banking_Fraud_Detection_Internship.ipynb

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      "metadata": {
        "id": "yaXfdE7OoroW"
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      "source": [
        "import numpy as np \n",
        "import pandas as pd \n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv(\"/content/Fraud.csv\")\n",
        "#df1=pd.read_csv(\"/content/Fraud.csv\",chunksize=10000)"
      ],
      "metadata": {
        "id": "LvvqP-vHo-1L"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#for chunk in df1:\n",
        "   # print(chunk.head())\n",
        "    "
      ],
      "metadata": {
        "id": "o-v1yQZ9pO_V"
      },
      "execution_count": 3,
      "outputs": []
    },
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      "source": [
        "df.tail()"
      ],
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      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         step      type      amount     nameOrig  oldbalanceOrg  \\\n",
              "6362615   743  CASH_OUT   339682.13   C786484425      339682.13   \n",
              "6362616   743  TRANSFER  6311409.28  C1529008245     6311409.28   \n",
              "6362617   743  CASH_OUT  6311409.28  C1162922333     6311409.28   \n",
              "6362618   743  TRANSFER   850002.52  C1685995037      850002.52   \n",
              "6362619   743  CASH_OUT   850002.52  C1280323807      850002.52   \n",
              "\n",
              "         newbalanceOrig     nameDest  oldbalanceDest  newbalanceDest  isFraud  \\\n",
              "6362615             0.0   C776919290            0.00       339682.13        1   \n",
              "6362616             0.0  C1881841831            0.00            0.00        1   \n",
              "6362617             0.0  C1365125890        68488.84      6379898.11        1   \n",
              "6362618             0.0  C2080388513            0.00            0.00        1   \n",
              "6362619             0.0   C873221189      6510099.11      7360101.63        1   \n",
              "\n",
              "         isFlaggedFraud  \n",
              "6362615               0  \n",
              "6362616               0  \n",
              "6362617               0  \n",
              "6362618               0  \n",
              "6362619               0  "
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              "      <th>6362618</th>\n",
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              "      <td>0.0</td>\n",
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              "      <td>7360101.63</td>\n",
              "      <td>1</td>\n",
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               step        amount  oldbalanceOrg  newbalanceOrig  \\\n",
              "count  6.362620e+06  6.362620e+06   6.362620e+06    6.362620e+06   \n",
              "mean   2.433972e+02  1.798619e+05   8.338831e+05    8.551137e+05   \n",
              "std    1.423320e+02  6.038582e+05   2.888243e+06    2.924049e+06   \n",
              "min    1.000000e+00  0.000000e+00   0.000000e+00    0.000000e+00   \n",
              "25%    1.560000e+02  1.338957e+04   0.000000e+00    0.000000e+00   \n",
              "50%    2.390000e+02  7.487194e+04   1.420800e+04    0.000000e+00   \n",
              "75%    3.350000e+02  2.087215e+05   1.073152e+05    1.442584e+05   \n",
              "max    7.430000e+02  9.244552e+07   5.958504e+07    4.958504e+07   \n",
              "\n",
              "       oldbalanceDest  newbalanceDest       isFraud  isFlaggedFraud  \n",
              "count    6.362620e+06    6.362620e+06  6.362620e+06    6.362620e+06  \n",
              "mean     1.100702e+06    1.224996e+06  1.290820e-03    2.514687e-06  \n",
              "std      3.399180e+06    3.674129e+06  3.590480e-02    1.585775e-03  \n",
              "min      0.000000e+00    0.000000e+00  0.000000e+00    0.000000e+00  \n",
              "25%      0.000000e+00    0.000000e+00  0.000000e+00    0.000000e+00  \n",
              "50%      1.327057e+05    2.146614e+05  0.000000e+00    0.000000e+00  \n",
              "75%      9.430367e+05    1.111909e+06  0.000000e+00    0.000000e+00  \n",
              "max      3.560159e+08    3.561793e+08  1.000000e+00    1.000000e+00  "
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              "      <th>count</th>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6.362620e+06</td>\n",
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              "      <th>mean</th>\n",
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              "      <td>1.798619e+05</td>\n",
              "      <td>8.338831e+05</td>\n",
              "      <td>8.551137e+05</td>\n",
              "      <td>1.100702e+06</td>\n",
              "      <td>1.224996e+06</td>\n",
              "      <td>1.290820e-03</td>\n",
              "      <td>2.514687e-06</td>\n",
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              "      <th>std</th>\n",
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              "      <td>2.888243e+06</td>\n",
              "      <td>2.924049e+06</td>\n",
              "      <td>3.399180e+06</td>\n",
              "      <td>3.674129e+06</td>\n",
              "      <td>3.590480e-02</td>\n",
              "      <td>1.585775e-03</td>\n",
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              "      <th>min</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.560000e+02</td>\n",
              "      <td>1.338957e+04</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.390000e+02</td>\n",
              "      <td>7.487194e+04</td>\n",
              "      <td>1.420800e+04</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.327057e+05</td>\n",
              "      <td>2.146614e+05</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.350000e+02</td>\n",
              "      <td>2.087215e+05</td>\n",
              "      <td>1.073152e+05</td>\n",
              "      <td>1.442584e+05</td>\n",
              "      <td>9.430367e+05</td>\n",
              "      <td>1.111909e+06</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
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              "      <th>max</th>\n",
              "      <td>7.430000e+02</td>\n",
              "      <td>9.244552e+07</td>\n",
              "      <td>5.958504e+07</td>\n",
              "      <td>4.958504e+07</td>\n",
              "      <td>3.560159e+08</td>\n",
              "      <td>3.561793e+08</td>\n",
              "      <td>1.000000e+00</td>\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 6362620 entries, 0 to 6362619\n",
            "Data columns (total 11 columns):\n",
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            " 9   isFraud         int64  \n",
            " 10  isFlaggedFraud  int64  \n",
            "dtypes: float64(5), int64(3), object(3)\n",
            "memory usage: 534.0+ MB\n"
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              "         step      type      amount     nameOrig  oldbalanceOrg  \\\n",
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              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b70b8b46-f9e6-4d65-b680-dacbe9f691d8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b70b8b46-f9e6-4d65-b680-dacbe9f691d8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cols=df.columns\n"
      ],
      "metadata": {
        "id": "9IeEWnipxDJ_"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aEofInsM72tX",
        "outputId": "d90a3d73-a134-41a4-ec50-ea97cc67f2e9"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "step              0\n",
              "type              0\n",
              "amount            0\n",
              "nameOrig          0\n",
              "oldbalanceOrg     0\n",
              "newbalanceOrig    0\n",
              "nameDest          0\n",
              "oldbalanceDest    0\n",
              "newbalanceDest    0\n",
              "isFraud           0\n",
              "isFlaggedFraud    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "# Example: Histogram of transaction amounts\n",
        "plt.hist(df['amount'], bins=20)\n",
        "plt.xlabel('Amount')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Distribution of Transaction Amounts')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "UEcYFnR2Bx6R",
        "outputId": "0b8d9d21-5709-415a-ad29-cf1e6dfef9ba"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Total transaction amount by type\n",
        "type_amount = df.groupby('type')['amount'].sum()\n",
        "print(type_amount)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KAMtSYYSGg93",
        "outputId": "a78e4e25-b81f-471a-96d8-889013e09d2c"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "type\n",
            "CASH_IN     2.363674e+11\n",
            "CASH_OUT    3.944130e+11\n",
            "DEBIT       2.271992e+08\n",
            "PAYMENT     2.809337e+10\n",
            "TRANSFER    4.852920e+11\n",
            "Name: amount, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Number of fraud transactions\n",
        "fraud_count = df['isFraud'].sum()\n",
        "print(\"Number of fraud transactions:\", fraud_count)\n",
        "\n",
        "# Proportion of fraud transactions\n",
        "fraud_proportion = df['isFraud'].mean()\n",
        "print(\"Proportion of fraud transactions:\", fraud_proportion)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Gui3w6_Gs7g",
        "outputId": "6f5fdf70-eacd-4533-a6d7-892473d4572c"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of fraud transactions: 8213\n",
            "Proportion of fraud transactions: 0.001290820448180152\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"isFraud\"].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w2nGfSr-G4-T",
        "outputId": "c58a6136-7046-4447-9340-a1a5fbc1082d"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"isFlaggedFraud\"].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5NzXnkzmHUex",
        "outputId": "71b2ea49-4dca-461c-b36c-831afee01029"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "7ZM8rnvtHgE5",
        "outputId": "e7faf998-5534-4ea7-d116-ce39acf6e8e9"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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",
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              "      <td>181.00</td>\n",
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              "    </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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    }\n",
              "\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-42bbde8f-6f70-4846-b830-904f30f740de button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-42bbde8f-6f70-4846-b830-904f30f740de');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Group by 'type' and calculate the count of fraud transactions for each type\n",
        "fraud_by_type = df[df['isFraud'] == 1].groupby('type')['isFraud'].count()\n",
        "print(fraud_by_type)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FVEC33llIrJh",
        "outputId": "39c5d406-7cf7-4c80-d6a3-71aaa3709359"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "type\n",
            "CASH_OUT    4116\n",
            "TRANSFER    4097\n",
            "Name: isFraud, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder"
      ],
      "metadata": {
        "id": "j1jXZMRrKWIP"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "le=LabelEncoder()"
      ],
      "metadata": {
        "id": "uALdPZ8tLPsK"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"type\"]=le.fit_transform(df[\"type\"])"
      ],
      "metadata": {
        "id": "o_YK92lrLSyV"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "IW5ZSj_GLlUJ",
        "outputId": "423c31ec-e97a-48e9-97fa-94fe45e0cd1f"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   step  type    amount     nameOrig  oldbalanceOrg  newbalanceOrig  \\\n",
              "0     1     3   9839.64  C1231006815       170136.0       160296.36   \n",
              "1     1     3   1864.28  C1666544295        21249.0        19384.72   \n",
              "2     1     4    181.00  C1305486145          181.0            0.00   \n",
              "3     1     1    181.00   C840083671          181.0            0.00   \n",
              "4     1     3  11668.14  C2048537720        41554.0        29885.86   \n",
              "\n",
              "      nameDest  oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \n",
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              "1  M2044282225             0.0             0.0        0               0  \n",
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              "3    C38997010         21182.0             0.0        1               0  \n",
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              "      <div>\n",
              "<style scoped>\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7beae764-c2e4-4742-b605-db512065bec3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7beae764-c2e4-4742-b605-db512065bec3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e2Ux9iYeNXc2",
        "outputId": "c8814a01-4f4a-4ac4-ac29-3569b6700891"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 6362620 entries, 0 to 6362619\n",
            "Data columns (total 11 columns):\n",
            " #   Column          Dtype  \n",
            "---  ------          -----  \n",
            " 0   step            int64  \n",
            " 1   type            int64  \n",
            " 2   amount          float64\n",
            " 3   nameOrig        object \n",
            " 4   oldbalanceOrg   float64\n",
            " 5   newbalanceOrig  float64\n",
            " 6   nameDest        object \n",
            " 7   oldbalanceDest  float64\n",
            " 8   newbalanceDest  float64\n",
            " 9   isFraud         int64  \n",
            " 10  isFlaggedFraud  int64  \n",
            "dtypes: float64(5), int64(4), object(2)\n",
            "memory usage: 534.0+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"nameDest\"]=df[\"nameDest\"].str[1:].astype(int)"
      ],
      "metadata": {
        "id": "nqKopP6lPgmS"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['nameOrig'] = df['nameOrig'].str[1:].astype(int)\n"
      ],
      "metadata": {
        "id": "mPtjr_ORNXKC"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "fXPxHpSvNW8m",
        "outputId": "44c0ea16-66cf-40fe-8df4-5e2999d2e845"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   step  type    amount    nameOrig  oldbalanceOrg  newbalanceOrig  \\\n",
              "0     1     3   9839.64  1231006815       170136.0       160296.36   \n",
              "1     1     3   1864.28  1666544295        21249.0        19384.72   \n",
              "2     1     4    181.00  1305486145          181.0            0.00   \n",
              "3     1     1    181.00   840083671          181.0            0.00   \n",
              "4     1     3  11668.14  2048537720        41554.0        29885.86   \n",
              "\n",
              "     nameDest  oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \n",
              "0  1979787155             0.0             0.0        0               0  \n",
              "1  2044282225             0.0             0.0        0               0  \n",
              "2   553264065             0.0             0.0        1               0  \n",
              "3    38997010         21182.0             0.0        1               0  \n",
              "4  1230701703             0.0             0.0        0               0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2728eaaa-b8a7-4669-a692-d50bdefb245b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
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              "</style>\n",
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              "      <th>step</th>\n",
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              "      <th>amount</th>\n",
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              "      <th>oldbalanceOrg</th>\n",
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              "      <th>0</th>\n",
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              "      <td>1864.28</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>4</td>\n",
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              "      <td>181.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>553264065</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
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              "      <td>181.00</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>11668.14</td>\n",
              "      <td>2048537720</td>\n",
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              "      <td>29885.86</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2728eaaa-b8a7-4669-a692-d50bdefb245b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2728eaaa-b8a7-4669-a692-d50bdefb245b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2728eaaa-b8a7-4669-a692-d50bdefb245b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "vmu1XExHLnZU"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cols"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rlLgoBeEL_j_",
        "outputId": "a6ce3dad-7f36-43ac-f6e1-afaec0bcdf65"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['step', 'type', 'amount', 'nameOrig', 'oldbalanceOrg', 'newbalanceOrig',\n",
              "       'nameDest', 'oldbalanceDest', 'newbalanceDest', 'isFraud',\n",
              "       'isFlaggedFraud'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feature=['step', 'type', 'amount', 'nameOrig', 'oldbalanceOrg', 'newbalanceOrig',\n",
        "       'nameDest', 'oldbalanceDest', 'newbalanceDest', \n",
        "       'isFlaggedFraud']"
      ],
      "metadata": {
        "id": "xkWEkHUzL2e2"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "target=[\"isFraud\"]"
      ],
      "metadata": {
        "id": "CsX0Sy4wMEnY"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xtrain,xtest,ytrain,ytest=train_test_split(df[feature],df[target],test_size=0.2,random_state=16)"
      ],
      "metadata": {
        "id": "U_HMIViHMHXX"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "metadata": {
        "id": "6BLp5XXFMvYB"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rfc=RandomForestClassifier()\n",
        "rfc.fit(xtrain,ytrain)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        },
        "id": "7YsHAMvMM6S6",
        "outputId": "66321b4b-9b6d-469a-d000-ffab13c7d3ac"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-35-11c9a9f90774>:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  rfc.fit(xtrain,ytrain)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ],
            "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()</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()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rfc.score(xtrain,ytrain)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QuKlbzmWNQEC",
        "outputId": "02a83276-f22e-457e-941d-2eec05451a8d"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9999996070801023"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix"
      ],
      "metadata": {
        "id": "Eo8Uok86aSFE"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = rfc.predict(xtest)\n"
      ],
      "metadata": {
        "id": "C2sdKC2XaYSX"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(ytest, y_pred)\n",
        "print(cm)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yG618AwhaYO2",
        "outputId": "2bb14a82-6530-4fca-bed6-322654e70c49"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1270841      21]\n",
            " [    368    1294]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rfc.score(xtest,ytest)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WZDRbForQGix",
        "outputId": "caf53cb4-e8b1-4b48-e196-ffa78ff41726"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9996943083195288"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Q1 - after seeing the boxplot there was no outliers and using isna() there was no missing value in the datset\n",
        "\n",
        "\n",
        "# Q2 - Fraud detection models aim to identify fraudulent activities or anomalies within a given dataset. \n",
        "#         They are designed to analyze patterns, behaviors, and characteristics of transactions or events to distinguish between legitimate and fraudulent ones. \n",
        "#            Here is a high-level overview of a typical fraud detection model\n",
        "\n",
        "\n",
        "# Q7 - When a company updates its infrastructure, \n",
        "#        it's essential to prioritize security and adopt preventive measures to safeguard the new environment. Here are some key prevention strategies to consider:\n",
        "#        - Security Assessment,Secure Configuration,Security Monitoring and Logging etc \n",
        "\n",
        "\n",
        "\n",
        "# Q8 m - Penetration Testing: Conduct regular penetration testing to simulate real-world attack scenarios and assess the security posture of the updated infrastructure.\n",
        "#        Penetration tests involve authorized security experts attempting to exploit vulnerabilities to identify weaknesses in the system.\n"
      ],
      "metadata": {
        "id": "LwPTN2uLWrHs"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}