fraud_detection.ipynb

{
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  "metadata": {
    "colab": {
      "name": "fraud-detection.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "QxydjwM2QM9R",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "os.environ['KAGGLE_USERNAME'] = 'xxxx'\n",
        "os.environ['KAGGLE_KEY'] = 'xxxx'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NTiIcyYKNyFT",
        "colab_type": "code",
        "outputId": "ae8f3c12-0a48-4f68-dd54-07f7ba40d002",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "!kaggle competitions download -c ieee-fraud-detection"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading train_transaction.csv.zip to /content\n",
            " 93% 49.0M/52.5M [00:00<00:00, 42.7MB/s]\n",
            "100% 52.5M/52.5M [00:00<00:00, 60.1MB/s]\n",
            "Downloading train_identity.csv.zip to /content\n",
            "  0% 0.00/3.02M [00:00<?, ?B/s]\n",
            "100% 3.02M/3.02M [00:00<00:00, 99.9MB/s]\n",
            "Downloading test_transaction.csv.zip to /content\n",
            " 97% 46.0M/47.3M [00:01<00:00, 31.0MB/s]\n",
            "100% 47.3M/47.3M [00:01<00:00, 31.6MB/s]\n",
            "Downloading test_identity.csv.zip to /content\n",
            "  0% 0.00/2.97M [00:00<?, ?B/s]\n",
            "100% 2.97M/2.97M [00:00<00:00, 185MB/s]\n",
            "Downloading sample_submission.csv.zip to /content\n",
            "  0% 0.00/1.14M [00:00<?, ?B/s]\n",
            "100% 1.14M/1.14M [00:00<00:00, 156MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B3dFWP4DRFSX",
        "colab_type": "code",
        "outputId": "46dbc071-4d0e-4375-dbc3-7f0709204e22",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        }
      },
      "source": [
        "!unzip train_identity.csv.zip\n",
        "!unzip train_transaction.csv.zip\n",
        "!unzip test_identity.csv.zip\n",
        "!unzip test_transaction.csv.zip"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  train_identity.csv.zip\n",
            "  inflating: train_identity.csv      \n",
            "Archive:  train_transaction.csv.zip\n",
            "  inflating: train_transaction.csv   \n",
            "Archive:  test_identity.csv.zip\n",
            "  inflating: test_identity.csv       \n",
            "Archive:  test_transaction.csv.zip\n",
            "  inflating: test_transaction.csv    \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4lxrTuKhPfw5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wnyBsrNeRGAX",
        "colab_type": "code",
        "outputId": "f128ad1d-f0b9-419b-ad1d-56e9d5a951f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "train_transaction = pd.read_csv('./train_transaction.csv')\n",
        "test_transaction = pd.read_csv('./test_transaction.csv')\n",
        "print('train transaction size:', len(train_transaction))\n",
        "print('test transaction size:', len(test_transaction))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "train transaction size: 590540\n",
            "test transaction size: 506691\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AmhzCmC4WdWo",
        "colab_type": "code",
        "outputId": "3f56c99d-f744-4b94-85ca-62ecd74c70d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        }
      },
      "source": [
        "train_transaction_new = pd.DataFrame(train_transaction, columns=train_transaction.isnull().sum().sort_values()[:196].index)\n",
        "train_transaction_new = train_transaction_new.drop(columns=['TransactionID', 'TransactionDT', 'card6', 'card4', 'P_emaildomain'])\n",
        "train_transaction_new_label = train_transaction_new.isFraud\n",
        "train_transaction_new = train_transaction_new.drop(columns=['isFraud'])\n",
        "train_transaction_new.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>24.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>111.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>H</td>\n",
              "      <td>4497</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 190 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   C14   C13  C12  C11  C10   C9   C8  ...  V49  V48  V51  V46  V45  V47  V43\n",
              "0  1.0   1.0  0.0  2.0  0.0  1.0  0.0  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
              "1  1.0   1.0  0.0  1.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  1.0  1.0  1.0  0.0\n",
              "2  1.0   1.0  0.0  1.0  0.0  1.0  0.0  ...  0.0  0.0  0.0  1.0  1.0  1.0  0.0\n",
              "3  1.0  25.0  0.0  1.0  0.0  1.0  0.0  ...  0.0  0.0  0.0  1.0  1.0  1.0  0.0\n",
              "4  1.0   1.0  0.0  1.0  1.0  0.0  1.0  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
              "\n",
              "[5 rows x 190 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "go7r5ValsrZS",
        "colab_type": "code",
        "outputId": "a6d53ee7-9a4a-47d6-c992-8be570963a45",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "sns.countplot(x='isFraud', data=train_transaction)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5f8b217b00>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ayJEOm23AoRVlG4EbRuoXtlVp64AH2qOwHcA5SU5qCwPOAXa0cw8mWddWoV0471rjxpAk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\nANfshOEE+WunAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3uo5GdddWyhE",
        "colab_type": "code",
        "outputId": "71e2c086-2315-4711-a781-1d06dcbd0621",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        }
      },
      "source": [
        "test_transaction_new = pd.DataFrame(test_transaction, columns=train_transaction.isnull().sum().sort_values()[:196].index)\n",
        "test_transaction_new = test_transaction_new.drop(columns=['isFraud', 'TransactionID', 'TransactionDT', 'card6', 'card4', 'P_emaildomain'])\n",
        "test_transaction_new.head()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>C14</th>\n",
              "      <th>C13</th>\n",
              "      <th>C12</th>\n",
              "      <th>C11</th>\n",
              "      <th>C10</th>\n",
              "      <th>C9</th>\n",
              "      <th>C8</th>\n",
              "      <th>C7</th>\n",
              "      <th>C6</th>\n",
              "      <th>C5</th>\n",
              "      <th>C4</th>\n",
              "      <th>C3</th>\n",
              "      <th>C2</th>\n",
              "      <th>C1</th>\n",
              "      <th>TransactionAmt</th>\n",
              "      <th>ProductCD</th>\n",
              "      <th>card1</th>\n",
              "      <th>V309</th>\n",
              "      <th>V306</th>\n",
              "      <th>V307</th>\n",
              "      <th>V299</th>\n",
              "      <th>V321</th>\n",
              "      <th>V320</th>\n",
              "      <th>V319</th>\n",
              "      <th>V318</th>\n",
              "      <th>V316</th>\n",
              "      <th>V312</th>\n",
              "      <th>V311</th>\n",
              "      <th>V305</th>\n",
              "      <th>V308</th>\n",
              "      <th>V310</th>\n",
              "      <th>V304</th>\n",
              "      <th>V317</th>\n",
              "      <th>V302</th>\n",
              "      <th>V303</th>\n",
              "      <th>V298</th>\n",
              "      <th>V297</th>\n",
              "      <th>V295</th>\n",
              "      <th>V294</th>\n",
              "      <th>V292</th>\n",
              "      <th>...</th>\n",
              "      <th>V71</th>\n",
              "      <th>V72</th>\n",
              "      <th>V74</th>\n",
              "      <th>V64</th>\n",
              "      <th>V73</th>\n",
              "      <th>D15</th>\n",
              "      <th>V94</th>\n",
              "      <th>V75</th>\n",
              "      <th>V76</th>\n",
              "      <th>V77</th>\n",
              "      <th>V78</th>\n",
              "      <th>V79</th>\n",
              "      <th>V80</th>\n",
              "      <th>V81</th>\n",
              "      <th>V83</th>\n",
              "      <th>V82</th>\n",
              "      <th>V86</th>\n",
              "      <th>V87</th>\n",
              "      <th>V88</th>\n",
              "      <th>V89</th>\n",
              "      <th>V90</th>\n",
              "      <th>V91</th>\n",
              "      <th>V92</th>\n",
              "      <th>V93</th>\n",
              "      <th>V84</th>\n",
              "      <th>V85</th>\n",
              "      <th>D4</th>\n",
              "      <th>V40</th>\n",
              "      <th>V41</th>\n",
              "      <th>V38</th>\n",
              "      <th>V52</th>\n",
              "      <th>V39</th>\n",
              "      <th>V37</th>\n",
              "      <th>V49</th>\n",
              "      <th>V48</th>\n",
              "      <th>V51</th>\n",
              "      <th>V46</th>\n",
              "      <th>V45</th>\n",
              "      <th>V47</th>\n",
              "      <th>V43</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>31.95</td>\n",
              "      <td>W</td>\n",
              "      <td>10409</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>47.950001</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>47.950001</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>409.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>398.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>49.00</td>\n",
              "      <td>W</td>\n",
              "      <td>4272</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>280.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>77.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>77.000000</td>\n",
              "      <td>280.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>634.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>634.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>171.00</td>\n",
              "      <td>W</td>\n",
              "      <td>4476</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1321.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>263.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1058.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>97.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>97.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>284.95</td>\n",
              "      <td>W</td>\n",
              "      <td>10989</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>242.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>242.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>67.95</td>\n",
              "      <td>W</td>\n",
              "      <td>18018</td>\n",
              "      <td>67.949997</td>\n",
              "      <td>67.949997</td>\n",
              "      <td>183.850006</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>67.949997</td>\n",
              "      <td>67.949997</td>\n",
              "      <td>1.0</td>\n",
              "      <td>67.949997</td>\n",
              "      <td>183.850006</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 190 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   C14    C13  C12  C11  C10   C9   C8  ...  V49  V48  V51  V46  V45  V47  V43\n",
              "0  6.0  115.0  1.0  5.0  0.0  6.0  0.0  ...  1.0  1.0  0.0  1.0  1.0  1.0  0.0\n",
              "1  2.0   12.0  1.0  1.0  0.0  2.0  0.0  ...  0.0  0.0  0.0  1.0  1.0  1.0  0.0\n",
              "2  2.0   22.0  0.0  2.0  0.0  4.0  0.0  ...  0.0  0.0  0.0  1.0  1.0  1.0  0.0\n",
              "3  4.0    7.0  0.0  2.0  0.0  2.0  0.0  ...  1.0  1.0  0.0  2.0  2.0  2.0  0.0\n",
              "4  6.0   14.0  0.0  6.0  0.0  5.0  0.0  ...  1.0  1.0  0.0  1.0  1.0  1.0  0.0\n",
              "\n",
              "[5 rows x 190 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lqWhjn26Xi80",
        "colab_type": "code",
        "outputId": "dba19155-cecb-4e30-ce4a-56aa4ddb7f39",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "labelencoder = LabelEncoder()\n",
        "test_transaction_new['ProductCD'] = labelencoder.fit_transform(test_transaction_new['ProductCD'])\n",
        "test_transaction_new.ProductCD[:5]"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    4\n",
              "1    4\n",
              "2    4\n",
              "3    4\n",
              "4    4\n",
              "Name: ProductCD, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BOoSHp17Xpw7",
        "colab_type": "code",
        "outputId": "e9d1ea20-7e6d-4722-e885-4cce9f636514",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "train_transaction_new['ProductCD'] = labelencoder.fit_transform(train_transaction_new['ProductCD'])\n",
        "train_transaction_new.ProductCD[:5]"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    4\n",
              "1    4\n",
              "2    4\n",
              "3    4\n",
              "4    1\n",
              "Name: ProductCD, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qq_AckiiXrYr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_transaction_new = train_transaction_new.fillna(-9999)\n",
        "test_transaction_new = test_transaction_new.fillna(-9999)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hrxBlPstXtf6",
        "colab_type": "code",
        "outputId": "afda4d37-6783-4066-ef14-d9d99fc7c17e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(train_transaction_new, train_transaction_new_label, test_size=0.2)\n",
        "lr = LogisticRegression(C=0.09, class_weight='balanced')\n",
        "lr = lr.fit(x_train, y_train)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JsRDMwtqX-2J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "40ca6cea-a2b3-4645-e3d3-14e8cb3b2486"
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "y_predict = lr.predict(x_test)\n",
        "accuracy_score(y_predict, y_test)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7307549022928167"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JfrrVrwxP0Ys",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# from sklearn.metrics import log_loss\n",
        "\n",
        "# loss = log_loss(x_test, model.predict_proba(x_test), eps=1e-15)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "swEhMtRQa_V3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "43450230-60bd-47dd-804f-87045459fb45"
      },
      "source": [
        "import csv\n",
        "\n",
        "y_sub_predict = lr.predict_proba(test_transaction_new)\n",
        "\n",
        "csv_data = [['TransactionID', 'isFraud']]\n",
        "\n",
        "fraud_dict = { 'fraud': 0, 'not_fraud': 0 }\n",
        "for i in range(0, len(y_sub_predict)):\n",
        "  csv_data.append([test_transaction['TransactionID'][i], y_sub_predict[i][0]])\n",
        "  if y_sub_predict[i][0] >= 0.5:\n",
        "    fraud_dict['fraud'] += 1\n",
        "  else:\n",
        "    fraud_dict['not_fraud'] += 1\n",
        "print(fraud_dict)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'fraud': 398480, 'not_fraud': 108211}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aPAoJRizcuJu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with open('submission.csv', 'w') as csv_file:\n",
        "  writer = csv.writer(csv_file)\n",
        "  writer.writerows(csv_data)\n",
        "\n",
        "csv_file.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lnXus9fhR7yo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}