Difference between revisions of "User:KWeathington"

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*Explored Kernel Density Estimates
 
*Explored Kernel Density Estimates
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<h2>Week Three</h2>
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*Expanding a dictionary for sanitizing address data
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*RCR Training
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*Made a poster for upcoming Northwestern Mutual event
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*Opened API keys
 +
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<h2> Week Four </h2>
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*Added basemap to DBScan visualization
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*Created several DBScan clustering models of crime in Milwaukee
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 +
*Presented research at Northwestern Mutual technology showcase at announcement of the Data Science Institute
 +
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<h2> Week Five </h2>
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 +
*Reporting halfway progress
 +
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*More DBScan and nearest neighbors based calculation of epsilon
 +
 +
*Began developing theory and equation for a "wastage index"
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<h2>Week Six</h2>
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*Implemented a K Nearest Neighbors plot to help identify optimal eps given a minPT value on certain subsections of the data
 +
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*Added hulls around individual clusters to make more user friendly
 +
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<h2>Week Seven</h2>
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*Further background reading
 +
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*Cultivating literature for paper
 +
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<h2>Week Eight</h2>
 +
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*Began write up about DBScan findings
 +
 +
*Made poster for end of summer poster session
 +
 +
<h2>Week Nine</h2>
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*Continued final paper writing
 +
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<h2> Week Ten</h2>
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*Poster session
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*Final presentations
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*Finished final write-up

Latest revision as of 17:08, 3 August 2018

Mentor: Dr. Shion Guha
Lab Github: https://github.com/marquettecomputationalsocialscience
Personal Github:https://github.com/KatyWeathington

Week One

  • Orientation
  • Data discussion meeting and updating new team members on previous work and lab goals
  • Background reading on various spatial clustering algorithms and use of data science concepts in police departments

Week Two

  • Compiled a preliminary list of clustering algorithms sorted into family, with a note of potential sources of biases and a summary of the steps
  • Demonstrated a variety of clustering algorithms on random data
  • Explored Kernel Density Estimates

Week Three

  • Expanding a dictionary for sanitizing address data
  • RCR Training
  • Made a poster for upcoming Northwestern Mutual event
  • Opened API keys

Week Four

  • Added basemap to DBScan visualization
  • Created several DBScan clustering models of crime in Milwaukee
  • Presented research at Northwestern Mutual technology showcase at announcement of the Data Science Institute

Week Five

  • Reporting halfway progress
  • More DBScan and nearest neighbors based calculation of epsilon
  • Began developing theory and equation for a "wastage index"

Week Six

  • Implemented a K Nearest Neighbors plot to help identify optimal eps given a minPT value on certain subsections of the data
  • Added hulls around individual clusters to make more user friendly

Week Seven

  • Further background reading
  • Cultivating literature for paper

Week Eight

  • Began write up about DBScan findings
  • Made poster for end of summer poster session

Week Nine

  • Continued final paper writing

Week Ten

  • Poster session
  • Final presentations
  • Finished final write-up