def models_combined(): df = load_data() df = process_df(df) X, y = get_features(df) print "running gradient boosted model with 4000 estimators..." gb4000_clf, gb4000_Xtest, gb4000_ytest = run_gradient_boosted(X, y, n_estimators = 4000) print "running gradient boosted model with 1000 estimators..." gb1000_clf, gb1000_Xtest, gb1000_ytest = run_gradient_boosted(X, y, n_estimators = 1000) print "running random forest model..." rf1_clf, rf1_Xtest, rf1_ytest = run_rf_churn(X,y) df = load_data() df = process_df(df) train_df, test_df = train_test_equal_weight(df) X_train, y_train = get_features(train_df) X_test, y_test = get_features(test_df) print "running gradient boosted model with even fraud and non-fraud..." gbEF_clf, gbEF_Xtest, gbEF_ytest = run_gradient_boosted_evenFraud(X_train, y_train, X_test, y_test) print "running random forest model with even fraud and non-fraud..." rf2_clf, rf2_Xtest, rf2_ytest = run_rf_churn2(X_train, y_train, X_test, y_test) return gb4000_clf, gb4000_Xtest, gb4000_ytest, gb1000_clf, gb1000_Xtest, gb1000_ytest, gbEF_clf, gbEF_Xtest, gbEF_ytest, rf1_clf, rf1_Xtest, rf1_ytest, rf2_clf, rf2_Xtest, rf2_ytest
return plt.savefig(filepath,
transparent=False,
bbox_inches='tight',
format='svg',
dpi=1200)
def map_html_file(map_name, filepath):
return map_name.save(filepath)
if __name__ == '__main__':
# read in week long cleaned dataframe (pride week, small scale model)
scooter_june_pride = load_data('../data/small_scooter.csv')
scooter_june_pride = drop_cols_update_names(scooter_june_pride,
['Unnamed: 0'])
scooter_june_pride = cols_to_datetime(scooter_june_pride,
['Start_Time', 'End_Time'])
'''EDA Histograms'''
fig, ax = plt.subplots(figsize=(10, 6))
histogram_of_column(scooter_june_pride, 'Trip_Distance', ax, '#DF2C04',
'Scooter Trip Distances - Pride Week June 2019',
'Trip Distance, miles', 0.5, 5000, 0, 10)
image_file('../images/trip_distance_hist.svg')
'''EDA Bar Plots'''
weekdays = [
'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday',
'Sunday'
]
roc_auc = auc(fpr, tpr)
print('auc done...')
plt.figure(figsize=(6, 6))
plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)
plt.plot([0, 1], [0, 1], linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Random Forest Fraud Prediction ROC Curve')
plt.show()
if __name__ == '__main__':
# Load Data
cleandf, fraud = load_data()
# Perform SMOTE-Overbalancing
sm = SMOTE(random_state=42)
X, y = sm.fit_sample(cleandf, fraud)
#Cross Validate on Various Classification Model
models = {
'XGBClassifier': XGBClassifier(),
'Linear SVC': LinearSVC(),
'Gradient Boosting': GradientBoostingClassifier(),
'Logistic Regression': LogisticRegression(),
'KNN': KNeighborsClassifier(n_neighbors=9),
'Decision Tree': DecisionTreeClassifier(),
'Naive Bayes': GaussianNB(),
'Random Forest': RandomForestClassifier(),
'AdaBoost': AdaBoostClassifier()
}
Args:
df: cleaned training dataframe
Returns:
Factorplot
"""
ax = sns.factorplot(x='delivery_method',
y='fraud',
data=df,
palette='coolwarm_r',
kind='bar',
ci=None,
estimator=lambda x: sum(x == 1.0) * 100.0 / len(x))
ax.set_axis_labels('Delivery Method', 'Probability of Fraud')
plt.title('Probability of Fraud by Delivery Method')
plt.tight_layout()
plt.show()
if __name__ == '__main__':
# Load Cleaned Data
X, y = load_data()
df = pd.concat([X, y], axis=1)
# Investigate Most-Correlated Features w/ Fraud
print(top_corr(df))
# Display Plots
display_plots(df)
