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This is a Data Mining project primarily implemented in R, which is based on the US National Prisoner Exoneration data, and looks into the relationship between the different features(given below) and the CTE(Conviction to Exoneration Time)

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Arnab777as3uj/SYS6018-Datamining-Project-Prisoner-Exonerations

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sys-project-prisoner-exonerations

NOTE: Please look at 'html' files for quick view of python code and results. All reusable functions are organised under "Helper Functions" section in every file.

ROLES & RESPONSIBILITIES:

Lucas Beane: Feature Engineering Random Forest Modeling Report Writing

Arnab Sarkar: Feature Engineering XGBoost Report Writing

Navin Kasa: EDA Linear Regression Report Writing

DATA DESCRIPTION:

The column labeled "Tags" allows you to sort and search cases by each of several additional common characteristics. To see the definitions of these terms, click on their full names:

A: Arson Case

CDC: Co-Defendant Confessed

CIU: Conviction Integrity Unit

CSH: Child Sex abuse Hysteria case

CV: Child Victim

F: Female exoneree

FED: Federal case

H: Homicide

IO: Innocence Organization

JI: Jailhouse Informant

M: Misdemeanor

NC: No Crime case

P: Guilty Plea case

PH: Posthumous exoneration

SBS: Shaken Baby Syndrome case

The six columns on the far right of this page display separately the Contributing Factors that are listed together on the Summary Page. To save space, we use abbreviations. To see definitions of these factors, click on their full names:

MWID: Mistaken Witness Identification

FC: False Confession

P/FA: Perjury or False Accusation

F/MFE: False or Misleading Forensic Evidence

OM: Official Misconduct

ILD: Inadequate Legal Defense      

Age: The exoneree's age on the date of the reported crime

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This is a Data Mining project primarily implemented in R, which is based on the US National Prisoner Exoneration data, and looks into the relationship between the different features(given below) and the CTE(Conviction to Exoneration Time)

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