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<h1 id="project-description">Project Description</h1>
<p>In this project I used the <a href="https://www.kaggle.com/mlg-ulb/creditcardfraud">Kaggle Creditcard Fraud</a>
data to determine whether the transaction is fraud or not.</p>
<p><strong>Assumptions</strong>:</p>
<ul>
<li>Here we have data for just two days, but we assume the data is representative
of the whole population of credit card transactions.</li>
<li>We assume the 28 variables V1-V28 are
obtained from correct method of PCA and are scaled properly.</li>
</ul>
<p><strong>Metric Used</strong></p>
<ul>
<li>Here 1 means fraud and 0 means no fraud.</li>
<li>For this imbalanced dataset, false negative (fraud classified as not fraud) is more important than false positive (not-fraud classified as fraud), so we use <code>Recall</code> as the metric of evaluation. (<code>Recall = TP / (TP + FN)</code>).</li>
<li>For the imbalanced dataset, AUCROC gives overly optimistic metric, instead we should use <code>precision_recall_curve</code> and after looking at the curve we should choose the value that we want for precision and recall.</li>
<li>We should also note that precision and recall does not involve TN, so we should use them only when specificity (TNR = TN/(TN+FP)) is not important.</li>
<li>For imbalanced dataset, we can use F_beta metric. If both precision and recall are equally important, we can use F1-score. If we consider recall beta times more important than precision, we can use <code>F_beta = (1+beta^2) PR/(beta^P + R)</code> where P is precision and R is recall. (Mnemonic: Look at the denominator and remember that Recall is beta^2 time important than Precision). (Common values are 2 and 0.5. If beta is 2, recall is twice important than precision.)</li>
<li>We should also note that F_beta depends on Precision and Recall only. It does not depend on TN (true negative), so for imbalanced classification, better metric could be MCC (Mathew's Correlation Coefficient.)</li>
</ul>
<p><strong>Resampling Techniques</strong></p>
<ul>
<li>Our dataset is imbalanced, we can try two sampling: undersampling and oversampling.</li>
<li>Under-sampling. (We have low number of frauds, choose randomly same number of non-frauds.)</li>
<li>Oversampling <code>SMOTE</code> method. Used external library <code>imblearn</code>.</li>
</ul>
<h1 style="background-color:tomato;">Best Model So Far</h1>
<table>
<thead>
<tr>
<th style="text-align:left">Model</th>
<th style="text-align:left">Description</th>
<th style="text-align:left">Accuracy</th>
<th style="text-align:left">Precision</th>
<th style="text-align:left">Recall</th>
<th style="text-align:left">F1</th>
<th style="text-align:left">AUC</th>
<th style="text-align:left">Untrue Frauds</th>
<th style="text-align:left">Missed Frauds</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left">keras</td>
<td style="text-align:left">1 layer, class_weight, early_stopping, scikit api</td>
<td style="text-align:left">0.987939</td>
<td style="text-align:left">0.111989</td>
<td style="text-align:left">0.867347</td>
<td style="text-align:left">0.198366</td>
<td style="text-align:left">0.927747</td>
<td style="text-align:left">674</td>
<td style="text-align:left">13</td>
</tr>
<tr>
<td style="text-align:left">cb_tuned pycaret</td>
<td style="text-align:left">fold=5</td>
<td style="text-align:left">0.9996</td>
<td style="text-align:left">0.9659</td>
<td style="text-align:left">0.7865</td>
<td style="text-align:left">0.9667</td>
<td style="text-align:left">0.8642</td>
<td style="text-align:left"></td>
<td style="text-align:left"></td>
</tr>
<tr>
<td style="text-align:left">catboost</td>
<td style="text-align:left">seed=100,depth=6,iter=1k</td>
<td style="text-align:left">0.999631</td>
<td style="text-align:left">1.000000</td>
<td style="text-align:left">0.785714</td>
<td style="text-align:left">0.880000</td>
<td style="text-align:left">0.892857</td>
<td style="text-align:left">0</td>
<td style="text-align:left">21</td>
</tr>
</tbody>
</table>
</br>
<h1 style="background-color:tomato;">Undersampling</h1>
<p><strong>Recall for all Classifiers with Grid Search for Undersampled Data</strong>
<img src="reports/screenshots/recall_all_models_undersample_grid.png" alt="">
<img src="reports/screenshots/cm_lr_undersample_grid.png" alt=""></p>
</br>
<h1 style="background-color:tomato;">SMOTE Oversampling: Logistic Regression</h1>
<p><img src="reports/screenshots/lr_model_evaluation_scalar_metrics.png" alt="">
<img src="reports/screenshots/cm_lr_smote_grid.png" alt=""></p>
</br>
<h1 style="background-color:tomato;">Anomaly Detection Methods</h1>
<table>
<thead>
<tr>
<th style="text-align:left">Model</th>
<th style="text-align:left">Description</th>
<th style="text-align:left">Accuracy</th>
<th style="text-align:left">Precision</th>
<th style="text-align:left">Recall</th>
<th style="text-align:left">F1(Weighted)</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left">Isolation Forest</td>
<td style="text-align:left">default</td>
<td style="text-align:left">0.997384</td>
<td style="text-align:left">0.261682</td>
<td style="text-align:left">0.285714</td>
<td style="text-align:left">0.997442</td>
</tr>
<tr>
<td style="text-align:left">Local Outlier Factor</td>
<td style="text-align:left">default</td>
<td style="text-align:left">0.996331</td>
<td style="text-align:left">0.025641</td>
<td style="text-align:left">0.030612</td>
<td style="text-align:left">0.996493</td>
</tr>
</tbody>
</table>
</br>
<h1 style="background-color:tomato;">Gradient Boosting Modelling</h1>
<table>
<thead>
<tr>
<th style="text-align:left">Model</th>
<th style="text-align:left">Description</th>
<th style="text-align:left">Accuracy</th>
<th style="text-align:left">Precision</th>
<th style="text-align:left">Recall</th>
<th style="text-align:left">F1</th>
<th style="text-align:left">AUC</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left">lightgbm</td>
<td style="text-align:left">grid search optuna</td>
<td style="text-align:left">0.999315</td>
<td style="text-align:left">0.873418</td>
<td style="text-align:left">0.704082</td>
<td style="text-align:left">0.779661</td>
<td style="text-align:left">0.851953</td>
</tr>
<tr>
<td style="text-align:left">lightgbm</td>
<td style="text-align:left">default</td>
<td style="text-align:left">0.997367</td>
<td style="text-align:left">0.275862</td>
<td style="text-align:left">0.326531</td>
<td style="text-align:left">0.299065</td>
<td style="text-align:left">0.662527</td>
</tr>
<tr>
<td style="text-align:left">Xgboost</td>
<td style="text-align:left">default, imbalanced</td>
<td style="text-align:left">0.999263</td>
<td style="text-align:left">0.850000</td>
<td style="text-align:left">0.693878</td>
<td style="text-align:left">0.764045</td>
<td style="text-align:left">0.846833</td>
</tr>
<tr>
<td style="text-align:left">Xgboost</td>
<td style="text-align:left">default, undersampling</td>
<td style="text-align:left">0.999263</td>
<td style="text-align:left">0.850000</td>
<td style="text-align:left">0.693878</td>
<td style="text-align:left">0.764045</td>
<td style="text-align:left">0.846833</td>
</tr>
<tr>
<td style="text-align:left">Xgboost</td>
<td style="text-align:left">n_estimators=150, imbalanced</td>
<td style="text-align:left">0.999263</td>
<td style="text-align:left">0.850000</td>
<td style="text-align:left">0.693878</td>
<td style="text-align:left">0.764045</td>
<td style="text-align:left">0.846833</td>
</tr>
<tr>
<td style="text-align:left">Xgboost</td>
<td style="text-align:left">undersample, hpo1</td>
<td style="text-align:left">0.999298</td>
<td style="text-align:left">0.881579</td>
<td style="text-align:left">0.683673</td>
<td style="text-align:left">0.770115</td>
<td style="text-align:left">0.841758</td>
</tr>
<tr>
<td style="text-align:left">Xgboost</td>
<td style="text-align:left">imbalanced, hpo</td>
<td style="text-align:left">0.999245</td>
<td style="text-align:left">0.898551</td>
<td style="text-align:left">0.632653</td>
<td style="text-align:left">0.742515</td>
<td style="text-align:left">0.816265</td>
</tr>
<tr>
<td style="text-align:left">xgboost</td>
<td style="text-align:left">grid search optuna</td>
<td style="text-align:left">0.999333</td>
<td style="text-align:left">0.875000</td>
<td style="text-align:left">0.714286</td>
<td style="text-align:left">0.786517</td>
<td style="text-align:left">0.857055</td>
</tr>
<tr>
<td style="text-align:left">catboost</td>
<td style="text-align:left">seed=100,depth=6,iter=1k</td>
<td style="text-align:left">0.999631</td>
<td style="text-align:left">1.000000</td>
<td style="text-align:left">0.785714</td>
<td style="text-align:left">0.880000</td>
<td style="text-align:left">0.892857</td>
</tr>
</tbody>
</table>
</br>
<h1 style="background-color:tomato;">Automatic Modelling: pycaret</h1>
<table>
<thead>
<tr>
<th style="text-align:left">Model</th>
<th style="text-align:left">Description</th>
<th style="text-align:left">Accuracy</th>
<th style="text-align:left">AUC</th>
<th style="text-align:left">Recall</th>
<th style="text-align:left">Precision</th>
<th style="text-align:left">F1</th>
<th style="text-align:left">Kappa</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left">cb_tuned</td>
<td style="text-align:left">fold=5</td>
<td style="text-align:left">0.9996</td>
<td style="text-align:left">0.9659</td>
<td style="text-align:left">0.7865</td>
<td style="text-align:left">0.9667</td>
<td style="text-align:left">0.8642</td>
<td style="text-align:left">0.8639</td>
</tr>
<tr>
<td style="text-align:left">lda_tuned</td>
<td style="text-align:left">fold=5</td>
<td style="text-align:left">0.9995</td>
<td style="text-align:left">0.9833</td>
<td style="text-align:left">0.7760</td>
<td style="text-align:left">0.9217</td>
<td style="text-align:left">0.8423</td>
<td style="text-align:left">0.8420</td>
</tr>
<tr>
<td style="text-align:left">xgb</td>
<td style="text-align:left">default</td>
<td style="text-align:left">0.9994</td>
<td style="text-align:left">0.9585</td>
<td style="text-align:left">0.7345</td>
<td style="text-align:left">0.9102</td>
<td style="text-align:left">0.8047</td>
<td style="text-align:left">0.8044</td>
</tr>
<tr>
<td style="text-align:left">cb</td>
<td style="text-align:left">default</td>
<td style="text-align:left">0.9995</td>
<td style="text-align:left">0.9554</td>
<td style="text-align:left">0.7345</td>
<td style="text-align:left">0.9548</td>
<td style="text-align:left">0.8215</td>
<td style="text-align:left">0.8212</td>
</tr>
<tr>
<td style="text-align:left">lda</td>
<td style="text-align:left">default</td>
<td style="text-align:left">0.9992</td>
<td style="text-align:left">0.9677</td>
<td style="text-align:left">0.7255</td>
<td style="text-align:left">0.8340</td>
<td style="text-align:left">0.7661</td>
<td style="text-align:left">0.7657</td>
</tr>
<tr>
<td style="text-align:left">xgb_tuned</td>
<td style="text-align:left">tuned</td>
<td style="text-align:left">0.9992</td>
<td style="text-align:left">0.9677</td>
<td style="text-align:left">0.7255</td>
<td style="text-align:left">0.8340</td>
<td style="text-align:left">0.7661</td>
<td style="text-align:left">0.7657</td>
</tr>
<tr>
<td style="text-align:left">lda_tuned</td>
<td style="text-align:left">n_iter=100,fold=10</td>
<td style="text-align:left">0.9992</td>
<td style="text-align:left">0.9677</td>
<td style="text-align:left">0.7255</td>
<td style="text-align:left">0.8340</td>
<td style="text-align:left">0.7661</td>
<td style="text-align:left">0.7657</td>
</tr>
</tbody>
</table>
</br>
<h1 style="background-color:tomato;">Big Data Modelling: PySpark</h1>
<p><img src="reports/screenshots/pyspark_clf_results.png" alt=""></p>
</br>
<h1 style="background-color:tomato;">Deep Learning Models</h1>
<table>
<thead>
<tr>
<th style="text-align:left">Model</th>
<th style="text-align:left">Description</th>
<th style="text-align:left">Accuracy</th>
<th style="text-align:left">Precision</th>
<th style="text-align:left">Recall</th>
<th style="text-align:left">F1</th>
<th style="text-align:left">AUC</th>
<th style="text-align:left">Missed Frauds</th>
<th style="text-align:left">Untrue Frauds</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left">keras</td>
<td style="text-align:left">3 layers, 2 dropouts, class_weight</td>
<td style="text-align:left">0.983744</td>
<td style="text-align:left">0.081818</td>
<td style="text-align:left">0.826531</td>
<td style="text-align:left">0.148897</td>
<td style="text-align:left">0.905273</td>
<td style="text-align:left">17</td>
<td style="text-align:left">909</td>
</tr>
<tr>
<td style="text-align:left">keras</td>
<td style="text-align:left">1 layer, dropout, early_stopping</td>
<td style="text-align:left">0.984990</td>
<td style="text-align:left">0.090811</td>
<td style="text-align:left">0.857143</td>
<td style="text-align:left">0.164223</td>
<td style="text-align:left">0.921177</td>
<td style="text-align:left">14</td>
<td style="text-align:left">841</td>
</tr>
<tr>
<td style="text-align:left">keras</td>
<td style="text-align:left">1 layer, dropout, steps_per_epoch, oversampling</td>
<td style="text-align:left">0.982796</td>
<td style="text-align:left">0.080000</td>
<td style="text-align:left">0.857143</td>
<td style="text-align:left">0.146341</td>
<td style="text-align:left">0.920077</td>
<td style="text-align:left">14</td>
<td style="text-align:left">966</td>
</tr>
<tr>
<td style="text-align:left">keras</td>
<td style="text-align:left">1 layer, class_weight, early_stopping, scikit api</td>
<td style="text-align:left">0.987939</td>
<td style="text-align:left">0.111989</td>
<td style="text-align:left">0.867347</td>
<td style="text-align:left">0.198366</td>
<td style="text-align:left">0.927747</td>
<td style="text-align:left">13</td>
<td style="text-align:left">674</td>
</tr>
</tbody>
</table>
<h1 id="references">References</h1>
<ul>
<li>https://www.tensorflow.org/tutorials/structured_data/imbalanced_data</li>
<li>https://keras.io/examples/structured_data/imbalanced_classification/</li>
<li>https://www.kaggle.com/residentmario/using-keras-models-with-scikit-learn-pipelines#</li>
<li>https://machinelearningmastery.com/grid-search-hyperparameters-deep-learning-models-python-keras/</li>
</ul>
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