Credit_card_fraud_detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d88339f9-595e-42fe-b843-33eb8596e0ed",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "40a017ca-661b-4ed6-9832-fe754b22af02",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\shiva\\Downloads\\archive (6)\\creditcard.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dc67e3e0-9ac1-4869-a30a-eef80441e67b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 284807 entries, 0 to 284806\n",
      "Data columns (total 31 columns):\n",
      " #   Column  Non-Null Count   Dtype  \n",
      "---  ------  --------------   -----  \n",
      " 0   Time    284807 non-null  float64\n",
      " 1   V1      284807 non-null  float64\n",
      " 2   V2      284807 non-null  float64\n",
      " 3   V3      284807 non-null  float64\n",
      " 4   V4      284807 non-null  float64\n",
      " 5   V5      284807 non-null  float64\n",
      " 6   V6      284807 non-null  float64\n",
      " 7   V7      284807 non-null  float64\n",
      " 8   V8      284807 non-null  float64\n",
      " 9   V9      284807 non-null  float64\n",
      " 10  V10     284807 non-null  float64\n",
      " 11  V11     284807 non-null  float64\n",
      " 12  V12     284807 non-null  float64\n",
      " 13  V13     284807 non-null  float64\n",
      " 14  V14     284807 non-null  float64\n",
      " 15  V15     284807 non-null  float64\n",
      " 16  V16     284807 non-null  float64\n",
      " 17  V17     284807 non-null  float64\n",
      " 18  V18     284807 non-null  float64\n",
      " 19  V19     284807 non-null  float64\n",
      " 20  V20     284807 non-null  float64\n",
      " 21  V21     284807 non-null  float64\n",
      " 22  V22     284807 non-null  float64\n",
      " 23  V23     284807 non-null  float64\n",
      " 24  V24     284807 non-null  float64\n",
      " 25  V25     284807 non-null  float64\n",
      " 26  V26     284807 non-null  float64\n",
      " 27  V27     284807 non-null  float64\n",
      " 28  V28     284807 non-null  float64\n",
      " 29  Amount  284807 non-null  float64\n",
      " 30  Class   284807 non-null  int64  \n",
      "dtypes: float64(30), int64(1)\n",
      "memory usage: 67.4 MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(df.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9f452d57-1967-4d89-8efc-3bd0b3bfbd60",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 31)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3f8f3fb2-08d2-4f78-b18e-073a2711404c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class\n",
      "0    284315\n",
      "1       492\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df['Class'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4e326d35-eb7d-458a-a8ca-e255d5e4097e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "37e82211-1e40-486b-a921-7321aa49d95f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Class',data=df)\n",
    "plt.title('Class Distribution (0 = Not Fraud, 1 = Fraud)')\n",
    "plt.xlabel('Class')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7aecd09f-3929-4d6e-b0c1-1bb674be7944",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "X = df.drop(columns=['Class'])\n",
    "y = df['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f53201ad-b444-4f51-823e-57d58f4ba968",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "print(df.isnull().sum().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a35619b5-07f2-4c6e-8e96-3a952c1baedd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bb8f3684-5fad-40ac-9216-d4a51740e3ab",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X['Time'] = scaler.fit_transform(X[['Time']])\n",
    "X['Amount'] = scaler.fit_transform(X[['Amount']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b2f00600-40de-4c0c-8fca-4490a42de547",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class\n",
      "0    284315\n",
      "1       492\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(y.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "33737ea9-ad37-4dc7-b6b2-dd073bfec905",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "abe682da-1bb6-47bf-a3a2-37fe8e195647",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "smote = SMOTE(random_state=42)\n",
    "X_resampled, y_resampled = smote.fit_resample(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "788bbf30-c62b-4ad5-8ba2-4a1be4778770",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class\n",
      "0    284315\n",
      "1    284315\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(pd.Series(y_resampled).value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3270d487-f4a3-462f-80fe-d5e6e7259605",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0e5dfb2e-4898-4caa-bc43-a7cba97f6468",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_resampled,y_resampled, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "782eeb0a-24e1-4111-aaac-13378dc1edec",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5c8dfa7a-c1b8-4d09-ab2f-5b8af829d549",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(random_state=42)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestClassifier(random_state=42,n_estimators=100)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4b1d9056-a2d7-4338-90b7-0c9d2736991d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "y_pred_prob = model.predict_proba(X_test)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "434f24c6-9261-478f-97f3-096d37701646",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "eecff0b9-abe6-4dd7-a05d-7a080bf5322f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     71023\n",
      "           1       1.00      1.00      1.00     71135\n",
      "\n",
      "    accuracy                           1.00    142158\n",
      "   macro avg       1.00      1.00      1.00    142158\n",
      "weighted avg       1.00      1.00      1.00    142158\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "fd27263d-c46d-4df7-ad7b-d30d34ee7c04",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap = 'Blues')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f8fb565d-3d3b-4d93-b437-90d2d2b29822",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "69ebc2f5-f6fe-4498-be12-a57c26894404",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9999085524557183\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "602c595f-7553-40ff-bc68-04c127cc4c3a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>284802</th>\n",
       "      <td>172786.0</td>\n",
       "      <td>-11.881118</td>\n",
       "      <td>10.071785</td>\n",
       "      <td>-9.834783</td>\n",
       "      <td>-2.066656</td>\n",
       "      <td>-5.364473</td>\n",
       "      <td>-2.606837</td>\n",
       "      <td>-4.918215</td>\n",
       "      <td>7.305334</td>\n",
       "      <td>1.914428</td>\n",
       "      <td>...</td>\n",
       "      <td>0.213454</td>\n",
       "      <td>0.111864</td>\n",
       "      <td>1.014480</td>\n",
       "      <td>-0.509348</td>\n",
       "      <td>1.436807</td>\n",
       "      <td>0.250034</td>\n",
       "      <td>0.943651</td>\n",
       "      <td>0.823731</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284803</th>\n",
       "      <td>172787.0</td>\n",
       "      <td>-0.732789</td>\n",
       "      <td>-0.055080</td>\n",
       "      <td>2.035030</td>\n",
       "      <td>-0.738589</td>\n",
       "      <td>0.868229</td>\n",
       "      <td>1.058415</td>\n",
       "      <td>0.024330</td>\n",
       "      <td>0.294869</td>\n",
       "      <td>0.584800</td>\n",
       "      <td>...</td>\n",
       "      <td>0.214205</td>\n",
       "      <td>0.924384</td>\n",
       "      <td>0.012463</td>\n",
       "      <td>-1.016226</td>\n",
       "      <td>-0.606624</td>\n",
       "      <td>-0.395255</td>\n",
       "      <td>0.068472</td>\n",
       "      <td>-0.053527</td>\n",
       "      <td>24.79</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284804</th>\n",
       "      <td>172788.0</td>\n",
       "      <td>1.919565</td>\n",
       "      <td>-0.301254</td>\n",
       "      <td>-3.249640</td>\n",
       "      <td>-0.557828</td>\n",
       "      <td>2.630515</td>\n",
       "      <td>3.031260</td>\n",
       "      <td>-0.296827</td>\n",
       "      <td>0.708417</td>\n",
       "      <td>0.432454</td>\n",
       "      <td>...</td>\n",
       "      <td>0.232045</td>\n",
       "      <td>0.578229</td>\n",
       "      <td>-0.037501</td>\n",
       "      <td>0.640134</td>\n",
       "      <td>0.265745</td>\n",
       "      <td>-0.087371</td>\n",
       "      <td>0.004455</td>\n",
       "      <td>-0.026561</td>\n",
       "      <td>67.88</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284805</th>\n",
       "      <td>172788.0</td>\n",
       "      <td>-0.240440</td>\n",
       "      <td>0.530483</td>\n",
       "      <td>0.702510</td>\n",
       "      <td>0.689799</td>\n",
       "      <td>-0.377961</td>\n",
       "      <td>0.623708</td>\n",
       "      <td>-0.686180</td>\n",
       "      <td>0.679145</td>\n",
       "      <td>0.392087</td>\n",
       "      <td>...</td>\n",
       "      <td>0.265245</td>\n",
       "      <td>0.800049</td>\n",
       "      <td>-0.163298</td>\n",
       "      <td>0.123205</td>\n",
       "      <td>-0.569159</td>\n",
       "      <td>0.546668</td>\n",
       "      <td>0.108821</td>\n",
       "      <td>0.104533</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284806</th>\n",
       "      <td>172792.0</td>\n",
       "      <td>-0.533413</td>\n",
       "      <td>-0.189733</td>\n",
       "      <td>0.703337</td>\n",
       "      <td>-0.506271</td>\n",
       "      <td>-0.012546</td>\n",
       "      <td>-0.649617</td>\n",
       "      <td>1.577006</td>\n",
       "      <td>-0.414650</td>\n",
       "      <td>0.486180</td>\n",
       "      <td>...</td>\n",
       "      <td>0.261057</td>\n",
       "      <td>0.643078</td>\n",
       "      <td>0.376777</td>\n",
       "      <td>0.008797</td>\n",
       "      <td>-0.473649</td>\n",
       "      <td>-0.818267</td>\n",
       "      <td>-0.002415</td>\n",
       "      <td>0.013649</td>\n",
       "      <td>217.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>284807 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Time         V1         V2        V3        V4        V5  \\\n",
       "0            0.0  -1.359807  -0.072781  2.536347  1.378155 -0.338321   \n",
       "1            0.0   1.191857   0.266151  0.166480  0.448154  0.060018   \n",
       "2            1.0  -1.358354  -1.340163  1.773209  0.379780 -0.503198   \n",
       "3            1.0  -0.966272  -0.185226  1.792993 -0.863291 -0.010309   \n",
       "4            2.0  -1.158233   0.877737  1.548718  0.403034 -0.407193   \n",
       "...          ...        ...        ...       ...       ...       ...   \n",
       "284802  172786.0 -11.881118  10.071785 -9.834783 -2.066656 -5.364473   \n",
       "284803  172787.0  -0.732789  -0.055080  2.035030 -0.738589  0.868229   \n",
       "284804  172788.0   1.919565  -0.301254 -3.249640 -0.557828  2.630515   \n",
       "284805  172788.0  -0.240440   0.530483  0.702510  0.689799 -0.377961   \n",
       "284806  172792.0  -0.533413  -0.189733  0.703337 -0.506271 -0.012546   \n",
       "\n",
       "              V6        V7        V8        V9  ...       V21       V22  \\\n",
       "0       0.462388  0.239599  0.098698  0.363787  ... -0.018307  0.277838   \n",
       "1      -0.082361 -0.078803  0.085102 -0.255425  ... -0.225775 -0.638672   \n",
       "2       1.800499  0.791461  0.247676 -1.514654  ...  0.247998  0.771679   \n",
       "3       1.247203  0.237609  0.377436 -1.387024  ... -0.108300  0.005274   \n",
       "4       0.095921  0.592941 -0.270533  0.817739  ... -0.009431  0.798278   \n",
       "...          ...       ...       ...       ...  ...       ...       ...   \n",
       "284802 -2.606837 -4.918215  7.305334  1.914428  ...  0.213454  0.111864   \n",
       "284803  1.058415  0.024330  0.294869  0.584800  ...  0.214205  0.924384   \n",
       "284804  3.031260 -0.296827  0.708417  0.432454  ...  0.232045  0.578229   \n",
       "284805  0.623708 -0.686180  0.679145  0.392087  ...  0.265245  0.800049   \n",
       "284806 -0.649617  1.577006 -0.414650  0.486180  ...  0.261057  0.643078   \n",
       "\n",
       "             V23       V24       V25       V26       V27       V28  Amount  \\\n",
       "0      -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053  149.62   \n",
       "1       0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724    2.69   \n",
       "2       0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752  378.66   \n",
       "3      -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458  123.50   \n",
       "4      -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153   69.99   \n",
       "...          ...       ...       ...       ...       ...       ...     ...   \n",
       "284802  1.014480 -0.509348  1.436807  0.250034  0.943651  0.823731    0.77   \n",
       "284803  0.012463 -1.016226 -0.606624 -0.395255  0.068472 -0.053527   24.79   \n",
       "284804 -0.037501  0.640134  0.265745 -0.087371  0.004455 -0.026561   67.88   \n",
       "284805 -0.163298  0.123205 -0.569159  0.546668  0.108821  0.104533   10.00   \n",
       "284806  0.376777  0.008797 -0.473649 -0.818267 -0.002415  0.013649  217.00   \n",
       "\n",
       "        Class  \n",
       "0           0  \n",
       "1           0  \n",
       "2           0  \n",
       "3           0  \n",
       "4           0  \n",
       "...       ...  \n",
       "284802      0  \n",
       "284803      0  \n",
       "284804      0  \n",
       "284805      0  \n",
       "284806      0  \n",
       "\n",
       "[284807 rows x 31 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0acb3939-c315-444e-8778-cb5e889dd64e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from joblib import dump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ad9f4c35-ba6b-4958-8a1e-1920c15fafd8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['credit_card_fraud_model.joblib']"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dump(model, 'credit_card_fraud_model.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "79297187-1a1a-4f52-a3ba-af90230a14fe",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: scikit-learn\n",
      "Version: 1.2.2\n",
      "Summary: A set of python modules for machine learning and data mining\n",
      "Home-page: http://scikit-learn.org\n",
      "Author: \n",
      "Author-email: \n",
      "License: new BSD\n",
      "Location: C:\\Users\\shiva\\anaconda3\\Lib\\site-packages\n",
      "Requires: joblib, numpy, scipy, threadpoolctl\n",
      "Required-by: daal4py, imbalanced-learn, scikit-learn-intelex, sklearn-compat\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip show scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "83921bec-df8f-4c51-abe9-5a5c6759b9cd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: numpy\n",
      "Version: 1.24.3\n",
      "Summary: Fundamental package for array computing in Python\n",
      "Home-page: https://www.numpy.org\n",
      "Author: Travis E. Oliphant et al.\n",
      "Author-email: \n",
      "License: BSD-3-Clause\n",
      "Location: C:\\Users\\shiva\\anaconda3\\Lib\\site-packages\n",
      "Requires: \n",
      "Required-by: astropy, bokeh, Bottleneck, contourpy, daal4py, datasets, datashader, datashape, gensim, h5py, holoviews, hvplot, imagecodecs, imageio, imbalanced-learn, keras, matplotlib, mkl-fft, mkl-random, ml-dtypes, numba, numexpr, opencv-contrib-python, opencv-python, opt-einsum, pandas, patsy, pyarrow, pydeck, pyerfa, PyWavelets, scikit-image, scikit-learn, scipy, seaborn, statsmodels, streamlit, tables, tensorboard, tensorflow-intel, tifffile, torchvision, transformers, xarray\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip show numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1bddb11f-e4f6-4cb1-a509-a8ecd5c4efff",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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