Survival Analysis Collection

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🔮Psychic

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“Survival analysis in Python”

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📦Normal

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“Machine Learning in R ”

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🔮Psychic

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“Survival analysis built on top of scikit-learn”

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🔮Psychic

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“Survival analysis with PyTorch”

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🔮Psychic

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“Reliability engineering toolkit for Python - https://reliability.readthedocs.io/en/latest/”

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🔥Flame

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“Open source package for Survival Analysis modeling”

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🔮Psychic

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“Auton Survival - an open source package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Events ”

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🔮Psychic

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“Improving XGBoost survival analysis with embeddings and debiased estimators”

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🔮Psychic

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“A unified framework for tabular probabilistic regression, time-to-event prediction, and probability distributions in python”

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📦Normal

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“In this project, I have utilized survival analysis models to see how the likelihood of the customer churn changes over time and to calculate customer LTV. I have also implemented the Random Forest model to predict if a customer is going to churn and deployed a model using the flask web app.”

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📦Normal

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“This repository contains morden baysian statistics and deep learning based research articles , software for survival analysis”

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🔮Psychic

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“Deep survival analysis made easy ”

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📦Normal

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“Code that might be useful to others for learning/demonstration purposes, specifically along the lines of modeling and various algorithms. **Superseded by the models-by-example repo**.”

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🔮Psychic

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“A system for automating the design of predictive modeling pipelines tailored for clinical prognosis.”

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📦Normal

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“GPstuff - Gaussian process models for Bayesian analysis”

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📦Normal

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“Datasets for Predictive Maintenance”

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📦Normal

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“Probabilistic Learning for mlr3”

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🔮Psychic

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“Deep Recurrent Survival Analysis, an auto-regressive deep model for time-to-event data analysis with censorship handling. An implementation of our AAAI 2019 paper and a benchmark for several (Python) implemented survival analysis methods.”