Crime project (Big Data Certification Course #6)
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The document outlines an evaluation model aimed at analyzing arrest rates, including data preparation and understanding a business case for predicting arrest probabilities. It describes key variables such as crime type, location, and whether an arrest was made, using a structured dataset with 6 million records. The methodology includes data transformation, feature selection, and model evaluation through decision trees and confusion matrix analysis.
Crime project (Big Data Certification Course #6)
- 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