Topic: spearman-rank-correlation Goto Github
Some thing interesting about spearman-rank-correlation
Some thing interesting about spearman-rank-correlation
spearman-rank-correlation,Singals applications
User: brianmuv
spearman-rank-correlation,Movie Recommendation System based on the Spearman's rank correlation 🎞️
User: bursasha
spearman-rank-correlation,Currently, there are 26.2 million COVID-19 cases in the US due to people taking lower precautions to reduce transmission at public venues. This project aims to create a tool that provides individuals with information to make a responsible decision about visiting an establishment to prevent unnecessary SARS-CoV-2 cases. A probabilistic model that predicts the risk of an individual contracting COVID-19 (transmission risk) during a visit to an establishment was created. This model was integrated into a web app built using the StreamLit framework in Python3.8. It acquired data through parsing COVID-19 databases and obtaining venue-specific data through user-inputs. The transmission risk model worked by multiplying the likelihood an individual will encounter a virus carrier (exposure risk) and the risk the individual will contract the virus if one carrier is present (contract risk). The exposure risk model was tested through the Spearman’s Rank Correlation by calculating the correlation between the model’s risk prediction for 200 random counties to new cases two weeks later of the aforementioned counties. Contract risk was tested using four different scenarios. Transmission risk was tested using these four scenarios factoring three counties with ranging incidence rates. The exposure risk model averaged a Spearman Rank correlation of 0.81, placing it in the “very strong” category. The contract and transmission risk provided sensible predictions for the scenarios provided. This model can be easily expanded to other databases and adapted to high-incidence countries. Since virus-specific aspects can apply to other illnesses, the model can be adjusted easily for other viruses.
User: daiwikpal
Home Page: https://transmission-risk-prediction.herokuapp.com/
spearman-rank-correlation,Continuation to Data Analysis using more mathematical approach.
User: danieljai
spearman-rank-correlation,Telecom Customer Churn Prediction with 9 Different Alghoritms
User: haluksumen
spearman-rank-correlation,My personal repository where I can keep files associated with my learning of Statistics
User: linggarm
spearman-rank-correlation,Calculating pairwise euclidean distance matrix for horizontally partitioned data in federated learning environment
User: mdshihabullah
spearman-rank-correlation,MediaEval challenge 2019 - to predict the memorability of the Videos
User: mrraghav
spearman-rank-correlation,In this repository, four famous correlation algorithms have been implemented. Pearson, spearman, Chatterjee, and MIC correlation algorithm implemented
User: parsa-mhmdi
spearman-rank-correlation,A machine learning project where we first detected and removed the outliers and then checked correlation among features and then applied different ML algorithms to check if the person might get a heart attack or not.
User: prathammehta16
spearman-rank-correlation,Implementation of Spearman and Kendall correlation coefficient for MS Excel (VBA)
User: quantumqu
spearman-rank-correlation,10 Days of Statistics Hackerrank Solutions
User: sharmasapna
spearman-rank-correlation,Feature importance refers to a measure of how important each feature/variable is in a dataset to the target variable or the model performance. It can be used to understand the relationships between variables and can also be used for feature selection to optimize the performance of machine learning models.
User: wangyuhsin
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