Credit Card Fraud Detection.ipynb

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   "source": [
    "# Credit Card Fraud Detection\n",
    "\n",
    "\n",
    "## Context:\n",
    "\n",
    "It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase.\n",
    "\n",
    "## Dataset:\n",
    "Link - https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud/data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "bdcd1112-72f4-4e07-af30-87a863811056",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Required Libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "44cf537e-efdf-4ac2-9628-f746f5aeba9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "36bda68b-31b5-45cf-b1ed-f7253dd71dd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the data into the dataframe\n",
    "\n",
    "df = pd.read_csv('creditcard.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "4e29b90c-184e-4898-9064-c46d5d752e74",
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       "\n",
       "              V6        V7        V8        V9  ...       V21       V22  \\\n",
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       "...          ...       ...       ...       ...       ...       ...     ...   \n",
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       "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",
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       "3           0  \n",
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       "[284807 rows x 31 columns]"
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     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df"
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  {
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   "source": [
    "## Data Understanding\n",
    "\n",
    "It contains only numerical input variables which are the result of a PCA transformation. \n",
    "\n",
    "Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. \n",
    "- Features V1, V2, … V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. \n",
    "- The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. \n",
    "- Feature 'Class' is the response variable and it takes value \n",
    "    - **1 - Fraud** and\n",
    "    - **0 - Legitimate**.\n"
   ]
  },
  {
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   "id": "47b6cacf-1eb6-45c5-9b4e-5a7f74d000d0",
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       "      <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>count</th>\n",
       "      <td>284807.000000</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>284807.000000</td>\n",
       "      <td>284807.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>94813.859575</td>\n",
       "      <td>1.168375e-15</td>\n",
       "      <td>3.416908e-16</td>\n",
       "      <td>-1.379537e-15</td>\n",
       "      <td>2.074095e-15</td>\n",
       "      <td>9.604066e-16</td>\n",
       "      <td>1.487313e-15</td>\n",
       "      <td>-5.556467e-16</td>\n",
       "      <td>1.213481e-16</td>\n",
       "      <td>-2.406331e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>1.654067e-16</td>\n",
       "      <td>-3.568593e-16</td>\n",
       "      <td>2.578648e-16</td>\n",
       "      <td>4.473266e-15</td>\n",
       "      <td>5.340915e-16</td>\n",
       "      <td>1.683437e-15</td>\n",
       "      <td>-3.660091e-16</td>\n",
       "      <td>-1.227390e-16</td>\n",
       "      <td>88.349619</td>\n",
       "      <td>0.001727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>47488.145955</td>\n",
       "      <td>1.958696e+00</td>\n",
       "      <td>1.651309e+00</td>\n",
       "      <td>1.516255e+00</td>\n",
       "      <td>1.415869e+00</td>\n",
       "      <td>1.380247e+00</td>\n",
       "      <td>1.332271e+00</td>\n",
       "      <td>1.237094e+00</td>\n",
       "      <td>1.194353e+00</td>\n",
       "      <td>1.098632e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>7.345240e-01</td>\n",
       "      <td>7.257016e-01</td>\n",
       "      <td>6.244603e-01</td>\n",
       "      <td>6.056471e-01</td>\n",
       "      <td>5.212781e-01</td>\n",
       "      <td>4.822270e-01</td>\n",
       "      <td>4.036325e-01</td>\n",
       "      <td>3.300833e-01</td>\n",
       "      <td>250.120109</td>\n",
       "      <td>0.041527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.640751e+01</td>\n",
       "      <td>-7.271573e+01</td>\n",
       "      <td>-4.832559e+01</td>\n",
       "      <td>-5.683171e+00</td>\n",
       "      <td>-1.137433e+02</td>\n",
       "      <td>-2.616051e+01</td>\n",
       "      <td>-4.355724e+01</td>\n",
       "      <td>-7.321672e+01</td>\n",
       "      <td>-1.343407e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.483038e+01</td>\n",
       "      <td>-1.093314e+01</td>\n",
       "      <td>-4.480774e+01</td>\n",
       "      <td>-2.836627e+00</td>\n",
       "      <td>-1.029540e+01</td>\n",
       "      <td>-2.604551e+00</td>\n",
       "      <td>-2.256568e+01</td>\n",
       "      <td>-1.543008e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54201.500000</td>\n",
       "      <td>-9.203734e-01</td>\n",
       "      <td>-5.985499e-01</td>\n",
       "      <td>-8.903648e-01</td>\n",
       "      <td>-8.486401e-01</td>\n",
       "      <td>-6.915971e-01</td>\n",
       "      <td>-7.682956e-01</td>\n",
       "      <td>-5.540759e-01</td>\n",
       "      <td>-2.086297e-01</td>\n",
       "      <td>-6.430976e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.283949e-01</td>\n",
       "      <td>-5.423504e-01</td>\n",
       "      <td>-1.618463e-01</td>\n",
       "      <td>-3.545861e-01</td>\n",
       "      <td>-3.171451e-01</td>\n",
       "      <td>-3.269839e-01</td>\n",
       "      <td>-7.083953e-02</td>\n",
       "      <td>-5.295979e-02</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>84692.000000</td>\n",
       "      <td>1.810880e-02</td>\n",
       "      <td>6.548556e-02</td>\n",
       "      <td>1.798463e-01</td>\n",
       "      <td>-1.984653e-02</td>\n",
       "      <td>-5.433583e-02</td>\n",
       "      <td>-2.741871e-01</td>\n",
       "      <td>4.010308e-02</td>\n",
       "      <td>2.235804e-02</td>\n",
       "      <td>-5.142873e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.945017e-02</td>\n",
       "      <td>6.781943e-03</td>\n",
       "      <td>-1.119293e-02</td>\n",
       "      <td>4.097606e-02</td>\n",
       "      <td>1.659350e-02</td>\n",
       "      <td>-5.213911e-02</td>\n",
       "      <td>1.342146e-03</td>\n",
       "      <td>1.124383e-02</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>139320.500000</td>\n",
       "      <td>1.315642e+00</td>\n",
       "      <td>8.037239e-01</td>\n",
       "      <td>1.027196e+00</td>\n",
       "      <td>7.433413e-01</td>\n",
       "      <td>6.119264e-01</td>\n",
       "      <td>3.985649e-01</td>\n",
       "      <td>5.704361e-01</td>\n",
       "      <td>3.273459e-01</td>\n",
       "      <td>5.971390e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.863772e-01</td>\n",
       "      <td>5.285536e-01</td>\n",
       "      <td>1.476421e-01</td>\n",
       "      <td>4.395266e-01</td>\n",
       "      <td>3.507156e-01</td>\n",
       "      <td>2.409522e-01</td>\n",
       "      <td>9.104512e-02</td>\n",
       "      <td>7.827995e-02</td>\n",
       "      <td>77.165000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>172792.000000</td>\n",
       "      <td>2.454930e+00</td>\n",
       "      <td>2.205773e+01</td>\n",
       "      <td>9.382558e+00</td>\n",
       "      <td>1.687534e+01</td>\n",
       "      <td>3.480167e+01</td>\n",
       "      <td>7.330163e+01</td>\n",
       "      <td>1.205895e+02</td>\n",
       "      <td>2.000721e+01</td>\n",
       "      <td>1.559499e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>2.720284e+01</td>\n",
       "      <td>1.050309e+01</td>\n",
       "      <td>2.252841e+01</td>\n",
       "      <td>4.584549e+00</td>\n",
       "      <td>7.519589e+00</td>\n",
       "      <td>3.517346e+00</td>\n",
       "      <td>3.161220e+01</td>\n",
       "      <td>3.384781e+01</td>\n",
       "      <td>25691.160000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Time            V1            V2            V3            V4  \\\n",
       "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean    94813.859575  1.168375e-15  3.416908e-16 -1.379537e-15  2.074095e-15   \n",
       "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
       "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
       "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
       "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
       "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
       "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
       "\n",
       "                 V5            V6            V7            V8            V9  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   9.604066e-16  1.487313e-15 -5.556467e-16  1.213481e-16 -2.406331e-15   \n",
       "std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   \n",
       "min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   \n",
       "25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   \n",
       "50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   \n",
       "75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   \n",
       "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   \n",
       "\n",
       "       ...           V21           V22           V23           V24  \\\n",
       "count  ...  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   ...  1.654067e-16 -3.568593e-16  2.578648e-16  4.473266e-15   \n",
       "std    ...  7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
       "min    ... -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
       "25%    ... -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
       "50%    ... -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
       "75%    ...  1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
       "max    ...  2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00   \n",
       "\n",
       "                V25           V26           V27           V28         Amount  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
       "mean   5.340915e-16  1.683437e-15 -3.660091e-16 -1.227390e-16      88.349619   \n",
       "std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
       "min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
       "25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
       "50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
       "75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
       "max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
       "\n",
       "               Class  \n",
       "count  284807.000000  \n",
       "mean        0.001727  \n",
       "std         0.041527  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         0.000000  \n",
       "max         1.000000  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# describe the data\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "976daa0f-3c30-4bf1-86a9-1ace92bdf984",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time      0\n",
       "V1        0\n",
       "V2        0\n",
       "V3        0\n",
       "V4        0\n",
       "V5        0\n",
       "V6        0\n",
       "V7        0\n",
       "V8        0\n",
       "V9        0\n",
       "V10       0\n",
       "V11       0\n",
       "V12       0\n",
       "V13       0\n",
       "V14       0\n",
       "V15       0\n",
       "V16       0\n",
       "V17       0\n",
       "V18       0\n",
       "V19       0\n",
       "V20       0\n",
       "V21       0\n",
       "V22       0\n",
       "V23       0\n",
       "V24       0\n",
       "V25       0\n",
       "V26       0\n",
       "V27       0\n",
       "V28       0\n",
       "Amount    0\n",
       "Class     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13cb2621-e117-4cb1-8c5e-f9188ff4dbe7",
   "metadata": {},
   "source": [
    "## Data Preprocessing\n",
    "\n",
    "- From the above it is clear that there are no missing values in the dataset and thus there is no need for preprocessing the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "327aad11-0e13-4ec7-8c3e-571423a9cb0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Class\n",
       "0    284315\n",
       "1       492\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# distribution of legit transactions & fraudulent transactions\n",
    "df['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "bd43fa3c-da9b-438f-b522-84e1ceec77ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting a graph to compare the fradulant vs legitamate transactions\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.countplot(x=df['Class'], data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b3eb78a-788b-4be0-a81b-9637a5cf6428",
   "metadata": {},
   "source": [
    "## Insights\n",
    "\n",
    "Credit Card Fraud Detection is a classic class-imbalance problem where the number of fraud transactions is much lesser than the number of legitimate transaction for any bank.\n",
    "\n",
    "This is very clear from the above cells that from the total 284807 transactions only 492 are fradulant transactions i.e only 0.17% of the data.\n",
    "\n",
    "The dataset is highly imbalanced thus if we run the model on such data we will end up with high-overfitting on the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "188b3760-9b5c-4899-a58f-e92b405fcbca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(284315, 31)\n",
      "(492, 31)\n"
     ]
    }
   ],
   "source": [
    "# separating the data for analysis\n",
    "\n",
    "legit = df[df['Class'] == 0]\n",
    "fraud = df[df['Class'] == 1]\n",
    "print(legit.shape)\n",
    "print(fraud.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "a5939623-cb2a-49cc-b0c8-c27b2e7adeb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    284315.000000\n",
       "mean         88.291022\n",
       "std         250.105092\n",
       "min           0.000000\n",
       "25%           5.650000\n",
       "50%          22.000000\n",
       "75%          77.050000\n",
       "max       25691.160000\n",
       "Name: Amount, dtype: float64"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# statistical measures of the data\n",
    "legit.Amount.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "f66cb218-3ed9-44f8-84d8-dea6b7140419",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     492.000000\n",
       "mean      122.211321\n",
       "std       256.683288\n",
       "min         0.000000\n",
       "25%         1.000000\n",
       "50%         9.250000\n",
       "75%       105.890000\n",
       "max      2125.870000\n",
       "Name: Amount, dtype: float64"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fraud.Amount.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "01788d95-2f94-465f-80c2-072b9f62fa8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 658.875x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graphical Representation of the nature of the transactions with respect to their amount\n",
    "\n",
    "sns.FacetGrid(df, hue=\"Class\", height=6).map(plt.scatter, \"Time\", \"Amount\").add_legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "08573264-8104-473e-90da-ccc5b53485d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 658.875x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(df, hue=\"Class\", height=6).map(plt.scatter, \"Amount\", \"Time\").add_legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3223c7de-abf3-45be-9fe2-c5ceb244479c",
   "metadata": {},
   "source": [
    "## Insight\n",
    "\n",
    "From the above data analysis of the fraud and legit transactions \n",
    "\n",
    "The **maximum amount in the fraud transactions is 2125.87**. So we know that all fraud transactions occur for an amount below 2250. With this insight, lets **find out how many transactions below the amount of 2250 are fraud and legit.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "a454c338-59c8-4e6f-b51b-0f5ef4a4ca32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "492"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = 0\n",
    "\n",
    "refine = df[[\"Time\", \"Amount\", \"Class\"]]\n",
    "\n",
    "for i in range(refine.shape[0]):\n",
    "    if refine.iloc[i][\"Amount\"] < 2250 and refine.iloc[i][\"Class\"] == 1:\n",
    "            f += 1\n",
    "\n",
    "f"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2efc531-ada1-4850-9c75-3ffb8aa12bef",
   "metadata": {},
   "source": [
    "From the above it is clear that all the fraud transactions are below the amount of 2250. As both the value from the above cell and the fraud.Amount.describe() are equal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96506883-9002-4570-b932-f7caac1b1fd1",
   "metadata": {},
   "source": [
    "## Is there any relationship between the time and the transactions?\n",
    "\n",
    "Let us plot a distribution plot and find if there is any relationship between both of them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "a11b8eab-b2d6-42f4-b256-acc615127e52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 658.875x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(df_refine, hue=\"Class\", height=6).map(sns.distplot,\"Time\").add_legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24653d40-1061-4828-a9c9-bf6c601723c4",
   "metadata": {},
   "source": [
    "From the above plot, it is evident that the fraud transactions are throughout the time period so there is no aparant relationship between bot the values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24d343a4-431d-4391-be72-697e3f12daf3",
   "metadata": {},
   "source": [
    "## Machine Learning Model to identify the credit card fraudulants\n",
    "\n",
    "Since the dataset is highly imbalanced, we need to create a sample dataset containing similar distribution of transactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "0067f0bf-e80b-447d-aaa4-dea10a10bf72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<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>233879</th>\n",
       "      <td>147740.0</td>\n",
       "      <td>2.030181</td>\n",
       "      <td>-0.143821</td>\n",
       "      <td>-1.988416</td>\n",
       "      <td>0.379731</td>\n",
       "      <td>0.212851</td>\n",
       "      <td>-1.632425</td>\n",
       "      <td>0.705438</td>\n",
       "      <td>-0.525514</td>\n",
       "      <td>0.315037</td>\n",
       "      <td>...</td>\n",
       "      <td>0.113343</td>\n",
       "      <td>0.384506</td>\n",
       "      <td>-0.055369</td>\n",
       "      <td>0.114201</td>\n",
       "      <td>0.302375</td>\n",
       "      <td>0.822920</td>\n",
       "      <td>-0.136864</td>\n",
       "      <td>-0.080336</td>\n",
       "      <td>49.95</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217233</th>\n",
       "      <td>140817.0</td>\n",
       "      <td>1.973879</td>\n",
       "      <td>-0.509845</td>\n",
       "      <td>-1.227686</td>\n",
       "      <td>0.081128</td>\n",
       "      <td>-0.208654</td>\n",
       "      <td>-0.508661</td>\n",
       "      <td>-0.163037</td>\n",
       "      <td>-0.005953</td>\n",
       "      <td>1.135357</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.148142</td>\n",
       "      <td>-0.352257</td>\n",
       "      <td>0.139703</td>\n",
       "      <td>-0.525682</td>\n",
       "      <td>-0.102873</td>\n",
       "      <td>-0.075850</td>\n",
       "      <td>-0.046174</td>\n",
       "      <td>-0.069476</td>\n",
       "      <td>34.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181843</th>\n",
       "      <td>125142.0</td>\n",
       "      <td>2.014619</td>\n",
       "      <td>0.281165</td>\n",
       "      <td>-2.213673</td>\n",
       "      <td>0.921595</td>\n",
       "      <td>0.378792</td>\n",
       "      <td>-1.353702</td>\n",
       "      <td>0.209212</td>\n",
       "      <td>-0.283793</td>\n",
       "      <td>0.782169</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018851</td>\n",
       "      <td>0.262605</td>\n",
       "      <td>-0.045560</td>\n",
       "      <td>-0.231925</td>\n",
       "      <td>0.185279</td>\n",
       "      <td>0.734789</td>\n",
       "      <td>-0.036630</td>\n",
       "      <td>-0.013429</td>\n",
       "      <td>12.31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167711</th>\n",
       "      <td>118848.0</td>\n",
       "      <td>0.597053</td>\n",
       "      <td>0.781116</td>\n",
       "      <td>-1.188694</td>\n",
       "      <td>-0.592401</td>\n",
       "      <td>0.940083</td>\n",
       "      <td>0.255621</td>\n",
       "      <td>-0.099588</td>\n",
       "      <td>-1.567813</td>\n",
       "      <td>0.047871</td>\n",
       "      <td>...</td>\n",
       "      <td>1.130061</td>\n",
       "      <td>-1.695215</td>\n",
       "      <td>0.094467</td>\n",
       "      <td>-0.273581</td>\n",
       "      <td>0.651872</td>\n",
       "      <td>0.251855</td>\n",
       "      <td>0.033543</td>\n",
       "      <td>0.086661</td>\n",
       "      <td>5.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223582</th>\n",
       "      <td>143440.0</td>\n",
       "      <td>1.834083</td>\n",
       "      <td>-1.242381</td>\n",
       "      <td>-1.404565</td>\n",
       "      <td>-0.128186</td>\n",
       "      <td>-0.696623</td>\n",
       "      <td>-0.548778</td>\n",
       "      <td>-0.272746</td>\n",
       "      <td>-0.156866</td>\n",
       "      <td>-0.526743</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.113660</td>\n",
       "      <td>-0.103548</td>\n",
       "      <td>-0.039105</td>\n",
       "      <td>-0.342416</td>\n",
       "      <td>-0.093963</td>\n",
       "      <td>0.443908</td>\n",
       "      <td>-0.072869</td>\n",
       "      <td>-0.047609</td>\n",
       "      <td>165.25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279863</th>\n",
       "      <td>169142.0</td>\n",
       "      <td>-1.927883</td>\n",
       "      <td>1.125653</td>\n",
       "      <td>-4.518331</td>\n",
       "      <td>1.749293</td>\n",
       "      <td>-1.566487</td>\n",
       "      <td>-2.010494</td>\n",
       "      <td>-0.882850</td>\n",
       "      <td>0.697211</td>\n",
       "      <td>-2.064945</td>\n",
       "      <td>...</td>\n",
       "      <td>0.778584</td>\n",
       "      <td>-0.319189</td>\n",
       "      <td>0.639419</td>\n",
       "      <td>-0.294885</td>\n",
       "      <td>0.537503</td>\n",
       "      <td>0.788395</td>\n",
       "      <td>0.292680</td>\n",
       "      <td>0.147968</td>\n",
       "      <td>390.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280143</th>\n",
       "      <td>169347.0</td>\n",
       "      <td>1.378559</td>\n",
       "      <td>1.289381</td>\n",
       "      <td>-5.004247</td>\n",
       "      <td>1.411850</td>\n",
       "      <td>0.442581</td>\n",
       "      <td>-1.326536</td>\n",
       "      <td>-1.413170</td>\n",
       "      <td>0.248525</td>\n",
       "      <td>-1.127396</td>\n",
       "      <td>...</td>\n",
       "      <td>0.370612</td>\n",
       "      <td>0.028234</td>\n",
       "      <td>-0.145640</td>\n",
       "      <td>-0.081049</td>\n",
       "      <td>0.521875</td>\n",
       "      <td>0.739467</td>\n",
       "      <td>0.389152</td>\n",
       "      <td>0.186637</td>\n",
       "      <td>0.76</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280149</th>\n",
       "      <td>169351.0</td>\n",
       "      <td>-0.676143</td>\n",
       "      <td>1.126366</td>\n",
       "      <td>-2.213700</td>\n",
       "      <td>0.468308</td>\n",
       "      <td>-1.120541</td>\n",
       "      <td>-0.003346</td>\n",
       "      <td>-2.234739</td>\n",
       "      <td>1.210158</td>\n",
       "      <td>-0.652250</td>\n",
       "      <td>...</td>\n",
       "      <td>0.751826</td>\n",
       "      <td>0.834108</td>\n",
       "      <td>0.190944</td>\n",
       "      <td>0.032070</td>\n",
       "      <td>-0.739695</td>\n",
       "      <td>0.471111</td>\n",
       "      <td>0.385107</td>\n",
       "      <td>0.194361</td>\n",
       "      <td>77.89</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281144</th>\n",
       "      <td>169966.0</td>\n",
       "      <td>-3.113832</td>\n",
       "      <td>0.585864</td>\n",
       "      <td>-5.399730</td>\n",
       "      <td>1.817092</td>\n",
       "      <td>-0.840618</td>\n",
       "      <td>-2.943548</td>\n",
       "      <td>-2.208002</td>\n",
       "      <td>1.058733</td>\n",
       "      <td>-1.632333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.583276</td>\n",
       "      <td>-0.269209</td>\n",
       "      <td>-0.456108</td>\n",
       "      <td>-0.183659</td>\n",
       "      <td>-0.328168</td>\n",
       "      <td>0.606116</td>\n",
       "      <td>0.884876</td>\n",
       "      <td>-0.253700</td>\n",
       "      <td>245.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281674</th>\n",
       "      <td>170348.0</td>\n",
       "      <td>1.991976</td>\n",
       "      <td>0.158476</td>\n",
       "      <td>-2.583441</td>\n",
       "      <td>0.408670</td>\n",
       "      <td>1.151147</td>\n",
       "      <td>-0.096695</td>\n",
       "      <td>0.223050</td>\n",
       "      <td>-0.068384</td>\n",
       "      <td>0.577829</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.164350</td>\n",
       "      <td>-0.295135</td>\n",
       "      <td>-0.072173</td>\n",
       "      <td>-0.450261</td>\n",
       "      <td>0.313267</td>\n",
       "      <td>-0.289617</td>\n",
       "      <td>0.002988</td>\n",
       "      <td>-0.015309</td>\n",
       "      <td>42.53</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>984 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Time        V1        V2        V3        V4        V5        V6  \\\n",
       "233879  147740.0  2.030181 -0.143821 -1.988416  0.379731  0.212851 -1.632425   \n",
       "217233  140817.0  1.973879 -0.509845 -1.227686  0.081128 -0.208654 -0.508661   \n",
       "181843  125142.0  2.014619  0.281165 -2.213673  0.921595  0.378792 -1.353702   \n",
       "167711  118848.0  0.597053  0.781116 -1.188694 -0.592401  0.940083  0.255621   \n",
       "223582  143440.0  1.834083 -1.242381 -1.404565 -0.128186 -0.696623 -0.548778   \n",
       "...          ...       ...       ...       ...       ...       ...       ...   \n",
       "279863  169142.0 -1.927883  1.125653 -4.518331  1.749293 -1.566487 -2.010494   \n",
       "280143  169347.0  1.378559  1.289381 -5.004247  1.411850  0.442581 -1.326536   \n",
       "280149  169351.0 -0.676143  1.126366 -2.213700  0.468308 -1.120541 -0.003346   \n",
       "281144  169966.0 -3.113832  0.585864 -5.399730  1.817092 -0.840618 -2.943548   \n",
       "281674  170348.0  1.991976  0.158476 -2.583441  0.408670  1.151147 -0.096695   \n",
       "\n",
       "              V7        V8        V9  ...       V21       V22       V23  \\\n",
       "233879  0.705438 -0.525514  0.315037  ...  0.113343  0.384506 -0.055369   \n",
       "217233 -0.163037 -0.005953  1.135357  ... -0.148142 -0.352257  0.139703   \n",
       "181843  0.209212 -0.283793  0.782169  ... -0.018851  0.262605 -0.045560   \n",
       "167711 -0.099588 -1.567813  0.047871  ...  1.130061 -1.695215  0.094467   \n",
       "223582 -0.272746 -0.156866 -0.526743  ... -0.113660 -0.103548 -0.039105   \n",
       "...          ...       ...       ...  ...       ...       ...       ...   \n",
       "279863 -0.882850  0.697211 -2.064945  ...  0.778584 -0.319189  0.639419   \n",
       "280143 -1.413170  0.248525 -1.127396  ...  0.370612  0.028234 -0.145640   \n",
       "280149 -2.234739  1.210158 -0.652250  ...  0.751826  0.834108  0.190944   \n",
       "281144 -2.208002  1.058733 -1.632333  ...  0.583276 -0.269209 -0.456108   \n",
       "281674  0.223050 -0.068384  0.577829  ... -0.164350 -0.295135 -0.072173   \n",
       "\n",
       "             V24       V25       V26       V27       V28  Amount  Class  \n",
       "233879  0.114201  0.302375  0.822920 -0.136864 -0.080336   49.95      0  \n",
       "217233 -0.525682 -0.102873 -0.075850 -0.046174 -0.069476   34.66      0  \n",
       "181843 -0.231925  0.185279  0.734789 -0.036630 -0.013429   12.31      0  \n",
       "167711 -0.273581  0.651872  0.251855  0.033543  0.086661    5.99      0  \n",
       "223582 -0.342416 -0.093963  0.443908 -0.072869 -0.047609  165.25      0  \n",
       "...          ...       ...       ...       ...       ...     ...    ...  \n",
       "279863 -0.294885  0.537503  0.788395  0.292680  0.147968  390.00      1  \n",
       "280143 -0.081049  0.521875  0.739467  0.389152  0.186637    0.76      1  \n",
       "280149  0.032070 -0.739695  0.471111  0.385107  0.194361   77.89      1  \n",
       "281144 -0.183659 -0.328168  0.606116  0.884876 -0.253700  245.00      1  \n",
       "281674 -0.450261  0.313267 -0.289617  0.002988 -0.015309   42.53      1  \n",
       "\n",
       "[984 rows x 31 columns]"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "legit = df[df['Class'] == 0]\n",
    "fraud = df[df['Class'] == 1]\n",
    "\n",
    "sampleLegit = df.sample(n=492)\n",
    "newdf = pd.concat([sampleLegit, fraud], axis=0)\n",
    "newdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "30abc0dc-c84d-4d01-af7e-a2c3efbc5598",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Class\n",
       "0    492\n",
       "1    492\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "50dc88ed-4f1a-4b58-86c8-725345ba6d5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting the data into Features & Targets\n",
    "X = newdf.drop(columns='Class', axis=1)\n",
    "Y = newdf['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "d4b55e83-1165-427c-8a4e-39bb8b684ed3",
   "metadata": {},
   "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>V20</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>233879</th>\n",
       "      <td>147740.0</td>\n",
       "      <td>2.030181</td>\n",
       "      <td>-0.143821</td>\n",
       "      <td>-1.988416</td>\n",
       "      <td>0.379731</td>\n",
       "      <td>0.212851</td>\n",
       "      <td>-1.632425</td>\n",
       "      <td>0.705438</td>\n",
       "      <td>-0.525514</td>\n",
       "      <td>0.315037</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.202954</td>\n",
       "      <td>0.113343</td>\n",
       "      <td>0.384506</td>\n",
       "      <td>-0.055369</td>\n",
       "      <td>0.114201</td>\n",
       "      <td>0.302375</td>\n",
       "      <td>0.822920</td>\n",
       "      <td>-0.136864</td>\n",
       "      <td>-0.080336</td>\n",
       "      <td>49.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217233</th>\n",
       "      <td>140817.0</td>\n",
       "      <td>1.973879</td>\n",
       "      <td>-0.509845</td>\n",
       "      <td>-1.227686</td>\n",
       "      <td>0.081128</td>\n",
       "      <td>-0.208654</td>\n",
       "      <td>-0.508661</td>\n",
       "      <td>-0.163037</td>\n",
       "      <td>-0.005953</td>\n",
       "      <td>1.135357</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.271966</td>\n",
       "      <td>-0.148142</td>\n",
       "      <td>-0.352257</td>\n",
       "      <td>0.139703</td>\n",
       "      <td>-0.525682</td>\n",
       "      <td>-0.102873</td>\n",
       "      <td>-0.075850</td>\n",
       "      <td>-0.046174</td>\n",
       "      <td>-0.069476</td>\n",
       "      <td>34.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181843</th>\n",
       "      <td>125142.0</td>\n",
       "      <td>2.014619</td>\n",
       "      <td>0.281165</td>\n",
       "      <td>-2.213673</td>\n",
       "      <td>0.921595</td>\n",
       "      <td>0.378792</td>\n",
       "      <td>-1.353702</td>\n",
       "      <td>0.209212</td>\n",
       "      <td>-0.283793</td>\n",
       "      <td>0.782169</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.171305</td>\n",
       "      <td>-0.018851</td>\n",
       "      <td>0.262605</td>\n",
       "      <td>-0.045560</td>\n",
       "      <td>-0.231925</td>\n",
       "      <td>0.185279</td>\n",
       "      <td>0.734789</td>\n",
       "      <td>-0.036630</td>\n",
       "      <td>-0.013429</td>\n",
       "      <td>12.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167711</th>\n",
       "      <td>118848.0</td>\n",
       "      <td>0.597053</td>\n",
       "      <td>0.781116</td>\n",
       "      <td>-1.188694</td>\n",
       "      <td>-0.592401</td>\n",
       "      <td>0.940083</td>\n",
       "      <td>0.255621</td>\n",
       "      <td>-0.099588</td>\n",
       "      <td>-1.567813</td>\n",
       "      <td>0.047871</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.436426</td>\n",
       "      <td>1.130061</td>\n",
       "      <td>-1.695215</td>\n",
       "      <td>0.094467</td>\n",
       "      <td>-0.273581</td>\n",
       "      <td>0.651872</td>\n",
       "      <td>0.251855</td>\n",
       "      <td>0.033543</td>\n",
       "      <td>0.086661</td>\n",
       "      <td>5.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223582</th>\n",
       "      <td>143440.0</td>\n",
       "      <td>1.834083</td>\n",
       "      <td>-1.242381</td>\n",
       "      <td>-1.404565</td>\n",
       "      <td>-0.128186</td>\n",
       "      <td>-0.696623</td>\n",
       "      <td>-0.548778</td>\n",
       "      <td>-0.272746</td>\n",
       "      <td>-0.156866</td>\n",
       "      <td>-0.526743</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.361627</td>\n",
       "      <td>-0.113660</td>\n",
       "      <td>-0.103548</td>\n",
       "      <td>-0.039105</td>\n",
       "      <td>-0.342416</td>\n",
       "      <td>-0.093963</td>\n",
       "      <td>0.443908</td>\n",
       "      <td>-0.072869</td>\n",
       "      <td>-0.047609</td>\n",
       "      <td>165.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279863</th>\n",
       "      <td>169142.0</td>\n",
       "      <td>-1.927883</td>\n",
       "      <td>1.125653</td>\n",
       "      <td>-4.518331</td>\n",
       "      <td>1.749293</td>\n",
       "      <td>-1.566487</td>\n",
       "      <td>-2.010494</td>\n",
       "      <td>-0.882850</td>\n",
       "      <td>0.697211</td>\n",
       "      <td>-2.064945</td>\n",
       "      <td>...</td>\n",
       "      <td>1.252967</td>\n",
       "      <td>0.778584</td>\n",
       "      <td>-0.319189</td>\n",
       "      <td>0.639419</td>\n",
       "      <td>-0.294885</td>\n",
       "      <td>0.537503</td>\n",
       "      <td>0.788395</td>\n",
       "      <td>0.292680</td>\n",
       "      <td>0.147968</td>\n",
       "      <td>390.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280143</th>\n",
       "      <td>169347.0</td>\n",
       "      <td>1.378559</td>\n",
       "      <td>1.289381</td>\n",
       "      <td>-5.004247</td>\n",
       "      <td>1.411850</td>\n",
       "      <td>0.442581</td>\n",
       "      <td>-1.326536</td>\n",
       "      <td>-1.413170</td>\n",
       "      <td>0.248525</td>\n",
       "      <td>-1.127396</td>\n",
       "      <td>...</td>\n",
       "      <td>0.226138</td>\n",
       "      <td>0.370612</td>\n",
       "      <td>0.028234</td>\n",
       "      <td>-0.145640</td>\n",
       "      <td>-0.081049</td>\n",
       "      <td>0.521875</td>\n",
       "      <td>0.739467</td>\n",
       "      <td>0.389152</td>\n",
       "      <td>0.186637</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280149</th>\n",
       "      <td>169351.0</td>\n",
       "      <td>-0.676143</td>\n",
       "      <td>1.126366</td>\n",
       "      <td>-2.213700</td>\n",
       "      <td>0.468308</td>\n",
       "      <td>-1.120541</td>\n",
       "      <td>-0.003346</td>\n",
       "      <td>-2.234739</td>\n",
       "      <td>1.210158</td>\n",
       "      <td>-0.652250</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247968</td>\n",
       "      <td>0.751826</td>\n",
       "      <td>0.834108</td>\n",
       "      <td>0.190944</td>\n",
       "      <td>0.032070</td>\n",
       "      <td>-0.739695</td>\n",
       "      <td>0.471111</td>\n",
       "      <td>0.385107</td>\n",
       "      <td>0.194361</td>\n",
       "      <td>77.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281144</th>\n",
       "      <td>169966.0</td>\n",
       "      <td>-3.113832</td>\n",
       "      <td>0.585864</td>\n",
       "      <td>-5.399730</td>\n",
       "      <td>1.817092</td>\n",
       "      <td>-0.840618</td>\n",
       "      <td>-2.943548</td>\n",
       "      <td>-2.208002</td>\n",
       "      <td>1.058733</td>\n",
       "      <td>-1.632333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.306271</td>\n",
       "      <td>0.583276</td>\n",
       "      <td>-0.269209</td>\n",
       "      <td>-0.456108</td>\n",
       "      <td>-0.183659</td>\n",
       "      <td>-0.328168</td>\n",
       "      <td>0.606116</td>\n",
       "      <td>0.884876</td>\n",
       "      <td>-0.253700</td>\n",
       "      <td>245.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281674</th>\n",
       "      <td>170348.0</td>\n",
       "      <td>1.991976</td>\n",
       "      <td>0.158476</td>\n",
       "      <td>-2.583441</td>\n",
       "      <td>0.408670</td>\n",
       "      <td>1.151147</td>\n",
       "      <td>-0.096695</td>\n",
       "      <td>0.223050</td>\n",
       "      <td>-0.068384</td>\n",
       "      <td>0.577829</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.017652</td>\n",
       "      <td>-0.164350</td>\n",
       "      <td>-0.295135</td>\n",
       "      <td>-0.072173</td>\n",
       "      <td>-0.450261</td>\n",
       "      <td>0.313267</td>\n",
       "      <td>-0.289617</td>\n",
       "      <td>0.002988</td>\n",
       "      <td>-0.015309</td>\n",
       "      <td>42.53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>984 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Time        V1        V2        V3        V4        V5        V6  \\\n",
       "233879  147740.0  2.030181 -0.143821 -1.988416  0.379731  0.212851 -1.632425   \n",
       "217233  140817.0  1.973879 -0.509845 -1.227686  0.081128 -0.208654 -0.508661   \n",
       "181843  125142.0  2.014619  0.281165 -2.213673  0.921595  0.378792 -1.353702   \n",
       "167711  118848.0  0.597053  0.781116 -1.188694 -0.592401  0.940083  0.255621   \n",
       "223582  143440.0  1.834083 -1.242381 -1.404565 -0.128186 -0.696623 -0.548778   \n",
       "...          ...       ...       ...       ...       ...       ...       ...   \n",
       "279863  169142.0 -1.927883  1.125653 -4.518331  1.749293 -1.566487 -2.010494   \n",
       "280143  169347.0  1.378559  1.289381 -5.004247  1.411850  0.442581 -1.326536   \n",
       "280149  169351.0 -0.676143  1.126366 -2.213700  0.468308 -1.120541 -0.003346   \n",
       "281144  169966.0 -3.113832  0.585864 -5.399730  1.817092 -0.840618 -2.943548   \n",
       "281674  170348.0  1.991976  0.158476 -2.583441  0.408670  1.151147 -0.096695   \n",
       "\n",
       "              V7        V8        V9  ...       V20       V21       V22  \\\n",
       "233879  0.705438 -0.525514  0.315037  ... -0.202954  0.113343  0.384506   \n",
       "217233 -0.163037 -0.005953  1.135357  ... -0.271966 -0.148142 -0.352257   \n",
       "181843  0.209212 -0.283793  0.782169  ... -0.171305 -0.018851  0.262605   \n",
       "167711 -0.099588 -1.567813  0.047871  ... -0.436426  1.130061 -1.695215   \n",
       "223582 -0.272746 -0.156866 -0.526743  ... -0.361627 -0.113660 -0.103548   \n",
       "...          ...       ...       ...  ...       ...       ...       ...   \n",
       "279863 -0.882850  0.697211 -2.064945  ...  1.252967  0.778584 -0.319189   \n",
       "280143 -1.413170  0.248525 -1.127396  ...  0.226138  0.370612  0.028234   \n",
       "280149 -2.234739  1.210158 -0.652250  ...  0.247968  0.751826  0.834108   \n",
       "281144 -2.208002  1.058733 -1.632333  ...  0.306271  0.583276 -0.269209   \n",
       "281674  0.223050 -0.068384  0.577829  ... -0.017652 -0.164350 -0.295135   \n",
       "\n",
       "             V23       V24       V25       V26       V27       V28  Amount  \n",
       "233879 -0.055369  0.114201  0.302375  0.822920 -0.136864 -0.080336   49.95  \n",
       "217233  0.139703 -0.525682 -0.102873 -0.075850 -0.046174 -0.069476   34.66  \n",
       "181843 -0.045560 -0.231925  0.185279  0.734789 -0.036630 -0.013429   12.31  \n",
       "167711  0.094467 -0.273581  0.651872  0.251855  0.033543  0.086661    5.99  \n",
       "223582 -0.039105 -0.342416 -0.093963  0.443908 -0.072869 -0.047609  165.25  \n",
       "...          ...       ...       ...       ...       ...       ...     ...  \n",
       "279863  0.639419 -0.294885  0.537503  0.788395  0.292680  0.147968  390.00  \n",
       "280143 -0.145640 -0.081049  0.521875  0.739467  0.389152  0.186637    0.76  \n",
       "280149  0.190944  0.032070 -0.739695  0.471111  0.385107  0.194361   77.89  \n",
       "281144 -0.456108 -0.183659 -0.328168  0.606116  0.884876 -0.253700  245.00  \n",
       "281674 -0.072173 -0.450261  0.313267 -0.289617  0.002988 -0.015309   42.53  \n",
       "\n",
       "[984 rows x 30 columns]"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "17b9519a-7206-4147-bef7-cb9bac705bb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "233879    0\n",
       "217233    0\n",
       "181843    0\n",
       "167711    0\n",
       "223582    0\n",
       "         ..\n",
       "279863    1\n",
       "280143    1\n",
       "280149    1\n",
       "281144    1\n",
       "281674    1\n",
       "Name: Class, Length: 984, dtype: int64"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "0cb50cca-06b1-46c4-befd-f43f633d32f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, stratify=Y, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "bffefcac-339c-4940-9852-35d79179f544",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(984, 30) (787, 30) (197, 30)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape, X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "3dbe9a48-d42f-4a7b-a2eb-9aba55bdc31c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "# training the Logistic Regression Model with Training Data\n",
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "fe310064-7555-44b1-81d9-9aeb27d20bd7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training data :  0.9529860228716646\n"
     ]
    }
   ],
   "source": [
    "# accuracy on training data\n",
    "X_train_prediction = model.predict(X_train)\n",
    "training_data_accuracy = accuracy_score(X_train_prediction, Y_train)\n",
    "print('Accuracy on training data : ', training_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "1a60014a-28b7-4278-abe6-7bb4a7b2b299",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score on Test Data :  0.9289340101522843\n"
     ]
    }
   ],
   "source": [
    "# accuracy on test data\n",
    "X_test_prediction = model.predict(X_test)\n",
    "test_data_accuracy = accuracy_score(X_test_prediction, Y_test)\n",
    "print('Accuracy score on Test Data : ', test_data_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1d3d7f4-8398-46a0-ae3d-80ea37f91e00",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "- The **amount of all fraud transactions are below 2250** inorder to avoid suspicious.\n",
    "- The fraud transactions are distributed throughout the time. So, there are **no solid relationships between the time and the fraud transactions**\n",
    "- The dataset is **highly imbalanced**, there need to be a fair comparison to avoid overfitting in the model. Therefore, I took a sample 492 data from the legit transactions and used **Logistic Regression** ML algorithm to predict whether a transaction is legit or fraud.\n",
    "- The Accuracy of this algorithm is **92.89%**\n",
    "- We can increase the accuracy of the algorithm by using the Synthetic Minority Oversampling (SMOTE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0f97455-cde9-45b1-8677-e043555bc3a1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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