89. Credit Card Fraud.ipynb

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      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading credit-card-fraud.zip to /content\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r  0%|          | 0.00/28.9M [00:00<?, ?B/s]\r 31%|███       | 9.00M/28.9M [00:00<00:00, 89.1MB/s]\r 62%|██████▏   | 18.0M/28.9M [00:00<00:00, 48.9MB/s]\r100%|██████████| 28.9M/28.9M [00:00<00:00, 68.8MB/s]\n"
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      ],
      "source": [
        "%%bash\n",
        "\n",
        "pip install -q kaggle\n",
        "mkdir -p ~/.kaggle\n",
        "cp drive/MyDrive/kaggle.json ~/.kaggle/\n",
        "chmod 600 /root/.kaggle/kaggle.json\n",
        "kaggle datasets download -d dhanushnarayananr/credit-card-fraud\n",
        "unzip -qq credit-card-fraud.zip"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install xgboost==1.7.1"
      ],
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      "execution_count": 3,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting xgboost==1.7.1\n",
            "  Downloading xgboost-1.7.1-py3-none-manylinux2014_x86_64.whl (193.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m193.6/193.6 MB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: scipy in /usr/local/lib/python3.8/dist-packages (from xgboost==1.7.1) (1.7.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from xgboost==1.7.1) (1.21.6)\n",
            "Installing collected packages: xgboost\n",
            "  Attempting uninstall: xgboost\n",
            "    Found existing installation: xgboost 0.90\n",
            "    Uninstalling xgboost-0.90:\n",
            "      Successfully uninstalled xgboost-0.90\n",
            "Successfully installed xgboost-1.7.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import xgboost \n",
        "xgboost.__version__"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "gt8zi9yVVnzc",
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      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'1.7.1'"
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          },
          "metadata": {},
          "execution_count": 4
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from xgboost import XGBClassifier"
      ],
      "metadata": {
        "id": "N33gPjrtVLYN"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"card_transdata.csv\")\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "iv9AJnQiVt64",
        "outputId": "8c2a365e-851c-4f49-bc1c-bebc69a0c7c0"
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      "execution_count": 6,
      "outputs": [
        {
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            "text/plain": [
              "   distance_from_home  distance_from_last_transaction  ...  online_order  fraud\n",
              "0           57.877857                        0.311140  ...           0.0    0.0\n",
              "1           10.829943                        0.175592  ...           0.0    0.0\n",
              "2            5.091079                        0.805153  ...           1.0    0.0\n",
              "3            2.247564                        5.600044  ...           1.0    0.0\n",
              "4           44.190936                        0.566486  ...           1.0    0.0\n",
              "\n",
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              "      <th>distance_from_home</th>\n",
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              "      <th>ratio_to_median_purchase_price</th>\n",
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        "np.max(df[\"distance_from_home\"])"
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        "np.mean(df[\"distance_from_home\"][df[\"fraud\"] == 1.0])"
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        "df[\"fraud\"].unique()"
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      "source": [
        "_, ax = plt.subplots()\n",
        "ax.bar(df[\"fraud\"].unique().astype(\"int64\"), [np.mean(df[\"distance_from_home\"][df[\"fraud\"] == 0.0]), np.mean(df[\"distance_from_home\"][df[\"fraud\"] == 1.0])])\n",
        "ax.set_xlim([-0.5, 1.5])\n",
        "ax.set_xticks([0, 1])\n",
        "ax.set_xticklabels([\"Not Fraud\", \"Fraud\"])\n",
        "plt.ylabel(\"Distance from home\")"
      ],
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              "Text(0, 0.5, 'Distance from home')"
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              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "_, ax = plt.subplots()\n",
        "ax.bar(df[\"fraud\"].unique().astype(\"int64\"), [np.mean(df[\"distance_from_last_transaction\"][df[\"fraud\"] == 0.0]), np.mean(df[\"distance_from_last_transaction\"][df[\"fraud\"] == 1.0])])\n",
        "ax.set_xlim([-0.5, 1.5])\n",
        "ax.set_xticks([0, 1])\n",
        "ax.set_xticklabels([\"Not Fraud\", \"Fraud\"])\n",
        "plt.ylabel(\"Distance from last transaction\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "q2VfZQ8AXc5p",
        "outputId": "6bf58a1a-7179-417a-e3e9-c9010e967fa5"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Distance from last transaction')"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "_, ax = plt.subplots()\n",
        "ax.bar(df[\"fraud\"].unique().astype(\"int64\"), [np.mean(df[\"ratio_to_median_purchase_price\"][df[\"fraud\"] == 0.0]), np.mean(df[\"ratio_to_median_purchase_price\"][df[\"fraud\"] == 1.0])])\n",
        "ax.set_xlim([-0.5, 1.5])\n",
        "ax.set_xticks([0, 1])\n",
        "ax.set_xticklabels([\"Not Fraud\", \"Fraud\"])\n",
        "plt.ylabel(\"Ration to median purchase price\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "sQZEaNv7YXm_",
        "outputId": "0d7b4532-05e9-4ebb-ecae-78862ae9f53e"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Ration to median purchase price')"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vk7Mpg_OYxxs",
        "outputId": "bda35aa1-8c0e-44d2-ac4c-28d480dbc03d"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "distance_from_home                0\n",
              "distance_from_last_transaction    0\n",
              "ratio_to_median_purchase_price    0\n",
              "repeat_retailer                   0\n",
              "used_chip                         0\n",
              "used_pin_number                   0\n",
              "online_order                      0\n",
              "fraud                             0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop(\"fraud\", axis=1)\n",
        "y = df[\"fraud\"]\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
      ],
      "metadata": {
        "id": "GbZ2h0rbY2kg"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xgclf = XGBClassifier()\n",
        "rclf = RandomForestClassifier()"
      ],
      "metadata": {
        "id": "oc6rSKCXY-63"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xgclf.fit(X_train.values, y_train.values)\n",
        "xgclf.score(X_test.values, y_test.values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o8qB8bPjZFNq",
        "outputId": "47b76240-ef03-4f4b-c7e2-a47a3e20f3d9"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.999965"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rclf.fit(X_train.values, y_train.values)\n",
        "rclf.score(X_test.values, y_test.values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o9ejgdw3ZKJU",
        "outputId": "1e1c0238-cd85-41c9-ad24-7bc0ce8cf4b3"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.99998"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xgclf.predict([X_test.values[43]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5qTpyvzsZO1a",
        "outputId": "dca7af58-ae84-40c2-daa4-07f3d84f4c45"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0])"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "y16frwKLZTq_"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}