/Driver-s-Race-Classifer-traffic-stops

To see if drivers were being profiled. I built a Support Vector Machine (SVM) classifier and a randomForest classifier to predict a driver's race given the traffic's stop's details. Successful classification will indicate the existence of bais in the traffic stops' data.

Primary LanguagePython

Driver-s-Race-Classifer-traffic-stops

To see if drivers were being profiled. I built a Support Vector Machine (SVM) classifier and a randomForest classifier to predict a driver's race given the traffic's stop's details. Successful classification will indicate the existence of bais in the traffic stops' data.

Data

Two Data Tables:

  1. Traffic Stop Data
  2. Socio Economic Data for Zip Codes where Stops happen

Traffic Stop Data

Unnamed: 0 Stop_Key Type TCOLE_Sex TCOLE_RACE_ETHNICITY Standardized_Race_Known Reason_for_Stop Street_Type Search_Yes_or_No TCOLE_Search_Based_On TCOLE_Search_Found TCOLE_Result_of_Stop TCOLE_Arrest_Based_On Council_District COUNTY Custody Location Sector Standardized_Race Stop_Time
0 20201-459626502-25962 WARNING Male White NO - RACE OR ETHNICITY WAS NOT KNOWN BEFORE STOP Moving Traffic Violation City Street NO N/A - No Search was conducted N/A - No Search was conducted Verbal Warning N/A - No Arrest Conducted Council District 9 TRAVIS COUNTY NOT APPLICABLE 500 E 8TH ST GEORGE WHITE 2154.0

Socio Economic Data for Zip Codes

index Zip_Code_1 Zip_Code latitude longitude propertyTaxRate numPriceChanges avgSchoolRating MedianStudentsPerTeacher
0 78617 78617 30.16451458598292 -97.63406638211984 1.9799999999999984 2.558139534883721 3.1589147286821717 13.965116279069768
1 78619 78619 30.136290550231934 -97.97578048706056 2.01 1.9166666666666667 7.388888888888889 15.666666666666666

Joined (merged) the Tables

racialProf = racialProfUpdated.merge(socioEcoZipCodesInfo, on ='Zip_Code', how = 'outer')
index Unnamed: 0 Stop_Key Type TCOLE_Sex TCOLE_RACE_ETHNICITY Standardized_Race_Known Reason_for_Stop Street_Type Search_Yes_or_No TCOLE_Search_Based_On TCOLE_Search_Found TCOLE_Result_of_Stop TCOLE_Arrest_Based_On Council_District COUNTY Custody Location Sector Standardized_Race Stop_Time
0 0.0 20201-459626502-25962 WARNING Male White NO - RACE OR ETHNICITY WAS NOT KNOWN BEFORE STOP Moving Traffic Violation City Street NO N/A - No Search was conducted N/A - No Search was conducted Verbal Warning N/A - No Arrest Conducted Council District 9 TRAVIS COUNTY NOT APPLICABLE 500 E 8TH ST GEORGE WHITE 2154.0

Shape: (45274, 33)

Data Cleaning Pipeline: Encoder and Imputer

numeric_transformer = Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),('scaler', StandardScaler())])
categorical_transformer = Pipeline(steps=[('imputer', SimpleImputer(strategy='constant',fill_value='missing')),
                                          ('onehot', OneHotEncoder(handle_unknown='ignore'))])
numeric_features = train.select_dtypes(include=['int64','float64']).columns
categorical_features = train.select_dtypes(include=['object']).drop(['TCOLE_RACE_ETHNICITY'], axis=1).columns
preprocessor = ColumnTransformer( transformers=[('num', numeric_transformer, numeric_features),
                                                ('cat', categorical_transformer, categorical_features)])

Train Test Split

train, test, = train_test_split(racialProf,
test_size=0.2)

RandomForest Model

rf = Pipeline(steps=[('preprocessor', preprocessor),
('classifier', RandomForestClassifier(n_estimators = 13, max_depth=10))])

Training Fit

X_train = train.drop('TCOLE_RACE_ETHNICITY', axis=1)
y_train = train['TCOLE_RACE_ETHNICITY']
rf.fit(X_train ,y_train)
train_pred = rf.predict (X_train)

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Test Fit

X_test = test.drop('TCOLE_RACE_ETHNICITY', axis=1)
y_test = test['TCOLE_RACE_ETHNICITY']
test_predRF = rf.predict (X_test)

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RF Grid Search

param_grid = {
'classifier__n_estimators': [200, 500],
'classifier__max_features': ['auto', 'sqrt', 'log2'],
'classifier__max_depth' : [4,5,6,7,8],
'classifier__criterion' :['gini', 'entropy']}
from sklearn.model_selection import GridSearchCV
CV = GridSearchCV(rf, param_grid, n_jobs= 1)
CV.fit(X_train, y_train)
print(CV.best_params_)
print(CV.best_score_)

# Suggested parameters from grid search
from sklearn.ensemble import RandomForestClassifier
rf_GridSearch = Pipeline(steps=[('preprocessor', preprocessor),
('classifier', RandomForestClassifier(criterion = 'entropy', 
                                      max_depth = 8, max_features= 'sqrt',
                                        n_estimators= 200))])

RF Grid Search Training Fit

rf_GridSearch.fit(X_train, y_train)

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RF Grid Search Test Fit

test_predRFGRID = rf_GridSearch.predict (X_test)

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SVM Model

svm_classifier = Pipeline(steps=[('preprocessor', preprocessor),
('classifier', SVC(kernel="poly", degree=3))])

Training Fit

X_train = train.drop('TCOLE_RACE_ETHNICITY', axis=1)
y_train = train['TCOLE_RACE_ETHNICITY']
svm_classifier.fit(X_train, y_train)
train_predSVM = svm_classifier.predict (X_train)

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Test Fit

X_test = test.drop('TCOLE_RACE_ETHNICITY', axis=1)
y_test = test['TCOLE_RACE_ETHNICITY']
test_predSVM = svm_classifier.predict (X_test)

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SVM Grid Search

param_grid = {
'classifier__kernel': ['poly'],
'classifier__degree': [1,2,3],
'classifier__decision_function_shape': ['ovr','ovo']}
from sklearn.model_selection import GridSearchCV
CV1 = GridSearchCV(svm_classifier, param_grid, n_jobs= 1)
CV1.fit(X_train, y_train)
print(CV1.best_params_)
print(CV1.best_score_)

# Suggested Parameters from Grid-Search
svm_classifierGridSearch = Pipeline(steps=[('preprocessor', preprocessor),
('classifier', SVC(decision_function_shape= 'ovr', degree= 1, kernel= 'poly'
))])

SVM Grid Search Training Fit

svm_classifierGridSearch.fit(X_train, y_train)
train_predSVMGRID = svm_classifierGridSearch.predict (X_train)

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SVM Grid Search Test Fit

test_predSVMGRID = svm_classifierGridSearch.predict (X_test)

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