paysim1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "# %matplotlib inline\n",
    "sns.set_theme()\n",
    "\n",
    "\n",
    "data_dir = './data/'\n",
    "seed = 42\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # Load Paysim1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load Banksim1\n",
    "paysim = pd.read_csv(f'{data_dir}paysim1/PS_20174392719_1491204439457_log.csv')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Paysim Transactions (6362620, 11)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>step</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>nameOrig</th>\n",
       "      <th>oldbalanceOrg</th>\n",
       "      <th>newbalanceOrig</th>\n",
       "      <th>nameDest</th>\n",
       "      <th>oldbalanceDest</th>\n",
       "      <th>newbalanceDest</th>\n",
       "      <th>isFraud</th>\n",
       "      <th>isFlaggedFraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>9839.64</td>\n",
       "      <td>C1231006815</td>\n",
       "      <td>170136.0</td>\n",
       "      <td>160296.36</td>\n",
       "      <td>M1979787155</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>1864.28</td>\n",
       "      <td>C1666544295</td>\n",
       "      <td>21249.0</td>\n",
       "      <td>19384.72</td>\n",
       "      <td>M2044282225</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>181.00</td>\n",
       "      <td>C1305486145</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C553264065</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>181.00</td>\n",
       "      <td>C840083671</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C38997010</td>\n",
       "      <td>21182.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>11668.14</td>\n",
       "      <td>C2048537720</td>\n",
       "      <td>41554.0</td>\n",
       "      <td>29885.86</td>\n",
       "      <td>M1230701703</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   step      type    amount     nameOrig  oldbalanceOrg  newbalanceOrig  \\\n",
       "0     1   PAYMENT   9839.64  C1231006815       170136.0       160296.36   \n",
       "1     1   PAYMENT   1864.28  C1666544295        21249.0        19384.72   \n",
       "2     1  TRANSFER    181.00  C1305486145          181.0            0.00   \n",
       "3     1  CASH_OUT    181.00   C840083671          181.0            0.00   \n",
       "4     1   PAYMENT  11668.14  C2048537720        41554.0        29885.86   \n",
       "\n",
       "      nameDest  oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \n",
       "0  M1979787155             0.0             0.0        0               0  \n",
       "1  M2044282225             0.0             0.0        0               0  \n",
       "2   C553264065             0.0             0.0        1               0  \n",
       "3    C38997010         21182.0             0.0        1               0  \n",
       "4  M1230701703             0.0             0.0        0               0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f'Paysim Transactions {paysim.shape}')\n",
    "paysim.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # Fraud distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fraud \n",
    "fraud = paysim.isFraud.value_counts()\n",
    "fig, ax = plt.subplots()\n",
    "bars = ax.bar(['Not Fraud', 'Fraud'],fraud.values, color=['#1f77b4', '#d62728'])\n",
    "\n",
    "# Add labels to the top of each bar\n",
    "ax.bar_label(bars, labels=fraud.values, label_type='edge', color='black', fontsize=10, weight='bold')\n",
    "\n",
    "# Customize the plot\n",
    "ax.set_ylabel('Count')\n",
    "ax.set_title('Count of Fraud Payments')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Amount Distribution - Max 92445516.64')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data=paysim, x='amount', fill=True, alpha=0.1, linewidth=1)\n",
    "plt.xlabel('Amount')\n",
    "plt.ylabel('Density')\n",
    "max_amount = max(paysim.amount)\n",
    "plt.title(f'Amount Distribution - Max {max_amount}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fraud = paysim.loc[paysim.isFraud== 1]\n",
    "not_fraud = paysim.loc[paysim.isFraud== 0]\n",
    "\n",
    "sns.kdeplot(data=fraud, x='amount', fill=True, color='#d62728', alpha=0.1, linewidth=1)\n",
    "plt.xlabel('Amount')\n",
    "plt.ylabel('Density')\n",
    "max_amount = max(fraud.amount)\n",
    "plt.title(f'Fraud Amount Distribution - Max {max_amount}')\n",
    "plt.show()\n",
    "\n",
    "plt.hist(not_fraud.amount, alpha=0.5,label='Not Fraud', bins=100, color='#1f77b4')\n",
    "plt.hist(fraud.amount, alpha=0.5,label='Fraud', bins=100, color='#d62728')\n",
    "plt.title('Amount Histogram split by Fraud')\n",
    "plt.xlabel('Amount')\n",
    "plt.ylabel('Count')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.hist(not_fraud.amount, alpha=0.5,label='Not Fraud', bins=100, color='#1f77b4')\n",
    "plt.hist(fraud.amount, alpha=0.5,label='Fraud', bins=100, color='#d62728')\n",
    "plt.ylim(0,10000)\n",
    "plt.xlim(0,1000)\n",
    "plt.title('Zoomed in Amount Histogram split by Fraud')\n",
    "plt.xlabel('Amount')\n",
    "plt.ylabel('Count')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # Split Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step                  743\n",
      "type                    5\n",
      "amount            5316900\n",
      "nameOrig          6353307\n",
      "oldbalanceOrg     1845844\n",
      "newbalanceOrig    2682586\n",
      "nameDest          2722362\n",
      "oldbalanceDest    3614697\n",
      "newbalanceDest    3555499\n",
      "isFraud                 2\n",
      "isFlaggedFraud          2\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "def label_encode(data, cat_cols):\n",
    "    label_encoders = {}\n",
    "    for col in cat_cols:\n",
    "        label_encoders[col] = LabelEncoder()\n",
    "        data[col] = label_encoders[col].fit_transform(data[col])\n",
    "    return label_encoders\n",
    "\n",
    "def label_decode(data, encoders):\n",
    "    for col, encoder in encoders.items():\n",
    "        data[col] = encoder.inverse_transform(data[col])\n",
    "\n",
    "# Clean up data\n",
    "data = paysim\n",
    "print(data.nunique())\n",
    "# data = data.drop(['zipcodeOri', 'zipMerchant'],axis=1) # drop single value columns\n",
    "# data.age = pd.to_numeric(data.age.str.strip(\"'\"), errors='coerce').fillna(-1).astype(int) # convert age to numbers, U's turn into -1's\n",
    "cat_cols = ['type','nameOrig', 'nameDest']\n",
    "label_encoders = label_encode(data, cat_cols)\n",
    "data.dtypes\n",
    "\n",
    "# Split data\n",
    "X = data.drop(['isFraud'], axis=1)\n",
    "y = data['isFraud']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3, random_state=seed, shuffle=True, stratify=y)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # Create Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "rand_forest = RandomForestClassifier(random_state=seed)\n",
    "rand_forest.fit(X_train, y_train)\n",
    "y_pred = rand_forest.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00   1906322\n",
      "           1       0.98      0.77      0.86      2464\n",
      "\n",
      "    accuracy                           1.00   1908786\n",
      "   macro avg       0.99      0.88      0.93   1908786\n",
      "weighted avg       1.00      1.00      1.00   1908786\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# Create a DataFrame from the confusion matrix for better visualization\n",
    "cm_df = pd.DataFrame(cm, index=['Actual Not Fraud', 'Actual Fraud'], columns=['Predicted Not Fraud', 'Predicted Fraud'])\n",
    "\n",
    "# Plot the heatmap using seaborn\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(cm_df, annot=True, fmt='d', cbar=False)\n",
    "plt.title('Confusion Matrix')\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "plt.show()\n",
    "\n",
    "print(classification_report(y_test,y_pred))\n",
    "\n"
   ]
  }
 ],
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