Devi2k20

covid19

SIR code in python to model the spread of COVID-19

Covid_Spatial_Analysis

Crime-Predictor-Chicago

One of the most important questions people ask before moving to a new place or traveling is: Is this locality safe? We answer this question using co-location pattern mining. Our crime predictor gives the possibility of all crimes that can happen given the presence of certain geographical entities and certain crimes in the locality. Given a set of boolean spatial features, the co-location pattern discovery process finds the subsets of features frequently located together. We have used a transaction free approach for this using Haversine distance as a metric for finding proximity neighborhood and measuring all spatial autocorrelations. We also propose an improvement to distance measure approach while calculating R-proximity neighborhood using boundary window. We used distance and participation thresholds for determining the correlation between features and crimes. Our algorithm exploits the anti-monotone property and optimizes the co-location pattern discovery algorithm by prevalence based pruning.

datasciencecoursera

data science course

datasharing

The Leek group guide to data sharing

Delhi_india-air-pollution

hello-world

exploring github

it-cert-automation-practice

Google IT Automation with Python Professional Certificate - Practice files

ProgrammingAssignment2

Repository for Programming Assignment 2 for R Programming on Coursera

Wordcloud

Devi2k20/covid19

SIR code in python to model the spread of COVID-19

Devi2k20/Covid_Spatial_Analysis

Devi2k20/Crime-Predictor-Chicago

One of the most important questions people ask before moving to a new place or traveling is: Is this locality safe? We answer this question using co-location pattern mining. Our crime predictor gives the possibility of all crimes that can happen given the presence of certain geographical entities and certain crimes in the locality. Given a set of boolean spatial features, the co-location pattern discovery process finds the subsets of features frequently located together. We have used a transaction free approach for this using Haversine distance as a metric for finding proximity neighborhood and measuring all spatial autocorrelations. We also propose an improvement to distance measure approach while calculating R-proximity neighborhood using boundary window. We used distance and participation thresholds for determining the correlation between features and crimes. Our algorithm exploits the anti-monotone property and optimizes the co-location pattern discovery algorithm by prevalence based pruning.

Devi2k20/datasciencecoursera

data science course

Devi2k20/datasharing

The Leek group guide to data sharing

Devi2k20/Delhi_india-air-pollution

Devi2k20/hello-world

exploring github

Devi2k20/it-cert-automation-practice

Google IT Automation with Python Professional Certificate - Practice files

Devi2k20/ProgrammingAssignment2

Repository for Programming Assignment 2 for R Programming on Coursera

Devi2k20/Wordcloud

Devi2k20/Rdatamining

This will be linked with my R studio