SlideShare a Scribd company logo
Evaluation
Model
Model Analysis
Data
Preparation
Data
Understanding
Business Case
• Explore potential determinant of arrest rate
• Predict probability of arrest
• To provide insights on arresting performance
• Prediction model of arrest
Column Name Description
Date
Date when the incident occurred. this is sometimes a best
estimate.
Primary Type The primary description of the crime
Description The secondary description of the crime,
Location
Description
Description of the location where the incident occurred.
Arrest Indicates whether an arrest was made.
Domestic Indicates whether the incident was domestic-related
Beat Indicates the beat where the incident occurred.
District Indicates the police district where the incident occurred.
Ward The ward (City Council district) where the incident occurred.
Community Area Indicates the community area where the incident occurred.
Year Year the incident occurred.
Updated On Date and time the record was last updated.
Latitude The latitude of the location where the incident occurred.
Longitude The longitude of the location where the incident occurred.
Location The location where the incident occurred
*Data source : CSV file
*Data Characteristics: Volumes
(6 million records)
*Data format : Structure / Batch
• Define day of week in crime
• Split data 80:20
• Transform data of arrest to ratio of arrest
• Transformation categorical variable to numerical one
( Primary Type, Location )
• Index label of arrest rate
• Feature
• Day of week
• Primary Type
• Location Description
• Label
• Arrest
Decision Tree
Data
Algorithm
ClassificationCategory
• Confusion matrix for model evaluation
Presentation
• Relation of factor crime
• Danger & Safe Zone
• Prediction Model
Proportion
• Arrest
• Planning day for safety
• Location analysis
Location
Description
Map
Location
Description
Type
Crime project (Big Data Certification Course #6)

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Crime project (Big Data Certification Course #6)

  • 1.
  • 3. • Explore potential determinant of arrest rate • Predict probability of arrest • To provide insights on arresting performance • Prediction model of arrest
  • 4. Column Name Description Date Date when the incident occurred. this is sometimes a best estimate. Primary Type The primary description of the crime Description The secondary description of the crime, Location Description Description of the location where the incident occurred. Arrest Indicates whether an arrest was made. Domestic Indicates whether the incident was domestic-related Beat Indicates the beat where the incident occurred. District Indicates the police district where the incident occurred. Ward The ward (City Council district) where the incident occurred. Community Area Indicates the community area where the incident occurred. Year Year the incident occurred. Updated On Date and time the record was last updated. Latitude The latitude of the location where the incident occurred. Longitude The longitude of the location where the incident occurred. Location The location where the incident occurred *Data source : CSV file *Data Characteristics: Volumes (6 million records) *Data format : Structure / Batch
  • 5. • Define day of week in crime • Split data 80:20 • Transform data of arrest to ratio of arrest • Transformation categorical variable to numerical one ( Primary Type, Location ) • Index label of arrest rate
  • 6. • Feature • Day of week • Primary Type • Location Description • Label • Arrest Decision Tree Data Algorithm ClassificationCategory • Confusion matrix for model evaluation
  • 7. Presentation • Relation of factor crime • Danger & Safe Zone • Prediction Model Proportion • Arrest • Planning day for safety • Location analysis
  • 8.
  • 9.