Heads up, we've moved! If you'd like to continue keeping up with the latest technical content from Square please visit us at our new home https://developer.squareup.com/blogPySurvival is an open source python package for Survival Analysis modeling. Today, we're excited to introduce PySurvival, a python package for Survival Analysis modeling. This article is the first installment in a four part series, which will include tutorials designed to demonstrate how to easily make the most of the package. You can also find these tutorials on the official website: Churn modeling Predictive maintenance Credit risk PySurvival LogoWhat is PySurvival ? PySurvival is an open source python package for Survival Analysis modeling -- the modeling concept used to analyze or predict when an event is likely to happen. It is built on top the most commonly used machine learning packages: NumPy, SciPy, and PyTorch. PySurvival provides a very easy way to navigate between theoretical knowledge on Survival Analysis and detailed tutorials on how to conduct a full analysis, as well as build and use a model. The package contains: 10+ models ranging from the Cox Proportional Hazard model and the Neural Multi-Task Logistic Regression, to Random Survival Forest Summaries of the theory behind each model as well as API descriptions and examples Detailed tutorials on how to perform exploratory data analysis, survival modeling, cross-validation and prediction, for churn modeling and credit risk, for example Performance metrics to assess the models' abilities like c-index or brier score Simple ways to load and save models Installation If you have already installed a working version of gcc, the easiest way to install Pysurvival is using pip. pip install pysurvival The complete installation steps can be found here. Introduction to Survival analysisWhat is Survival Analysis ? Survival analysis is used to analyze or predict when an event is likely to happen. It originated in medical research, but its use has ## RealityMining, a Case Study in the Woes of Data Processing DevFeed: [RealityMining, a Case Study in the Woes of Data Processing]() Original publisher: [Read original article]() Published: 2014-01-21T22:11:49Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [data-processing](), [Python](), [dataset](), [SciPy](), [MATLAB](), [NumPy]() Tags: [array](), [big-data](), [data-analysis](), [data-mining](), [data-processing](), [dataset](), [how-to](), [library](), [matlab](), [python](), [reality-mining](), [research](), [social-networks](), [tutorial]() ### AI overview A tutorial and critique of accessing the RealityMining smartphone dataset with Python. It explains the dataset's communication, cell-tower, survey, and friendship data; the access process; the proprietary Matlab file format; and difficulties loading and inspecting the data with SciPy and NumPy. ### Source excerpt This post is intended to be a tutorial on how to access the RealityMining dataset using Python (because who likes Matlab?), and a rant on how annoying the process was to figure out. RealityMining is a dataset of smart-phone data logs from a group of about one hundred MIT students over the course of a year. The data includes communication and cell tower data, the latter being recorded every time a signal changes from one tower to the next.