link dataset:
http://dados.recife.pe.gov.br/dataset/registro-das-infracoes-de-transito
all the analysis:
https://github.com/MarcoAurello/Estudo-DataSets/blob/master/multas.ipynb
I am strengthening my bases in statistics to present studies and prospecting and linear regression, I bring this dataset because I found the analysis interesting.
amount of infractions 335.748, I look for the first 2 and the dates of beginning and end of the measurements of the dataset.
The first information that we can already take and the daily average of infractions of 1519, and I must explain that these are the infractions, will be analyzed to be converted or not into fines.
Organizing the infractions by the responsible of the assessment, and their respective amounts of assessments
Note that half of the notices are made by Traffic Agents.
Tracking the locations with the most notices.
Rescuing the 10 laws with the most assessments
As in the dataset we do not have information on the prices of the fines I resolve to enrich the information creating a function to find out the value of the fines. I did not post the whole function because it was too big but follow the logic below
Applying the function
Converting dates and times to make calculations
Separating the assessments in shift from 6am - morning, afternoon, night 1 and night 2
penalty chart per turn
Separating the assessments per month between January and June 2017
http://dados.recife.pe.gov.br/dataset/registro-das-infracoes-de-transito
all the analysis:
https://github.com/MarcoAurello/Estudo-DataSets/blob/master/multas.ipynb
I am strengthening my bases in statistics to present studies and prospecting and linear regression, I bring this dataset because I found the analysis interesting.
amount of infractions 335.748, I look for the first 2 and the dates of beginning and end of the measurements of the dataset.
The first information that we can already take and the daily average of infractions of 1519, and I must explain that these are the infractions, will be analyzed to be converted or not into fines.
Organizing the infractions by the responsible of the assessment, and their respective amounts of assessments
Note that half of the notices are made by Traffic Agents.
Tracking the locations with the most notices.
Rescuing the 10 laws with the most assessments
As in the dataset we do not have information on the prices of the fines I resolve to enrich the information creating a function to find out the value of the fines. I did not post the whole function because it was too big but follow the logic below
Applying the function
This data and what caught my attention, if all infractions really turn fines the value
would be 55,110,983.46 in fines for 8 months of the year
Agent, the type of fine and the amount of fines applied by each equipment.
Listing major fines:
Converting dates and times to make calculations
Separating the assessments in shift from 6am - morning, afternoon, night 1 and night 2
penalty chart per turn
Separating the assessments per month between January and June 2017