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Chirag Jain

cj8664

Currently pursuing Master of Computer Science at North Carolina State University. I am a versatile and productive Team player with 3+ years of work experience
United States
Commits
504
Repos
19
Lines of code
64845
Following
0
2
Overview
19 repos
Last updated: 2019/02/10 — 07:02:00
3
Languages
19 repos
Last updated: 2019/02/10 — 07:02:00
Python
 
Commits:
299
LOC:
15850
JavaScript
 
Commits:
166
LOC:
7681
HTML
 
Commits:
77
LOC:
4666
SQL
 
Commits:
76
LOC:
184
CSS
 
Commits:
73
LOC:
1330
Shell
 
Commits:
54
LOC:
313
4
Technologies
19 repos
Last updated: 2019/02/10 — 07:02:00
Computational Science
124 commits
Machine Learning
56 commits
Natural Language Processing
56 commits
Storage
27 commits
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5
Fun facts
19 repos
Last updated: 2019/02/10 — 07:02:00

I'm most productive during

daytime
49% of users
d

I'm most productive on

Wednesdays
16% of users
We

I prefer

CamelCase

for naming variables

I prefer

spaces

for indentation

I prefer

list comprehensions
6
Repositories
19 repos
Last updated: 2019/02/10 — 07:02:00
#
Repository
Commits
Team
Language
Timeline
2
Crime-Predictor-Chicago
64
3
Python
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. 
3
Twitter-Mining
64
4
Python
4
Twitter-Mining
64
4
Python
5
Crime-Predictor-Chicago
62
3
Python
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. 
6
my-app
58
1
Shell
Test repo
7
CJ8664.github.io
37
2
HTML
My portfolio
8
carsuggestor
37
5
JavaScript
A small application that suggests a car based on user's personality and lifestyle
9
fedapp
28
2
JavaScript
Web site for Fed Day
10
smartARt
22
4
Java
Packhacks 2018
Show more
7
⭐ Coworker Superstars
paulrouget
shanbhag10
gurudarshan266
wseng
niravjain
pratikkumar-jain
rohitnaik246