Fraud_Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fraud Detection Project - CSE 891 @ Michigan State University\n",
    "Justin Gould, Baylee Adams, Kyle Shope"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using a fraud dataset from Kaggle (https://www.kaggle.com/ntnu-testimon/banksim1), we attempt to apply data mining techniques to distinguish legitimate and fraudulent transactions, based on a variety of attributes.\n",
    "\n",
    "The integrity of financial transactions is an incredibly important issue facing financial institutions and credit card companies alike. Having the ability to classify the legitimacy of a transaction to protect clients not only gives customers peace of mind, but is expensive. In 2016, according to CNBC, consumers lost more than $16B due to fraudulent transactions. With the scope of this issue in mind, our goal is to apply clustering and classification algorithms to determine if any given transaction follows similar behavior (classified as legitimate) or is an anomaly among that purchaser (flag as potentially fraudulent).\n",
    "\n",
    "The data we have selected for this project can be found on Kaggle. This data is a table of transactions, transaction attributes (e.g. customer age, customer gender, ZIP code, merchant, purchase category, amount, etc.), and whether or not a transaction is fraudulent (0/1). With the amount of attribute data features within this dataset, we are confident that we will be able to build and train a classifier to detect fraudulent transactions by identifying transactions outside of typical purchase behavior across customer IDs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Required Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import pandas\n",
    "import numpy\n",
    "import string\n",
    "from sklearn.model_selection import train_test_split\n",
    "import sklearn.decomposition\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "%pylab inline\n",
    "import math\n",
    "import statistics\n",
    "import sklearn\n",
    "from sklearn import neighbors\n",
    "from sklearn import metrics\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import mean_squared_error, completeness_score\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn import tree\n",
    "import graphviz\n",
    "import pydot\n",
    "import pydotplus\n",
    "from sklearn import svm\n",
    "import os\n",
    "os.environ[\"PATH\"] += os.pathsep + \"C:\\\\Program Files (x86)\\\\Graphviz2.38\\\\bin\\\\\"\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Original Data\n",
    "fraud = pandas.read_csv(\"fraudData.csv\")\n",
    "\n",
    "#Upsampled Data - Training\n",
    "fraudUp = pandas.read_csv(\"Upsampled.csv\")\n",
    "\n",
    "#Test Data (from Upsampled)\n",
    "fraudTest = pandas.read_csv(\"Test Data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pre-process the Original Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove Apostrophes from Columns for Data Type Adjustment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "fraud[\"customer\"] = fraud[\"customer\"].str.replace('[^\\w\\s]','')\n",
    "fraud[\"age\"] = fraud[\"age\"].str.replace('[^\\w\\s]','')\n",
    "fraud[\"gender\"] = fraud[\"gender\"].str.replace('[^\\w\\s]','')\n",
    "fraud[\"zipcodeOri\"] = fraud[\"zipcodeOri\"].str.replace('[^\\w\\s]','')\n",
    "fraud[\"merchant\"] = fraud[\"merchant\"].str.replace('[^\\w\\s]','')\n",
    "fraud[\"zipMerchant\"] = fraud[\"zipMerchant\"].str.replace('[^\\w\\s]','')\n",
    "fraud[\"category\"] = fraud[\"category\"].str.replace('[^\\w\\s]','')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fix Column Data Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "fraud[\"step\"] = fraud[\"step\"].astype(\"category\")\n",
    "fraud[\"customer\"] = fraud[\"customer\"].astype(\"category\")\n",
    "fraud[\"age\"] = fraud[\"age\"].astype(\"category\")\n",
    "fraud[\"gender\"] = fraud[\"gender\"].astype(\"category\")\n",
    "fraud[\"zipcodeOri\"] = fraud[\"zipcodeOri\"].astype(\"category\")\n",
    "fraud[\"merchant\"] = fraud[\"merchant\"].astype(\"category\")\n",
    "fraud[\"zipMerchant\"] = fraud[\"zipMerchant\"].astype(\"category\")\n",
    "fraud[\"category\"] = fraud[\"category\"].astype(\"category\")\n",
    "fraud[\"amount\"] = fraud[\"amount\"].astype(float)\n",
    "fraud[\"fraud\"] = fraud[\"fraud\"].astype(\"category\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Zip Columns not Necessary in Clustering Analysis - Eliminate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "fraud = fraud.drop([\"zipcodeOri\"], axis = 1)\n",
    "fraud = fraud.drop([\"zipMerchant\"], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change Categorical Columns to Binary Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "fraudBinaryCols = pandas.get_dummies(fraud,columns = [\"age\",\"gender\",\"merchant\",\"category\"])\n",
    "fraudBinaryCols = fraudBinaryCols.drop([\"customer\"], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Distribution of Amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Amount Rug Distribution')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(fraud[\"amount\"], kde=False, rug=True)\n",
    "title(\"Amount Rug Distribution\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Standardize the Data for Customer Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Original Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "fraudElim = fraudBinaryCols\n",
    "fraudElim = fraudElim.drop([\"step\", \"fraud\"], axis = 1)\n",
    "fraudStandScaler = scaler.fit_transform(fraudBinaryCols)\n",
    "fraudStand = pandas.DataFrame(fraudStandScaler)\n",
    "fraudStand.columns = list(fraudBinaryCols)\n",
    "dataStandardized = fraudStand\n",
    "dataStandardized[\"fraud\"] = fraudBinaryCols[\"fraud\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Upsampled Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "fraudStandScaler = scaler.fit_transform(fraudUp)\n",
    "fraudStand = pandas.DataFrame(fraudStandScaler)\n",
    "fraudStand.columns = list(fraudBinaryCols)\n",
    "dataStandardizedUP = fraudStand"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test Data (from Upsampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "fraudTestTest = fraudTest\n",
    "fraudTestTest = fraudTestTest.drop(\"fraud\", axis = 1)\n",
    "scaler = StandardScaler()\n",
    "fraudStandScaler = scaler.fit_transform(fraudTestTest)\n",
    "fraudStand = pandas.DataFrame(fraudStandScaler)\n",
    "fraudStand.columns = list(fraudTestTest)\n",
    "dataStandardizedTest = fraudStand"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Attempt to Cluster Customers by Purchase Behavior"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Original Data - Standardized"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-Means Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k: 2  cost: 45582627.414760284\n",
      "k: 3  cost: 44082528.24596801\n",
      "k: 4  cost: 43113145.498852685\n",
      "k: 5  cost: 41905419.66589378\n",
      "k: 6  cost: 40600675.23316435\n",
      "k: 7  cost: 39879098.315167114\n",
      "k: 8  cost: 39451977.14252325\n",
      "k: 9  cost: 38151369.41422901\n",
      "k: 10  cost: 37548416.16936904\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "error = list()\n",
    "kList = list()\n",
    "for k in range(2, 11):\n",
    "    kmeans_model = KMeans(n_clusters = k, random_state = 891).fit(dataStandardized)\n",
    "    labels = kmeans_model.labels_\n",
    "    labels = labels.tolist()\n",
    "    cost = kmeans_model.inertia_\n",
    "    error.append(cost)\n",
    "    kList.append(k)\n",
    "    print(\"k:\", k, \" cost:\", cost)\n",
    "plt.plot(kList, error)\n",
    "plt.title(\"K Values And Error Score - Original Data\")\n",
    "plt.xlabel(\"K Value\")\n",
    "plt.ylabel(\"Error Score\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using the Best Value of k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters = 4, random_state = 891).fit(dataStandardized)\n",
    "labs = kmeans.labels_\n",
    "labsList = labs.tolist()\n",
    "dataStandardized[\"customerGroup\"] = labsList\n",
    "dataStandardized[\"fraud\"] = fraud[\"fraud\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examine Customer Group Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Customer Group Breakdown - Original Data')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts = dataStandardized.groupby(\"customerGroup\").count()\n",
    "counts = counts[\"step\"].values.tolist()\n",
    "\n",
    "dataStandardized.groupby(['customerGroup', 'fraud']).size().unstack().plot(kind='bar', stacked=True)\n",
    "annotate(counts[0], [-0.21, 325000])\n",
    "annotate(counts[1], [0.9, 50000])\n",
    "annotate(counts[2], [1.76, 280000])\n",
    "annotate(counts[3], [2.9, 50000])\n",
    "\n",
    "ylabel(\"Number of Transactions\")\n",
    "xlabel(\"Customer Group\")\n",
    "grid()\n",
    "title(\"Customer Group Breakdown - Original Data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Customer Group 0: 0.9869006968371935 not fraud | 0.013099303162806508 fraud\n",
      "Customer Group 1: 0.75 not fraud | 0.25 fraud\n",
      "Customer Group 2: 0.9920006830043282 not fraud | 0.00799931699567183 fraud\n",
      "Customer Group 3: 0.20604395604395603 not fraud | 0.7939560439560439 fraud\n"
     ]
    }
   ],
   "source": [
    "cust0 = dataStandardized.loc[dataStandardized['customerGroup'] == 0]\n",
    "cust0 = cust0.reset_index(drop = True)\n",
    "cust1 = dataStandardized.loc[dataStandardized['customerGroup'] == 1]\n",
    "cust1 = cust1.reset_index(drop = True)\n",
    "cust2 = dataStandardized.loc[dataStandardized['customerGroup'] == 2]\n",
    "cust2 = cust2.reset_index(drop = True)\n",
    "cust3 = dataStandardized.loc[dataStandardized['customerGroup'] == 3]\n",
    "cust3 = cust3.reset_index(drop = True)\n",
    "\n",
    "counts0 = cust0.groupby(\"fraud\").count()\n",
    "counts0 = counts0[\"step\"].values.tolist()\n",
    "nofraud0 = counts0[0]/sum(counts0)\n",
    "fraud0 = counts0[1]/sum(counts0)\n",
    "print(\"Customer Group 0:\", \"%s not fraud\" % nofraud0, \"| %s fraud\" % fraud0)\n",
    "\n",
    "counts1 = cust1.groupby(\"fraud\").count()\n",
    "counts1 = counts1[\"step\"].values.tolist()\n",
    "nofraud1 = counts1[0]/sum(counts1)\n",
    "fraud1 = counts1[1]/sum(counts1)\n",
    "print(\"Customer Group 1:\", \"%s not fraud\" % nofraud1, \"| %s fraud\" % fraud1)\n",
    "\n",
    "counts2 = cust2.groupby(\"fraud\").count()\n",
    "counts2 = counts2[\"step\"].values.tolist()\n",
    "nofraud2 = counts2[0]/sum(counts2)\n",
    "fraud2 = counts2[1]/sum(counts2)\n",
    "print(\"Customer Group 2:\", \"%s not fraud\" % nofraud2, \"| %s fraud\" % fraud2)\n",
    "\n",
    "counts3 = cust3.groupby(\"fraud\").count()\n",
    "counts3 = counts3[\"step\"].values.tolist()\n",
    "nofraud3 = counts3[0]/sum(counts3)\n",
    "fraud3 = counts3[1]/sum(counts3)\n",
    "print(\"Customer Group 3:\", \"%s not fraud\" % nofraud3, \"| %s fraud\" % fraud3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Upsampled Data - Standardized"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-Means Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k: 2  cost: 70679353.74244852\n",
      "k: 3  cost: 68894406.45436603\n",
      "k: 4  cost: 66690476.96307046\n",
      "k: 5  cost: 64420003.92912575\n",
      "k: 6  cost: 62993960.50975509\n",
      "k: 7  cost: 60770439.98079894\n",
      "k: 8  cost: 59677998.21574084\n",
      "k: 9  cost: 58416067.61473298\n",
      "k: 10  cost: 55712832.18699259\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "error = list()\n",
    "kList = list()\n",
    "for k in range(2, 11):\n",
    "    kmeans_model = KMeans(n_clusters = k, random_state = 891).fit(dataStandardizedUP)\n",
    "    labels = kmeans_model.labels_\n",
    "    labels = labels.tolist()\n",
    "    cost = kmeans_model.inertia_\n",
    "    error.append(cost)\n",
    "    kList.append(k)\n",
    "    print(\"k:\", k, \" cost:\", cost)\n",
    "plot(kList, error)\n",
    "title(\"K Values And Error Score - Upsampled Data\")\n",
    "xlabel(\"K Value\")\n",
    "ylabel(\"Error Score\")\n",
    "grid()\n",
    "show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using the Best Value of k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters = 5, random_state = 891).fit(dataStandardizedUP)\n",
    "labs = kmeans.labels_\n",
    "labsList = labs.tolist()\n",
    "dataStandardizedUP[\"customerGroup\"] = labsList\n",
    "dataStandardizedUP[\"fraud\"] = fraudUp[\"fraud\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examine Customer Group Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Customer Group Breakdown - Upsampled Data')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts = dataStandardizedUP.groupby(\"customerGroup\").count()\n",
    "counts = counts[\"amount\"].values.tolist()\n",
    "\n",
    "dataStandardizedUP.groupby(['customerGroup', 'fraud']).size().unstack().plot(kind='bar', stacked=True)\n",
    "annotate(counts[0], [-0.21, 50000])\n",
    "annotate(counts[1], [0.73, 439000])\n",
    "annotate(counts[2], [1.76, 50000])\n",
    "annotate(counts[3], [2.74, 50000])\n",
    "annotate(counts[4], [3.73, 410000])\n",
    "\n",
    "ylabel(\"Number of Transactions\")\n",
    "xlabel(\"Customer Group\")\n",
    "grid()\n",
    "title(\"Customer Group Breakdown - Upsampled Data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Customer Group 0: 0.0006251172094767769 not fraud | 0.9993748827905232 fraud\n",
      "Customer Group 1: 0.09862720324023047 not fraud | 0.9013727967597696 fraud\n",
      "Customer Group 2: 0.003275934481310374 not fraud | 0.9967240655186896 fraud\n",
      "Customer Group 3: 1.0 not fraud | 0 fraud\n",
      "Customer Group 4: 1.0 not fraud | 0 fraud\n"
     ]
    }
   ],
   "source": [
    "cust0 = dataStandardizedUP.loc[dataStandardizedUP['customerGroup'] == 0]\n",
    "cust0 = cust0.reset_index(drop = True)\n",
    "cust1 = dataStandardizedUP.loc[dataStandardizedUP['customerGroup'] == 1]\n",
    "cust1 = cust1.reset_index(drop = True)\n",
    "cust2 = dataStandardizedUP.loc[dataStandardizedUP['customerGroup'] == 2]\n",
    "cust2 = cust2.reset_index(drop = True)\n",
    "cust3 = dataStandardizedUP.loc[dataStandardizedUP['customerGroup'] == 3]\n",
    "cust3 = cust3.reset_index(drop = True)\n",
    "cust4 = dataStandardizedUP.loc[dataStandardizedUP['customerGroup'] == 4]\n",
    "cust4 = cust4.reset_index(drop = True)\n",
    "\n",
    "counts0 = cust0.groupby(\"fraud\").count()\n",
    "counts0 = counts0[\"amount\"].values.tolist()\n",
    "nofraud0 = counts0[0]/sum(counts0)\n",
    "fraud0 = counts0[1]/sum(counts0)\n",
    "print(\"Customer Group 0:\", \"%s not fraud\" % nofraud0, \"| %s fraud\" % fraud0)\n",
    "\n",
    "counts1 = cust1.groupby(\"fraud\").count()\n",
    "counts1 = counts1[\"amount\"].values.tolist()\n",
    "nofraud1 = counts1[0]/sum(counts1)\n",
    "fraud1 = counts1[1]/sum(counts1)\n",
    "print(\"Customer Group 1:\", \"%s not fraud\" % nofraud1, \"| %s fraud\" % fraud1)\n",
    "\n",
    "counts2 = cust2.groupby(\"fraud\").count()\n",
    "counts2 = counts2[\"amount\"].values.tolist()\n",
    "nofraud2 = counts2[0]/sum(counts2)\n",
    "fraud2 = counts2[1]/sum(counts2)\n",
    "print(\"Customer Group 2:\", \"%s not fraud\" % nofraud2, \"| %s fraud\" % fraud2)\n",
    "\n",
    "counts3 = cust3.groupby(\"fraud\").count()\n",
    "counts3 = counts3[\"amount\"].values.tolist()\n",
    "nofraud3 = counts3[0]/sum(counts3)\n",
    "print(\"Customer Group 3:\", \"%s not fraud\" % nofraud3, \"| %s fraud\" % 0)\n",
    "\n",
    "counts4 = cust4.groupby(\"fraud\").count()\n",
    "counts4 = counts4[\"amount\"].values.tolist()\n",
    "nofraud4 = counts4[0]/sum(counts4)\n",
    "print(\"Customer Group 4:\", \"%s not fraud\" % nofraud4, \"| %s fraud\" % 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test Data - Standardized"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-Means Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k: 2  cost: 8983754.79033209\n",
      "k: 3  cost: 8759730.472061029\n",
      "k: 4  cost: 8560138.352494117\n",
      "k: 5  cost: 8313444.254985454\n",
      "k: 6  cost: 8180582.2624179255\n",
      "k: 7  cost: 8026945.636027608\n",
      "k: 8  cost: 7644403.490412499\n",
      "k: 9  cost: 7617370.704876197\n",
      "k: 10  cost: 7444527.622420674\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "error = list()\n",
    "kList = list()\n",
    "for k in range(2, 11):\n",
    "    kmeans_model = KMeans(n_clusters = k, random_state = 891).fit(dataStandardizedTest)\n",
    "    labels = kmeans_model.labels_\n",
    "    labels = labels.tolist()\n",
    "    cost = kmeans_model.inertia_\n",
    "    error.append(cost)\n",
    "    kList.append(k)\n",
    "    print(\"k:\", k, \" cost:\", cost)\n",
    "plot(kList, error)\n",
    "title(\"K Values And Error Score - Test Data\")\n",
    "xlabel(\"K Value\")\n",
    "ylabel(\"Error Score\")\n",
    "grid()\n",
    "show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using the Best Value of k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters = 5, random_state = 891).fit(dataStandardizedTest)\n",
    "labs = kmeans.labels_\n",
    "labsList = labs.tolist()\n",
    "dataStandardizedTest[\"customerGroup\"] = labsList\n",
    "dataStandardizedTest[\"fraud\"] = fraudTest[\"fraud\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examine Customer Group Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Customer Group Breakdown - Test Data')"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts = dataStandardizedTest.groupby(\"customerGroup\").count()\n",
    "counts = counts[\"amount\"].values.tolist()\n",
    "\n",
    "dataStandardizedTest.groupby(['customerGroup', 'fraud']).size().unstack().plot(kind='bar', stacked=True)\n",
    "annotate(counts[0], [-0.21, 52000])\n",
    "annotate(counts[1], [0.73, 60000])\n",
    "annotate(counts[2], [1.76, 3000])\n",
    "annotate(counts[3], [2.74, 1000])\n",
    "annotate(counts[4], [3.73, 8200])\n",
    "\n",
    "ylabel(\"Number of Transactions\")\n",
    "xlabel(\"Customer Group\")\n",
    "grid()\n",
    "title(\"Customer Group Breakdown - Test Data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Customer Group 0: 1.0 not fraud\n",
      "Customer Group 1: 0.9791281589923939 not fraud | 0.02087184100760612 fraud\n",
      "Customer Group 2: 0.9748987854251012 not fraud | 0.025101214574898785 fraud\n",
      "Customer Group 3: 0.2097902097902098 not fraud | 0.7902097902097902 fraud\n",
      "Customer Group 4: 1.0 not fraud\n"
     ]
    }
   ],
   "source": [
    "cust0 = dataStandardizedTest.loc[dataStandardizedTest['customerGroup'] == 0]\n",
    "cust0 = cust0.reset_index(drop = True)\n",
    "cust1 = dataStandardizedTest.loc[dataStandardizedTest['customerGroup'] == 1]\n",
    "cust1 = cust1.reset_index(drop = True)\n",
    "cust2 = dataStandardizedTest.loc[dataStandardizedTest['customerGroup'] == 2]\n",
    "cust2 = cust2.reset_index(drop = True)\n",
    "cust3 = dataStandardizedTest.loc[dataStandardizedTest['customerGroup'] == 3]\n",
    "cust3 = cust3.reset_index(drop = True)\n",
    "cust4 = dataStandardizedTest.loc[dataStandardizedTest['customerGroup'] == 4]\n",
    "cust4 = cust4.reset_index(drop = True)\n",
    "\n",
    "counts0 = cust0.groupby(\"fraud\").count()\n",
    "counts0 = counts0[\"amount\"].values.tolist()\n",
    "nofraud0 = counts0[0]/sum(counts0)\n",
    "print(\"Customer Group 0:\", \"%s not fraud\" % nofraud0)#, \"%s fraud\" % fraud0)\n",
    "\n",
    "counts1 = cust1.groupby(\"fraud\").count()\n",
    "counts1 = counts1[\"amount\"].values.tolist()\n",
    "nofraud1 = counts1[0]/sum(counts1)\n",
    "fraud1 = counts1[1]/sum(counts1)\n",
    "print(\"Customer Group 1:\", \"%s not fraud\" % nofraud1, \"| %s fraud\" % fraud1)\n",
    "\n",
    "counts2 = cust2.groupby(\"fraud\").count()\n",
    "counts2 = counts2[\"amount\"].values.tolist()\n",
    "nofraud2 = counts2[0]/sum(counts2)\n",
    "fraud2 = counts2[1]/sum(counts2)\n",
    "print(\"Customer Group 2:\", \"%s not fraud\" % nofraud2, \"| %s fraud\" % fraud2)\n",
    "\n",
    "counts3 = cust3.groupby(\"fraud\").count()\n",
    "counts3 = counts3[\"amount\"].values.tolist()\n",
    "nofraud3 = counts3[0]/sum(counts3)\n",
    "fraud3 = counts3[1]/sum(counts3)\n",
    "print(\"Customer Group 3:\", \"%s not fraud\" % nofraud3, \"| %s fraud\" % fraud3)\n",
    "\n",
    "counts4 = cust4.groupby(\"fraud\").count()\n",
    "counts4 = counts4[\"amount\"].values.tolist()\n",
    "nofraud4 = counts4[0]/sum(counts4)\n",
    "print(\"Customer Group 4:\", \"%s not fraud\" % nofraud4)#, \"%s fraud\" % fraud4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split Data into Train Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataStandardized1 = dataStandardized\n",
    "dataStandardized1 = dataStandardized1.drop(\"fraud\", axis = 1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(dataStandardized1, dataStandardized['fraud'], test_size = 0.40, random_state = 10, stratify = dataStandardized['fraud'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Data: 0.9507042088251954\n",
      "Test Data: 0.9501341129450445\n"
     ]
    }
   ],
   "source": [
    "gnb = GaussianNB()\n",
    "nb = cross_val_score(gnb, X_train, y_train, cv = 10)\n",
    "print(\"Train Data:\", numpy.mean(nb))\n",
    "\n",
    "gnb = GaussianNB()\n",
    "nb = cross_val_score(gnb, X_test, y_test, cv = 10)\n",
    "print(\"Test Data:\", numpy.mean(nb))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.34564236  1.45480284 -0.06327624  0.01653141  0.00553531 -0.02211753\n",
      "   0.01278538  0.01781141 -0.0161141  -0.0214958  -0.0214958   0.09324961\n",
      "  -0.08612507 -0.08888809 -0.28946347 -0.01800635  0.294898    0.098587\n",
      "  -0.02336559 -0.08820967  0.08558719 -0.11031536 -0.06295951  0.26915115\n",
      "   0.26849323 -0.09250767  0.07869496 -0.03713408  0.09439651  0.10722117\n",
      "   0.10784619 -0.09594644 -0.16007575 -0.12007918 -0.09326862  0.16978337\n",
      "   0.13507215 -0.22846964 -0.27896592  0.13845099  0.01721911  0.29293434\n",
      "   0.17480885  0.0596751  -0.06294638 -0.1013012   0.1081484  -0.11461935\n",
      "   0.54606914 -0.13131509  0.15851804  0.26758384 -0.01483232  0.12059642\n",
      "   0.06384526  0.18116047  0.20074922  0.30185636  0.10018849 -0.13240982\n",
      "   0.15088436  0.02576749 -0.09346524  0.31070779  0.26915115 -0.02747386\n",
      "  -0.02397785 -0.13240982  0.02001663  0.01457554  0.06923648  0.30185636\n",
      "   0.14598932  0.13507215  0.29679227  0.06745923 -0.30750631  0.01994153\n",
      "   0.22909168]]\n",
      "Precision: 0.8886993603411514\n",
      "Recall: 0.7236111111111111\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[234717,    261],\n",
       "       [   796,   2084]], dtype=int64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = sklearn.linear_model.LogisticRegression()\n",
    "reg.fit(X_train, y_train)\n",
    "print(reg.coef_)\n",
    "y_pred = reg.predict(X_test)\n",
    "confMat = sklearn.metrics.confusion_matrix(y_test, y_pred)\n",
    "confMatList = confMat.tolist()\n",
    "TN = confMatList[0][0]\n",
    "TP = confMatList[1][1]\n",
    "FN = confMatList[1][0]\n",
    "FP = confMatList[0][1]\n",
    "\n",
    "precision = (TP) / (TP + FP)\n",
    "recall = (TP) / (TP + FN)\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall:\", recall)\n",
    "confMat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.8720699245133094\n",
      "Recall: 0.7624175060784995\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[234656,    322,      0],\n",
       "       [   684,   2195,      1],\n",
       "       [     0,      0,      0]], dtype=int64)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = MLPRegressor()\n",
    "reg.fit(X_train, y_train)\n",
    "y_pred = reg.predict(X_test)\n",
    "y_pred = numpy.rint(y_pred)\n",
    "\n",
    "confMat = sklearn.metrics.confusion_matrix(y_test, y_pred)\n",
    "confMatList = confMat.tolist()\n",
    "TN = confMatList[0][0]\n",
    "TP = confMatList[1][1]\n",
    "FN = confMatList[1][0]\n",
    "FP = confMatList[0][1]\n",
    "\n",
    "precision = (TP) / (TP + FP)\n",
    "recall = (TP) / (TP + FN)\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall:\", recall)\n",
    "confMat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree - Different Max_Depth Values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Determine Best Max_Depth Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
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      "99\n",
      "100\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>k</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f-measure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>0.903128</td>\n",
       "      <td>0.721875</td>\n",
       "      <td>0.802393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>0.900415</td>\n",
       "      <td>0.678125</td>\n",
       "      <td>0.773619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>0.900312</td>\n",
       "      <td>0.702431</td>\n",
       "      <td>0.789155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>0.893855</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.763723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>0.888984</td>\n",
       "      <td>0.720139</td>\n",
       "      <td>0.795703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>0.884826</td>\n",
       "      <td>0.730903</td>\n",
       "      <td>0.800532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>0.879768</td>\n",
       "      <td>0.736806</td>\n",
       "      <td>0.801965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11</td>\n",
       "      <td>0.879079</td>\n",
       "      <td>0.729514</td>\n",
       "      <td>0.797343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12</td>\n",
       "      <td>0.875517</td>\n",
       "      <td>0.735069</td>\n",
       "      <td>0.799169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>0.875224</td>\n",
       "      <td>0.679514</td>\n",
       "      <td>0.765051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>13</td>\n",
       "      <td>0.861382</td>\n",
       "      <td>0.735764</td>\n",
       "      <td>0.793633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>15</td>\n",
       "      <td>0.858647</td>\n",
       "      <td>0.736111</td>\n",
       "      <td>0.792672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>14</td>\n",
       "      <td>0.853874</td>\n",
       "      <td>0.738542</td>\n",
       "      <td>0.792031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>0.849392</td>\n",
       "      <td>0.679514</td>\n",
       "      <td>0.755015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>16</td>\n",
       "      <td>0.844875</td>\n",
       "      <td>0.741319</td>\n",
       "      <td>0.789717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>19</td>\n",
       "      <td>0.836143</td>\n",
       "      <td>0.740625</td>\n",
       "      <td>0.785491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>17</td>\n",
       "      <td>0.836066</td>\n",
       "      <td>0.743750</td>\n",
       "      <td>0.787211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>18</td>\n",
       "      <td>0.835552</td>\n",
       "      <td>0.740972</td>\n",
       "      <td>0.785425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>20</td>\n",
       "      <td>0.826960</td>\n",
       "      <td>0.743403</td>\n",
       "      <td>0.782958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>21</td>\n",
       "      <td>0.813796</td>\n",
       "      <td>0.749653</td>\n",
       "      <td>0.780408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>23</td>\n",
       "      <td>0.811822</td>\n",
       "      <td>0.753472</td>\n",
       "      <td>0.781560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>22</td>\n",
       "      <td>0.809720</td>\n",
       "      <td>0.752083</td>\n",
       "      <td>0.779838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>24</td>\n",
       "      <td>0.807979</td>\n",
       "      <td>0.752431</td>\n",
       "      <td>0.779216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>25</td>\n",
       "      <td>0.796038</td>\n",
       "      <td>0.753472</td>\n",
       "      <td>0.774171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>26</td>\n",
       "      <td>0.788574</td>\n",
       "      <td>0.752431</td>\n",
       "      <td>0.770078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>27</td>\n",
       "      <td>0.784687</td>\n",
       "      <td>0.757986</td>\n",
       "      <td>0.771106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>28</td>\n",
       "      <td>0.777897</td>\n",
       "      <td>0.755208</td>\n",
       "      <td>0.766385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>29</td>\n",
       "      <td>0.772952</td>\n",
       "      <td>0.760069</td>\n",
       "      <td>0.766457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>30</td>\n",
       "      <td>0.772134</td>\n",
       "      <td>0.760069</td>\n",
       "      <td>0.766054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>31</td>\n",
       "      <td>0.761971</td>\n",
       "      <td>0.756944</td>\n",
       "      <td>0.759450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>52</td>\n",
       "      <td>0.752479</td>\n",
       "      <td>0.764236</td>\n",
       "      <td>0.758312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>71</td>\n",
       "      <td>0.752316</td>\n",
       "      <td>0.761458</td>\n",
       "      <td>0.756859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>63</td>\n",
       "      <td>0.752309</td>\n",
       "      <td>0.763542</td>\n",
       "      <td>0.757884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>41</td>\n",
       "      <td>0.751970</td>\n",
       "      <td>0.762153</td>\n",
       "      <td>0.757027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>76</td>\n",
       "      <td>0.751900</td>\n",
       "      <td>0.755556</td>\n",
       "      <td>0.753724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>81</td>\n",
       "      <td>0.751810</td>\n",
       "      <td>0.757292</td>\n",
       "      <td>0.754541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>42</td>\n",
       "      <td>0.751805</td>\n",
       "      <td>0.759375</td>\n",
       "      <td>0.755571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>67</td>\n",
       "      <td>0.751643</td>\n",
       "      <td>0.754514</td>\n",
       "      <td>0.753076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>92</td>\n",
       "      <td>0.751551</td>\n",
       "      <td>0.757292</td>\n",
       "      <td>0.754410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>88</td>\n",
       "      <td>0.751290</td>\n",
       "      <td>0.758333</td>\n",
       "      <td>0.754795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>65</td>\n",
       "      <td>0.750600</td>\n",
       "      <td>0.759722</td>\n",
       "      <td>0.755134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>96</td>\n",
       "      <td>0.750432</td>\n",
       "      <td>0.753819</td>\n",
       "      <td>0.752122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>46</td>\n",
       "      <td>0.750171</td>\n",
       "      <td>0.760069</td>\n",
       "      <td>0.755088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>53</td>\n",
       "      <td>0.750086</td>\n",
       "      <td>0.756597</td>\n",
       "      <td>0.753328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>79</td>\n",
       "      <td>0.750085</td>\n",
       "      <td>0.763889</td>\n",
       "      <td>0.756924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>82</td>\n",
       "      <td>0.749743</td>\n",
       "      <td>0.759375</td>\n",
       "      <td>0.754528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>51</td>\n",
       "      <td>0.749658</td>\n",
       "      <td>0.762153</td>\n",
       "      <td>0.755854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>78</td>\n",
       "      <td>0.749485</td>\n",
       "      <td>0.757292</td>\n",
       "      <td>0.753368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>73</td>\n",
       "      <td>0.749399</td>\n",
       "      <td>0.757986</td>\n",
       "      <td>0.753668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>90</td>\n",
       "      <td>0.749228</td>\n",
       "      <td>0.758333</td>\n",
       "      <td>0.753753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>62</td>\n",
       "      <td>0.749144</td>\n",
       "      <td>0.760069</td>\n",
       "      <td>0.754567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>84</td>\n",
       "      <td>0.749056</td>\n",
       "      <td>0.757639</td>\n",
       "      <td>0.753323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>64</td>\n",
       "      <td>0.748797</td>\n",
       "      <td>0.756597</td>\n",
       "      <td>0.752677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>40</td>\n",
       "      <td>0.748714</td>\n",
       "      <td>0.758333</td>\n",
       "      <td>0.753493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>74</td>\n",
       "      <td>0.748714</td>\n",
       "      <td>0.758333</td>\n",
       "      <td>0.753493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>77</td>\n",
       "      <td>0.748538</td>\n",
       "      <td>0.755556</td>\n",
       "      <td>0.752030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>91</td>\n",
       "      <td>0.748462</td>\n",
       "      <td>0.760417</td>\n",
       "      <td>0.754392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>72</td>\n",
       "      <td>0.748201</td>\n",
       "      <td>0.758333</td>\n",
       "      <td>0.753233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>56</td>\n",
       "      <td>0.748107</td>\n",
       "      <td>0.754861</td>\n",
       "      <td>0.751469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>55</td>\n",
       "      <td>0.746918</td>\n",
       "      <td>0.757292</td>\n",
       "      <td>0.752069</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     k  precision    recall  f-measure\n",
       "6    8   0.903128  0.721875   0.802393\n",
       "3    5   0.900415  0.678125   0.773619\n",
       "4    6   0.900312  0.702431   0.789155\n",
       "2    4   0.893855  0.666667   0.763723\n",
       "5    7   0.888984  0.720139   0.795703\n",
       "8   10   0.884826  0.730903   0.800532\n",
       "7    9   0.879768  0.736806   0.801965\n",
       "9   11   0.879079  0.729514   0.797343\n",
       "10  12   0.875517  0.735069   0.799169\n",
       "1    3   0.875224  0.679514   0.765051\n",
       "11  13   0.861382  0.735764   0.793633\n",
       "13  15   0.858647  0.736111   0.792672\n",
       "12  14   0.853874  0.738542   0.792031\n",
       "0    2   0.849392  0.679514   0.755015\n",
       "14  16   0.844875  0.741319   0.789717\n",
       "17  19   0.836143  0.740625   0.785491\n",
       "15  17   0.836066  0.743750   0.787211\n",
       "16  18   0.835552  0.740972   0.785425\n",
       "18  20   0.826960  0.743403   0.782958\n",
       "19  21   0.813796  0.749653   0.780408\n",
       "21  23   0.811822  0.753472   0.781560\n",
       "20  22   0.809720  0.752083   0.779838\n",
       "22  24   0.807979  0.752431   0.779216\n",
       "23  25   0.796038  0.753472   0.774171\n",
       "24  26   0.788574  0.752431   0.770078\n",
       "25  27   0.784687  0.757986   0.771106\n",
       "26  28   0.777897  0.755208   0.766385\n",
       "27  29   0.772952  0.760069   0.766457\n",
       "28  30   0.772134  0.760069   0.766054\n",
       "29  31   0.761971  0.756944   0.759450\n",
       "..  ..        ...       ...        ...\n",
       "50  52   0.752479  0.764236   0.758312\n",
       "69  71   0.752316  0.761458   0.756859\n",
       "61  63   0.752309  0.763542   0.757884\n",
       "39  41   0.751970  0.762153   0.757027\n",
       "74  76   0.751900  0.755556   0.753724\n",
       "79  81   0.751810  0.757292   0.754541\n",
       "40  42   0.751805  0.759375   0.755571\n",
       "65  67   0.751643  0.754514   0.753076\n",
       "90  92   0.751551  0.757292   0.754410\n",
       "86  88   0.751290  0.758333   0.754795\n",
       "63  65   0.750600  0.759722   0.755134\n",
       "94  96   0.750432  0.753819   0.752122\n",
       "44  46   0.750171  0.760069   0.755088\n",
       "51  53   0.750086  0.756597   0.753328\n",
       "77  79   0.750085  0.763889   0.756924\n",
       "80  82   0.749743  0.759375   0.754528\n",
       "49  51   0.749658  0.762153   0.755854\n",
       "76  78   0.749485  0.757292   0.753368\n",
       "71  73   0.749399  0.757986   0.753668\n",
       "88  90   0.749228  0.758333   0.753753\n",
       "60  62   0.749144  0.760069   0.754567\n",
       "82  84   0.749056  0.757639   0.753323\n",
       "62  64   0.748797  0.756597   0.752677\n",
       "38  40   0.748714  0.758333   0.753493\n",
       "72  74   0.748714  0.758333   0.753493\n",
       "75  77   0.748538  0.755556   0.752030\n",
       "89  91   0.748462  0.760417   0.754392\n",
       "70  72   0.748201  0.758333   0.753233\n",
       "54  56   0.748107  0.754861   0.751469\n",
       "53  55   0.746918  0.757292   0.752069\n",
       "\n",
       "[99 rows x 4 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy = []\n",
    "for x in range(2, 101):\n",
    "    print(x)\n",
    "    clf = tree.DecisionTreeClassifier(max_depth = x)\n",
    "    clf.fit(X_train, y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    confMat = sklearn.metrics.confusion_matrix(y_test, y_pred)\n",
    "    confMatList = confMat.tolist()\n",
    "    TN = confMatList[0][0]\n",
    "    TP = confMatList[1][1]\n",
    "    FN = confMatList[1][0]\n",
    "    FP = confMatList[0][1]\n",
    "\n",
    "    precision = (TP) / (TP + FP)\n",
    "    recall = (TP) / (TP + FN)\n",
    "    f1 = 2*((precision * recall) / (precision + recall))\n",
    "    accuracy.append([x, precision, recall, f1])\n",
    "    \n",
    "accuracyDF = pandas.DataFrame(data = accuracy, columns = [\"k\", \"precision\", \"recall\", \"f-measure\"])\n",
    "accuracyDF = accuracyDF.sort_values(by = [\"precision\", \"recall\"], ascending = False)\n",
    "accuracyDF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply Best Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.9026510212950891\n",
      "Recall: 0.7211805555555556\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[234754,    224],\n",
       "       [   803,   2077]], dtype=int64)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maxVals = list(accuracyDF[\"k\"])\n",
    "best = maxVals[0]\n",
    "clf = tree.DecisionTreeClassifier(max_depth = best)\n",
    "clf.fit(X_train, y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "accuracy.append([x, sklearn.metrics.accuracy_score(y_test, y_pred)])\n",
    "dot_data = tree.export_graphviz(clf, out_file = None) \n",
    "graph = graphviz.Source(dot_data) \n",
    "graph.render(\"fraudDT\")\n",
    "\n",
    "#print(\"Accuracy:\", sklearn.metrics.accuracy_score(y_test, y_pred))\n",
    "confMat = sklearn.metrics.confusion_matrix(y_test, y_pred)\n",
    "confMatList = confMat.tolist()\n",
    "TN = confMatList[0][0]\n",
    "TP = confMatList[1][1]\n",
    "FN = confMatList[1][0]\n",
    "FP = confMatList[0][1]\n",
    "\n",
    "precision = (TP) / (TP + FP)\n",
    "recall = (TP) / (TP + FN)\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall:\", recall)\n",
    "confMat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "<text text-anchor=\"middle\" x=\"1893\" y=\"-453.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[0] &lt;= 0.049</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1893\" y=\"-438.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.317</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1893\" y=\"-423.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 993</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1893\" y=\"-408.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [797, 196]</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"742\" y=\"-349.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[16] &lt;= 9.661</text>\r\n",
       "<text text-anchor=\"middle\" x=\"742\" y=\"-334.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.003</text>\r\n",
       "<text text-anchor=\"middle\" x=\"742\" y=\"-319.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 347303</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"1219\" y=\"-349.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[0] &lt;= &#45;0.352</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1219\" y=\"-334.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.482</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1219\" y=\"-319.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 42</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"394\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[28] &lt;= 22.944</text>\r\n",
       "<text text-anchor=\"middle\" x=\"394\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.002</text>\r\n",
       "<text text-anchor=\"middle\" x=\"394\" y=\"-215.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 346534</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"742\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[0] &lt;= &#45;0.049</text>\r\n",
       "<text text-anchor=\"middle\" x=\"742\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.174</text>\r\n",
       "<text text-anchor=\"middle\" x=\"742\" y=\"-215.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 769</text>\r\n",
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       "<!-- 5&#45;&gt;13 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"208\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[39] &lt;= 24.668</text>\r\n",
       "<text text-anchor=\"middle\" x=\"208\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.002</text>\r\n",
       "<text text-anchor=\"middle\" x=\"208\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 346462</text>\r\n",
       "<text text-anchor=\"middle\" x=\"208\" y=\"-96.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [346086, 376]</text>\r\n",
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       "<!-- 6&#45;&gt;7 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"394\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[0] &lt;= &#45;0.93</text>\r\n",
       "<text text-anchor=\"middle\" x=\"394\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.424</text>\r\n",
       "<text text-anchor=\"middle\" x=\"394\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 72</text>\r\n",
       "<text text-anchor=\"middle\" x=\"394\" y=\"-96.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [50, 22]</text>\r\n",
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       "<!-- 6&#45;&gt;10 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"69\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.002</text>\r\n",
       "<text text-anchor=\"middle\" x=\"69\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 346416</text>\r\n",
       "<text text-anchor=\"middle\" x=\"69\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [346055, 361]</text>\r\n",
       "</g>\r\n",
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       "<text text-anchor=\"middle\" x=\"208\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.44</text>\r\n",
       "<text text-anchor=\"middle\" x=\"208\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 46</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"324\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.48</text>\r\n",
       "<text text-anchor=\"middle\" x=\"324\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 15</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"440\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.352</text>\r\n",
       "<text text-anchor=\"middle\" x=\"440\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 57</text>\r\n",
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       "<!-- 10&#45;&gt;12 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"678\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[1] &lt;= 0.877</text>\r\n",
       "<text text-anchor=\"middle\" x=\"678\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.106</text>\r\n",
       "<text text-anchor=\"middle\" x=\"678\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 479</text>\r\n",
       "<text text-anchor=\"middle\" x=\"678\" y=\"-96.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [452, 27]</text>\r\n",
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       "<!-- 13&#45;&gt;14 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"807\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[0] &lt;= 0.421</text>\r\n",
       "<text text-anchor=\"middle\" x=\"807\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.272</text>\r\n",
       "<text text-anchor=\"middle\" x=\"807\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 290</text>\r\n",
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       "<!-- 13&#45;&gt;17 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"566\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.103</text>\r\n",
       "<text text-anchor=\"middle\" x=\"566\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 478</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"685\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"685\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 1</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"801\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.388</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"1277\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[1] &lt;= 0.46</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1277\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.308</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1277\" y=\"-215.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 21</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"1047\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[1] &lt;= &#45;0.272</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1047\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.432</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1047\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 19</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"1161\" y=\"-134.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"1046\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1046\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 2</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"1159\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 17</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"1275\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[0] &lt;= 1.391</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1275\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.375</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1275\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 16</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"1388\" y=\"-134.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1388\" y=\"-119.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 5</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"1275\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.32</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1275\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 15</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"1388\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1388\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 1</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"1799\" y=\"-334.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.244</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1799\" y=\"-319.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 704</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"1987\" y=\"-349.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[0] &lt;= 1.538</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1987\" y=\"-334.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.444</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1987\" y=\"-319.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 289</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"1617\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.132</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1617\" y=\"-215.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 127</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"1799\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[1] &lt;= &#45;0.308</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1799\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.266</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1799\" y=\"-215.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 577</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1799\" y=\"-200.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [486, 91]</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"1501\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.202</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1501\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 79</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"1617\" y=\"-134.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1617\" y=\"-119.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 48</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"1733\" y=\"-134.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1733\" y=\"-119.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 32</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1733\" y=\"-104.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [32, 0]</text>\r\n",
       "</g>\r\n",
       "<!-- 38&#45;&gt;39 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"1856\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[1] &lt;= &#45;0.29</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1856\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.278</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1856\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 545</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1856\" y=\"-96.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [454, 91]</text>\r\n",
       "</g>\r\n",
       "<!-- 38&#45;&gt;40 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"1733\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.494</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1733\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 9</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"1852\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.272</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1852\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 536</text>\r\n",
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       "<!-- 40&#45;&gt;42 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"1987\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[0] &lt;= 0.911</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1987\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.451</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1987\" y=\"-215.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 279</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1987\" y=\"-200.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [183, 96]</text>\r\n",
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       "<!-- 43&#45;&gt;44 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"2110\" y=\"-238.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2110\" y=\"-223.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 10</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2110\" y=\"-208.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [10, 0]</text>\r\n",
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       "<!-- 43&#45;&gt;51 -->\r\n",
       "<g id=\"edge51\" class=\"edge\"><title>43&#45;&gt;51</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"1987\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[3] &lt;= 1.354</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1987\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.415</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1987\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 180</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1987\" y=\"-96.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [127, 53]</text>\r\n",
       "</g>\r\n",
       "<!-- 44&#45;&gt;45 -->\r\n",
       "<g id=\"edge45\" class=\"edge\"><title>44&#45;&gt;45</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"2229\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[4] &lt;= 0.398</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2229\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.491</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2229\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 99</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2229\" y=\"-96.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [56, 43]</text>\r\n",
       "</g>\r\n",
       "<!-- 44&#45;&gt;48 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"1981\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.387</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1981\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 156</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1981\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [115, 41]</text>\r\n",
       "</g>\r\n",
       "<!-- 45&#45;&gt;46 -->\r\n",
       "<g id=\"edge46\" class=\"edge\"><title>45&#45;&gt;46</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"2107\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2107\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 24</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2107\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [12, 12]</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"2229\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.459</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2229\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 70</text>\r\n",
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       "</g>\r\n",
       "<!-- 48&#45;&gt;49 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"2351\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.471</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2351\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 29</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2351\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [11, 18]</text>\r\n",
       "</g>\r\n",
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       "<text text-anchor=\"middle\" x=\"3356\" y=\"-453.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[57] &lt;= 4.861</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3356\" y=\"-438.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.22</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3356\" y=\"-423.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 4656</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3356\" y=\"-408.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [4069, 587]</text>\r\n",
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       "<!-- 52&#45;&gt;53 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"3486\" y=\"-446.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3486\" y=\"-431.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 81</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3486\" y=\"-416.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [0, 81]</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"3182\" y=\"-349.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[48] &lt;= 6.452</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3182\" y=\"-334.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.201</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3182\" y=\"-319.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 4586</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"3529\" y=\"-349.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[1] &lt;= 0.967</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3529\" y=\"-334.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.108</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3529\" y=\"-319.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 70</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"2811\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.16</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2811\" y=\"-215.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 4114</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"3182\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[0] &lt;= 0.304</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3182\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.45</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3182\" y=\"-215.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 472</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3182\" y=\"-200.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [311, 161]</text>\r\n",
       "</g>\r\n",
       "<!-- 54&#45;&gt;62 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"2617\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[16] &lt;= 9.661</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2617\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.273</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2617\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 2182</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2617\" y=\"-96.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [1826, 356]</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"2811\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[50] &lt;= 12.71</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2811\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.004</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2811\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 1932</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2811\" y=\"-96.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [1928, 4]</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"2484\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.246</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2484\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 2094</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2484\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [1793, 301]</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"2617\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.469</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2617\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 88</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2617\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [33, 55]</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"2743\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.001</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2743\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 1790</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"2869\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.041</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2869\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 142</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2869\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [139, 3]</text>\r\n",
       "</g>\r\n",
       "<!-- 59&#45;&gt;61 -->\r\n",
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       "<!-- 63 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"3119\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[1] &lt;= 1.596</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3119\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.395</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3119\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 332</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3119\" y=\"-96.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [242, 90]</text>\r\n",
       "</g>\r\n",
       "<!-- 62&#45;&gt;63 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"3245\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[1] &lt;= 1.787</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3245\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3245\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 140</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3245\" y=\"-96.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [69, 71]</text>\r\n",
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       "<polygon fill=\"none\" stroke=\"black\" points=\"3050.5,-53 2939.5,-53 2939.5,-0 3050.5,-0 3050.5,-53\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"2995\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.332</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2995\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 252</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2995\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [199, 53]</text>\r\n",
       "</g>\r\n",
       "<!-- 63&#45;&gt;64 -->\r\n",
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       "<path fill=\"none\" stroke=\"black\" d=\"M3075.63,-88.9485C3063.03,-79.3431 3049.29,-68.8747 3036.79,-59.345\"/>\r\n",
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       "<text text-anchor=\"middle\" x=\"3121\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.497</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3121\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 80</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3121\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [43, 37]</text>\r\n",
       "</g>\r\n",
       "<!-- 63&#45;&gt;65 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"3243\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.498</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3243\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 125</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3243\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [66, 59]</text>\r\n",
       "</g>\r\n",
       "<!-- 66&#45;&gt;67 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"3362\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.32</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3362\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 15</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3362\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [3, 12]</text>\r\n",
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       "<!-- 66&#45;&gt;68 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"3529\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[1] &lt;= 0.961</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3529\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.469</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3529\" y=\"-215.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 8</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3529\" y=\"-200.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [3, 5]</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"3699\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[1] &lt;= 1.186</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3699\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.032</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3699\" y=\"-215.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 62</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3699\" y=\"-200.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [1, 61]</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"3475\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[0] &lt;= &#45;1.43</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3475\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.408</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3475\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 7</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"3586\" y=\"-134.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3586\" y=\"-119.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 1</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3586\" y=\"-104.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [1, 0]</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"3475\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"3584\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.278</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3584\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 6</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"3699\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[1] &lt;= 1.176</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3699\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.117</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3699\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 16</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"3810\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3810\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 1</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"4703\" y=\"-557.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[0] &lt;= 1.332</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4703\" y=\"-542.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4703\" y=\"-527.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 265</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4703\" y=\"-512.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [130, 135]</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"4999\" y=\"-557.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[1] &lt;= 1.069</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4999\" y=\"-542.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.31</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4999\" y=\"-527.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 323</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"4500\" y=\"-453.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[1] &lt;= &#45;0.317</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4500\" y=\"-438.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.499</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4500\" y=\"-423.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 247</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"4703\" y=\"-453.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[1] &lt;= 0.456</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4703\" y=\"-438.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.105</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4703\" y=\"-423.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 18</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"4377\" y=\"-342.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4377\" y=\"-327.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4377\" y=\"-312.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [5, 0]</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"4500\" y=\"-349.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[5] &lt;= 0.585</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4500\" y=\"-334.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4500\" y=\"-319.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 242</text>\r\n",
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       "<!-- 83&#45;&gt;85 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"4219\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.498</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4219\" y=\"-215.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 183</text>\r\n",
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       "<!-- 85&#45;&gt;86 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"4500\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[0] &lt;= &#45;1.616</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4500\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.458</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4500\" y=\"-215.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 59</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4500\" y=\"-200.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [38, 21]</text>\r\n",
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       "<!-- 85&#45;&gt;93 -->\r\n",
       "<g id=\"edge93\" class=\"edge\"><title>85&#45;&gt;93</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"4045\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[0] &lt;= 0.833</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4045\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.494</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4045\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 169</text>\r\n",
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       "<!-- 86&#45;&gt;87 -->\r\n",
       "<g id=\"edge87\" class=\"edge\"><title>86&#45;&gt;87</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"4219\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[0] &lt;= 0.911</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4219\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.337</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4219\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 14</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"3926\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.488</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3926\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 152</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3926\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [64, 88]</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"4045\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.457</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4045\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 17</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"4274\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"4487\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[1] &lt;= 0.137</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4487\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.32</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4487\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 5</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"4606\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[1] &lt;= &#45;0.292</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4606\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.431</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"4383\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4383\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 2</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"4492\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4492\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 3</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"4601\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4601\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 2</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"4717\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 52</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"4645\" y=\"-342.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4645\" y=\"-327.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 16</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"4761\" y=\"-349.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[4] &lt;= 0.398</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4761\" y=\"-334.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4761\" y=\"-319.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 2</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"4678\" y=\"-238.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4678\" y=\"-223.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 1</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"4787\" y=\"-238.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4787\" y=\"-223.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 1</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"4999\" y=\"-438.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.38</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4999\" y=\"-423.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 153</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"5567\" y=\"-453.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[0] &lt;= &#45;1.283</text>\r\n",
       "<text text-anchor=\"middle\" x=\"5567\" y=\"-438.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.234</text>\r\n",
       "<text text-anchor=\"middle\" x=\"5567\" y=\"-423.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 170</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"4939\" y=\"-342.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4939\" y=\"-327.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 9</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"5058\" y=\"-334.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.395</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"4955\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.417</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4955\" y=\"-215.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 125</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"5124\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.188</text>\r\n",
       "<text text-anchor=\"middle\" x=\"5124\" y=\"-215.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 19</text>\r\n",
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       "<!-- 108&#45;&gt;114 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"4955\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[1] &lt;= 1.067</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"4955\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"5124\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[5] &lt;= 0.585</text>\r\n",
       "<text text-anchor=\"middle\" x=\"5124\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.111</text>\r\n",
       "<text text-anchor=\"middle\" x=\"5124\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 17</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"5290\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[1] &lt;= 0.903</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"5290\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 2</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"5181\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.32</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"5290\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"5567\" y=\"-334.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.426</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"5626\" y=\"-238.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"5626\" y=\"-223.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 3</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"5745\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[1] &lt;= 1.848</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"5745\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[1] &lt;= 1.832</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"5744\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.18</text>\r\n",
       "<text text-anchor=\"middle\" x=\"5744\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 120</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"5863\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 2</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"6598\" y=\"-661.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[37] &lt;= 6.044</text>\r\n",
       "<text text-anchor=\"middle\" x=\"6598\" y=\"-646.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.233</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"6731\" y=\"-654.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"6731\" y=\"-639.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 161</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"6598\" y=\"-557.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[1] &lt;= 2.529</text>\r\n",
       "<text text-anchor=\"middle\" x=\"6598\" y=\"-542.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.188</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"6728\" y=\"-550.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"6598\" y=\"-453.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[69] &lt;= 8.608</text>\r\n",
       "<text text-anchor=\"middle\" x=\"6598\" y=\"-438.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.417</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"7179\" y=\"-438.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.106</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"6446\" y=\"-334.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.372</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"6446\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.364</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"6101\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[19] &lt;= 20.026</text>\r\n",
       "<text text-anchor=\"middle\" x=\"6101\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.171</text>\r\n",
       "<text text-anchor=\"middle\" x=\"6101\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 328</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"6260\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[48] &lt;= 6.452</text>\r\n",
       "<text text-anchor=\"middle\" x=\"6260\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.488</text>\r\n",
       "<text text-anchor=\"middle\" x=\"6260\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 149</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"5982\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.128</text>\r\n",
       "<text text-anchor=\"middle\" x=\"5982\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 319</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"6101\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"6101\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 9</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"6214\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.161</text>\r\n",
       "<text text-anchor=\"middle\" x=\"6214\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 34</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"6333\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.402</text>\r\n",
       "<text text-anchor=\"middle\" x=\"6333\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 115</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"6384\" y=\"-134.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"6384\" y=\"-119.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 13</text>\r\n",
       "<text text-anchor=\"middle\" x=\"6384\" y=\"-104.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [13, 0]</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"6503\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[0] &lt;= &#45;0.254</text>\r\n",
       "<text text-anchor=\"middle\" x=\"6503\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.444</text>\r\n",
       "<text text-anchor=\"middle\" x=\"6503\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 33</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"6449\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.444</text>\r\n",
       "<text text-anchor=\"middle\" x=\"6449\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 9</text>\r\n",
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       "<!-- 150&#45;&gt;151 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"6562\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.33</text>\r\n",
       "<text text-anchor=\"middle\" x=\"6562\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 24</text>\r\n",
       "<text text-anchor=\"middle\" x=\"6562\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [19, 5]</text>\r\n",
       "</g>\r\n",
       "<!-- 150&#45;&gt;152 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"6751\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[42] &lt;= 20.862</text>\r\n",
       "<text text-anchor=\"middle\" x=\"6751\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.249</text>\r\n",
       "<text text-anchor=\"middle\" x=\"6751\" y=\"-215.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 48</text>\r\n",
       "<text text-anchor=\"middle\" x=\"6751\" y=\"-200.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [41, 7]</text>\r\n",
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       "<!-- 153&#45;&gt;154 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"6892\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[0] &lt;= &#45;0.871</text>\r\n",
       "<text text-anchor=\"middle\" x=\"6892\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.32</text>\r\n",
       "<text text-anchor=\"middle\" x=\"6892\" y=\"-215.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 10</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"6639\" y=\"-134.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"6639\" y=\"-119.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 35</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"6755\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[1] &lt;= 2.411</text>\r\n",
       "<text text-anchor=\"middle\" x=\"6755\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.497</text>\r\n",
       "<text text-anchor=\"middle\" x=\"6755\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 13</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"6784\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 3</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"6892\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"6892\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 4</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"7127\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[53] &lt;= 12.823</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"7251\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.469</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"7364\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[8] &lt;= 2.194</text>\r\n",
       "<text text-anchor=\"middle\" x=\"7364\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.153</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"7480\" y=\"-223.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 11</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"7364\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">X[0] &lt;= &#45;1.714</text>\r\n",
       "<text text-anchor=\"middle\" x=\"7364\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.083</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"7478\" y=\"-134.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"7364\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.375</text>\r\n",
       "<text text-anchor=\"middle\" x=\"7364\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 4</text>\r\n",
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