KNN missing value imputer, Label Encoder, Feature Engineering, Standard Scaler, SMOTE
Logistic Regression, Decision Tree, Naive Bayes Classifier, Random Forest, XGBoost, LightGBM, CatBoost, Tensorflow Multi Layer Peceptron, Encoder Decoder w KNN, Support Vector Machine
from q2_fraud_detection.model import LRegression
evaluation_metics = LRegression().run()
{'auc_train': 0.6363008375787929, 'auc_valid': 0.6155861712594572,
'acc_train': 0.6363008375787929, 'acc_valid': 0.7210041309183349,
'matthew_corr_train': 0.2795725460502018, 'matthew_corr_valid': 0.14368101202678438,
'f1_score_train': 0.5909090909090909, 'f1_score_valid': 0.22847100175746926}
from q2_fraud_detection.model import LRegression
evaluation_metics = LRegression(onehot_encode=True, polyfeature=True).run("evaluate", gridsearch=True)
{'auc_train': 0.9999568258354201, 'auc_valid': 0.9033798049445029,
'acc_train': 0.9999568258354201, 'acc_valid': 0.9574197648554179,
'matthew_corr_train': 0.9999136553985353, 'matthew_corr_valid': 0.7488045651603159,
'f1_score_train': 0.999956823971331, 'f1_score_valid': 0.7689655172413793}
from q2_fraud_detection.model import LRegression
prediction = LRegression().run("predict")
Insp
0 1
1 0
2 1
3 0
4 0
from q2_fraud_detection.model import LRegression
prediction = LRegression(onehot_encode=True, polyfeature=True).run("predict")
Insp
0 1
1 0
2 1
3 0
4 0
Univariate: StepWiseArima, Univariate Multi Step LSTM
Multivariate: Vector AutoRegression, Multivariate Multi Step LSTM
from q3_time_series.model import VectorAutoRegression
evaluate_metrics = VectorAutoRegression().run("evaluate")
[{'China': {'rmse_val': 0.10033340062857614, 'mae_val': 0.08651686327283652, 'mape_val': '0.08505443829568742 %'}},
{'India': {'rmse_val': 0.22945699053781246, 'mae_val': 0.16033218959840193, 'mape_val': '0.23784733993852525 %'}},
{'Singapore': {'rmse_val': 1.251872932175908, 'mae_val': 1.1537627135242137, 'mape_val': '1.4047582232972406 %'}}]
from q3_time_series.model import UnivariateMultiStepLSTM
evaluate_metrics = UnivariateMultiStepLSTM(3,2).run('Singapore', "evaluate")
{'Singapore': {'rmse_train': 1.4304630397766138, 'rmse_val': 1.5401814488031627,
'mae_train': 1.2334213245388463, 'mae_val': 1.535661061311849,
'mape_train': '1.5851514521362737 %', 'mape_val': '1.8713769392807569 %'}}
from q3_time_series.model import VectorAutoRegression
prediction = VectorAutoRegression().run("predict")
China India Singapore
2008 93.111763 70.077187 81.695653
2009 93.087358 70.002008 81.873667
from q3_time_series.model import UnivariateMultiStepLSTM
prediction = UnivariateMultiStepLSTM(3,2).run('Singapore', 'predict')
Singapore
2008 85.778084
2009 87.609962