Predicting Property Maintenance Fines
This assignment is based on a data challenge from the Michigan Data Science Team (MDST).
The Michigan Data Science Team (MDST) and the Michigan Student Symposium for Interdisciplinary Statistical Sciences (MSSISS) have partnered with the City of Detroit to help solve one of the most pressing problems facing Detroit - blight. Blight violations are issued by the city to individuals who allow their properties to remain in a deteriorated condition. Every year, the city of Detroit issues millions of dollars in fines to residents and every year, many of these fines remain unpaid. Enforcing unpaid blight fines is a costly and tedious process, so the city wants to know: how can we increase blight ticket compliance?
The first step in answering this question is understanding when and why a resident might fail to comply with a blight ticket. This is where predictive modeling comes in. For this assignment, your task is to predict whether a given blight ticket will be paid on time.
All data for this assignment has been provided to us through the Detroit Open Data Portal. Only the data already included in your Coursera directory can be used for training the model for this assignment.
You may also find data related to this project from the links below:
- Building Permits
- Trades Permits
- Improve Detroit: Submitted Issues
- DPD: Citizen Complaints
- Parcel Map
For this project two data files are used for training and validating models: train.csv and test.csv. Each row in these two files corresponds to a single blight ticket, and includes information about when, why, and to whom each ticket was issued. The target variable is compliance, which is True if the ticket was paid early, on time, or within one month of the hearing data, False if the ticket was paid after the hearing date or not at all, and Null if the violator was found not responsible. Compliance, as well as a handful of other variables that will not be available at test-time, are only included in train.csv.
Note: All tickets where the violators were found not responsible are not considered during evaluation. They are included in the training set as an additional source of data for visualization, and to enable unsupervised and semi-supervised approaches. However, they are not included in the test set.
train.csv - the training set (all tickets issued 2004-2011) test.csv - the test set (all tickets issued 2012-2016) addresses.csv & latlons.csv - mapping from ticket id to addresses, and from addresses to lat/lon coordinates. Note: misspelled addresses may be incorrectly geolocated.
train.csv & test.csv
ticket_id - unique identifier for tickets agency_name - Agency that issued the ticket inspector_name - Name of inspector that issued the ticket violator_name - Name of the person/organization that the ticket was issued to violation_street_number, violation_street_name, violation_zip_code - Address where the violation occurred mailing_address_str_number, mailing_address_str_name, city, state, zip_code, non_us_str_code, country - Mailing address of the violator ticket_issued_date - Date and time the ticket was issued hearing_date - Date and time the violator's hearing was scheduled violation_code, violation_description - Type of violation disposition - Judgment and judgement type fine_amount - Violation fine amount, excluding fees admin_fee - $20 fee assigned to responsible judgments
state_fee - $10 fee assigned to responsible judgments late_fee - 10% fee assigned to responsible judgments discount_amount - discount applied, if any clean_up_cost - DPW clean-up or graffiti removal cost judgment_amount - Sum of all fines and fees grafitti_status - Flag for graffiti violations
payment_amount - Amount paid, if any payment_date - Date payment was made, if it was received payment_status - Current payment status as of Feb 1 2017 balance_due - Fines and fees still owed collection_status - Flag for payments in collections compliance [target variable for prediction] Null = Not responsible 0 = Responsible, non-compliant 1 = Responsible, compliant compliance_detail - More information on why each ticket was marked compliant or non-compliant
Your predictions will be given as the probability that the corresponding blight ticket will be paid on time.
For this assignment, I created a function that trains a model to predict blight ticket compliance in Detroit using
train.csv. Returning a series of length 61001 with the data being the probability that each corresponding ticket from
test.csv will be paid, and the index being the ticket_id.
ticket_id 284932 0.531842 285362 0.401958 285361 0.105928 285338 0.018572 ... 376499 0.208567 376500 0.818759 369851 0.018528 Name: compliance, dtype: float32