Fraud.ipynb

{
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      "cell_type": "code",
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      "metadata": {
        "id": "MCa78edMkT53"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import accuracy_score,classification_report\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.svm import OneClassSVM\n",
        "from sklearn.ensemble import IsolationForest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "outputId": "ee24c74d-e8f2-4011-aaf2-c86263574de6"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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          "height": 463
        },
        "id": "cvZg4SovkLak",
        "outputId": "7b1be4ec-da1e-4225-a309-be0ef036b0d8"
      },
      "outputs": [
        {
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            "text/plain": [
              "      trans_date_trans_time               cc_num  \\\n",
              "99995   2019-02-28 15:36:49     2475085306462014   \n",
              "99996   2019-02-28 15:37:27     4005676619255478   \n",
              "99997   2019-02-28 15:37:34     3519232971341141   \n",
              "99998   2019-02-28 15:38:11  4040099974063068803   \n",
              "99999   2019-02-28 15:40:20     6595970453799027   \n",
              "\n",
              "                             merchant        category    amt    first    last  \\\n",
              "99995  fraud_O'Reilly, Mohr and Purdy            home  95.14     John  Miller   \n",
              "99996                   fraud_Kub PLC   personal_care   8.75  William   Perry   \n",
              "99997           fraud_Schuppe-Schuppe     food_dining  34.20  Michael   Jones   \n",
              "99998          fraud_Rippin-VonRueden  health_fitness  73.11  Jeffrey   Lewis   \n",
              "99999             fraud_Kessler Group          travel   9.08     Gina   Gomez   \n",
              "\n",
              "      gender                           street            city  ...      lat  \\\n",
              "99995      M  153 Mccullough Springs Apt. 857       Lamberton  ...  44.2378   \n",
              "99996      M     458 Phillips Island Apt. 768  Denham Springs  ...  30.4590   \n",
              "99997      M                   754 Smith Isle       Amsterdam  ...  40.4731   \n",
              "99998      M               24255 Bryan Square         Palermo  ...  48.3396   \n",
              "99999      F  72030 Shelton Crossing Apt. 742        Stirling  ...  40.6774   \n",
              "\n",
              "           long  city_pop                      job         dob  \\\n",
              "99995  -95.2739      1507  Land/geomatics surveyor  1993-10-12   \n",
              "99996  -90.9027     71335                Herbalist  1994-05-31   \n",
              "99997  -80.9596      2208      Mental health nurse  1961-09-10   \n",
              "99998 -102.2400       229            Administrator  1983-03-20   \n",
              "99999  -74.4968      2307           Science writer  1969-09-11   \n",
              "\n",
              "                              trans_num   unix_time  merch_lat  merch_long  \\\n",
              "99995  085e8f89f41378f4bd21ed8a59065fee  1330443409  44.718105  -95.843397   \n",
              "99996  9988dd4de1bb2182a7b3ec5f1ede1e34  1330443447  29.931844  -90.610715   \n",
              "99997  70151fb544afe365dc163889d67f5be6  1330443454  41.076153  -80.506107   \n",
              "99998  bc2adb0fad8f733cdc21ebefcc79ddf3  1330443491  48.535070 -102.524262   \n",
              "99999  fd4a6a1b893c2045d7310c70c1d9ea1f  1330443620  39.718206  -73.627417   \n",
              "\n",
              "       is_fraud  \n",
              "99995         0  \n",
              "99996         0  \n",
              "99997         0  \n",
              "99998         0  \n",
              "99999         0  \n",
              "\n",
              "[5 rows x 22 columns]"
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              "      <th>unix_time</th>\n",
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              "      <th>is_fraud</th>\n",
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              "      <th>99995</th>\n",
              "      <td>2019-02-28 15:36:49</td>\n",
              "      <td>2475085306462014</td>\n",
              "      <td>fraud_O'Reilly, Mohr and Purdy</td>\n",
              "      <td>home</td>\n",
              "      <td>95.14</td>\n",
              "      <td>John</td>\n",
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              "      <td>153 Mccullough Springs Apt. 857</td>\n",
              "      <td>Lamberton</td>\n",
              "      <td>...</td>\n",
              "      <td>44.2378</td>\n",
              "      <td>-95.2739</td>\n",
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              "      <td>Land/geomatics surveyor</td>\n",
              "      <td>1993-10-12</td>\n",
              "      <td>085e8f89f41378f4bd21ed8a59065fee</td>\n",
              "      <td>1330443409</td>\n",
              "      <td>44.718105</td>\n",
              "      <td>-95.843397</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>99996</th>\n",
              "      <td>2019-02-28 15:37:27</td>\n",
              "      <td>4005676619255478</td>\n",
              "      <td>fraud_Kub PLC</td>\n",
              "      <td>personal_care</td>\n",
              "      <td>8.75</td>\n",
              "      <td>William</td>\n",
              "      <td>Perry</td>\n",
              "      <td>M</td>\n",
              "      <td>458 Phillips Island Apt. 768</td>\n",
              "      <td>Denham Springs</td>\n",
              "      <td>...</td>\n",
              "      <td>30.4590</td>\n",
              "      <td>-90.9027</td>\n",
              "      <td>71335</td>\n",
              "      <td>Herbalist</td>\n",
              "      <td>1994-05-31</td>\n",
              "      <td>9988dd4de1bb2182a7b3ec5f1ede1e34</td>\n",
              "      <td>1330443447</td>\n",
              "      <td>29.931844</td>\n",
              "      <td>-90.610715</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99997</th>\n",
              "      <td>2019-02-28 15:37:34</td>\n",
              "      <td>3519232971341141</td>\n",
              "      <td>fraud_Schuppe-Schuppe</td>\n",
              "      <td>food_dining</td>\n",
              "      <td>34.20</td>\n",
              "      <td>Michael</td>\n",
              "      <td>Jones</td>\n",
              "      <td>M</td>\n",
              "      <td>754 Smith Isle</td>\n",
              "      <td>Amsterdam</td>\n",
              "      <td>...</td>\n",
              "      <td>40.4731</td>\n",
              "      <td>-80.9596</td>\n",
              "      <td>2208</td>\n",
              "      <td>Mental health nurse</td>\n",
              "      <td>1961-09-10</td>\n",
              "      <td>70151fb544afe365dc163889d67f5be6</td>\n",
              "      <td>1330443454</td>\n",
              "      <td>41.076153</td>\n",
              "      <td>-80.506107</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>99998</th>\n",
              "      <td>2019-02-28 15:38:11</td>\n",
              "      <td>4040099974063068803</td>\n",
              "      <td>fraud_Rippin-VonRueden</td>\n",
              "      <td>health_fitness</td>\n",
              "      <td>73.11</td>\n",
              "      <td>Jeffrey</td>\n",
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              "      <td>M</td>\n",
              "      <td>24255 Bryan Square</td>\n",
              "      <td>Palermo</td>\n",
              "      <td>...</td>\n",
              "      <td>48.3396</td>\n",
              "      <td>-102.2400</td>\n",
              "      <td>229</td>\n",
              "      <td>Administrator</td>\n",
              "      <td>1983-03-20</td>\n",
              "      <td>bc2adb0fad8f733cdc21ebefcc79ddf3</td>\n",
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              "      <td>fraud_Kessler Group</td>\n",
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              "      <td>9.08</td>\n",
              "      <td>Gina</td>\n",
              "      <td>Gomez</td>\n",
              "      <td>F</td>\n",
              "      <td>72030 Shelton Crossing Apt. 742</td>\n",
              "      <td>Stirling</td>\n",
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              "      <td>40.6774</td>\n",
              "      <td>-74.4968</td>\n",
              "      <td>2307</td>\n",
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              "      <td>fd4a6a1b893c2045d7310c70c1d9ea1f</td>\n",
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              "      <td>0</td>\n",
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      "source": [
        "data=pd.read_csv('/content/drive/MyDrive/fraudTrain.csv',index_col=0,nrows=100000)\n",
        "data=data.dropna()\n",
        "data.tail()"
      ]
    },
    {
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      "execution_count": null,
      "metadata": {
        "id": "JjCNAOqDkQ7a",
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        {
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 100000 entries, 0 to 99999\n",
            "Data columns (total 22 columns):\n",
            " #   Column                 Non-Null Count   Dtype  \n",
            "---  ------                 --------------   -----  \n",
            " 0   trans_date_trans_time  100000 non-null  object \n",
            " 1   cc_num                 100000 non-null  int64  \n",
            " 2   merchant               100000 non-null  object \n",
            " 3   category               100000 non-null  object \n",
            " 4   amt                    100000 non-null  float64\n",
            " 5   first                  100000 non-null  object \n",
            " 6   last                   100000 non-null  object \n",
            " 7   gender                 100000 non-null  object \n",
            " 8   street                 100000 non-null  object \n",
            " 9   city                   100000 non-null  object \n",
            " 10  state                  100000 non-null  object \n",
            " 11  zip                    100000 non-null  int64  \n",
            " 12  lat                    100000 non-null  float64\n",
            " 13  long                   100000 non-null  float64\n",
            " 14  city_pop               100000 non-null  int64  \n",
            " 15  job                    100000 non-null  object \n",
            " 16  dob                    100000 non-null  object \n",
            " 17  trans_num              100000 non-null  object \n",
            " 18  unix_time              100000 non-null  int64  \n",
            " 19  merch_lat              100000 non-null  float64\n",
            " 20  merch_long             100000 non-null  float64\n",
            " 21  is_fraud               100000 non-null  int64  \n",
            "dtypes: float64(5), int64(5), object(12)\n",
            "memory usage: 17.5+ MB\n"
          ]
        }
      ],
      "source": [
        "data.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ggKknqRwzRpx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0f6093a8-f2d9-41f7-d146-ea5c9a73dddc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "data.isnull().values.any()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RP9d_Pm80guV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "534abdb7-5c6e-45b8-ea1a-67535ae4bbcb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(990, 22) (99010, 22)\n"
          ]
        }
      ],
      "source": [
        "fraud=data[data[\"is_fraud\"]==1]\n",
        "\n",
        "normal=data[data[\"is_fraud\"]==0]\n",
        "\n",
        "print(fraud.shape,normal.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zzmKt6zw1mpU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "outputId": "f4d69471-2fa3-4d45-f189-cad18e5b6380"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  trans_date_trans_time            cc_num                            merchant  \\\n",
              "0   2019-01-01 00:00:18  2703186189652095          fraud_Rippin, Kub and Mann   \n",
              "1   2019-01-01 00:00:44      630423337322     fraud_Heller, Gutmann and Zieme   \n",
              "2   2019-01-01 00:00:51    38859492057661                fraud_Lind-Buckridge   \n",
              "3   2019-01-01 00:01:16  3534093764340240  fraud_Kutch, Hermiston and Farrell   \n",
              "4   2019-01-01 00:03:06   375534208663984                 fraud_Keeling-Crist   \n",
              "\n",
              "        category     amt      first     last  gender  \\\n",
              "0       misc_net    4.97   Jennifer    Banks       1   \n",
              "1    grocery_pos  107.23  Stephanie     Gill       1   \n",
              "2  entertainment  220.11     Edward  Sanchez       0   \n",
              "3  gas_transport   45.00     Jeremy    White       0   \n",
              "4       misc_pos   41.96      Tyler   Garcia       0   \n",
              "\n",
              "                         street            city  ...      lat      long  \\\n",
              "0                561 Perry Cove  Moravian Falls  ...  36.0788  -81.1781   \n",
              "1  43039 Riley Greens Suite 393          Orient  ...  48.8878 -118.2105   \n",
              "2      594 White Dale Suite 530      Malad City  ...  42.1808 -112.2620   \n",
              "3   9443 Cynthia Court Apt. 038         Boulder  ...  46.2306 -112.1138   \n",
              "4              408 Bradley Rest        Doe Hill  ...  38.4207  -79.4629   \n",
              "\n",
              "   city_pop                                job         dob  \\\n",
              "0      3495          Psychologist, counselling  1988-03-09   \n",
              "1       149  Special educational needs teacher  1978-06-21   \n",
              "2      4154        Nature conservation officer  1962-01-19   \n",
              "3      1939                    Patent attorney  1967-01-12   \n",
              "4        99     Dance movement psychotherapist  1986-03-28   \n",
              "\n",
              "                          trans_num   unix_time  merch_lat  merch_long  \\\n",
              "0  0b242abb623afc578575680df30655b9  1325376018  36.011293  -82.048315   \n",
              "1  1f76529f8574734946361c461b024d99  1325376044  49.159047 -118.186462   \n",
              "2  a1a22d70485983eac12b5b88dad1cf95  1325376051  43.150704 -112.154481   \n",
              "3  6b849c168bdad6f867558c3793159a81  1325376076  47.034331 -112.561071   \n",
              "4  a41d7549acf90789359a9aa5346dcb46  1325376186  38.674999  -78.632459   \n",
              "\n",
              "   is_fraud  \n",
              "0         0  \n",
              "1         0  \n",
              "2         0  \n",
              "3         0  \n",
              "4         0  \n",
              "\n",
              "[5 rows x 22 columns]"
            ],
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              "  <div id=\"df-28c61f47-a84d-401d-b051-6560b5adf19b\" class=\"colab-df-container\">\n",
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              "      <th></th>\n",
              "      <th>trans_date_trans_time</th>\n",
              "      <th>cc_num</th>\n",
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              "      <th>...</th>\n",
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              "      <th>merch_lat</th>\n",
              "      <th>merch_long</th>\n",
              "      <th>is_fraud</th>\n",
              "    </tr>\n",
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              "      <th>0</th>\n",
              "      <td>2019-01-01 00:00:18</td>\n",
              "      <td>2703186189652095</td>\n",
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              "      <td>misc_net</td>\n",
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              "      <td>561 Perry Cove</td>\n",
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              "      <td>-81.1781</td>\n",
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              "      <td>Psychologist, counselling</td>\n",
              "      <td>1988-03-09</td>\n",
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              "      <td>-82.048315</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-01-01 00:00:44</td>\n",
              "      <td>630423337322</td>\n",
              "      <td>fraud_Heller, Gutmann and Zieme</td>\n",
              "      <td>grocery_pos</td>\n",
              "      <td>107.23</td>\n",
              "      <td>Stephanie</td>\n",
              "      <td>Gill</td>\n",
              "      <td>1</td>\n",
              "      <td>43039 Riley Greens Suite 393</td>\n",
              "      <td>Orient</td>\n",
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              "      <td>48.8878</td>\n",
              "      <td>-118.2105</td>\n",
              "      <td>149</td>\n",
              "      <td>Special educational needs teacher</td>\n",
              "      <td>1978-06-21</td>\n",
              "      <td>1f76529f8574734946361c461b024d99</td>\n",
              "      <td>1325376044</td>\n",
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              "      <td>-118.186462</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-01-01 00:00:51</td>\n",
              "      <td>38859492057661</td>\n",
              "      <td>fraud_Lind-Buckridge</td>\n",
              "      <td>entertainment</td>\n",
              "      <td>220.11</td>\n",
              "      <td>Edward</td>\n",
              "      <td>Sanchez</td>\n",
              "      <td>0</td>\n",
              "      <td>594 White Dale Suite 530</td>\n",
              "      <td>Malad City</td>\n",
              "      <td>...</td>\n",
              "      <td>42.1808</td>\n",
              "      <td>-112.2620</td>\n",
              "      <td>4154</td>\n",
              "      <td>Nature conservation officer</td>\n",
              "      <td>1962-01-19</td>\n",
              "      <td>a1a22d70485983eac12b5b88dad1cf95</td>\n",
              "      <td>1325376051</td>\n",
              "      <td>43.150704</td>\n",
              "      <td>-112.154481</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-01-01 00:01:16</td>\n",
              "      <td>3534093764340240</td>\n",
              "      <td>fraud_Kutch, Hermiston and Farrell</td>\n",
              "      <td>gas_transport</td>\n",
              "      <td>45.00</td>\n",
              "      <td>Jeremy</td>\n",
              "      <td>White</td>\n",
              "      <td>0</td>\n",
              "      <td>9443 Cynthia Court Apt. 038</td>\n",
              "      <td>Boulder</td>\n",
              "      <td>...</td>\n",
              "      <td>46.2306</td>\n",
              "      <td>-112.1138</td>\n",
              "      <td>1939</td>\n",
              "      <td>Patent attorney</td>\n",
              "      <td>1967-01-12</td>\n",
              "      <td>6b849c168bdad6f867558c3793159a81</td>\n",
              "      <td>1325376076</td>\n",
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              "      <td>-112.561071</td>\n",
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              "      <th>4</th>\n",
              "      <td>2019-01-01 00:03:06</td>\n",
              "      <td>375534208663984</td>\n",
              "      <td>fraud_Keeling-Crist</td>\n",
              "      <td>misc_pos</td>\n",
              "      <td>41.96</td>\n",
              "      <td>Tyler</td>\n",
              "      <td>Garcia</td>\n",
              "      <td>0</td>\n",
              "      <td>408 Bradley Rest</td>\n",
              "      <td>Doe Hill</td>\n",
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              "      <td>-79.4629</td>\n",
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              "      <td>1986-03-28</td>\n",
              "      <td>a41d7549acf90789359a9aa5346dcb46</td>\n",
              "      <td>1325376186</td>\n",
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              "      <td>-78.632459</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 22 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-28c61f47-a84d-401d-b051-6560b5adf19b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-28c61f47-a84d-401d-b051-6560b5adf19b 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-28c61f47-a84d-401d-b051-6560b5adf19b');\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",
              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f5e9d372-f1d2-4423-87cf-0529494a2e07')\"\n",
              "            title=\"Suggest charts\"\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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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f5e9d372-f1d2-4423-87cf-0529494a2e07 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "gender_mapping={\"F\":1,\"M\":0}\n",
        "data[\"gender\"]=data[\"gender\"].map(gender_mapping)\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the existing columns in your DataFrame\n",
        "print(data.columns)\n",
        "\n",
        "# Attempt to drop the columns, handling potential KeyError\n",
        "columns_to_drop = [\"merch_lat\", \"merch_long\", \"dob\", \"lat\", \"long\",\"first\",\"last\"]\n",
        "for col in columns_to_drop:\n",
        "    try:\n",
        "        data = data.drop(col, axis=1)\n",
        "        print(f\"Dropped column: {col}\")\n",
        "    except KeyError:\n",
        "        print(f\"Column not found: {col}\")\n",
        "\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 712
        },
        "id": "-Kd-njLt_Oh0",
        "outputId": "e13999ee-5278-419e-f548-1738669bd283"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['trans_date_trans_time', 'cc_num', 'merchant', 'category', 'amt',\n",
            "       'first', 'last', 'gender', 'street', 'city', 'state', 'zip', 'lat',\n",
            "       'long', 'city_pop', 'job', 'dob', 'trans_num', 'unix_time', 'merch_lat',\n",
            "       'merch_long', 'is_fraud'],\n",
            "      dtype='object')\n",
            "Dropped column: merch_lat\n",
            "Dropped column: merch_long\n",
            "Dropped column: dob\n",
            "Dropped column: lat\n",
            "Dropped column: long\n",
            "Dropped column: first\n",
            "Dropped column: last\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  trans_date_trans_time            cc_num                            merchant  \\\n",
              "0   2019-01-01 00:00:18  2703186189652095          fraud_Rippin, Kub and Mann   \n",
              "1   2019-01-01 00:00:44      630423337322     fraud_Heller, Gutmann and Zieme   \n",
              "2   2019-01-01 00:00:51    38859492057661                fraud_Lind-Buckridge   \n",
              "3   2019-01-01 00:01:16  3534093764340240  fraud_Kutch, Hermiston and Farrell   \n",
              "4   2019-01-01 00:03:06   375534208663984                 fraud_Keeling-Crist   \n",
              "\n",
              "        category     amt  gender                        street  \\\n",
              "0       misc_net    4.97       1                561 Perry Cove   \n",
              "1    grocery_pos  107.23       1  43039 Riley Greens Suite 393   \n",
              "2  entertainment  220.11       0      594 White Dale Suite 530   \n",
              "3  gas_transport   45.00       0   9443 Cynthia Court Apt. 038   \n",
              "4       misc_pos   41.96       0              408 Bradley Rest   \n",
              "\n",
              "             city state    zip  city_pop                                job  \\\n",
              "0  Moravian Falls    NC  28654      3495          Psychologist, counselling   \n",
              "1          Orient    WA  99160       149  Special educational needs teacher   \n",
              "2      Malad City    ID  83252      4154        Nature conservation officer   \n",
              "3         Boulder    MT  59632      1939                    Patent attorney   \n",
              "4        Doe Hill    VA  24433        99     Dance movement psychotherapist   \n",
              "\n",
              "                          trans_num   unix_time  is_fraud  \n",
              "0  0b242abb623afc578575680df30655b9  1325376018         0  \n",
              "1  1f76529f8574734946361c461b024d99  1325376044         0  \n",
              "2  a1a22d70485983eac12b5b88dad1cf95  1325376051         0  \n",
              "3  6b849c168bdad6f867558c3793159a81  1325376076         0  \n",
              "4  a41d7549acf90789359a9aa5346dcb46  1325376186         0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8ef1f320-cd3a-48f7-9808-9c8659cab8ac\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        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>trans_date_trans_time</th>\n",
              "      <th>cc_num</th>\n",
              "      <th>merchant</th>\n",
              "      <th>category</th>\n",
              "      <th>amt</th>\n",
              "      <th>gender</th>\n",
              "      <th>street</th>\n",
              "      <th>city</th>\n",
              "      <th>state</th>\n",
              "      <th>zip</th>\n",
              "      <th>city_pop</th>\n",
              "      <th>job</th>\n",
              "      <th>trans_num</th>\n",
              "      <th>unix_time</th>\n",
              "      <th>is_fraud</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-01 00:00:18</td>\n",
              "      <td>2703186189652095</td>\n",
              "      <td>fraud_Rippin, Kub and Mann</td>\n",
              "      <td>misc_net</td>\n",
              "      <td>4.97</td>\n",
              "      <td>1</td>\n",
              "      <td>561 Perry Cove</td>\n",
              "      <td>Moravian Falls</td>\n",
              "      <td>NC</td>\n",
              "      <td>28654</td>\n",
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              "      <td>Psychologist, counselling</td>\n",
              "      <td>0b242abb623afc578575680df30655b9</td>\n",
              "      <td>1325376018</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-01-01 00:00:44</td>\n",
              "      <td>630423337322</td>\n",
              "      <td>fraud_Heller, Gutmann and Zieme</td>\n",
              "      <td>grocery_pos</td>\n",
              "      <td>107.23</td>\n",
              "      <td>1</td>\n",
              "      <td>43039 Riley Greens Suite 393</td>\n",
              "      <td>Orient</td>\n",
              "      <td>WA</td>\n",
              "      <td>99160</td>\n",
              "      <td>149</td>\n",
              "      <td>Special educational needs teacher</td>\n",
              "      <td>1f76529f8574734946361c461b024d99</td>\n",
              "      <td>1325376044</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-01-01 00:00:51</td>\n",
              "      <td>38859492057661</td>\n",
              "      <td>fraud_Lind-Buckridge</td>\n",
              "      <td>entertainment</td>\n",
              "      <td>220.11</td>\n",
              "      <td>0</td>\n",
              "      <td>594 White Dale Suite 530</td>\n",
              "      <td>Malad City</td>\n",
              "      <td>ID</td>\n",
              "      <td>83252</td>\n",
              "      <td>4154</td>\n",
              "      <td>Nature conservation officer</td>\n",
              "      <td>a1a22d70485983eac12b5b88dad1cf95</td>\n",
              "      <td>1325376051</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-01-01 00:01:16</td>\n",
              "      <td>3534093764340240</td>\n",
              "      <td>fraud_Kutch, Hermiston and Farrell</td>\n",
              "      <td>gas_transport</td>\n",
              "      <td>45.00</td>\n",
              "      <td>0</td>\n",
              "      <td>9443 Cynthia Court Apt. 038</td>\n",
              "      <td>Boulder</td>\n",
              "      <td>MT</td>\n",
              "      <td>59632</td>\n",
              "      <td>1939</td>\n",
              "      <td>Patent attorney</td>\n",
              "      <td>6b849c168bdad6f867558c3793159a81</td>\n",
              "      <td>1325376076</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-01-01 00:03:06</td>\n",
              "      <td>375534208663984</td>\n",
              "      <td>fraud_Keeling-Crist</td>\n",
              "      <td>misc_pos</td>\n",
              "      <td>41.96</td>\n",
              "      <td>0</td>\n",
              "      <td>408 Bradley Rest</td>\n",
              "      <td>Doe Hill</td>\n",
              "      <td>VA</td>\n",
              "      <td>24433</td>\n",
              "      <td>99</td>\n",
              "      <td>Dance movement psychotherapist</td>\n",
              "      <td>a41d7549acf90789359a9aa5346dcb46</td>\n",
              "      <td>1325376186</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8ef1f320-cd3a-48f7-9808-9c8659cab8ac')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-8ef1f320-cd3a-48f7-9808-9c8659cab8ac 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-8ef1f320-cd3a-48f7-9808-9c8659cab8ac');\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",
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              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9131649f-d708-4b1e-91bc-72dca7c8fcda')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
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              "      --hover-fill-color: #174EA6;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
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              "    }\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "      quickchartButtonEl.style.display =\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 100000,\n  \"fields\": [\n    {\n      \"column\": \"trans_date_trans_time\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 98825,\n        \"samples\": [\n          \"2019-01-13 23:22:04\",\n          \"2019-01-02 11:10:15\",\n          \"2019-01-14 15:18:36\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cc_num\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1309964004704921856,\n        \"min\": 60416207185,\n        \"max\": 4992346398065154184,\n        \"num_unique_values\": 922,\n        \"samples\": [\n          4328928491302401,\n          2297447006766555,\n          6592243974328236\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"merchant\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 693,\n        \"samples\": [\n          \"fraud_Eichmann-Kilback\",\n          \"fraud_Swaniawski, Bahringer and Ledner\",\n          \"fraud_Moen, Reinger and Murphy\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 14,\n        \"samples\": [\n          \"personal_care\",\n          \"travel\",\n          \"misc_net\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"amt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 145.89539987564356,\n        \"min\": 1.0,\n        \"max\": 12788.07,\n        \"num_unique_values\": 21184,\n        \"samples\": [\n          171.26,\n          83.41,\n          170.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"street\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 922,\n        \"samples\": [\n          \"927 Cummings Manor Suite 318\",\n          \"611 Michael Rue\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 848,\n        \"samples\": [\n          \"Leonard\",\n          \"Fordoche\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 50,\n        \"samples\": [\n          \"NJ\",\n          \"VT\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"zip\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26895,\n        \"min\": 1257,\n        \"max\": 99783,\n        \"num_unique_values\": 911,\n        \"samples\": [\n          76905,\n          68723\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city_pop\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 301578,\n        \"min\": 23,\n        \"max\": 2906700,\n        \"num_unique_values\": 833,\n        \"samples\": [\n          4603,\n          47\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"job\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 479,\n        \"samples\": [\n          \"Financial trader\",\n          \"Furniture designer\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"trans_num\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 100000,\n        \"samples\": [\n          \"f01f8ccbb4ae94687c665544565518c0\",\n          \"574f29c7623cce43fedd25fae4f9feb7\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unix_time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1459295,\n        \"min\": 1325376018,\n        \"max\": 1330443620,\n        \"num_unique_values\": 98825,\n        \"samples\": [\n          1326496924,\n          1325502615\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"is_fraud\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "def remove_char(text):\n",
        "  return (re.sub(r'<.*?>-:', '', text))\n",
        "\n",
        "columns=[\"merchant\",\"category\",\"job\",\"trans_date_trans_time\",\"street\",\"city\",\"state\",\"job\",\"trans_num\"]\n",
        "for col in columns:\n",
        "  data[col]=data[col].apply(remove_char)\n",
        "\n",
        "data.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "_YLfLse-AOrN",
        "outputId": "ffd055bc-f66b-4f3d-d57f-36479410a4e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  trans_date_trans_time            cc_num                            merchant  \\\n",
              "0   2019-01-01 00:00:18  2703186189652095          fraud_Rippin, Kub and Mann   \n",
              "1   2019-01-01 00:00:44      630423337322     fraud_Heller, Gutmann and Zieme   \n",
              "2   2019-01-01 00:00:51    38859492057661                fraud_Lind-Buckridge   \n",
              "3   2019-01-01 00:01:16  3534093764340240  fraud_Kutch, Hermiston and Farrell   \n",
              "4   2019-01-01 00:03:06   375534208663984                 fraud_Keeling-Crist   \n",
              "\n",
              "        category     amt  gender                        street  \\\n",
              "0       misc_net    4.97       1                561 Perry Cove   \n",
              "1    grocery_pos  107.23       1  43039 Riley Greens Suite 393   \n",
              "2  entertainment  220.11       0      594 White Dale Suite 530   \n",
              "3  gas_transport   45.00       0   9443 Cynthia Court Apt. 038   \n",
              "4       misc_pos   41.96       0              408 Bradley Rest   \n",
              "\n",
              "             city state    zip  city_pop                                job  \\\n",
              "0  Moravian Falls    NC  28654      3495          Psychologist, counselling   \n",
              "1          Orient    WA  99160       149  Special educational needs teacher   \n",
              "2      Malad City    ID  83252      4154        Nature conservation officer   \n",
              "3         Boulder    MT  59632      1939                    Patent attorney   \n",
              "4        Doe Hill    VA  24433        99     Dance movement psychotherapist   \n",
              "\n",
              "                          trans_num   unix_time  is_fraud  \n",
              "0  0b242abb623afc578575680df30655b9  1325376018         0  \n",
              "1  1f76529f8574734946361c461b024d99  1325376044         0  \n",
              "2  a1a22d70485983eac12b5b88dad1cf95  1325376051         0  \n",
              "3  6b849c168bdad6f867558c3793159a81  1325376076         0  \n",
              "4  a41d7549acf90789359a9aa5346dcb46  1325376186         0  "
            ],
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              "      <th></th>\n",
              "      <th>trans_date_trans_time</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-01 00:00:18</td>\n",
              "      <td>2703186189652095</td>\n",
              "      <td>fraud_Rippin, Kub and Mann</td>\n",
              "      <td>misc_net</td>\n",
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              "      <td>1</td>\n",
              "      <td>561 Perry Cove</td>\n",
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              "      <td>1</td>\n",
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              "      <td>Orient</td>\n",
              "      <td>WA</td>\n",
              "      <td>99160</td>\n",
              "      <td>149</td>\n",
              "      <td>Special educational needs teacher</td>\n",
              "      <td>1f76529f8574734946361c461b024d99</td>\n",
              "      <td>1325376044</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-01-01 00:00:51</td>\n",
              "      <td>38859492057661</td>\n",
              "      <td>fraud_Lind-Buckridge</td>\n",
              "      <td>entertainment</td>\n",
              "      <td>220.11</td>\n",
              "      <td>0</td>\n",
              "      <td>594 White Dale Suite 530</td>\n",
              "      <td>Malad City</td>\n",
              "      <td>ID</td>\n",
              "      <td>83252</td>\n",
              "      <td>4154</td>\n",
              "      <td>Nature conservation officer</td>\n",
              "      <td>a1a22d70485983eac12b5b88dad1cf95</td>\n",
              "      <td>1325376051</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-01-01 00:01:16</td>\n",
              "      <td>3534093764340240</td>\n",
              "      <td>fraud_Kutch, Hermiston and Farrell</td>\n",
              "      <td>gas_transport</td>\n",
              "      <td>45.00</td>\n",
              "      <td>0</td>\n",
              "      <td>9443 Cynthia Court Apt. 038</td>\n",
              "      <td>Boulder</td>\n",
              "      <td>MT</td>\n",
              "      <td>59632</td>\n",
              "      <td>1939</td>\n",
              "      <td>Patent attorney</td>\n",
              "      <td>6b849c168bdad6f867558c3793159a81</td>\n",
              "      <td>1325376076</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-01-01 00:03:06</td>\n",
              "      <td>375534208663984</td>\n",
              "      <td>fraud_Keeling-Crist</td>\n",
              "      <td>misc_pos</td>\n",
              "      <td>41.96</td>\n",
              "      <td>0</td>\n",
              "      <td>408 Bradley Rest</td>\n",
              "      <td>Doe Hill</td>\n",
              "      <td>VA</td>\n",
              "      <td>24433</td>\n",
              "      <td>99</td>\n",
              "      <td>Dance movement psychotherapist</td>\n",
              "      <td>a41d7549acf90789359a9aa5346dcb46</td>\n",
              "      <td>1325376186</td>\n",
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "type": "dataframe",
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              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 100000,\n  \"fields\": [\n    {\n      \"column\": \"trans_date_trans_time\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 98825,\n        \"samples\": [\n          \"2019-01-13 23:22:04\",\n          \"2019-01-02 11:10:15\",\n          \"2019-01-14 15:18:36\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cc_num\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1309964004704921856,\n        \"min\": 60416207185,\n        \"max\": 4992346398065154184,\n        \"num_unique_values\": 922,\n        \"samples\": [\n          4328928491302401,\n          2297447006766555,\n          6592243974328236\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"merchant\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 693,\n        \"samples\": [\n          \"fraud_Eichmann-Kilback\",\n          \"fraud_Swaniawski, Bahringer and Ledner\",\n          \"fraud_Moen, Reinger and Murphy\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 14,\n        \"samples\": [\n          \"personal_care\",\n          \"travel\",\n          \"misc_net\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"amt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 145.89539987564356,\n        \"min\": 1.0,\n        \"max\": 12788.07,\n        \"num_unique_values\": 21184,\n        \"samples\": [\n          171.26,\n          83.41,\n          170.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"street\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 922,\n        \"samples\": [\n          \"927 Cummings Manor Suite 318\",\n          \"611 Michael Rue\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 848,\n        \"samples\": [\n          \"Leonard\",\n          \"Fordoche\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 50,\n        \"samples\": [\n          \"NJ\",\n          \"VT\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"zip\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26895,\n        \"min\": 1257,\n        \"max\": 99783,\n        \"num_unique_values\": 911,\n        \"samples\": [\n          76905,\n          68723\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city_pop\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 301578,\n        \"min\": 23,\n        \"max\": 2906700,\n        \"num_unique_values\": 833,\n        \"samples\": [\n          4603,\n          47\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"job\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 479,\n        \"samples\": [\n          \"Financial trader\",\n          \"Furniture designer\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"trans_num\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 100000,\n        \"samples\": [\n          \"f01f8ccbb4ae94687c665544565518c0\",\n          \"574f29c7623cce43fedd25fae4f9feb7\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unix_time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1459295,\n        \"min\": 1325376018,\n        \"max\": 1330443620,\n        \"num_unique_values\": 98825,\n        \"samples\": [\n          1326496924,\n          1325502615\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"is_fraud\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "o93Y1WyMCoP0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "columns_to_lower=[\"state\",\"city\",\"job\"]\n",
        "for col in columns_to_lower:\n",
        "  data[col]=data[col].str.lower()\n",
        "\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "tj1Lg8M7BWvw",
        "outputId": "7a97c9f2-6cfe-4732-9ad1-fdb6fd3fa5a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  trans_date_trans_time            cc_num                            merchant  \\\n",
              "0   2019-01-01 00:00:18  2703186189652095          fraud_Rippin, Kub and Mann   \n",
              "1   2019-01-01 00:00:44      630423337322     fraud_Heller, Gutmann and Zieme   \n",
              "2   2019-01-01 00:00:51    38859492057661                fraud_Lind-Buckridge   \n",
              "3   2019-01-01 00:01:16  3534093764340240  fraud_Kutch, Hermiston and Farrell   \n",
              "4   2019-01-01 00:03:06   375534208663984                 fraud_Keeling-Crist   \n",
              "\n",
              "        category     amt  gender                        street  \\\n",
              "0       misc_net    4.97       1                561 Perry Cove   \n",
              "1    grocery_pos  107.23       1  43039 Riley Greens Suite 393   \n",
              "2  entertainment  220.11       0      594 White Dale Suite 530   \n",
              "3  gas_transport   45.00       0   9443 Cynthia Court Apt. 038   \n",
              "4       misc_pos   41.96       0              408 Bradley Rest   \n",
              "\n",
              "             city state    zip  city_pop                                job  \\\n",
              "0  moravian falls    nc  28654      3495          psychologist, counselling   \n",
              "1          orient    wa  99160       149  special educational needs teacher   \n",
              "2      malad city    id  83252      4154        nature conservation officer   \n",
              "3         boulder    mt  59632      1939                    patent attorney   \n",
              "4        doe hill    va  24433        99     dance movement psychotherapist   \n",
              "\n",
              "                          trans_num   unix_time  is_fraud  \n",
              "0  0b242abb623afc578575680df30655b9  1325376018         0  \n",
              "1  1f76529f8574734946361c461b024d99  1325376044         0  \n",
              "2  a1a22d70485983eac12b5b88dad1cf95  1325376051         0  \n",
              "3  6b849c168bdad6f867558c3793159a81  1325376076         0  \n",
              "4  a41d7549acf90789359a9aa5346dcb46  1325376186         0  "
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              "      <th>2</th>\n",
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              "      <td>2019-01-01 00:01:16</td>\n",
              "      <td>3534093764340240</td>\n",
              "      <td>fraud_Kutch, Hermiston and Farrell</td>\n",
              "      <td>gas_transport</td>\n",
              "      <td>45.00</td>\n",
              "      <td>0</td>\n",
              "      <td>9443 Cynthia Court Apt. 038</td>\n",
              "      <td>boulder</td>\n",
              "      <td>mt</td>\n",
              "      <td>59632</td>\n",
              "      <td>1939</td>\n",
              "      <td>patent attorney</td>\n",
              "      <td>6b849c168bdad6f867558c3793159a81</td>\n",
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              "      <td>2019-01-01 00:03:06</td>\n",
              "      <td>375534208663984</td>\n",
              "      <td>fraud_Keeling-Crist</td>\n",
              "      <td>misc_pos</td>\n",
              "      <td>41.96</td>\n",
              "      <td>0</td>\n",
              "      <td>408 Bradley Rest</td>\n",
              "      <td>doe hill</td>\n",
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              "        document.querySelector('#df-3e364f48-7c12-4709-a13b-d8d611b33d74 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 100000,\n  \"fields\": [\n    {\n      \"column\": \"trans_date_trans_time\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 98825,\n        \"samples\": [\n          \"2019-01-13 23:22:04\",\n          \"2019-01-02 11:10:15\",\n          \"2019-01-14 15:18:36\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cc_num\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1309964004704921856,\n        \"min\": 60416207185,\n        \"max\": 4992346398065154184,\n        \"num_unique_values\": 922,\n        \"samples\": [\n          4328928491302401,\n          2297447006766555,\n          6592243974328236\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"merchant\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 693,\n        \"samples\": [\n          \"fraud_Eichmann-Kilback\",\n          \"fraud_Swaniawski, Bahringer and Ledner\",\n          \"fraud_Moen, Reinger and Murphy\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 14,\n        \"samples\": [\n          \"personal_care\",\n          \"travel\",\n          \"misc_net\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"amt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 145.89539987564356,\n        \"min\": 1.0,\n        \"max\": 12788.07,\n        \"num_unique_values\": 21184,\n        \"samples\": [\n          171.26,\n          83.41,\n          170.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"street\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 922,\n        \"samples\": [\n          \"927 Cummings Manor Suite 318\",\n          \"611 Michael Rue\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 848,\n        \"samples\": [\n          \"leonard\",\n          \"fordoche\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 50,\n        \"samples\": [\n          \"nj\",\n          \"vt\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"zip\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26895,\n        \"min\": 1257,\n        \"max\": 99783,\n        \"num_unique_values\": 911,\n        \"samples\": [\n          76905,\n          68723\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city_pop\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 301578,\n        \"min\": 23,\n        \"max\": 2906700,\n        \"num_unique_values\": 833,\n        \"samples\": [\n          4603,\n          47\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"job\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 479,\n        \"samples\": [\n          \"financial trader\",\n          \"furniture designer\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"trans_num\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 100000,\n        \"samples\": [\n          \"f01f8ccbb4ae94687c665544565518c0\",\n          \"574f29c7623cce43fedd25fae4f9feb7\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unix_time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1459295,\n        \"min\": 1325376018,\n        \"max\": 1330443620,\n        \"num_unique_values\": 98825,\n        \"samples\": [\n          1326496924,\n          1325502615\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"is_fraud\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "cv=CountVectorizer()\n",
        "\n",
        "def transform(text):\n",
        "  # Check if the input is a string before applying fit_transform\n",
        "  if isinstance(text, str):\n",
        "    return cv.fit_transform([text]).toarray()[0] # Transform and extract single row\n",
        "  else:\n",
        "    return text # Return the original value if not a string\n",
        "\n",
        "for i in columns:\n",
        "  # Apply the transformation only if the column contains strings\n",
        "  if data[i].dtype == 'object':\n",
        "    data[i]=data[i].apply(transform)\n",
        "    print(data[i])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TcH0M5CICr5h",
        "outputId": "461f5e49-bd17-4589-f7a9-997e9354a347"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0        [1]\n",
            "1        [1]\n",
            "2        [1]\n",
            "3        [1]\n",
            "4        [1]\n",
            "        ... \n",
            "99995    [1]\n",
            "99996    [1]\n",
            "99997    [1]\n",
            "99998    [1]\n",
            "99999    [1]\n",
            "Name: trans_num, Length: 100000, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "j5g_0qLCDGQd",
        "outputId": "75822cff-d12b-4cde-956a-42ff965d4552"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       trans_date_trans_time               cc_num  merchant  category    amt  \\\n",
              "99995                      0     2475085306462014         0         0  95.14   \n",
              "99996                      0     4005676619255478         0         0   8.75   \n",
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              "99999                      0     6595970453799027         0         0   9.08   \n",
              "\n",
              "       gender  street  city  state    zip  city_pop  job trans_num  \\\n",
              "99995       0       0     0      0  56152      1507    0       [1]   \n",
              "99996       0       0     0      0  70726     71335    0       [1]   \n",
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              "99998       0       0     0      0  58769       229    0       [1]   \n",
              "99999       1       0     0      0   7980      2307    0       [1]   \n",
              "\n",
              "        unix_time  is_fraud  \n",
              "99995  1330443409         0  \n",
              "99996  1330443447         0  \n",
              "99997  1330443454         0  \n",
              "99998  1330443491         0  \n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-90c65402-6431-4462-91c4-1853b4fd7c1c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-90c65402-6431-4462-91c4-1853b4fd7c1c 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-90c65402-6431-4462-91c4-1853b4fd7c1c');\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",
              "\n",
              "\n",
              "<div id=\"df-ea163037-11e4-4a49-b7eb-c4b92542babe\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ea163037-11e4-4a49-b7eb-c4b92542babe')\"\n",
              "            title=\"Suggest charts\"\n",
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              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ea163037-11e4-4a49-b7eb-c4b92542babe button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"trans_date_trans_time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cc_num\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1804932787860278528,\n        \"min\": 2475085306462014,\n        \"max\": 4040099974063068803,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4005676619255478\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"merchant\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"category\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"amt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38.79653631447014,\n        \"min\": 8.75,\n        \"max\": 95.14,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          8.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"street\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"zip\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24062,\n        \"min\": 7980,\n        \"max\": 70726,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          70726\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city_pop\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31214,\n        \"min\": 229,\n        \"max\": 71335,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          71335\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"job\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"trans_num\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unix_time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 81,\n        \"min\": 1330443409,\n        \"max\": 1330443620,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1330443447\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"is_fraud\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "columns_not_included=data[[\"cc_num\",\"amt\",\"gender\",\"zip\",\"unix_time\"]]\n",
        "X=data.drop('is_fraud',axis=1)\n",
        "#X.columns=X.columns.astype(str)\n",
        "#X[0]=X[0].fillna(data[0].mean())\n",
        "X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "kCqQcpwyDQSp",
        "outputId": "c38a63d2-f80a-466b-e1d7-7a6de06eb4dc"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   trans_date_trans_time            cc_num  merchant  category     amt  \\\n",
              "0                      2  2703186189652095         0         0    4.97   \n",
              "1                      2      630423337322         0         0  107.23   \n",
              "2                      2    38859492057661         0         1  220.11   \n",
              "3                      1  3534093764340240         0         0   45.00   \n",
              "4                      1   375534208663984         0         0   41.96   \n",
              "\n",
              "   gender  street  city  state    zip  city_pop  job trans_num   unix_time  \n",
              "0       1       0     0      0  28654      3495    0       [1]  1325376018  \n",
              "1       1       0     0      0  99160       149    0       [1]  1325376044  \n",
              "2       0       0     0      0  83252      4154    0       [1]  1325376051  \n",
              "3       0       0     0      0  59632      1939    0       [1]  1325376076  \n",
              "4       0       0     0      0  24433        99    0       [1]  1325376186  "
            ],
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            }
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install imblearn\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "CGAmZag96wKc",
        "outputId": "a488dcbf-0b2a-4327-b088-8ebba25804d3"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: imblearn in /usr/local/lib/python3.10/dist-packages (0.0)\n",
            "Requirement already satisfied: imbalanced-learn in /usr/local/lib/python3.10/dist-packages (from imblearn) (0.12.3)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn->imblearn) (1.26.4)\n",
            "Requirement already satisfied: scipy>=1.5.0 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn->imblearn) (1.13.1)\n",
            "Requirement already satisfied: scikit-learn>=1.0.2 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn->imblearn) (1.3.2)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn->imblearn) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn->imblearn) (3.5.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y=data[\"is_fraud\"]\n",
        "y.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "vdXWSAAKE7qR",
        "outputId": "1603c6cc-9388-4b69-b76f-cd1c20f727d8"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "99995    0\n",
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      "source": [
        "Xtrain,Xtest,ytrain,ytest=train_test_split(X,y,test_size=0.3,random_state=42)\n",
        "Xtest.head()"
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          "height": 206
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        "outputId": "8b4d9a5f-9aa7-43a1-bd4b-50bbaaf2d544"
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      "execution_count": 62,
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              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c1f43a21-123c-4c49-979f-725b413346b9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "Xtest",
              "summary": "{\n  \"name\": \"Xtest\",\n  \"rows\": 30000,\n  \"fields\": [\n    {\n      \"column\": \"trans_date_trans_time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1,\n          3,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cc_num\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1320925902357952000,\n        \"min\": 60416207185,\n        \"max\": 4992346398065154184,\n        \"num_unique_values\": 922,\n        \"samples\": [\n          6011860238257910,\n          3538520143479972,\n          213195754007681\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"merchant\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"category\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"amt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 133.51484159755842,\n        \"min\": 1.0,\n        \"max\": 5234.79,\n        \"num_unique_values\": 13065,\n        \"samples\": [\n          33.27,\n          94.27\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"street\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"zip\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26957,\n        \"min\": 1257,\n        \"max\": 99783,\n        \"num_unique_values\": 911,\n        \"samples\": [\n          95148,\n          89084\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city_pop\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 301384,\n        \"min\": 23,\n        \"max\": 2906700,\n        \"num_unique_values\": 833,\n        \"samples\": [\n          4913,\n          133\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"job\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"trans_num\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unix_time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1457249,\n        \"min\": 1325376318,\n        \"max\": 1330443620,\n        \"num_unique_values\": 29898,\n        \"samples\": [\n          1330175087,\n          1325624391\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -U imbalanced-learn  # Upgrade imblearn to the latest version\n",
        "\n",
        "from imblearn.over_sampling import SMOTE # Import SMOTE from the correct module\n",
        "smote=SMOTE()\n",
        "Xtrain,ytrain=smote.fit_resample(Xtrain,ytrain)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Hy3NcKQA8Ig7",
        "outputId": "6e4caa81-b004-4fbf-93e7-c5bc20b248fd"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: imbalanced-learn in /usr/local/lib/python3.10/dist-packages (0.12.3)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (1.26.4)\n",
            "Requirement already satisfied: scipy>=1.5.0 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (1.13.1)\n",
            "Requirement already satisfied: scikit-learn>=1.0.2 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (1.3.2)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (3.5.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Xtrain.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "0Ibu0KPJzd7k",
        "outputId": "4871f5d9-c910-409e-e595-cd71adc30114"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 138624 entries, 0 to 138623\n",
            "Data columns (total 14 columns):\n",
            " #   Column                 Non-Null Count   Dtype  \n",
            "---  ------                 --------------   -----  \n",
            " 0   trans_date_trans_time  138624 non-null  int64  \n",
            " 1   cc_num                 138624 non-null  int64  \n",
            " 2   merchant               138624 non-null  int64  \n",
            " 3   category               138624 non-null  int64  \n",
            " 4   amt                    138624 non-null  float64\n",
            " 5   gender                 138624 non-null  int64  \n",
            " 6   street                 138624 non-null  int64  \n",
            " 7   city                   138624 non-null  int64  \n",
            " 8   state                  138624 non-null  int64  \n",
            " 9   zip                    138624 non-null  int64  \n",
            " 10  city_pop               138624 non-null  int64  \n",
            " 11  job                    138624 non-null  int64  \n",
            " 12  trans_num              138624 non-null  object \n",
            " 13  unix_time              138624 non-null  int64  \n",
            "dtypes: float64(1), int64(12), object(1)\n",
            "memory usage: 14.8+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "model1=RandomForestClassifier(max_depth=5)\n",
        "model1.fit(Xtrain,ytrain)\n",
        "ypred1=model1.predict(Xtest)\n",
        "print(classification_report(ytest,ypred1))"
      ],
      "metadata": {
        "id": "4Q4guVWPGN1C",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "ad983563-5591-4bd8-cee9-46e827cd10bb"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.96      0.98     29698\n",
            "           1       0.15      0.77      0.26       302\n",
            "\n",
            "    accuracy                           0.95     30000\n",
            "   macro avg       0.58      0.87      0.62     30000\n",
            "weighted avg       0.99      0.95      0.97     30000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "model2=LogisticRegression()\n",
        "model2.fit(Xtrain,ytrain)\n",
        "ypred2=model2.predict(Xtest)\n",
        "print(classification_report(ytest,ypred2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "1ZXF3e2w00JV",
        "outputId": "ba74da49-6eec-411b-b81a-564bc49bfca7"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      1.00      0.99     29698\n",
            "           1       0.00      0.00      0.00       302\n",
            "\n",
            "    accuracy                           0.99     30000\n",
            "   macro avg       0.49      0.50      0.50     30000\n",
            "weighted avg       0.98      0.99      0.98     30000\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "acc=accuracy_score(ytest,ypred1)\n",
        "print(acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "cUOdeT2E9oXR",
        "outputId": "6bbbe10c-7fb8-48ba-937a-0f1a883fc2bc"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9548666666666666\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "acc=accuracy_score(ytest,ypred2)\n",
        "print(acc)"
      ],
      "metadata": {
        "id": "h4ziNVi6GltI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "61db01d9-20cf-4a5d-8db4-51ed974ddab2"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9899333333333333\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred_0=model.predict(Xtest.iloc[[0]])\n",
        "print(ytest.iloc[[0]]-pred_0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "K-kdgJ9dzHGk",
        "outputId": "6358f3e9-29f5-4bbd-a67a-db383b8cbc89"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "75721    0\n",
            "Name: is_fraud, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "b1n8gwTHzn3T"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "TPU",
    "colab": {
      "gpuType": "V28",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}