Police Data

📹Project Video available on YouTube - https://youtu.be/GyUbo45mVSE

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Police Check-posts Data

This dataset contains detailed records of police traffic stops. Each row represents a single stop, with information about the date, time, driver demographics, the reason for the stop, whether a search was conducted, and the outcome. It can be useful for analysing traffic stop patterns, demographic trends, law enforcement behaviour, and correlations with violations or arrests.

Using this dataset, we answered multiple questions with Python in our Project.

Q.1) Instruction ( For Data Cleaning ) - Remove the column that only contains missing values

Q.2) For Speeding , were Men or Women stopped more often ?

Q.3) Does gender affect who gets searched during a stop ?

Q.4) What is the mean stop_duration ?

Q.5) Compare the age distributions for each violation

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These are the main Features/Columns available in the dataset :

1. stop_date – The date on which the traffic stop occurred.

2. stop_time – The exact time when the stop took place.

3. driver_gender – Gender of the driver (M for male, F for female).

4. driver_age_raw – Raw recorded birth year of the driver.

5. driver_age – Calculated or cleaned driver’s age at the time of the stop.

6. driver_race – Race or ethnicity of the driver (e.g., White, Black, Asian, Hispanic).

7. violation_raw – Original recorded reason for the stop.

8. violation – Categorized reason for the stop (e.g., Speeding, Other).

9. search_conducted – Boolean value indicating whether a search was performed (True/False).

10. search_type – Type of search conducted, if any (e.g., vehicle search, driver search).

11. stop_outcome – The result of the stop (e.g., Citation, Arrest, Warning).

12. is_arrested – Boolean value indicating if the driver was arrested (True/False).

13. stop_duration – Approximate length of the stop (e.g., 0-15 Min, 16-30 Min).

14. drugs_related_stop – Boolean value indicating if the stop was related to drugs (True/False).