{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Let's have a look at how to implement a decision tree in Python. Again, we need to first import the required packages and load the data" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2026-01-19T18:46:42.745017Z", "iopub.status.busy": "2026-01-19T18:46:42.744687Z", "iopub.status.idle": "2026-01-19T18:46:46.911683Z", "shell.execute_reply": "2026-01-19T18:46:46.910926Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dataset already downloaded!\n" ] } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n", "from sklearn.tree import DecisionTreeClassifier, plot_tree\n", "from sklearn.ensemble import RandomForestClassifier\n", "from xgboost import XGBClassifier\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, recall_score, precision_score, roc_curve\n", "from sklearn.inspection import permutation_importance\n", "pd.set_option('display.max_columns', 50) # Display up to 50 columns\n", "from io import BytesIO\n", "from urllib.request import urlopen\n", "from zipfile import ZipFile\n", "import os.path\n", "\n", "# Check if the file exists\n", "if not os.path.isfile('data/card_transdata.csv'):\n", "\n", " print('Downloading dataset...')\n", "\n", " # Define the dataset to be downloaded\n", " zipurl = 'https://www.kaggle.com/api/v1/datasets/download/dhanushnarayananr/credit-card-fraud'\n", "\n", " # Download and unzip the dataset in the data folder\n", " with urlopen(zipurl) as zipresp:\n", " with ZipFile(BytesIO(zipresp.read())) as zfile:\n", " zfile.extractall('data')\n", "\n", " print('DONE!')\n", "\n", "else:\n", "\n", " print('Dataset already downloaded!')\n", "\n", "# Load the data\n", "df = pd.read_csv('data/card_transdata.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is the **dataset of credit card transactions** from [Kaggle.com](https://www.kaggle.com/datasets/dhanushnarayananr/credit-card-fraud/data) which we have used before. Recall that the target variable $y$ is `fraud`, which indicates whether the transaction is fraudulent or not. The other variables are the features $x$ of the transactions." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2026-01-19T18:46:46.914466Z", "iopub.status.busy": "2026-01-19T18:46:46.914198Z", "iopub.status.idle": "2026-01-19T18:46:46.934580Z", "shell.execute_reply": "2026-01-19T18:46:46.934080Z" } }, "outputs": [ { "data": { "text/html": [ "
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distance_from_homedistance_from_last_transactionratio_to_median_purchase_pricerepeat_retailerused_chipused_pin_numberonline_orderfraud
057.8778570.3111401.9459401.01.00.00.00.0
110.8299430.1755921.2942191.00.00.00.00.0
25.0910790.8051530.4277151.00.00.01.00.0
32.2475645.6000440.3626631.01.00.01.00.0
444.1909360.5664862.2227671.01.00.01.00.0
55.58640813.2610730.0647681.00.00.00.00.0
63.7240190.9568380.2784651.00.00.01.00.0
74.8482470.3207351.2730501.00.01.00.00.0
80.8766322.5036091.5169990.00.00.00.00.0
98.8390472.9705122.3616831.00.00.01.00.0
1014.2635300.1587581.1361021.01.00.01.00.0
1113.5923680.2405401.3703301.01.00.01.00.0
12765.2825590.3715620.5512451.01.00.00.00.0
132.13195656.3724016.3586671.00.00.01.01.0
1413.9559720.2715222.7989011.00.00.01.00.0
15179.6651480.1209200.5356401.01.01.01.00.0
16114.5197890.7070030.5169901.00.00.00.00.0
173.5896496.2474581.8464511.00.00.00.00.0
1811.08515234.6613512.5307581.00.00.01.00.0
196.1946711.1420140.3072171.00.00.00.00.0
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" ], "text/plain": [ " distance_from_home distance_from_last_transaction \\\n", "0 57.877857 0.311140 \n", "1 10.829943 0.175592 \n", "2 5.091079 0.805153 \n", "3 2.247564 5.600044 \n", "4 44.190936 0.566486 \n", "5 5.586408 13.261073 \n", "6 3.724019 0.956838 \n", "7 4.848247 0.320735 \n", "8 0.876632 2.503609 \n", "9 8.839047 2.970512 \n", "10 14.263530 0.158758 \n", "11 13.592368 0.240540 \n", "12 765.282559 0.371562 \n", "13 2.131956 56.372401 \n", "14 13.955972 0.271522 \n", "15 179.665148 0.120920 \n", "16 114.519789 0.707003 \n", "17 3.589649 6.247458 \n", "18 11.085152 34.661351 \n", "19 6.194671 1.142014 \n", "\n", " ratio_to_median_purchase_price repeat_retailer used_chip \\\n", "0 1.945940 1.0 1.0 \n", "1 1.294219 1.0 0.0 \n", "2 0.427715 1.0 0.0 \n", "3 0.362663 1.0 1.0 \n", "4 2.222767 1.0 1.0 \n", "5 0.064768 1.0 0.0 \n", "6 0.278465 1.0 0.0 \n", "7 1.273050 1.0 0.0 \n", "8 1.516999 0.0 0.0 \n", "9 2.361683 1.0 0.0 \n", "10 1.136102 1.0 1.0 \n", "11 1.370330 1.0 1.0 \n", "12 0.551245 1.0 1.0 \n", "13 6.358667 1.0 0.0 \n", "14 2.798901 1.0 0.0 \n", "15 0.535640 1.0 1.0 \n", "16 0.516990 1.0 0.0 \n", "17 1.846451 1.0 0.0 \n", "18 2.530758 1.0 0.0 \n", "19 0.307217 1.0 0.0 \n", "\n", " used_pin_number online_order fraud \n", "0 0.0 0.0 0.0 \n", "1 0.0 0.0 0.0 \n", "2 0.0 1.0 0.0 \n", "3 0.0 1.0 0.0 \n", "4 0.0 1.0 0.0 \n", "5 0.0 0.0 0.0 \n", "6 0.0 1.0 0.0 \n", "7 1.0 0.0 0.0 \n", "8 0.0 0.0 0.0 \n", "9 0.0 1.0 0.0 \n", "10 0.0 1.0 0.0 \n", "11 0.0 1.0 0.0 \n", "12 0.0 0.0 0.0 \n", "13 0.0 1.0 1.0 \n", "14 0.0 1.0 0.0 \n", "15 1.0 1.0 0.0 \n", "16 0.0 0.0 0.0 \n", "17 0.0 0.0 0.0 \n", "18 0.0 1.0 0.0 \n", "19 0.0 0.0 0.0 " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head(20)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2026-01-19T18:46:46.936878Z", "iopub.status.busy": "2026-01-19T18:46:46.936637Z", "iopub.status.idle": "2026-01-19T18:46:47.223798Z", "shell.execute_reply": "2026-01-19T18:46:47.223277Z" } }, "outputs": [ { "data": { "text/html": [ "
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distance_from_homedistance_from_last_transactionratio_to_median_purchase_pricerepeat_retailerused_chipused_pin_numberonline_orderfraud
count1000000.0000001000000.0000001000000.0000001000000.0000001000000.0000001000000.0000001000000.0000001000000.000000
mean26.6287925.0365191.8241820.8815360.3503990.1006080.6505520.087403
std65.39078425.8430932.7995890.3231570.4770950.3008090.4767960.282425
min0.0048740.0001180.0043990.0000000.0000000.0000000.0000000.000000
25%3.8780080.2966710.4756731.0000000.0000000.0000000.0000000.000000
50%9.9677600.9986500.9977171.0000000.0000000.0000001.0000000.000000
75%25.7439853.3557482.0963701.0000001.0000000.0000001.0000000.000000
max10632.72367211851.104565267.8029421.0000001.0000001.0000001.0000001.000000
\n", "
" ], "text/plain": [ " distance_from_home distance_from_last_transaction \\\n", "count 1000000.000000 1000000.000000 \n", "mean 26.628792 5.036519 \n", "std 65.390784 25.843093 \n", "min 0.004874 0.000118 \n", "25% 3.878008 0.296671 \n", "50% 9.967760 0.998650 \n", "75% 25.743985 3.355748 \n", "max 10632.723672 11851.104565 \n", "\n", " ratio_to_median_purchase_price repeat_retailer used_chip \\\n", "count 1000000.000000 1000000.000000 1000000.000000 \n", "mean 1.824182 0.881536 0.350399 \n", "std 2.799589 0.323157 0.477095 \n", "min 0.004399 0.000000 0.000000 \n", "25% 0.475673 1.000000 0.000000 \n", "50% 0.997717 1.000000 0.000000 \n", "75% 2.096370 1.000000 1.000000 \n", "max 267.802942 1.000000 1.000000 \n", "\n", " used_pin_number online_order fraud \n", "count 1000000.000000 1000000.000000 1000000.000000 \n", "mean 0.100608 0.650552 0.087403 \n", "std 0.300809 0.476796 0.282425 \n", "min 0.000000 0.000000 0.000000 \n", "25% 0.000000 0.000000 0.000000 \n", "50% 0.000000 1.000000 0.000000 \n", "75% 0.000000 1.000000 0.000000 \n", "max 1.000000 1.000000 1.000000 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2026-01-19T18:46:47.226119Z", "iopub.status.busy": "2026-01-19T18:46:47.225869Z", "iopub.status.idle": "2026-01-19T18:46:47.242618Z", "shell.execute_reply": "2026-01-19T18:46:47.242100Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 1000000 entries, 0 to 999999\n", "Data columns (total 8 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 distance_from_home 1000000 non-null float64\n", " 1 distance_from_last_transaction 1000000 non-null float64\n", " 2 ratio_to_median_purchase_price 1000000 non-null float64\n", " 3 repeat_retailer 1000000 non-null float64\n", " 4 used_chip 1000000 non-null float64\n", " 5 used_pin_number 1000000 non-null float64\n", " 6 online_order 1000000 non-null float64\n", " 7 fraud 1000000 non-null float64\n", "dtypes: float64(8)\n", "memory usage: 61.0 MB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Data Preprocessing\n", "\n", "Since we have already explored the dataset in the previous section, we can skip that part and move directly to the data preprocessing. \n", "\n", "We will again split the data into training and test sets using the `train_test_split` function" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2026-01-19T18:46:47.245225Z", "iopub.status.busy": "2026-01-19T18:46:47.244991Z", "iopub.status.idle": "2026-01-19T18:46:47.829362Z", "shell.execute_reply": "2026-01-19T18:46:47.828443Z" } }, "outputs": [], "source": [ "X = df.drop('fraud', axis=1) # All variables except `fraud`\n", "y = df['fraud'] # Only our fraud variables\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, test_size = 0.3, random_state = 42)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we can do the feature scaling to ensure our non-binary variables have mean zero and variance 1" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2026-01-19T18:46:47.832653Z", "iopub.status.busy": "2026-01-19T18:46:47.832339Z", "iopub.status.idle": "2026-01-19T18:46:47.895294Z", "shell.execute_reply": "2026-01-19T18:46:47.894497Z" } }, "outputs": [], "source": [ "def scale_features(scaler, df, col_names, only_transform=False):\n", "\n", " # Extract the features we want to scale\n", " features = df[col_names] \n", "\n", " # Fit the scaler to the features and transform them\n", " if only_transform:\n", " features = scaler.transform(features.values)\n", " else:\n", " features = scaler.fit_transform(features.values)\n", "\n", " # Replace the original features with the scaled features\n", " df[col_names] = features\n", "\n", "col_names = ['distance_from_home', 'distance_from_last_transaction', 'ratio_to_median_purchase_price'] \n", "scaler = StandardScaler() \n", "scale_features(scaler, X_train, col_names)\n", "scale_features(scaler, X_test, col_names, only_transform=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Implementing a Decision Tree Classifier\n", "\n", "We can now implement a decision tree model using the `DecisionTreeClassifier` class from the `sklearn.tree` module. Fitting the model to the data is almost the same as when we used logistic regression" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2026-01-19T18:46:47.898308Z", "iopub.status.busy": "2026-01-19T18:46:47.898019Z", "iopub.status.idle": "2026-01-19T18:46:49.719317Z", "shell.execute_reply": "2026-01-19T18:46:49.718513Z" } }, "outputs": [], "source": [ "clf_dt = DecisionTreeClassifier(random_state=0).fit(X_train, y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can visualize the tree using the `plot_tree` function from the `sklearn.tree` module" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2026-01-19T18:46:49.721820Z", "iopub.status.busy": "2026-01-19T18:46:49.721586Z", "iopub.status.idle": "2026-01-19T18:46:50.955535Z", "shell.execute_reply": "2026-01-19T18:46:50.954762Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_tree(clf_dt, filled=True, feature_names = X_train.columns.to_list())\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The tree is quite large and it's difficult to see details. Let's only look at the first level of the tree" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2026-01-19T18:46:50.958433Z", "iopub.status.busy": "2026-01-19T18:46:50.958155Z", "iopub.status.idle": "2026-01-19T18:46:51.091639Z", "shell.execute_reply": "2026-01-19T18:46:51.091004Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_tree(clf_dt, max_depth=1, filled=True, feature_names = X_train.columns.to_list(), fontsize=10)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Recall from the data exploration that `ratio_to_median_purchase_price` was highly correlated with fraud. The decision tree model seems to have picked up on this as well since the first split is based on this variable. Also, note that the order in which the variables are split can differ between different branches of the tree.\n", "\n", "We can also make predictions using the model and evaluate its performance using the same functions as before" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2026-01-19T18:46:51.093973Z", "iopub.status.busy": "2026-01-19T18:46:51.093753Z", "iopub.status.idle": "2026-01-19T18:46:51.248657Z", "shell.execute_reply": "2026-01-19T18:46:51.248095Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.9999833333333333\n", "Precision: 0.9999237223493517\n", "Recall: 0.999885587887571\n", "ROC AUC: 0.999939141362689\n" ] } ], "source": [ "y_pred_dt = clf_dt.predict(X_test)\n", "y_proba_dt = clf_dt.predict_proba(X_test)\n", "\n", "print(f\"Accuracy: {accuracy_score(y_test, y_pred_dt)}\")\n", "print(f\"Precision: {precision_score(y_test, y_pred_dt)}\")\n", "print(f\"Recall: {recall_score(y_test, y_pred_dt)}\")\n", "print(f\"ROC AUC: {roc_auc_score(y_test, y_proba_dt[:, 1])}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The decision tree performs substantially better than the logistic regression. The ROC AUC score is much closer to the maximum value of 1 and we have an almost perfect classifier" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2026-01-19T18:46:51.251168Z", "iopub.status.busy": "2026-01-19T18:46:51.250927Z", "iopub.status.idle": "2026-01-19T18:46:51.378712Z", "shell.execute_reply": "2026-01-19T18:46:51.378208Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Compute the ROC curve\n", "fpr, tpr, thresholds = roc_curve(y_test, y_proba_dt[:, 1])\n", "\n", "# Plot the ROC curve\n", "plt.plot(fpr, tpr)\n", "plt.plot([0, 1], [0, 1], linestyle='--', color='grey')\n", "plt.xlabel('False Positive Rate (FPR)')\n", "plt.ylabel('True Positive Rate (TPR)')\n", "plt.title('ROC Curve')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's also check the confusion matrix to see where we still make mistakes" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2026-01-19T18:46:51.381073Z", "iopub.status.busy": "2026-01-19T18:46:51.380828Z", "iopub.status.idle": "2026-01-19T18:46:51.660312Z", "shell.execute_reply": "2026-01-19T18:46:51.659814Z" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "conf_mat = confusion_matrix(y_test, y_pred_dt, labels=[1, 0]).transpose() # Transpose the sklearn confusion matrix to match the convention in the lecture\n", "sns.heatmap(conf_mat, annot=True, cmap='Blues', fmt='g', xticklabels=['Fraud', 'No Fraud'], yticklabels=['Fraud', 'No Fraud'])\n", "plt.xlabel(\"Actual\")\n", "plt.ylabel(\"Predicted\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are only 3 false negatives, i.e., fraudulent transactions that we did not detect. There are also 2 false positives, i.e., \"false alarms\", where non-fraudulent transactions were classified as fraudulent. The decision tree classifier is almost perfect which is a bit suspicious. We might have been lucky in the sense that the training and test sets were split in a way that the model performs very well. We should not expect this to be the case in general. It might be better to use cross-validation to get a more reliable estimate of the model's performance.\n", "\n", "\n", "### Implementing a Random Forest Classifier\n", "\n", "We can also implement a random forest model using the `RandomForestClassifier` class from the `sklearn.ensemble` module. Fitting the model to the data is almost the same as when we used logistic regression and decision trees" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2026-01-19T18:46:51.662602Z", "iopub.status.busy": "2026-01-19T18:46:51.662384Z", "iopub.status.idle": "2026-01-19T18:48:13.923413Z", "shell.execute_reply": "2026-01-19T18:48:13.922732Z" } }, "outputs": [], "source": [ "clf_rf = RandomForestClassifier(random_state = 0).fit(X_train, y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that it takes a bit longer to train the Random Forest since we have to train many trees (the default setting is 100). We can also make predictions using the model and evaluate its performance using the same functions as before" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2026-01-19T18:48:13.926778Z", "iopub.status.busy": "2026-01-19T18:48:13.926452Z", "iopub.status.idle": "2026-01-19T18:48:16.144487Z", "shell.execute_reply": "2026-01-19T18:48:16.143829Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.9999833333333333\n", "Precision: 1.0\n", "Recall: 0.9998093131459517\n", "ROC AUC: 0.999999999164201\n" ] } ], "source": [ "y_pred_rf = clf_rf.predict(X_test)\n", "y_proba_rf = clf_rf.predict_proba(X_test)\n", "\n", "print(f\"Accuracy: {accuracy_score(y_test, y_pred_rf)}\")\n", "print(f\"Precision: {precision_score(y_test, y_pred_rf)}\")\n", "print(f\"Recall: {recall_score(y_test, y_pred_rf)}\")\n", "print(f\"ROC AUC: {roc_auc_score(y_test, y_proba_rf[:, 1])}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As expected, the Random Forest performs better than the Decision Tree in the metrics we have used. Now, let's also check the confusion matrix to see where we still make mistakes" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "execution": { "iopub.execute_input": "2026-01-19T18:48:16.146775Z", "iopub.status.busy": "2026-01-19T18:48:16.146553Z", "iopub.status.idle": "2026-01-19T18:48:16.493825Z", "shell.execute_reply": "2026-01-19T18:48:16.493157Z" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "conf_mat = confusion_matrix(y_test, y_pred_rf, labels=[1, 0]).transpose() # Transpose the sklearn confusion matrix to match the convention in the lecture\n", "sns.heatmap(conf_mat, annot=True, cmap='Blues', fmt='g', xticklabels=['Fraud', 'No Fraud'], yticklabels=['Fraud', 'No Fraud'])\n", "plt.xlabel(\"Actual\")\n", "plt.ylabel(\"Predicted\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are still some false negatives, but the number of false positives has decreased compared to the Decision Tree model. \n", "\n", "\n", "### Implementing a XGBoost Classifier\n", "\n", "Let's also have a look at the XGBoost classifier. We can implement the model using the `XGBClassifier` class from the `xgboost` package. Fitting the model to the data is almost the same as when we used logistic regression, decision trees, and random forests, even though it is not part of the `sklearn` package. This is because the `xgboost` package is designed to work well with the `sklearn` package. Let's fit the model to the data" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "execution": { "iopub.execute_input": "2026-01-19T18:48:16.496814Z", "iopub.status.busy": "2026-01-19T18:48:16.496528Z", "iopub.status.idle": "2026-01-19T18:48:18.256360Z", "shell.execute_reply": "2026-01-19T18:48:18.255445Z" } }, "outputs": [], "source": [ "clf_xgb = XGBClassifier(random_state = 0).fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "execution": { "iopub.execute_input": "2026-01-19T18:48:18.260144Z", "iopub.status.busy": "2026-01-19T18:48:18.259792Z", "iopub.status.idle": "2026-01-19T18:48:18.671263Z", "shell.execute_reply": "2026-01-19T18:48:18.669122Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.9983333333333333\n", "Precision: 0.9896440129449838\n", "Recall: 0.9913046794553984\n", "ROC AUC: 0.9999735930686904\n" ] } ], "source": [ "y_pred_xgb = clf_xgb.predict(X_test)\n", "y_proba_xgb = clf_xgb.predict_proba(X_test)\n", "\n", "print(f\"Accuracy: {accuracy_score(y_test, y_pred_xgb)}\")\n", "print(f\"Precision: {precision_score(y_test, y_pred_xgb)}\")\n", "print(f\"Recall: {recall_score(y_test, y_pred_xgb)}\")\n", "print(f\"ROC AUC: {roc_auc_score(y_test, y_proba_xgb[:, 1])}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's also check the confusion matrix to see where we still make mistakes" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "execution": { "iopub.execute_input": "2026-01-19T18:48:18.676281Z", "iopub.status.busy": "2026-01-19T18:48:18.676049Z", "iopub.status.idle": "2026-01-19T18:48:18.945825Z", "shell.execute_reply": "2026-01-19T18:48:18.945300Z" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "conf_mat = confusion_matrix(y_test, y_pred_xgb, labels=[1, 0]).transpose() # Transpose the sklearn confusion matrix to match the convention in the lecture\n", "sns.heatmap(conf_mat, annot=True, cmap='Blues', fmt='g', xticklabels=['Fraud', 'No Fraud'], yticklabels=['Fraud', 'No Fraud'])\n", "plt.xlabel(\"Actual\")\n", "plt.ylabel(\"Predicted\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The XGBoost model seems to perform a bit worse than the Random Forest model. There are more false negatives and false positives. However, the model is still very good at detecting fraudulent transactions and has a high ROC AUC score. Adjusting the hyperparameters of the model might improve its performance.\n", "\n", "\n", "### Feature Importance\n", "\n", "We can also look at the feature importance of each model. The feature importance is a measure of how much each feature contributes to the model's predictions. Let's start with the Decision Tree model" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "execution": { "iopub.execute_input": "2026-01-19T18:48:18.948170Z", "iopub.status.busy": "2026-01-19T18:48:18.947932Z", "iopub.status.idle": "2026-01-19T18:48:19.063385Z", "shell.execute_reply": "2026-01-19T18:48:19.062929Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create a DataFrame with the feature importance\n", "df_feature_importance_dt = pd.DataFrame({'Feature': X_train.columns, 'Importance': clf_dt.feature_importances_})\n", "df_feature_importance_dt = df_feature_importance_dt.sort_values('Importance', ascending=False)\n", "\n", "# Plot the feature importance\n", "plt.barh(df_feature_importance_dt['Feature'], df_feature_importance_dt['Importance'])\n", "plt.xlabel('Importance')\n", "plt.ylabel('Feature')\n", "plt.title('Feature Importance - Decision Tree')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This shows that the `ratio_to_median_purchase_price` is the most important feature for determining whether a transaction is fraudulent or not. Whether a transaction is online, is important as well.\n", "\n", "Let's also look at the feature importance of the Random Forest model" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "execution": { "iopub.execute_input": "2026-01-19T18:48:19.065814Z", "iopub.status.busy": "2026-01-19T18:48:19.065606Z", "iopub.status.idle": "2026-01-19T18:48:19.169731Z", "shell.execute_reply": "2026-01-19T18:48:19.169289Z" } }, "outputs": [ { "data": { "image/png": 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y7QXR1NSEl5cXLl26BBcXlzw/+5yE6GP6+vr46quvMHbsWDx//lz2w4MF/X+DKjbeESAiqqRsbGwQGhqK6dOn4+7du+jYsSMMDAzw33//4fz589DS0kJISAh0dXXh7u6ORYsWoXr16rCxscGJEyfw66+/5vrxHycnJwDAL7/8Ah0dHairq6NWrVowMjLCoEGDMHDgQIwZMwY9e/bEvXv3sHDhQhgbGxcpXm1tbaxYsQKDBw/G8+fP8dVXX8HExARPnjzB5cuX8eTJE6xataq0h6lIlJWV0aFDB0ycOBFSqRQLFixAWlqa3NtT5s+fjw4dOsDLywuTJ0+GqqoqVq5ciWvXrmHz5s1FugOQc2K9fPlyDB48GNWqVYO9vT1atmwJAwMDjBo1CrNmzUK1atWwadMmXL58ucR96tevH9avX49Ro0bh5s2b8PLyglQqxblz5+Dg4IC+ffuiVatWGDFiBIYMGYKYmBi4u7tDS0sLKSkpOHXqFJydnTF69OgSx1BUs2fPxtatWxEUFIQjR46UyXj4+flh6dKl8PPzw9y5c1G3bl3s378fBw8elKunpKSEhQsXYsCAAejcuTNGjhyJzMxMLFq0CC9fvkRYWNjndvez+fn54aeffsLgwYORlJQEZ2dnnDp1CvPmzYOPjw/at29faBvLly9H69at0aZNG4wePRo2NjZ49eoV7ty5gz179uDYsWMAgC5dush+y8TY2Bj37t3DsmXLYG1tLZvKld/3uiSJIZUzBT6oTEREBch5+8yFCxcKrLdr1y7By8tL0NXVFdTU1ARra2vhq6++Eo4cOSKr8++//wo9e/YUDAwMBB0dHaFjx47CtWvX8nwT0LJly4RatWoJysrKcm9TkUqlwsKFC4XatWsL6urqgqurq3Ds2LF83xr0xx9/5BnviRMnBF9fX8HQ0FCoVq2aYGFhIfj6+uZbP0dBbw36dIxy3uby5MkTufLBgwcLWlpaudpcsGCBEBISItSsWVNQVVUVGjVqJBw8eDBXDFFRUULbtm0FLS0tQUNDQ2jRooWwZ88euTqFfW7Tpk0TatSoISgpKcm9oSk6Olpwc3MTNDU1BWNjY+Hrr78WLl68KPcZ5NWHT/v8sTdv3ggzZ84U6tatK6iqqgpGRkZC27ZthejoaLl669atE5o3by7rV506dQQ/P78C3+RTEgCEsWPH5rltypQpAgDhxIkTgiCUzXjk/D3Q1tYWdHR0hJ49ewrR0dG52hSED3+vmjdvLqirqwtaWlpCu3bthNOnT+d5jMK+Zzk8PDyE+vXr5zs+OaytrQVfX98C6zx79kwYNWqUYG5uLqioqAjW1tbCtGnThLdv38rVK2jMExMThaFDhwoWFhZCtWrVBGNjY6Fly5bCnDlzZHWWLFkitGzZUqhevbqgqqoqWFlZCcOGDROSkpLk2srve00Vm0QQivCIPxERURWU88DrokWLMHnyZEWHQ0RUrviMABERERGRCDERICIiIiISIU4NIiIiIiISId4RICIiIiISISYCREREREQixESAiIiIiEiE+INiRJQnqVSKhw8fQkdHp0g/lERERESKJwgCXr16hRo1akBJqeBr/kwEiChPDx8+hKWlpaLDICIiohK4f/8+atasWWAdJgJElKecn4a/f/8+dHV1FRwNERERFUVaWhosLS1l/44XhIkAEeUpZzqQrq4uEwEiIqJKpijTevmwMBERERGRCDERICIiIiISISYCREREREQixESAiIiIiEiEmAgQEREREYkQEwEiIiIiIhFiIkBEREREJEJMBIiIiIiIRIiJABERERGRCDERICIiIiISISYCREREREQixESAiIiIiEiEmAgQEREREYkQEwEiIiIiIhFSUXQARFSxOc06CCU1zVzlSWG+CoiGiIiISgvvCBARERERiRATASIiIiIiEWIiQEREREQkQkwEiIiIiIhEiIkAEREREZEIMREgIiIiIhIhJgJERERERCLERICIiIiISISYCBARERERiRATASIiIiIiEWIiQEREREQkQkwEiIiIiIhEiIkAEREREZEIMREgIiIiIhIhJgJEFVhSUhIkEgni4uIAAJGRkZBIJHj58qVC4yIiIqLKj4kAiZqnpycmTJhQ5scp6Qm8paUlUlJS4OTkVDaBERERkWgxEaBy9+7dO0WHUGoEQUBWVlaZta+srAwzMzOoqKiUWptVafyJiIio5JgIUJnz9PTEuHHjMHHiRFSvXh0dOnTAjRs34OPjA21tbZiammLQoEF4+vRprn3GjRsHfX19GBkZYcaMGRAEQVbn3bt3CAwMhIWFBbS0tNC8eXNERkbKtj979gz9+vVDzZo1oampCWdnZ2zevFm23d/fHydOnMDy5cshkUggkUiQlJRUYF9yruwfPHgQrq6uUFNTQ1RUFARBwMKFC1G7dm1oaGigQYMG+PPPPwF8mN7j5eUFADAwMIBEIoG/vz8A4MCBA2jdurWsj507d0ZCQoLseJ9ODcpLdHQ03N3doaGhAUtLSwQEBCAjI0O23cbGBnPmzIG/vz/09PQwfPjwAvtIRERE4sBEgMpFREQEVFRUcPr0aYSFhcHDwwMNGzZETEwMDhw4gP/++w+9e/fOc59z587hhx9+wNKlS7F27VrZ9iFDhuD06dPYsmULrly5gl69eqFjx464ffs2AODt27do0qQJ9u7di2vXrmHEiBEYNGgQzp07BwBYvnw53NzcMHz4cKSkpCAlJQWWlpZF6k9gYCDmz5+P+Ph4uLi4YMaMGVi/fj1WrVqF69ev49tvv8XAgQNx4sQJWFpaYvv27QCAmzdvIiUlBcuXLwcAZGRkYOLEibhw4QKOHj0KJSUlfPnll5BKpUWK4+rVq/D29kaPHj1w5coVbN26FadOncK4cePk6i1atAhOTk6IjY1FUFBQnm1lZmYiLS1NbiEiIqIqTCAqYx4eHkLDhg1l60FBQcIXX3whV+f+/fsCAOHmzZuyfRwcHASpVCqr89133wkODg6CIAjCnTt3BIlEIjx48ECunXbt2gnTpk3LNxYfHx9h0qRJcrGNHz++yH05fvy4AEDYtWuXrCw9PV1QV1cXoqOj5eoOGzZM6Nevn9x+L168KLD9x48fCwCEq1evCoIgCImJiQIA4dKlS3m2M2jQIGHEiBFybURFRQlKSkrCmzdvBEEQBGtra6F79+6F9m3WrFkCgFyL5YRtgvV3e3MtREREVPGkpqYKAITU1NRC65bexGOiAri6usr+HBsbi+PHj0NbWztXvYSEBNjZ2QEAWrRoAYlEItvm5uaGJUuWIDs7GxcvXoQgCLK6OTIzM2FkZAQAyM7ORlhYGLZu3YoHDx4gMzMTmZmZ0NLSKtX+3LhxA2/fvkWHDh3k6rx79w6NGjUqsJ2EhAQEBQXh7NmzePr0qexOQHJycpEeEI6NjcWdO3ewadMmWZkgCJBKpUhMTISDg0OuePMzbdo0TJw4UbaelpZW5DskREREVPkwEaBy8fHJt1QqRZcuXbBgwYJc9czNzYvUnlQqhbKyMmJjY6GsrCy3LSfBWLJkCZYuXYply5bB2dkZWlpamDBhQqk8LPtpfwBg3759sLCwkKunpqZWYDtdunSBpaUl1qxZgxo1akAqlcLJyanIMUqlUowcORIBAQG5tllZWeUZb37U1NQKjZeIiIiqDiYCVO4aN26M7du3w8bGpsC34Zw9ezbXet26daGsrIxGjRohOzsbjx8/Rps2bfLcPyoqCt26dcPAgQMBfDhpvn37tuwqOQCoqqoiOzv7s/rj6OgINTU1JCcnw8PDI886qqqqACB3rGfPniE+Ph6rV6+W9eHUqVPFOnbjxo1x/fp12NraljB6IiIiEis+LEzlbuzYsXj+/Dn69euH8+fP4+7duzh06BCGDh0qd6J8//59TJw4ETdv3sTmzZuxYsUKjB8/HgBgZ2eHAQMGwM/PDzt27EBiYiIuXLiABQsWYP/+/QAAW1tbHD58GNHR0YiPj8fIkSPx6NEjuVhsbGxw7tw5JCUlyU3NKQ4dHR1MnjwZ3377LSIiIpCQkIBLly7hp59+QkREBADA2toaEokEe/fuxZMnT5Ceng4DAwMYGRnhl19+wZ07d3Ds2DG5qTlF8d133+HMmTMYO3Ys4uLicPv2bezevRvffPNNsftBRERE4sJEgMpdjRo1cPr0aWRnZ8Pb2xtOTk4YP3489PT0oKT0f19JPz8/vHnzBs2aNcPYsWPxzTffYMSIEbLt69evh5+fHyZNmgR7e3t07doV586dk81rDwoKQuPGjeHt7Q1PT0+YmZmhe/fucrFMnjwZysrKcHR0hLGxMZKTk0vUp9mzZ2PmzJmYP38+HBwc4O3tjT179qBWrVoAAAsLC4SEhGDq1KkwNTXFuHHjoKSkhC1btiA2NhZOTk749ttvsWjRomId18XFBSdOnMDt27fRpk0bNGrUCEFBQUWeYkVERETiJRGEj17MTlRBeHp6omHDhli2bJmiQxGttLQ06OnpwXLCNiipaebanhTmq4CoiIiIqCA5/36npqZCV1e3wLq8I0BEREREJEJMBIg+MmrUKGhra+e5jBo1StHhEREREZUaTg0i+sjjx4/z/UVdXV1dmJiYlHNEisOpQURERJVPcaYG8fWhRB8xMTER1ck+ERERiRenBhERERERiRATASIiIiIiEWIiQEREREQkQkwEiIiIiIhEiIkAEREREZEIMREgIiIiIhIhJgJERERERCLERICIiIiISIT4g2JEVKBrId6F/jIhERERVT68I0BEREREJEJMBIiIiIiIRIiJABERERGRCDERICIiIiISISYCREREREQixESAiIiIiEiEmAgQEREREYkQEwEiIiIiIhFiIkBEREREJEL8ZWEiKpDTrINQUtNUdBj5SgrzVXQIRERElRLvCBARERERiRATASIiIiIiEWIiQEREREQkQkwEiIiIiIhEiIkAEREREZEIMREgIiIiIhIhJgJERERERCLERICIiIiISISYCBARERERiRATASIiIiIiEWIiQEREREQkQkwEiIiIiIhEiIkAEREREZEIMREgIiIiIhKhSpkIeHp6YsKECQAAGxsbLFu2TKHxlLXXr1+jZ8+e0NXVhUQiwcuXLxUdkkx4eDj09fUVHUaV9/F3noiIiKg0VMpE4GMXLlzAiBEjilS3siYNERERiIqKQnR0NFJSUqCnp6fokMpMcU94IyMjK1xy9Dny68+OHTswe/ZsxQRFREREVZKKogP4XMbGxooOocwlJCTAwcEBTk5O+dZ59+4dVFVVyzGqyqWyj4+hoaGiQyAiIqIqpsLfEcjIyICfnx+0tbVhbm6OJUuWyG3/9Cp/cHAwrKysoKamhho1aiAgIADAhyvN9+7dw7fffguJRAKJRAIAePbsGfr164eaNWtCU1MTzs7O2Lx5s9wxPD09ERAQgMDAQBgaGsLMzAzBwcFydV6+fIkRI0bA1NQU6urqcHJywt69e2Xbo6Oj4e7uDg0NDVhaWiIgIAAZGRmF9t/T0xNLlizByZMnIZFI4OnpKev3nDlz4O/vDz09PQwfPhwAsH37dtSvXx9qamqwsbHJc7zmzJkjG1Nra2v89ddfePLkCbp16wZtbW04OzsjJiam0NjykpCQgG7dusHU1BTa2tpo2rQpjhw5Ildn5cqVqFu3LtTV1WFqaoqvvvoKAODv748TJ05g+fLlss8oKSkp32MlJSXBy8sLAGBgYACJRAJ/f3/ZuI0bNw4TJ05E9erV0aFDBwDA999/D2dnZ2hpacHS0hJjxoxBenq6rM2cqU4HDx6Eg4MDtLW10bFjR6SkpMjqREZGolmzZtDS0oK+vj5atWqFe/fuFbn/mZmZCAwMhKWlJdTU1FC3bl38+uuvhfbn4zslL168gJ+fHwwMDKCpqYlOnTrh9u3bxeoHERERiVuFTwSmTJmC48ePY+fOnTh06BAiIyMRGxubZ90///wTS5cuxerVq3H79m3s2rULzs7OAD5MrahZsyZCQ0ORkpIiOyF6+/YtmjRpgr179+LatWsYMWIEBg0ahHPnzsm1HRERAS0tLZw7dw4LFy5EaGgoDh8+DACQSqXo1KkToqOj8dtvv+HGjRsICwuDsrIyAODq1avw9vZGjx49cOXKFWzduhWnTp3CuHHjCu3/jh07MHz4cLi5uSElJQU7duyQbVu0aBGcnJwQGxuLoKAgxMbGonfv3ujbty+uXr2K4OBgBAUFITw8XK7NpUuXolWrVrh06RJ8fX0xaNAg+Pn5YeDAgbh48SJsbW3h5+cHQRCK9iF9JD09HT4+Pjhy5AguXboEb29vdOnSBcnJyQCAmJgYBAQEIDQ0FDdv3sSBAwfg7u4OAFi+fDnc3NwwfPhw2WdkaWmZ77EsLS2xfft2AMDNmzeRkpKC5cuXy7ZHRERARUUFp0+fxurVqwEASkpK+OGHH3Dt2jVERETg2LFjCAwMlGv39evXWLx4MTZu3IiTJ08iOTkZkydPBgBkZWWhe/fu8PDwwJUrV3DmzBmMGDFCllgW1n8A8PPzw5YtW/DDDz8gPj4eP//8M7S1tQvtz8f8/f0RExOD3bt348yZMxAEAT4+Pnj//n2R+pGXzMxMpKWlyS1ERERUdVXoqUHp6en49ddfsWHDBtkV3YiICNSsWTPP+snJyTAzM0P79u1RrVo1WFlZoVmzZgA+TK1QVlaGjo4OzMzMZPtYWFjInRx98803OHDgAP744w80b95cVu7i4oJZs2YBAOrWrYsff/wRR48eRYcOHXDkyBGcP38e8fHxsLOzAwDUrl1btu+iRYvQv39/2RXdunXr4ocffoCHhwdWrVoFdXX1fMfA0NAQmpqaUFVVlYsbANq2bSsX+4ABA9CuXTsEBQUBAOzs7HDjxg0sWrRIdmUZAHx8fDBy5EgAwMyZM7Fq1So0bdoUvXr1AgB89913cHNzw3///ZfrmIVp0KABGjRoIFufM2cOdu7cid27d2PcuHFITk6GlpYWOnfuDB0dHVhbW6NRo0YAAD09PaiqqkJTU7NIx1VWVpZNmTExMcn10LKtrS0WLlwoV/bxVfVatWph9uzZGD16NFauXCkrf//+PX7++WfUqVMHADBu3DiEhoYCANLS0pCamorOnTvLtjs4OBS5/7du3cK2bdtw+PBhtG/fHoD8d6Wg/uS4ffs2du/ejdOnT6Nly5YAgE2bNsHS0hK7du2SfY4F9SMv8+fPR0hISL7biYiIqGqp0HcEEhIS8O7dO7i5ucnKDA0NYW9vn2f9Xr164c2bN6hduzaGDx+OnTt3Iisrq8BjZGdnY+7cuXBxcYGRkRG0tbVx6NAhuSu4wIdE4GPm5uZ4/PgxACAuLg41a9aUJQGfio2NRXh4OLS1tWWLt7c3pFIpEhMTCx2H/Li6usqtx8fHo1WrVnJlrVq1wu3bt5GdnZ1nX0xNTQFAdufk47Kc/hVHRkYGAgMD4ejoCH19fWhra+Off/6RjWeHDh1gbW2N2rVrY9CgQdi0aRNev35d7OMUxafjAwDHjx9Hhw4dYGFhAR0dHfj5+eHZs2dy07Q0NTVlJ8+A/GdtaGgIf39/2ZX+5cuXy023Kaz/cXFxUFZWhoeHR4n7FR8fDxUVFblE1cjICPb29oiPjy9SP/Iybdo0pKamypb79++XOEYiIiKq+Cp0IlDcqSmWlpa4efMmfvrpJ2hoaGDMmDFwd3eXmy7xqSVLlmDp0qUIDAzEsWPHEBcXB29vb7x7906uXrVq1eTWJRIJpFIpAEBDQ6PAuKRSKUaOHIm4uDjZcvnyZdy+fVvuRK24tLS05NYFQZBNUfm47FMf9yWnfl5lOf0rjilTpmD79u2YO3cuoqKiEBcXB2dnZ9l46ujo4OLFi9i8eTPMzc0xc+ZMNGjQoEze+vPp+Ny7dw8+Pj5wcnLC9u3bERsbi59++gkA5L4jeX3WH4/j+vXrcebMGbRs2RJbt26FnZ0dzp49W6T+F/ZdKYr8/l58+vkX1o9PqampQVdXV24hIiKiqqtCJwK2traoVq2a7CQL+PCQ5K1bt/LdR0NDA127dsUPP/yAyMhInDlzBlevXgUAqKqqyl0ZB4CoqCh069YNAwcORIMGDVC7dm25hy6LwsXFBf/++2++cTVu3BjXr1+Hra1trqU032Tj6OiIU6dOyZVFR0fDzs5O9rxCWYuKioK/vz++/PJLODs7w8zMLNcDvyoqKmjfvj0WLlyIK1euICkpCceOHQOQ92dUkJzxK8o+MTExyMrKwpIlS9CiRQvY2dnh4cOHRe/cRxo1aoRp06YhOjoaTk5O+P333wEU3n9nZ2dIpVKcOHGixP1xdHREVlaW3HMsz549w61bt+SmKREREREVpEInAtra2hg2bBimTJmCo0eP4tq1a/D394eSUt5hh4eH49dff8W1a9dw9+5dbNy4ERoaGrC2tgbw4Y05J0+exIMHD/D06VMAH5KNw4cPIzo6GvHx8Rg5ciQePXpUrDg9PDzg7u6Onj174vDhw0hMTMTff/+NAwcOAPgw5/7MmTMYO3Ys4uLiZHO8v/nmm88YndwmTZqEo0ePYvbs2bh16xYiIiLw448/FviAaGmztbXFjh07ZHc9+vfvL3dnYe/evfjhhx8QFxeHe/fuYcOGDZBKpbLpXjY2Njh37hySkpLw9OnTQu9KWFtbQyKRYO/evXjy5IncG4A+VadOHWRlZWHFihWy78fPP/9crP4lJiZi2rRpOHPmDO7du4dDhw7JnYAX1n8bGxsMHjwYQ4cOxa5du5CYmIjIyEhs27atyP2pW7cuunXrhuHDh+PUqVO4fPkyBg4cCAsLC3Tr1q1Y/SEiIiLxqtCJAPDhQVt3d3d07doV7du3R+vWrdGkSZM86+rr62PNmjVo1aoVXFxccPToUezZswdGRkYAgNDQUCQlJaFOnTqy3x8ICgpC48aN4e3tDU9PT5iZmaF79+7FjnP79u1o2rQp+vXrB0dHRwQGBsqu6rq4uODEiRO4ffs22rRpg0aNGiEoKAjm5uYlG5R8NG7cGNu2bcOWLVvg5OSEmTNnIjQ0VO5B4bK2dOlSGBgYoGXLlujSpQu8vb3RuHFj2XZ9fX3s2LEDbdu2hYODA37++Wds3rwZ9evXBwBMnjwZysrKcHR0hLGxca5nNT5lYWGBkJAQTJ06FaampgW+ialhw4b4/vvvsWDBAjg5OWHTpk2YP39+sfqnqamJf/75Bz179oSdnR1GjBiBcePGyR6+Lqz/ALBq1Sp89dVXGDNmDOrVq4fhw4fLnlEoan/Wr1+PJk2aoHPnznBzc4MgCNi/f3+u6UBERERE+ZEIJXlHJBFVeWlpadDT04PlhG1QUtNUdDj5SgrzVXQIREREFUbOv9+pqamFPu9X4e8IEBERERFR6WMioGBRUVFyrxX9dFG0Tp065RvbvHnzyvz4o0aNyvf4o0aNKvPjExEREVVVnBqkYG/evMGDBw/y3W5ra1uO0eT24MEDvHnzJs9thoaGsh/AKiuPHz/O9xdudXV1YWJiUqbHFzNODSIiIqp8ijM1qEL/srAYaGhoKPxkvyAWFhYKPb6JiQlP9omIiIjKAKcGERERERGJEBMBIiIiIiIRYiJARERERCRCTASIiIiIiESIiQARERERkQgxESAiIiIiEiEmAkREREREIsREgIiIiIhIhJgIEBERERGJEH9ZmIgKdC3Eu9CfKCciIqLKh3cEiIiIiIhEiIkAEREREZEIMREgIiIiIhIhJgJERERERCLERICIiIiISISYCBARERERiRATASIiIiIiEWIiQEREREQkQkwEiIiIiIhEiL8sTEQFcpp1EEpqmooOo0wkhfkqOgQiIiKF4R0BIiIiIiIRYiJARERERCRCTASIiIiIiESIiQARERERkQgxESAiIiIiEiEmAkREREREIsREgIiIiIhIhJgIEBERERGJEBMBIiIiIiIRYiJARERERCRCTASIiIiIiESIiQARERERkQgxESAiIiIiEiEmAkRlIDw8HPr6+qXSlo2NDZYtW5bv9qSkJEgkEsTFxZXK8YiIiEgcVBQdABF9HktLS6SkpKB69eqKDoWIiIgqESYCRJWcsrIyzMzMFB0GERERVTKcGkSikNf0moYNGyI4OBgAEBwcDCsrK6ipqaFGjRoICAiQ1Xv37h0CAwNhYWEBLS0tNG/eHJGRkXJthYeHw8rKCpqamvjyyy/x7NmzYsW3e/duuLq6Ql1dHdWrV0ePHj3ktr9+/RpDhw6Fjo4OrKys8Msvv8i2fTo1KDIyEhKJBPv27UODBg2grq6O5s2b4+rVq8WKiYiIiKo2JgIken/++SeWLl2K1atX4/bt29i1axecnZ1l24cMGYLTp09jy5YtuHLlCnr16oWOHTvi9u3bAIBz585h6NChGDNmDOLi4uDl5YU5c+YU+fj79u1Djx494Ovri0uXLuHo0aNwdXWVq7NkyRK4urri0qVLGDNmDEaPHo1//vmnwHanTJmCxYsX48KFCzAxMUHXrl3x/v37YowMERERVWWcGkSil5ycDDMzM7Rv3x7VqlWDlZUVmjVrBgBISEjA5s2b8e+//6JGjRoAgMmTJ+PAgQNYv3495s2bh+XLl8Pb2xtTp04FANjZ2SE6OhoHDhwo0vHnzp2Lvn37IiQkRFbWoEEDuTo+Pj4YM2YMAOC7777D0qVLERkZiXr16uXb7qxZs9ChQwcAQEREBGrWrImdO3eid+/eedbPzMxEZmambD0tLa1I8RMREVHlxDsCJHq9evXCmzdvULt2bQwfPhw7d+5EVlYWAODixYsQBAF2dnbQ1taWLSdOnEBCQgIAID4+Hm5ubnJtfrpekLi4OLRr167AOi4uLrI/SyQSmJmZ4fHjxwXu83EMhoaGsLe3R3x8fL7158+fDz09PdliaWlZxB4QERFRZcQ7AiQKSkpKEARBrixnmoylpSVu3ryJw4cP48iRIxgzZgwWLVqEEydOQCqVQllZGbGxsVBWVpbbX1tbGwBytVtcGhoahdapVq2a3LpEIoFUKi32sSQSSb7bpk2bhokTJ8rW09LSmAwQERFVYUwESBSMjY2RkpIiW09LS0NiYqJsXUNDA127dkXXrl0xduxY1KtXD1evXkWjRo2QnZ2Nx48fo02bNnm27ejoiLNnz8qVfbpeEBcXFxw9ehRDhgwpZq8KdvbsWVhZWQEAXrx4gVu3bhU4lUhNTQ1qamqlGgMRERFVXEwESBTatm2L8PBwdOnSBQYGBggKCpJd4Q8PD0d2djaaN28OTU1NbNy4ERoaGrC2toaRkREGDBgAPz8/LFmyBI0aNcLTp09x7NgxODs7w8fHBwEBAWjZsiUWLlyI7t2749ChQ0V+PgD4MJe/Xbt2qFOnDvr27YusrCz8/fffCAwM/Kw+h4aGwsjICKamppg+fTqqV6+O7t27f1abREREVHXwGQEShWnTpsHd3R2dO3eGj48Punfvjjp16gAA9PX1sWbNGrRq1Up2dX7Pnj0wMjICAKxfvx5+fn6YNGkS7O3t0bVrV5w7d042baZFixZYu3YtVqxYgYYNG+LQoUOYMWNGkWPz9PTEH3/8gd27d6Nhw4Zo27Ytzp0799l9DgsLw/jx49GkSROkpKRg9+7dUFVV/ex2iYiIqGqQCJ87wZmIKpTIyEh4eXnhxYsX0NfXL3E7aWlpHx4anrANSmqapRdgBZIU5qvoEIiIiEpVzr/fqamp0NXVLbAu7wgQEREREYkQEwGiMla/fn25V49+vGzatEnR4REREZFI8WFhojK2f//+fH/R19TUtNSP5+np+dmvNCUiIqKqj4kAURmztrZWdAhEREREuXBqEBERERGRCDERICIiIiISISYCREREREQixESAiIiIiEiEmAgQEREREYkQEwEiIiIiIhFiIkBEREREJEJMBIiIiIiIRIiJABERERGRCPGXhYmoQNdCvKGrq6voMIiIiKiU8Y4AEREREZEIMREgIiIiIhIhJgJERERERCLERICIiIiISISYCBARERERiRATASIiIiIiESpxIrBx40a0atUKNWrUwL179wAAy5Ytw19//VVqwRERERERUdkoUSKwatUqTJw4ET4+Pnj58iWys7MBAPr6+li2bFlpxkdERERERGWgRInAihUrsGbNGkyfPh3KysqycldXV1y9erXUgiMiIiIiorJRol8WTkxMRKNGjXKVq6mpISMj47ODIqKKw2nWQSipaSo6jHKRFOar6BCIiIjKTYnuCNSqVQtxcXG5yv/++284Ojp+bkxERERERFTGSnRHYMqUKRg7dizevn0LQRBw/vx5bN68GfPnz8fatWtLO0YiIiIiIiplJUoEhgwZgqysLAQGBuL169fo378/LCwssHz5cvTt27e0YyQiIiIiolJW7EQgKysLmzZtQpcuXTB8+HA8ffoUUqkUJiYmZREfERERERGVgWI/I6CiooLRo0cjMzMTAFC9enUmAURERERElUyJHhZu3rw5Ll26VNqxEBERERFROSnRMwJjxozBpEmT8O+//6JJkybQ0tKS2+7i4lIqwRERERERUdkoUSLQp08fAEBAQICsTCKRQBAESCQS2S8NExERERFRxVTiHxQjIiIiIqLKq0SJgLW1dWnHQURERERE5ahEicCGDRsK3O7n51eiYIiIiIiIqHyUKBEYP3683Pr79+/x+vVrqKqqQlNTk4kAEREREVEFV6LXh7548UJuSU9Px82bN9G6dWts3ry5tGMkIiIiIqJSVqJEIC9169ZFWFhYrrsFRERERERU8ZRaIgAAysrKePjwYWk2SSIQHh4OfX39UmvP398f3bt3L7X2FEkikWDXrl2KDoOIiIiqoBI9I7B79265dUEQkJKSgh9//BGtWrUqlcCISmr58uUQBEHRYRARERFVaCVKBD692iqRSGBsbIy2bdtiyZIlpREXUYnp6ekpOoQK7d27d1BVVVV0GERERKRgJZoaJJVK5Zbs7Gw8evQIv//+O8zNzUs7RipnNjY2WLZsmVxZw4YNERwcDAAIDg6GlZUV1NTUUKNGDblfmH737h0CAwNhYWEBLS0tNG/eHJGRkXJthYeHw8rKCpqamvjyyy/x7NmzIscWHByMhg0bYvXq1bC0tISmpiZ69eqFly9fyup8OjXI09MTAQEBCAwMhKGhIczMzGR9KQqJRIK1a9fiyy+/hKamJurWrSt3VyyvqU27du2CRCLJFfe6detgZWUFbW1tjB49GtnZ2Vi4cCHMzMxgYmKCuXPn5jp+SkoKOnXqBA0NDdSqVQt//PGH3PYHDx6gT58+MDAwgJGREbp164akpKRc4zF//nzUqFEDdnZ2Re47ERERVV0lSgRCQ0Px+vXrXOVv3rxBaGjoZwdFFdeff/6JpUuXYvXq1bh9+zZ27doFZ2dn2fYhQ4bg9OnT2LJlC65cuYJevXqhY8eOuH37NgDg3LlzGDp0KMaMGYO4uDh4eXlhzpw5xYrhzp072LZtG/bs2YMDBw4gLi4OY8eOLXCfiIgIaGlp4dy5c1i4cCFCQ0Nx+PDhIh8zJCQEvXv3xpUrV+Dj44MBAwbg+fPnxYo7ISEBf//9Nw4cOIDNmzdj3bp18PX1xb///osTJ05gwYIFmDFjBs6ePSu3X1BQEHr27InLly9j4MCB6NevH+Lj4wEAr1+/hpeXF7S1tXHy5EmcOnUK2tra6NixI969eydr4+jRo4iPj8fhw4exd+/ePOPLzMxEWlqa3EJERERVV4kSgZCQEKSnp+cqf/36NUJCQj47KKq4kpOTYWZmhvbt28PKygrNmjXD8OHDAXw40d28eTP++OMPtGnTBnXq1MHkyZPRunVrrF+/HsCH+fve3t6YOnUq7OzsEBAQAG9v72LF8PbtW0RERKBhw4Zwd3fHihUrsGXLFjx69CjffVxcXDBr1izUrVsXfn5+cHV1xdGjR4t8TH9/f/Tr1w+2traYN28eMjIycP78+WLFLZVKsW7dOjg6OqJLly7w8vLCzZs3sWzZMtjb22PIkCGwt7fPdQelV69e+Prrr2FnZ4fZs2fD1dUVK1asAABs2bIFSkpKWLt2LZydneHg4ID169cjOTlZrh0tLS2sXbsW9evXh5OTU57xzZ8/H3p6erLF0tKyWP0jIiKiyqVEiYAgCHLTHnJcvnwZhoaGnx0UVVy9evXCmzdvULt2bQwfPhw7d+5EVlYWAODixYsQBAF2dnbQ1taWLSdOnEBCQgIAID4+Hm5ubnJtfrpeGCsrK9SsWVNuf6lUips3b+a7j4uLi9y6ubk5Hj9+XORjfry/lpYWdHR0irU/8GHKlY6Ojmzd1NQUjo6OUFJSkiv7tN28xivnjkBsbCzu3LkDHR0d2XgbGhri7du3sjEHAGdn50KfC5g2bRpSU1Nly/3794vVPyIiIqpcivWwsIGBASQSCSQSCezs7OSSgezsbKSnp2PUqFGlHiSVLyUlpVxv3Xn//j0AwNLSEjdv3sThw4dx5MgRjBkzBosWLcKJEycglUqhrKyM2NhYKCsry+2vra0NAGXyNp+c72FeyWmOatWq5dpHKpUW+RgF7V/QeBXWRknjyumrVCpFkyZNsGnTplx1jI2NZX/W0tIqtE01NTWoqakVWo+IiIiqhmIlAsuWLYMgCBg6dChCQkLk3s6iqqoKGxubYl/dpYrH2NgYKSkpsvW0tDQkJibK1jU0NNC1a1d07doVY8eORb169XD16lU0atQI2dnZePz4Mdq0aZNn246OjrnmwH+6Xpjk5GQ8fPgQNWrUAACcOXMGSkpKCnsI1tjYGK9evUJGRobshDsuLq7U2j979iz8/Pzk1hs1agQAaNy4MbZu3QoTExPo6uqW2jGJiIio6itWIjB48GAAQK1atdCyZctcVzOpamjbti3Cw8PRpUsXGBgYICgoSHaFPzw8HNnZ2WjevDk0NTWxceNGaGhowNraGkZGRhgwYAD8/PywZMkSNGrUCE+fPsWxY8fg7OwMHx8fBAQEoGXLlli4cCG6d++OQ4cO4cCBA8WKT11dHYMHD8bixYuRlpaGgIAA9O7dG2ZmZmUxHIXKGYv//e9/+Oabb3D+/HmEh4eXWvt//PEHXF1d0bp1a2zatAnnz5/Hr7/+CgAYMGAAFi1ahG7duiE0NBQ1a9ZEcnIyduzYgSlTpshNoSIiIiL6WImeEfDw8JAlAW/evOGbRqqYadOmwd3dHZ07d4aPjw+6d++OOnXqAAD09fWxZs0atGrVCi4uLjh69Cj27NkDIyMjAMD69evh5+eHSZMmwd7eHl27dsW5c+dkD562aNECa9euxYoVK9CwYUMcOnQIM2bMKFZ8tra26NGjB3x8fPDFF1/AyckJK1euLN1BKAZDQ0P89ttv2L9/P5ydnbF58+ZivZ60MCEhIdiyZQtcXFwQERGBTZs2wdHREQCgqamJkydPwsrKCj169ICDgwOGDh2KN2/e8A4BERERFUgilGDS9uvXrxEYGIht27bl+Q747OzsUgmO6FPBwcHYtWtXqU69obylpaV9eHvQhG1QUtNUdDjlIinMV9EhEBERfZacf79TU1MLvShYojsCU6ZMwbFjx7By5Uqoqalh7dq1CAkJQY0aNbBhw4YSBU1EREREROWnRInAnj17sHLlSnz11VdQUVFBmzZtMGPGDMybNy/Pt5cQFVX9+vXlXj368VIW361Nmzble7z69euX+vGIiIiIKopiPSyc4/nz56hVqxYAQFdXV/YLq61bt8bo0aNLLzoSnf379+f56k3gwzv2dXR0SnX+fdeuXdG8efM8t/FheCIiIqrKSpQI1K5dG0lJSbC2toajoyO2bduGZs2aYc+ePdDX1y/lEElMrK2ty/V4Ojo6cj/yRURERCQWJZoaNGTIEFy+fBnAhzfM5Dwr8O2332LKlCmlGiAREREREZW+Et0R+Pbbb2V/9vLywj///IOYmBjUqVMHDRo0KLXgiIiIiIiobJQoEfjY27dvYWVlBSsrq9KIh4iIiIiIykGJpgZlZ2dj9uzZsLCwgLa2Nu7evQsACAoKkv3iKRERERERVVwlSgTmzp2L8PBwLFy4EKqqqrJyZ2dnrF27ttSCIyIiIiKislGiRGDDhg345ZdfMGDAACgrK8vKXVxc8M8//5RacEREREREVDZKlAg8ePAAtra2ucqlUmm+74AnIiIiIqKKo0SJQP369REVFZWr/I8//kCjRo0+OygiIiIiIipbJXpr0KxZszBo0CA8ePAAUqkUO3bswM2bN7Fhwwbs3bu3tGMkIiIiIqJSJhEEQShq5bt376JWrVqQSCQ4ePAg5s2bh9jYWEilUjRu3BgzZ87EF198UZbxElE5SUtLg56eHlJTU6Grq6vocIiIiKgIivPvd7HuCNStWxcpKSkwMTGBt7c31q1bhzt37sDMzOyzAiYiIiIiovJVrGcEPr158Pfff+P169elGhAREREREZW9Ej0snKMYs4qIiIiIiKgCKVYiIJFIIJFIcpUREREREVHlUqxnBARBgL+/P9TU1AAAb9++xahRo6ClpSVXb8eOHaUXIRERERERlbpiJQKDBw+WWx84cGCpBkNEREREROWjWInA+vXryyoOIiIiIiIqR5/1sDAREREREVVOTASIiIiIiESoWFODiEh8nGYdhJKapqLDoHKWFOar6BCIiKiM8Y4AEREREZEIMREgIiIiIhIhJgJERERERCLERICIiIiISISYCBARERERiRATASIiIiIiEWIiQEREREQkQkwEiIiIiIhEiIkAEREREZEIMREgIiIiIhIhJgJERERERCLERICIiIiISISYCBARERERiRATgSrG09MTEyZMAADY2Nhg2bJlCo2nrL1+/Ro9e/aErq4uJBIJXr58qeiQZMLDw6Gvr6/oMIiIiIjyxESgCrtw4QJGjBhRpLqVNWmIiIhAVFQUoqOjkZKSAj09PUWHRERERFQpqCg6ACo7xsbGig6hzCUkJMDBwQFOTk751nn37h1UVVXLMSoiIiKiio93BCqxjIwM+Pn5QVtbG+bm5liyZInc9k+v8gcHB8PKygpqamqoUaMGAgICAHyYTnTv3j18++23kEgkkEgkAIBnz56hX79+qFmzJjQ1NeHs7IzNmzfLHcPT0xMBAQEIDAyEoaEhzMzMEBwcLFfn5cuXGDFiBExNTaGurg4nJyfs3btXtj06Ohru7u7Q0NCApaUlAgICkJGRUWj/PT09sWTJEpw8eRISiQSenp6yfs+ZMwf+/v7Q09PD8OHDAQDbt29H/fr1oaamBhsbmzzHa86cObIxtba2xl9//YUnT56gW7du0NbWhrOzM2JiYgqN7WMHDx6Eg4MDtLW10bFjR6SkpMi2SaVShIaGombNmlBTU0PDhg1x4MAB2fakpCRIJBJs27YNbdq0gYaGBpo2bYpbt27hwoULcHV1lbX75MkTueOuX78eDg4OUFdXR7169bBy5cpixU1ERERVGxOBSmzKlCk4fvw4du7ciUOHDiEyMhKxsbF51v3zzz+xdOlSrF69Grdv38auXbvg7OwMANixYwdq1qyJ0NBQpKSkyE5U3759iyZNmmDv3r24du0aRowYgUGDBuHcuXNybUdEREBLSwvnzp3DwoULERoaisOHDwP4cKLbqVMnREdH47fffsONGzcQFhYGZWVlAMDVq1fh7e2NHj164MqVK9i6dStOnTqFcePGFdr/HTt2YPjw4XBzc0NKSgp27Ngh27Zo0SI4OTkhNjYWQUFBiI2NRe/evdG3b19cvXoVwcHBCAoKQnh4uFybS5cuRatWrXDp0iX4+vpi0KBB8PPzw8CBA3Hx4kXY2trCz88PgiAU6TN6/fo1Fi9ejI0bN+LkyZNITk7G5MmTZduXL1+OJUuWYPHixbhy5Qq8vb3RtWtX3L59W66dWbNmYcaMGbh48SJUVFTQr18/BAYGYvny5YiKikJCQgJmzpwpq79mzRpMnz4dc+fORXx8PObNm4egoCBERETkG2tmZibS0tLkFiIiIqq6JEJRz2ioQklPT4eRkRE2bNiAPn36AACeP3+OmjVrYsSIEVi2bBlsbGwwYcIETJgwAd9//z1Wr16Na9euoVq1arna+7huQXx9feHg4IDFixcD+HBVPjs7G1FRUbI6zZo1Q9u2bREWFoZDhw6hU6dOiI+Ph52dXa72/Pz8oKGhgdWrV8vKTp06BQ8PD2RkZEBdXb3AeCZMmIC4uDhERkbK9aVRo0bYuXOnrGzAgAF48uQJDh06JCsLDAzEvn37cP36ddl+bdq0wcaNGwEAjx49grm5OYKCghAaGgoAOHv2rCzxMDMzKzC28PBwDBkyBHfu3EGdOnUAACtXrkRoaCgePXoEALCwsMDYsWPxv//9T278mjZtip9++glJSUmoVasW1q5di2HDhgEAtmzZgn79+uHo0aNo27YtACAsLAzh4eH4559/AABWVlZYsGAB+vXrJ2t3zpw52L9/P6Kjo/OMNzg4GCEhIbnKLSdsg5KaZoF9paonKcxX0SEQEVEJpKWlQU9PD6mpqdDV1S2wLu8IVFIJCQl49+4d3NzcZGWGhoawt7fPs36vXr3w5s0b1K5dG8OHD8fOnTuRlZVV4DGys7Mxd+5cuLi4wMjICNra2jh06BCSk5Pl6rm4uMitm5ub4/HjxwCAuLg41KxZM88kAABiY2MRHh4ObW1t2eLt7Q2pVIrExMRCxyE/rq6ucuvx8fFo1aqVXFmrVq1w+/ZtZGdn59kXU1NTAJDdOfm4LKd/hdHU1JQlAYD82KSlpeHhw4d5xhUfHy9XVpS4ctp98uQJ7t+/j2HDhsmN65w5c5CQkJBvrNOmTUNqaqpsuX//fpH6SERERJUTHxaupIp7I8fS0hI3b97E4cOHceTIEYwZMwaLFi3CiRMn8rxDAABLlizB0qVLsWzZMjg7O0NLSwsTJkzAu3fv5Op9ur9EIoFUKgUAaGhoFBiXVCrFyJEjZc8rfMzKyqo4XZSjpaUlty4IguzZh4/LPvVxX3Lq51WW07/C5DU2nx43r7g+LStKXDkx5fx3zZo1aN68uVw7OVOy8qKmpgY1NbUC+0NERERVBxOBSsrW1hbVqlXD2bNnZSfML168wK1bt+Dh4ZHnPhoaGujatSu6du2KsWPHol69erh69SoaN24MVVVVuSvjABAVFYVu3bph4MCBAD6cYN6+fRsODg5FjtPFxQX//vsvbt26leddgcaNG+P69euwtbUtcpsl4ejoiFOnTsmVRUdHw87OrsCT47Kkq6uLGjVq4NSpU3B3d5eLq1mzZiVu19TUFBYWFrh79y4GDBhQGqESERFRFcREoJLS1tbGsGHDMGXKFBgZGcHU1BTTp0+HklLes73Cw8ORnZ2N5s2bQ1NTExs3boSGhgasra0BfJgff/LkSfTt2xdqamqoXr06bG1tsX37dkRHR8PAwADff/89Hj16VKxEwMPDA+7u7ujZsye+//572Nra4p9//oFEIkHHjh3x3XffoUWLFhg7diyGDx8OLS0txMfH4/Dhw1ixYkWpjBUATJo0CU2bNsXs2bPRp08fnDlzBj/++KPC36QzZcoUzJo1C3Xq1EHDhg2xfv16xMXFYdOmTZ/VbnBwMAICAqCrq4tOnTohMzMTMTExePHiBSZOnFhK0RMREVFlxkSgElu0aBHS09PRtWtX6OjoYNKkSUhNTc2zrr6+PsLCwjBx4kRkZ2fD2dkZe/bsgZGREQAgNDQUI0eORJ06dZCZmQlBEBAUFITExER4e3tDU1MTI0aMQPfu3fM9Rn62b9+OyZMno1+/fsjIyICtrS3CwsIAfLhjcOLECUyfPh1t2rSBIAioU6eO7AHo0tK4cWNs27YNM2fOxOzZs2Fubo7Q0FD4+/uX6nGKKyAgAGlpaZg0aRIeP34MR0dH7N69G3Xr1v2sdr/++mtoampi0aJFCAwMhJaWFpydnQt9GJyIiIjEg28NIqI85bx1gG8NEie+NYiIqHLiW4OIiIiIiKhATASowoqKipJ7/eWni6J16tQp39jmzZun6PCIiIiICsRnBKjCcnV1RVxcnKLDyNfatWvx5s2bPLcZGhqWczRERERExcNEgCosDQ2NMn+t6OewsLBQdAhEREREJcapQUREREREIsREgIiIiIhIhJgIEBERERGJEBMBIiIiIiIRYiJARERERCRCTASIiIiIiESIiQARERERkQgxESAiIiIiEiEmAkREREREIsRfFiaiAl0L8Yaurq6iwyAiIqJSxjsCREREREQixESAiIiIiEiEmAgQEREREYkQEwEiIiIiIhFiIkBEREREJEJMBIiIiIiIRIiJABERERGRCDERICIiIiISISYCREREREQixF8WJqICOc06CCU1TUWHQSKWFOar6BCIiKok3hEgIiIiIhIhJgJERERERCLERICIiIiISISYCBARERERiRATASIiIiIiEWIiQEREREQkQkwEiIiIiIhEiIkAEREREZEIMREgIiIiIhIhJgJERERERCLERICIiIiISISYCBARERERiRATASIiIiIiEWIiQEREREQkQkwESBSCg4PRsGFD2bq/vz+6d++usHg+x6d9ISIiIioJFUUHQKQIy5cvhyAIig6DiIiISGGYCJAo6enpKTqEQr1//x7VqlWrdG0TERFR5cCpQVQpZGZmIiAgACYmJlBXV0fr1q1x4cIFAEBkZCQkEgmOHj0KV1dXaGpqomXLlrh582a+7X06NcjT0xMBAQEIDAyEoaEhzMzMEBwcLLdPamoqRowYARMTE+jq6qJt27a4fPlykfuwatUq1KlTB6qqqrC3t8fGjRvltkskEvz888/o1q0btLS0MGfOHABAWFgYTE1NoaOjg2HDhuHt27e52l6/fj0cHBygrq6OevXqYeXKlbJtSUlJkEgk2LZtGzw9PaGuro7ffvutyHETERFR1cREgCqFwMBAbN++HREREbh48SJsbW3h7e2N58+fy+pMnz4dS5YsQUxMDFRUVDB06NBiHSMiIgJaWlo4d+4cFi5ciNDQUBw+fBgAIAgCfH198ejRI+zfvx+xsbFo3Lgx2rVrJxdDfnbu3Inx48dj0qRJuHbtGkaOHIkhQ4bg+PHjcvVmzZqFbt264erVqxg6dCi2bduGWbNmYe7cuYiJiYG5ubncST4ArFmzBtOnT8fcuXMRHx+PefPmISgoCBEREXL1vvvuOwQEBCA+Ph7e3t65YszMzERaWprcQkRERFWXROBEaargMjIyYGBggPDwcPTv3x/Ah6ktNjY2mDBhApo2bQovLy8cOXIE7dq1AwDs378fvr6+ePPmDdTV1REcHIxdu3YhLi4OwIc7Ai9fvsSuXbsAfLgjkJ2djaioKNlxmzVrhrZt2yIsLAzHjh3Dl19+icePH0NNTU1Wx9bWFoGBgRgxYkSBfWjVqhXq16+PX375RVbWu3dvZGRkYN++fQA+3BGYMGECli5dKqvTsmVLNGjQAKtWrZKVtWjRAm/fvpX1xcrKCgsWLEC/fv1kdebMmYP9+/cjOjoaSUlJqFWrFpYtW4bx48fnG2NwcDBCQkJylVtO2AYlNc0C+0dUlpLCfBUdAhFRpZGWlgY9PT2kpqZCV1e3wLq8I0AVXkJCAt6/f49WrVrJyqpVq4ZmzZohPj5eVubi4iL7s7m5OQDg8ePHRT7Ox/vntJGzf2xsLNLT02FkZARtbW3ZkpiYiISEhELbjo+Pl4sf+JAcfBw/ALi6uubaz83NTa7s4/UnT57g/v37GDZsmFxcc+bMyRXXp21/atq0aUhNTZUt9+/fL7RfREREVHnxYWGq8HJuWkkkklzlH5d9/PBrTrlUKi3ycT59eFYikcj2l0qlMDc3R2RkZK799PX1i9R+YfEDgJaWVpHjzYkL+DA9qHnz5nLblJWVi9W2mpqa3N0OIiIiqtp4R4AqPFtbW6iqquLUqVOysvfv3yMmJgYODg7lEkPjxo3x6NEjqKiowNbWVm6pXr16ofs7ODjIxQ8A0dHRhcbv4OCAs2fPypV9vG5qagoLCwvcvXs3V1y1atUqRg+JiIhIbHhHgCo8LS0tjB49GlOmTIGhoSGsrKywcOFCvH79GsOGDSvWm3tKqn379nBzc0P37t2xYMEC2Nvb4+HDh9i/fz+6d+9e6LSbKVOmoHfv3rIHjPfs2YMdO3bgyJEjBe43fvx4DB48GK6urmjdujU2bdqE69evo3bt2rI6wcHBCAgIgK6uLjp16oTMzEzExMTgxYsXmDhxYqn0n4iIiKoeJgJUKYSFhUEqlWLQoEF49eoVXF1dcfDgQRgYGJTL8SUSCfbv34/p06dj6NChePLkCczMzODu7g5TU9NC9+/evTuWL1+ORYsWISAgALVq1cL69evh6elZ4H59+vRBQkICvvvuO7x9+xY9e/bE6NGjcfDgQVmdr7/+Gpqamli0aBECAwOhpaUFZ2dnTJgw4TN7TURERFUZ3xpERHnKeesA3xpEisa3BhERFR3fGkRERERERAViIkBUCurXry/3+s6Pl02bNik6PCIiIqJc+IwAUSnYv38/3r9/n+e2ojxDQERERFTemAgQlQJra2tFh0BERERULJwaREREREQkQkwEiIiIiIhEiIkAEREREZEIMREgIiIiIhIhJgJERERERCLERICIiIiISISYCBARERERiRATASIiIiIiEWIiQEREREQkQvxlYSIq0LUQb+jq6io6DCIiIiplvCNARERERCRCTASIiIiIiESIiQARERERkQgxESAiIiIiEiEmAkREREREIsREgIiIiIhIhJgIEBERERGJEBMBIiIiIiIR4g+KEVGBnGYdhJKapqLDICIiqjKSwnwVHQIA3hEgIiIiIhIlJgJERERERCLERICIiIiISISYCBARERERiRATASIiIiIiEWIiQEREREQkQkwEiIiIiIhEiIkAEREREZEIMREgIiIiIhIhJgJERERERCLERICIiIiISISYCBARERERiRATASIiIiIiEWIiQEREREQkQlUmEbCxscGyZcsUHUaFEBwcjIYNG8rW/f390b17d4XFU56qcl8lEgl27dql6DCIiIioilBRdADFFR4ejgkTJuDly5dy5RcuXICWllapHCM4OBi7du1CXFxcqbSnaMuXL4cgCIoOgz5TSkoKDAwMFB0GERERVREVKhF49+4dVFVVS7SvsbFxKUdTdejp6Sk6hFLxOd+Pyiyn32ZmZooOhYiIiKoQhU4N8vT0xLhx4zBx4kRUr14dHTp0wPfffw9nZ2doaWnB0tISY8aMQXp6OgAgMjISQ4YMQWpqKiQSCSQSCYKDgwHknhqUnJyMbt26QVtbG7q6uujduzf++++/QmMKDw9HSEgILl++LDtGeHj4Z7UJ/N90nXXr1sHKygra2toYPXo0srOzsXDhQpiZmcHExARz586V2y81NRUjRoyAiYkJdHV10bZtW1y+fFmuTlhYGExNTaGjo4Nhw4bh7du3cts/nS5z4MABtG7dGvr6+jAyMkLnzp2RkJAg256UlASJRIIdO3bAy8sLmpqaaNCgAc6cOVOkvoaHh0NfXx+7du2CnZ0d1NXV0aFDB9y/fz/fmABgwoQJ8PT0lK3n9f0AgOvXr8PX1xe6urrQ0dFBmzZt5OIHgMWLF8Pc3BxGRkYYO3Ys3r9/L9v222+/wdXVFTo6OjAzM0P//v3x+PFj2fYXL15gwIABMDY2hoaGBurWrYv169fLtj948AB9+vSBgYEBjIyM0K1bNyQlJRVpbHL6HRISIvtMR44ciXfv3hXa70+nBv3777/o27cvDA0NoaWlBVdXV5w7d062fc+ePWjSpAnU1dVRu3ZthISEICsrq0hxEhERUdWn8GcEIiIioKKigtOnT2P16tVQUlLCDz/8gGvXriEiIgLHjh1DYGAgAKBly5ZYtmwZdHV1kZKSgpSUFEyePDlXm4IgoHv37nj+/DlOnDiBw4cPIyEhAX369Ck0nj59+mDSpEmoX7++7Bh9+vT5rDZzJCQk4O+//8aBAwewefNmrFu3Dr6+vvj3339x4sQJLFiwADNmzMDZs2dl/fD19cWjR4+wf/9+xMbGonHjxmjXrh2eP38OANi2bRtmzZqFuXPnIiYmBubm5li5cmWBcWRkZGDixIm4cOECjh49CiUlJXz55ZeQSqVy9aZPn47JkycjLi4OdnZ26NevX5FPJF+/fo25c+ciIiICp0+fRlpaGvr27Vvkscrx6ffjwYMHcHd3h7q6Oo4dO4bY2FgMHTpULq7jx48jISEBx48fR0REBMLDw2XJHPDhCvvs2bNx+fJl7Nq1C4mJifD395dtDwoKwo0bN/D3338jPj4eq1atQvXq1WX98vLygra2Nk6ePIlTp05BW1sbHTt2lDuZL8jRo0cRHx+P48ePY/Pmzdi5cydCQkIK7Pen0tPT4eHhgYcPH2L37t24fPkyAgMDZZ/hwYMHMXDgQAQEBODGjRtYvXo1wsPDcyWaH8vMzERaWprcQkRERFWXwqcG2draYuHChbL1evXqyf5cq1YtzJ49G6NHj8bKlSuhqqoKPT09SCSSAqdJHDlyBFeuXEFiYiIsLS0BABs3bkT9+vVx4cIFNG3aNN99NTQ0oK2tDRUVFbljHD58uMRt5pBKpVi3bh10dHTg6OgILy8v3Lx5E/v374eSkhLs7e2xYMECREZGokWLFjh+/DiuXr2Kx48fQ01NDcCHK927du3Cn3/+iREjRmDZsmUYOnQovv76awDAnDlzcOTIkVx3BT7Ws2dPufVff/0VJiYmuHHjBpycnGTlkydPhq+vLwAgJCQE9evXx507d+Q+o/y8f/8eP/74I5o3bw7gw4mtg4MDzp8/j2bNmhW6f45Pvx//+9//oKenhy1btqBatWoAADs7O7l9DAwM8OOPP0JZWRn16tWDr68vjh49iuHDhwMAhg4dKqtbu3Zt/PDDD2jWrBnS09Ohra2N5ORkNGrUCK6urgA+3G3KsWXLFigpKWHt2rWQSCQAgPXr10NfXx+RkZH44osvCu2Tqqoq1q1bB01NTdSvXx+hoaGYMmUKZs+eDSUlpTz7/anff/8dT548wYULF2BoaCjbJ8fcuXMxdepUDB48WNbP2bNnIzAwELNmzcqzzfnz5+dKSIiIiKjqUvgdgZyTrRzHjx9Hhw4dYGFhAR0dHfj5+eHZs2fIyMgocpvx8fGwtLSUnbADgKOjI/T19REfH1+iOEujTRsbG+jo6MjWTU1N4ejoKDv5yynLmaYSGxuL9PR0GBkZQVtbW7YkJibKpsLEx8fDzc1N7jifrn8qISEB/fv3R+3ataGrq4tatWoB+DD16WMuLi6yP5ubmwOA3BSagqioqMh9tvXq1SvR+H/6/YiLi0ObNm1kSUBe6tevD2VlZdm6ubm5XNyXLl1Ct27dYG1tDR0dHdl0pJz+jx49Glu2bEHDhg0RGBiI6Oho2b6xsbG4c+cOdHR0ZJ+HoaEh3r59m2t6Un4aNGgATU1N2bqbmxvS09Plpk592u9PxcXFoVGjRrIk4FOxsbEIDQ2V+94MHz4cKSkpeP36dZ77TJs2DampqbLl43iIiIio6lH4HYGP3/Rz7949+Pj4YNSoUZg9ezYMDQ1x6tQpDBs2TG6Od2EEQZBdrS1KeXm1+enJq0QiybMsZ3qHVCqFubk5IiMjc7Wlr69ftMDz0KVLF1haWmLNmjWoUaMGpFIpnJycck1t+Ti2nD5+On2oIHmNS06ZkpJSrjcZ5fUZf/omKA0NjUKPW9CYZmRk4IsvvsAXX3yB3377DcbGxkhOToa3t7es/506dcK9e/ewb98+HDlyBO3atcPYsWOxePFiSKVSNGnSBJs2bcp13M99YP3j8SrsDViFjYNUKkVISAh69OiRa5u6unqe+6ipqcnuPBEREVHVp/BE4GMxMTHIysrCkiVLZFfJt23bJldHVVUV2dnZBbbj6OiI5ORk3L9/X3YF/8aNG0hNTYWDg0OhceR1jM9tsyQaN26MR48eQUVFRW56ysccHBxw9uxZ+Pn5ycpynjHIy7NnzxAfH4/Vq1ejTZs2AIBTp06VatwAkJWVhZiYGNk0oJs3b+Lly5eyaUXGxsa4du2a3D5xcXEFXukHPtyliIiIwPv37wutm5d//vkHT58+RVhYmOxzjImJyVXP2NgY/v7+8Pf3R5s2bTBlyhQsXrwYjRs3xtatW2UP+pbE5cuX8ebNG9nJ/NmzZ6GtrY2aNWsWuQ0XFxesXbsWz58/z/OuQOPGjXHz5k256UJEREREH1P41KCP1alTB1lZWVixYgXu3r2LjRs34ueff5arY2Njg/T0dBw9ehRPnz7Nc5pD+/bt4eLiggEDBuDixYs4f/48/Pz84OHhUeiUi5xjJCYmIi4uDk+fPkVmZuZnt1kS7du3h5ubG7p3746DBw8iKSkJ0dHRmDFjhuzkdfz48Vi3bh3WrVuHW7duYdasWbh+/Xq+bea86eaXX37BnTt3cOzYMUycOLHUY69WrRq++eYbnDt3DhcvXsSQIUPQokULWWLQtm1bxMTEYMOGDbh9+zZmzZqVKzHIy7hx42QPHsfExOD27dvYuHEjbt68WaS4rKysoKqqKvuO7d69G7Nnz5arM3PmTPz111+4c+cOrl+/jr1798qSvQEDBqB69ero1q0boqKikJiYiBMnTmD8+PH4999/ixTDu3fvMGzYMNkDybNmzcK4cePkpogVpl+/fjAzM0P37t1x+vRp3L17F9u3b5e92WnmzJnYsGEDgoODcf36dcTHx2Pr1q2YMWNGkY9BREREVVuFSgQaNmyI77//HgsWLICTkxM2bdqE+fPny9Vp2bIlRo0ahT59+sDY2DjPBypzXrNoYGAAd3d3tG/fHrVr18bWrVuLFEfPnj3RsWNHeHl5wdjYGJs3b/7sNktCIpFg//79cHd3x9ChQ2FnZ4e+ffsiKSkJpqamAD685WjmzJn47rvv0KRJE9y7dw+jR4/Ot00lJSVs2bIFsbGxcHJywrfffotFixaVeuyampr47rvv0L9/f7i5uUFDQwNbtmyRbff29kZQUBACAwPRtGlTvHr1Su6uRn6MjIxw7Ngx2VtzmjRpgjVr1hT57oCxsTHCw8Pxxx9/wNHREWFhYVi8eLFcHVVVVUybNg0uLi5wd3eHsrKyLHZNTU2cPHkSVlZW6NGjBxwcHDB06FC8efOmyHcI2rVrh7p168Ld3R29e/dGly5dZK/BLSpVVVUcOnQIJiYm8PHxgbOzM8LCwmTPRnh7e2Pv3r04fPgwmjZtihYtWuD777+HtbV1sY5DREREVZdE4E/OUinL79ef6cPvCLx8+VLu9wAqqrS0NOjp6cFywjYoqWkWvgMREREVSVKYb5m1nfPvd2pqaqEXKSvUHQEiIiIiIiofokwE6tevL/daxY+XvN4Go6g2K6pOnTrl29d58+YpOjyFym9ctLW1ERUVpejwiIiIiGREOTXo3r17+b6O1NTUVO5d/4pss6J68OAB3rx5k+c2Q0PDfN9tLwZ37tzJd5uFhUWRXn9aUXBqEBERUdmoKFODKtTrQ8tLWTwwKaaHMC0sLBQdQoXF13USERFRZSHKqUFERERERGLHRICIiIiISISYCBARERERiRATASIiIiIiEWIiQEREREQkQkwEiIiIiIhEiIkAEREREZEIMREgIiIiIhIhJgJERERERCIkyl8WJqKiuxbiXehPlBMREVHlwzsCREREREQixESAiIiIiEiEmAgQEREREYkQEwEiIiIiIhFiIkBEREREJEJMBIiIiIiIRIiJABERERGRCDERICIiIiISISYCREREREQixESAiIiIiEiEmAgQEREREYkQEwEiIiIiIhFiIkBEREREJEJMBIiIiIiIRIiJABERERGRCKkoOgAiqpgEQQAApKWlKTgSIiIiKqqcf7dz/h0vCBMBIsrTs2fPAACWlpYKjoSIiIiK69WrV9DT0yuwDhMBIsqToaEhACA5ObnQ/5FQ6UhLS4OlpSXu378PXV1dRYdT5XG8yx/HvHxxvMtfRRhzQRDw6tUr1KhRo9C6TASIKE9KSh8eIdLT0+M/IOVMV1eXY16OON7lj2Nevjje5U/RY17UC3h8WJiIiIiISISYCBARERERiRATASLKk5qaGmbNmgU1NTVFhyIaHPPyxfEufxzz8sXxLn+VbcwlQlHeLURERERERFUK7wgQEREREYkQEwEiIiIiIhFiIkBEREREJEJMBIiIiIiIRIiJAJGIrVy5ErVq1YK6ujqaNGmCqKioAuufOHECTZo0gbq6OmrXro2ff/65nCKtGooz3ikpKejfvz/s7e2hpKSECRMmlF+gVUhxxnzHjh3o0KEDjI2NoaurCzc3Nxw8eLAco638ijPep06dQqtWrWBkZAQNDQ3Uq1cPS5cuLcdoq4bi/n88x+nTp6GiooKGDRuWbYBVTHHGOzIyEhKJJNfyzz//lGPEBWMiQCRSW7duxYQJEzB9+nRcunQJbdq0QadOnZCcnJxn/cTERPj4+KBNmza4dOkS/ve//yEgIADbt28v58grp+KOd2ZmJoyNjTF9+nQ0aNCgnKOtGoo75idPnkSHDh2wf/9+xMbGwsvLC126dMGlS5fKOfLKqbjjraWlhXHjxuHkyZOIj4/HjBkzMGPGDPzyyy/lHHnlVdwxz5Gamgo/Pz+0a9eunCKtGko63jdv3kRKSopsqVu3bjlFXAQCEYlSs2bNhFGjRsmV1atXT5g6dWqe9QMDA4V69erJlY0cOVJo0aJFmcVYlRR3vD/m4eEhjB8/vowiq7o+Z8xzODo6CiEhIaUdWpVUGuP95ZdfCgMHDizt0Kqsko55nz59hBkzZgizZs0SGjRoUIYRVi3FHe/jx48LAIQXL16UQ3QlwzsCRCL07t07xMbG4osvvpAr/+KLLxAdHZ3nPmfOnMlV39vbGzExMXj//n2ZxVoVlGS86fOUxphLpVK8evUKhoaGZRFilVIa433p0iVER0fDw8OjLEKscko65uvXr0dCQgJmzZpV1iFWKZ/zHW/UqBHMzc3Rrl07HD9+vCzDLDYVRQdAROXv6dOnyM7OhqmpqVy5qakpHj16lOc+jx49yrN+VlYWnj59CnNz8zKLt7IryXjT5ymNMV+yZAkyMjLQu3fvsgixSvmc8a5ZsyaePHmCrKwsBAcH4+uvvy7LUKuMkoz57du3MXXqVERFRUFFhaeAxVGS8TY3N8cvv/yCJk2aIDMzExs3bkS7du0QGRkJd3f38gi7UPwWEImYRCKRWxcEIVdZYfXzKqe8FXe86fOVdMw3b96M4OBg/PXXXzAxMSmr8Kqckox3VFQU0tPTcfbsWUydOhW2trbo169fWYZZpRR1zLOzs9G/f3+EhITAzs6uvMKrcorzHbe3t4e9vb1s3c3NDffv38fixYuZCBCR4lSvXh3Kysq5rmI8fvw419WOHGZmZnnWV1FRgZGRUZnFWhWUZLzp83zOmG/duhXDhg3DH3/8gfbt25dlmFXG54x3rVq1AADOzs7477//EBwczESgCIo75q9evUJMTAwuXbqEcePGAfgw/U0QBKioqODQoUNo27ZtucReGZXW/8dbtGiB3377rbTDKzE+I0AkQqqqqmjSpAkOHz4sV3748GG0bNkyz33c3Nxy1T906BBcXV1RrVq1Mou1KijJeNPnKemYb968Gf7+/vj999/h6+tb1mFWGaX1HRcEAZmZmaUdXpVU3DHX1dXF1atXERcXJ1tGjRoFe3t7xMXFoXnz5uUVeqVUWt/xS5cuVayptAp7TJmIFGrLli1CtWrVhF9//VW4ceOGMGHCBEFLS0tISkoSBEEQpk6dKgwaNEhW/+7du4Kmpqbw7bffCjdu3BB+/fVXoVq1asKff/6pqC5UKsUdb0EQhEuXLgmXLl0SmjRpIvTv31+4dOmScP36dUWEXykVd8x///13QUVFRfjpp5+ElJQU2fLy5UtFdaFSKe54//jjj8Lu3buFW7duCbdu3RLWrVsn6OrqCtOnT1dUFyqdkvx/5WN8a1DxFHe8ly5dKuzcuVO4deuWcO3aNWHq1KkCAGH79u2K6kIuTASIROynn34SrK2tBVVVVaFx48bCiRMnZNsGDx4seHh4yNWPjIwUGjVqJKiqqgo2NjbCqlWryjniyq244w0g12JtbV2+QVdyxRlzDw+PPMd88ODB5R94JVWc8f7hhx+E+vXrC5qamoKurq7QqFEjYeXKlUJ2drYCIq+8ivv/lY8xESi+4oz3ggULhDp16gjq6uqCgYGB0Lp1a2Hfvn0KiDp/EkH4/0/7ERERERGRaPAZASIiIiIiEWIiQEREREQkQkwEiIiIiIhEiIkAEREREZEIMREgIiIiIhIhJgJERERERCLERICIiIiISISYCBARERERiRATASIiojLk7++P7t27KzqMPCUlJUEikSAuLk7RoRCRAjARICIiEqF3794pOgQiUjAmAkREROXE09MT33zzDSZMmAADAwOYmpril19+QUZGBoYMGQIdHR3UqVMHf//9t2yfyMhISCQS7Nu3Dw0aNIC6ujqaN2+Oq1evyrW9fft21K9fH2pqarCxscGSJUvkttvY2GDOnDnw9/eHnp4ehg8fjlq1agEAGjVqBIlEAk9PTwDAhQsX0KFDB1SvXh16enrw8PDAxYsX5dqTSCRYu3YtvvzyS2hqaqJu3brYvXu3XJ3r16/D19cXurq60NHRQZs2bZCQkCDbvn79ejg4OEBdXR316tXDypUrP3uMiajomAgQERGVo4iICFSvXh3nz5/HN998g9GjR6NXr15o2bIlLl68CG9vbwwaNAivX7+W22/KlClYvHgxLly4ABMTE3Tt2hXv378HAMTGxqJ3797o27cvrl69iuDgYAQFBSE8PFyujUWLFsHJyQmxsbEICgrC+fPnAQBHjhxBSkoKduzYAQB49eoVBg8ejKioKJw9exZ169aFj48PXr16JddeSEgIevfujStXrsDHxwcDBgzA8+fPAQAPHjyAu7s71NXVcezYMcTGxmLo0KHIysoCAKxZswbTp0/H3LlzER8fj3nz5iEoKAgRERGlPuZElA+BiIiIyszgwYOFbt26CYIgCB4eHkLr1q1l27KysgQtLS1h0KBBsrKUlBQBgHDmzBlBEATh+PHjAgBhy5YtsjrPnj0TNDQ0hK1btwqCIAj9+/cXOnToIHfcKVOmCI6OjrJ1a2troXv37nJ1EhMTBQDCpUuXCuxDVlaWoKOjI+zZs0dWBkCYMWOGbD09PV2QSCTC33//LQiCIEybNk2oVauW8O7duzzbtLS0FH7//Xe5stmzZwtubm4FxkJEpYd3BIiIiMqRi4uL7M/KysowMjKCs7OzrMzU1BQA8PjxY7n93NzcZH82NDSEvb094uPjAQDx8fFo1aqVXP1WrVrh9u3byM7OlpW5uroWKcbHjx9j1KhRsLOzg56eHvT09JCeno7k5OR8+6KlpQUdHR1Z3HFxcWjTpg2qVauWq/0nT57g/v37GDZsGLS1tWXLnDlz5KYOEVHZUlF0AERERGLy6YmxRCKRK5NIJAAAqVRaaFs5dQVBkP05hyAIuepraWkVKUZ/f388efIEy5Ytg7W1NdTU1ODm5pbrAeO8+pITt4aGRr7t59RZs2YNmjdvLrdNWVm5SDES0edjIkBERFQJnD17FlZWVgCAFy9e4NatW6hXrx4AwNHREadOnZKrHx0dDTs7uwJPrFVVVQFA7q4BAERFRWHlypXw8fEBANy/fx9Pnz4tVrwuLi6IiIjA+/fvcyUMpqamsLCwwN27dzFgwIBitUtEpYeJABERUSUQGhoKIyMjmJqaYvr06ahevbrs9wkmTZqEpk2bYvbs2ejTpw/OnDmDH3/8sdC38JiYmEBDQwMHDhxAzZo1oa6uDj09Pdja2mLjxo1wdXVFWloapkyZUuAV/ryMGzcOK1asQN++fTFt2jTo6enh7NmzaNasGezt7REcHIyAgADo6uqiU6dOyMzMRExMDF68eIGJEyeWdJiIqBj4jAAREVElEBYWhvHjx6NJkyZISUnB7t27ZVf0GzdujG3btmHLli1wcnLCzJkzERoaCn9//wLbVFFRwQ8//IDVq1ejRo0a6NatGwBg3bp1ePHiBRo1aoRBgwYhICAAJiYmxYrXyMgIx44dQ3p6Ojw8PNCkSROsWbNGdnfg66+/xtq1axEeHg5nZ2d4eHggPDxc9kpTIip7EiGvSYRERERUIURGRsLLywsvXryAvr6+osMhoiqEdwSIiIiIiESIiQARERERkQhxahARERERkQjxjgARERERkQgxESAiIiIiEiEmAkREREREIsREgIiIiIhIhJgIEBERERGJEBMBIiIiIiIRYiJARERERCRCTASIiIiIiESIiQARERERkQj9P12e6HFB2HuRAAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create a DataFrame with the feature importance\n", "df_feature_importance_rf = pd.DataFrame({'Feature': X_train.columns, 'Importance': clf_rf.feature_importances_})\n", "df_feature_importance_rf = df_feature_importance_rf.sort_values('Importance', ascending=False)\n", "\n", "# Plot the feature importance\n", "plt.barh(df_feature_importance_rf['Feature'], df_feature_importance_rf['Importance'])\n", "plt.xlabel('Importance')\n", "plt.ylabel('Feature')\n", "plt.title('Feature Importance - Random Forest')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Somewhat surprisingly, XGBoost seems to have picked up on different features than the Decision Tree and Random Forest models. The most important feature is `online_order`, followed by `ratio_to_median_purchase_price` as you can see from the plot below" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "execution": { "iopub.execute_input": "2026-01-19T18:48:19.172932Z", "iopub.status.busy": "2026-01-19T18:48:19.172677Z", "iopub.status.idle": "2026-01-19T18:48:19.265975Z", "shell.execute_reply": "2026-01-19T18:48:19.265380Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create a DataFrame with the feature importance\n", "df_feature_importance_xgb = pd.DataFrame({'Feature': X_train.columns, 'Importance': clf_xgb.feature_importances_})\n", "df_feature_importance_xgb = df_feature_importance_xgb.sort_values('Importance', ascending=False)\n", "\n", "# Plot the feature importance\n", "plt.barh(df_feature_importance_xgb['Feature'], df_feature_importance_xgb['Importance'])\n", "plt.xlabel('Importance')\n", "plt.ylabel('Feature')\n", "plt.title('Feature Importance - XGBoost')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Permutation Importance\n", "\n", "We can also look at the permutation importance of each model. The permutation importance is a measure of how much each feature contributes to the model's predictions. The permutation importance is calculated by permuting the values of each feature and measuring how much the model's performance decreases. Let's start with the Decision Tree model" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "execution": { "iopub.execute_input": "2026-01-19T18:48:19.270148Z", "iopub.status.busy": "2026-01-19T18:48:19.269904Z", "iopub.status.idle": "2026-01-19T18:48:22.916274Z", "shell.execute_reply": "2026-01-19T18:48:22.915677Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Calculate the permutation importance\n", "result_dt = permutation_importance(clf_dt, X_test, y_test, n_repeats=10, random_state=0)\n", "\n", "# Create a DataFrame with the permutation importance\n", "df_permutation_importance_dt = pd.DataFrame({'Feature': X_train.columns, 'Importance': result_dt.importances_mean})\n", "df_permutation_importance_dt = df_permutation_importance_dt.sort_values('Importance', ascending=False)\n", "\n", "# Plot the permutation importance\n", "plt.barh(df_permutation_importance_dt['Feature'], df_permutation_importance_dt['Importance'])\n", "plt.xlabel('Accuracy Decrease')\n", "plt.ylabel('Feature')\n", "plt.title('Permutation Importance - Decision Tree')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's also look at the permutation importance of the Random Forest model" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "execution": { "iopub.execute_input": "2026-01-19T18:48:22.918743Z", "iopub.status.busy": "2026-01-19T18:48:22.918507Z", "iopub.status.idle": "2026-01-19T18:49:40.979278Z", "shell.execute_reply": "2026-01-19T18:49:40.978764Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Calculate the permutation importance\n", "result_rf = permutation_importance(clf_rf, X_test, y_test, n_repeats=10, random_state=0)\n", "\n", "# Create a DataFrame with the permutation importance\n", "df_permutation_importance_rf = pd.DataFrame({'Feature': X_train.columns, 'Importance': result_rf.importances_mean})\n", "df_permutation_importance_rf = df_permutation_importance_rf.sort_values('Importance', ascending=False)\n", "\n", "# Plot the permutation importance\n", "plt.barh(df_permutation_importance_rf['Feature'], df_permutation_importance_rf['Importance'])\n", "plt.xlabel('Accuracy Decrease')\n", "plt.ylabel('Feature')\n", "plt.title('Permutation Importance - Random Forest')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's also look at the permutation importance of the XGBoost model" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "execution": { "iopub.execute_input": "2026-01-19T18:49:40.981787Z", "iopub.status.busy": "2026-01-19T18:49:40.981537Z", "iopub.status.idle": "2026-01-19T18:49:47.843430Z", "shell.execute_reply": "2026-01-19T18:49:47.842719Z" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Calculate the permutation importance\n", "result_xgb = permutation_importance(clf_xgb, X_test, y_test, n_repeats=10, random_state=0)\n", "\n", "# Create a DataFrame with the permutation importance\n", "df_permutation_importance_xgb = pd.DataFrame({'Feature': X_train.columns, 'Importance': result_xgb.importances_mean})\n", "df_permutation_importance_xgb = df_permutation_importance_xgb.sort_values('Importance', ascending=False)\n", "\n", "# Plot the permutation importance\n", "plt.barh(df_permutation_importance_xgb['Feature'], df_permutation_importance_xgb['Importance'])\n", "plt.xlabel('Accuracy Decrease')\n", "plt.ylabel('Feature')\n", "plt.title('Permutation Importance - XGBoost')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here the results for the three models are quite similar. The most important feature is `ratio_to_median_purchase_price`, followed by `online_order`.\n", "\n", "\n", "### Conclusions\n", "\n", "In this section, we have seen how to implement decision trees, random forests, and XGBoost classifiers in Python. We have also seen how to evaluate the performance of these models using metrics such as accuracy, precision, recall, and ROC AUC. We have seen that the Random Forest and XGBoost models perform better than the Decision Tree model. Furthermore, we looked at the feature and permutation importance of each model to see which features are most important for determining whether a transaction is fraudulent or not.\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3", "path": "/usr/local/share/jupyter/kernels/python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.5" } }, "nbformat": 4, "nbformat_minor": 4 }