The project is dedicated to all murder victims and their families whose justice has not been served yet.
The project aims to conduct data science research and demonstrate the importance of accurately accounting for unsolved homicides within communities.
The model data source is Murder Accountability Project
Feature importance plot based on gain. It quantifies the model’s accuracy improvement achieved by using specific features for splitting.
Feature importance plot based on split. This measures the number of times a feature is used to split the data across all trees in the model.
All in all, important features for homicide solvability:
Relationship between the victim and offender
The circumstance (or theory) of the crime
Year of the homicide
Victim's Age
| Feature | Description |
|---|---|
| Agentype | Type of the law enforcement agency |
| Year | Year of homicide (or victim’s body was recovered) |
| Month | The month of homicide (or victim’s body was recovered) |
| Murder | 1: Murder & 0: Negligent Manslaughter |
| VicAge | Victim’s age |
| VicSex | Victim’s sex (“Unknown” gender: incomplete remains were recovered) |
| VicRace | Victim’s race |
| Weapon | Weapon used in the crime |
| Relationship | The relationship between victim and offender |
| Circumstance | The circumstances (or theory) of the crime |
| VicCount | Victim number in the crime |
| Region | USA region in which the homicide was reported |
For any questions or inquiries, feel free to reach out:
- My Website
- LinkedIn: Sevilay Munire Girgin
Thank you for visiting my project repository. Happy and accurate classification! 💕
| One's destination is never a place but rather a new way of seeing things. - Henry Miller |
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