Applied Machine Learning in Python week4 Assignment solution

These solutions are for reference only.

It is recommended that you should solve the assignments amd quizes by yourself honestly then only it makes sense to complete the course.
but if you are stuck in between refer thses solutions

make sure you understand the solution 
dont just copy paste it

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Assignment 4 - Understanding and 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. Nonetheless, we encourage you to look into data from other Detroit datasets to help inform feature creation and model selection. We recommend taking a look at the following related datasets:

*Building Permits
*Trades Permits
*Improve Detroit: Submitted Issues
*DPD: Citizen Complaints
*Parcel Map

We provide you with two data files for use in training and validating your 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.

File descriptions (Use only this data for training your model!)

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.

Data fields

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

train.csv only

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.

The evaluation metric for this assignment is the Area Under the ROC Curve (AUC).

Your grade will be based on the AUC score computed for your classifier. A model which with an AUROC of 0.7 passes this assignment, over 0.75 will recieve full points.

For this assignment, create a function that trains a model to predict blight ticket compliance in Detroit using train.csv. Using this model, return 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.


   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

import pandas as pd
import numpy as np
import math
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score
from sklearn.metrics import roc_curve, auc
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import GridSearchCV

def blight_model():
    # Your code here
    df = pd.read_csv('train.csv', encoding = "ISO-8859-1")
    df.index = df['ticket_id']

    features_name = ['fine_amount', 'admin_fee', 'state_fee', 'late_fee']
    df.compliance = df.compliance.fillna(value=-1)
    df = df[df.compliance != -1]
#     le = LabelEncoder().fit(df['inspector_name'])
#     inspector_name_transformed = le.transform(df['inspector_name'])
    X = df[features_name]
#     X['inspector_name'] = le.transform(df['inspector_name'])
#     print(X)
    X.fillna(value = -1)
    y = df.compliance
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 0)
    clf = RandomForestClassifier(n_estimators = 10, max_depth = 5).fit(X_train, y_train)

    # default metric to optimize over grid parameters: accuracy
#     grid_clf = GridSearchCV(clf, param_grid = grid_values)
#, y_train)

#     y_score = clf.predict(X_test)
#     fpr, tpr, _ = roc_curve(y_test, y_score)
#     roc_auc = auc(fpr, tpr)
#     print(roc_auc)

    features_name = ['fine_amount', 'admin_fee', 'state_fee', 'late_fee']
    df_test = pd.read_csv('test.csv', encoding = "ISO-8859-1")
    df_test.index = df_test['ticket_id']
    X_predict = clf.predict_proba(df_test[features_name])
    ans = pd.Series(data = X_predict[:,1], index = df_test['ticket_id'], dtype='float32')

#     print(ans)
    return ans

/opt/conda/lib/python3.5/site-packages/IPython/core/ DtypeWarning: Columns (11,12,31) have mixed types. Specify dtype option on import or set low_memory=False.
  if self.run_code(code, result):

284932    0.060788
285362    0.026533
285361    0.068650
285338    0.060788
285346    0.068650
285345    0.060788
285347    0.055858
285342    0.401352
285530    0.026533
284989    0.029734
285344    0.055858
285343    0.026533
285340    0.026533
285341    0.055858
285349    0.068650
285348    0.060788
284991    0.029734
285532    0.029734
285406    0.029734
285001    0.029734
285006    0.026533
285405    0.026533
285337    0.029734
285496    0.055858
285497    0.060788
285378    0.026533
285589    0.029734
285585    0.060788
285501    0.068650
285581    0.026533
376367    0.029734
376366    0.035475
376362    0.035475
376363    0.060788
376365    0.029734
376364    0.035475
376228    0.035475
376265    0.035475
376286    0.369236
376320    0.035475
376314    0.035475
376327    0.369236
376385    0.369236
376435    0.475758
376370    0.369236
376434    0.055858
376459    0.068650
376478    0.008845
376473    0.035475
376484    0.024999
376482    0.029734
376480    0.029734
376479    0.029734
376481    0.029734
376483    0.035475
376496    0.026533
376497    0.026533
376499    0.068650
376500    0.068650
369851    0.308120
dtype: float32