The project aims to analyze a police dataset comprising various attributes such as driver_gender, driver_dob, driver_age, driver_race, violation_raw, violation, search_conducted, stop_outcome, is_arrested, stop_duration, and drugs_related_stop. The objective is to conduct comprehensive data analysis and visualize key insights through six distinct graphs using Matplotlib, Seaborn, and Plotly libraries. The analysis encompasses various aspects such as demographic patterns, enforcement trends, and potential biases within the dataset, facilitating deeper understanding and insights into law enforcement practices.
The dataset comprises demographic and enforcement-related attributes, including driver gender, age, race, violation type, stop outcome, and search conduct. Each entry provides insights into police stops, highlighting patterns in law enforcement actions and demographic disparities.
- Exploratory Data Analysis (EDA): Understand patterns and trends in police stops by examining demographic distributions, violation types, and stop outcomes.
- Insight Generation: Identify potential biases or disparities in law enforcement practices based on gender, race, or age through statistical analysis and visualization.
- Inform Policy and Decision Making: Provide actionable insights to policymakers and law enforcement agencies for improving fairness, transparency, and accountability in traffic enforcement procedures.