Stay Alive. A Reliable and Interpretable Survival Analysis Library

Data analysis workflow kit for AI coding agents. Structure your analysis with the ALIVE loop — Ask, Look, Investigate, Voice, Evolve.

Mai is an emotionally intelligent, voice-enabled AI assistant built with FastAPI, Together.ai LLMs, memory persistence via ChromaDB, and real-time sentiment analysis. Designed to feel alive, empathetic, and human-like, Mai blends the charm of a flirty cyberpunk companion with the power of modern multimodal AI.

Data analysis project using python , based on the information that is collected and exploring it for any insights. Data handling, data analysis, data visualization, valuation of the data. Analyzing literacy rate of the cities and further state. Top 10 cities with most number of literates alive. Male and female literacy rates. Analyzing effective literacy rate, graduates. Correlation between urbanization and literacy. Sex ratio Which states have most of its male population in urban areas. Which states have most of its younger population in urban areas. Top 10 States. Problems solved : Correlation between urban and rural areas Education % in areas Male and female education Urbanization effects Tools & skills : Jupyter , IPython, Spyder Numpy, Pandas, Matplotlib, Statsmodel, Scipy

Nadia's birds come alive! Explore 58,000+ sightings of 382 species - by month, location, and more. Discover birding hotspots like Jaguli & Hijuli, plus frequent flyers like Spotted Doves & Red-vented Bulbuls. Dive in with interactive visuals & data analysis.

ip_mapper is a Shell-based tool designed to resolve hosts, identify which hosts are currently alive, and efficiently export all domain IP addresses as well as the resolved hosts. Its main purpose is to streamline the process of host enumeration and IP mapping for network analysis or penetration testing tasks.

Our analysis will use data from the UK Met Office together with records from the Global Biodiversity Information Facility to build our very own species distribution model using machine learning. This model will be able to predict where our bird species of interest is likely to occur in the future - information that is invaluable to conservation organization working on the ground to preserve these species and save them from extinction! In this notebook, we will model the Scottish crossbill (Loxia scotica). The Scottish crossbills is a small bird that inhabits the cool Scottish forests and feeds on pine seeds. Only ~ 20,000 individuals of this species are alive today. The code and the data sources in this project can be reapplied to any other species we may be interested in studying.

TCP tool to measure FIN packet timing for upstream keep-alive analysis

Analysis based on people who are alive and those who have died of a heart attack!

Analysis and Visualization of Missing Person Data Data Overview The dataset contains information about missing persons, including their case number, name, missing date, whether they were found alive or dead, image availability, gender, age, and the last seen location.

In statistics, the logistic model (or logit model) is a widely used statistical model that, in its basic form, uses a logistic function to model a binary dependent variable; many more complex extensions exist. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic model; it is a form of binomial regression. Mathematically, a binary logistic model has a dependent variable with two possible values, such as pass/fail, win/lose, alive/dead or healthy/sick; these are represented by an indicator variable, where the two values are labeled "0" and "1". In the logistic model, the log-odds (the logarithm of the odds) for the value labeled "1" is a linear combination of one or more independent variables ("predictors"); the independent variables can each be a binary variable (two classes, coded by an indicator variable) or a continuous variable (any real value). The corresponding probability of the value labeled "1" can vary between 0 (certainly the value "0") and 1 (certainly the value "1"), hence the labeling; the function that converts log-odds to probability is the logistic function, hence the name. The unit of measurement for the log-odds scale is called a logit, from logistic unit, hence the alternative names. Analogous models with a different sigmoid function instead of the logistic function can also be used, such as the probit model; the defining characteristic of the logistic model is that increasing one of the independent variables multiplicatively scales the odds of the given outcome at a constant rate, with each dependent variable having its own parameter; for a binary independent variable this generalizes the odds ratio.

Problem Statement: In a PUBG game, up to 100 players start in each match (matchId). Players (Id) can be on teams (groupId) which get ranked at the end of the game (winPlacePerc) based on how many other teams are still alive when they are eliminated. During the game, players can pick up different amunitions, revive downed-but-not-dead (knocked) teammates, drive vehicles, swim, run, shoot, and experience all of the consequences -- such as falling too far or running themselves over and eliminating themselves. The team at PUBG has made official game data available for the public to explore and scavenge outside of "The Blue Circle." This workshop is not an official or affiliated PUBG site. Its based on the data collected by Kaggle and made available through the PUBG Developer API. You are provided with a large number of anonymized PUBG game stats, formatted so that each row contains one player's post-game stats. The data comes from matches of all types: solos, duos, squads, and custom; there is no guarantee of there being 100 players per match, nor at most 4 player per group. Goal: Perform the PUBG data analysis and answer the following questions: Does killing more people increases the chance of winning the game? Hint: Use the correlation between the match winning percentage and number of kills to determine the relationship How do we catch the fraudsters in the game? Hint: Use various logical conditions based on game knowledge to determine fraudsters in the game Can we predict the finishing position of a player in the game? Hint: Regression Problem: Train and test a model using regression algorithm to predict the final position of the player at the end of the game. Create a model which predicts players' finishing placement based on their final stats, on a scale from 1 (first place) to 0 (last place).

In statistics, the logistic model (or logit model) is used to model the probability of a certain class or event existing such as pass/fail, win/lose, alive/dead or healthy/sick. This can be extended to model several classes of events such as determining whether an image contains a cat, dog, lion, etc... Each object being detected in the image would be assigned a probability between 0 and 1 and the sum adding to one. Logistic regression is a statistical model that in its basic form uses a logistic function to model a binary dependent variable, although many more complex extensions exist. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic model (a form of binary regression). Mathematically, a binary logistic model has a dependent variable with two possible values, such as pass/fail which is represented by an indicator variable, where the two values are labeled "0" and "1". In the logistic model, the log-odds (the logarithm of the odds) for the value labeled "1" is a linear combination of one or more independent variables ("predictors"); the independent variables can each be a binary variable (two classes, coded by an indicator variable) or a continuous variable (any real value). The corresponding probability of the value labeled "1" can vary between 0 (certainly the value "0") and 1 (certainly the value "1"), hence the labeling; the function that converts log-odds to probability is the logistic function, hence the name. The unit of measurement for the log-odds scale is called a logit, from logistic unit, hence the alternative names. Analogous models with a different sigmoid function instead of the logistic function can also be used, such as the probit model; the defining characteristic of the logistic model is that increasing one of the independent variables multiplicatively scales the odds of the given outcome at a constant rate, with each independent variable having its own parameter; for a binary dependent variable this generalizes the odds ratio.

In statistics, the logistic model (or logit model) is used to model the probability of a certain class or event existing such as pass/fail, win/lose, alive/dead or healthy/sick. This can be extended to model several classes of events such as determining whether an image contains a cat, dog, lion, etc... Each object being detected in the image would be assigned a probability between 0 and 1 and the sum adding to one. Logistic regression is a statistical model that in its basic form uses a logistic function to model a binary dependent variable, although many more complex extensions exist. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic model (a form of binary regression). Mathematically, a binary logistic model has a dependent variable with two possible values, such as pass/fail which is represented by an indicator variable, where the two values are labeled "0" and "1". In the logistic model, the log-odds (the logarithm of the odds) for the value labeled "1" is a linear combination of one or more independent variables ("predictors"); the independent variables can each be a binary variable (two classes, coded by an indicator variable) or a continuous variable (any real value). The corresponding probability of the value labeled "1" can vary between 0 (certainly the value "0") and 1 (certainly the value "1"), hence the labeling; the function that converts log-odds to probability is the logistic function, hence the name. The unit of measurement for the log-odds scale is called a logit, from logistic unit, hence the alternative names. Analogous models with a different sigmoid function instead of the logistic function can also be used, such as the probit model; the defining characteristic of the logistic model is that increasing one of the independent variables multiplicatively scales the odds of the given outcome at a constant rate, with each independent variable having its own parameter; for a binary dependent variable this generalizes the odds ratio.

alive analysis

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Spatial Analysis Models and Pipelines for Jurassic World Alive

Data Analysis Presentation on APA(Austin Pets Alive) Hackathon data

Development and implementation of alive variable analysis method on a control flow graph

It consists of exploratory data analysis and prediction of people alive on board of TITANIC.

Replication materials: What Keeps Budget Surpluses Alive? A Multi-State Hazard Analysis of 34 OECD Economies

USI Hackaton 2019, Making Data Alive | Bike-sharing dataset analysis and visualization through Python and Shiny R.

The analysis of the death rate and alive people left in the titanic ship accident and the calculation of how many of the alive people were men,women and children.

The Source Codes and Datasets of the Paper "How Long Will Your Case Stay Alive? Survival Analysis Beyond Instances"

End-to-end R/Bioconductor pipeline for LC–MS serum proteomics analysis of colorectal cancer survival (Alive vs Dead) using PRIDE PXD013150.

why is it so expensive to be alive? I am answering this question through my statistical analysis of this dataset using regression models

Exploring the job search as a survival analysis problem — visualizing how long applications stay alive and what paths lead to interviews, tests, and offers.