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    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, ConfusionMatrixDisplay\n",
        "from sklearn.ensemble import GradientBoostingClassifier"
      ],
      "metadata": {
        "id": "pTNV9noRtUNZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('creditcard.csv')"
      ],
      "metadata": {
        "id": "gFE_sJ7fuJMt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "QOWHOnn3uURp",
        "outputId": "7f40f75f-ebcf-44c7-f81e-49f4f2296028"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
              "0     0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
              "1     0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
              "2     1 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
              "3     1 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
              "4     2 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
              "\n",
              "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
              "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
              "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
              "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
              "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
              "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
              "\n",
              "        V26       V27       V28  Amount  Class  \n",
              "0 -0.189115  0.133558 -0.021053  149.62    0.0  \n",
              "1  0.125895 -0.008983  0.014724    2.69    0.0  \n",
              "2 -0.139097 -0.055353 -0.059752  378.66    0.0  \n",
              "3 -0.221929  0.062723  0.061458  123.50    0.0  \n",
              "4  0.502292  0.219422  0.215153   69.99    0.0  \n",
              "\n",
              "[5 rows x 31 columns]"
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              "      <th></th>\n",
              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
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              "      <th>V21</th>\n",
              "      <th>V22</th>\n",
              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
              "      <th>V25</th>\n",
              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Class</th>\n",
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              "      <td>-0.072781</td>\n",
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              "      <td>-0.338321</td>\n",
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              "      <td>0.239599</td>\n",
              "      <td>0.098698</td>\n",
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              "      <td>0.277838</td>\n",
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              "      <td>0.128539</td>\n",
              "      <td>-0.189115</td>\n",
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              "      <td>-0.021053</td>\n",
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              "      <th>1</th>\n",
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              "      <td>0.266151</td>\n",
              "      <td>0.166480</td>\n",
              "      <td>0.448154</td>\n",
              "      <td>0.060018</td>\n",
              "      <td>-0.082361</td>\n",
              "      <td>-0.078803</td>\n",
              "      <td>0.085102</td>\n",
              "      <td>-0.255425</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.225775</td>\n",
              "      <td>-0.638672</td>\n",
              "      <td>0.101288</td>\n",
              "      <td>-0.339846</td>\n",
              "      <td>0.167170</td>\n",
              "      <td>0.125895</td>\n",
              "      <td>-0.008983</td>\n",
              "      <td>0.014724</td>\n",
              "      <td>2.69</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>-1.358354</td>\n",
              "      <td>-1.340163</td>\n",
              "      <td>1.773209</td>\n",
              "      <td>0.379780</td>\n",
              "      <td>-0.503198</td>\n",
              "      <td>1.800499</td>\n",
              "      <td>0.791461</td>\n",
              "      <td>0.247676</td>\n",
              "      <td>-1.514654</td>\n",
              "      <td>...</td>\n",
              "      <td>0.247998</td>\n",
              "      <td>0.771679</td>\n",
              "      <td>0.909412</td>\n",
              "      <td>-0.689281</td>\n",
              "      <td>-0.327642</td>\n",
              "      <td>-0.139097</td>\n",
              "      <td>-0.055353</td>\n",
              "      <td>-0.059752</td>\n",
              "      <td>378.66</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>-0.966272</td>\n",
              "      <td>-0.185226</td>\n",
              "      <td>1.792993</td>\n",
              "      <td>-0.863291</td>\n",
              "      <td>-0.010309</td>\n",
              "      <td>1.247203</td>\n",
              "      <td>0.237609</td>\n",
              "      <td>0.377436</td>\n",
              "      <td>-1.387024</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.108300</td>\n",
              "      <td>0.005274</td>\n",
              "      <td>-0.190321</td>\n",
              "      <td>-1.175575</td>\n",
              "      <td>0.647376</td>\n",
              "      <td>-0.221929</td>\n",
              "      <td>0.062723</td>\n",
              "      <td>0.061458</td>\n",
              "      <td>123.50</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>-1.158233</td>\n",
              "      <td>0.877737</td>\n",
              "      <td>1.548718</td>\n",
              "      <td>0.403034</td>\n",
              "      <td>-0.407193</td>\n",
              "      <td>0.095921</td>\n",
              "      <td>0.592941</td>\n",
              "      <td>-0.270533</td>\n",
              "      <td>0.817739</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.009431</td>\n",
              "      <td>0.798278</td>\n",
              "      <td>-0.137458</td>\n",
              "      <td>0.141267</td>\n",
              "      <td>-0.206010</td>\n",
              "      <td>0.502292</td>\n",
              "      <td>0.219422</td>\n",
              "      <td>0.215153</td>\n",
              "      <td>69.99</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>5 rows × 31 columns</p>\n",
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              "      const quickchartButtonEl =\n",
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              "type": "dataframe",
              "variable_name": "df"
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          "metadata": {},
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      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "HvJMAHvruW1P",
        "outputId": "77529721-b41f-4dd9-98b4-b96af54667a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(87240, 31)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ha_cR-S5uaOr",
        "outputId": "cecdc24d-b846-4762-f71a-410bc43de10a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10',\n",
              "       'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20',\n",
              "       'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount',\n",
              "       'Class'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Eqgz_mkgudId",
        "outputId": "464f8aa8-14c2-4d6e-c499-aace2c3513af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 87240 entries, 0 to 87239\n",
            "Data columns (total 31 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   Time    87240 non-null  int64  \n",
            " 1   V1      87240 non-null  float64\n",
            " 2   V2      87240 non-null  float64\n",
            " 3   V3      87240 non-null  float64\n",
            " 4   V4      87240 non-null  float64\n",
            " 5   V5      87239 non-null  float64\n",
            " 6   V6      87239 non-null  float64\n",
            " 7   V7      87239 non-null  float64\n",
            " 8   V8      87239 non-null  float64\n",
            " 9   V9      87239 non-null  float64\n",
            " 10  V10     87239 non-null  float64\n",
            " 11  V11     87239 non-null  float64\n",
            " 12  V12     87239 non-null  float64\n",
            " 13  V13     87239 non-null  float64\n",
            " 14  V14     87239 non-null  float64\n",
            " 15  V15     87239 non-null  float64\n",
            " 16  V16     87239 non-null  float64\n",
            " 17  V17     87239 non-null  float64\n",
            " 18  V18     87239 non-null  float64\n",
            " 19  V19     87239 non-null  float64\n",
            " 20  V20     87239 non-null  float64\n",
            " 21  V21     87239 non-null  float64\n",
            " 22  V22     87239 non-null  float64\n",
            " 23  V23     87239 non-null  float64\n",
            " 24  V24     87239 non-null  float64\n",
            " 25  V25     87239 non-null  float64\n",
            " 26  V26     87239 non-null  float64\n",
            " 27  V27     87239 non-null  float64\n",
            " 28  V28     87239 non-null  float64\n",
            " 29  Amount  87239 non-null  float64\n",
            " 30  Class   87239 non-null  float64\n",
            "dtypes: float64(30), int64(1)\n",
            "memory usage: 20.6 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "wg_mF_AHugDq",
        "outputId": "decfad9d-655e-4b04-94a0-7908e2ea2629"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Time            V1            V2            V3            V4  \\\n",
              "count  87240.000000  87240.000000  87240.000000  87240.000000  87240.000000   \n",
              "mean   39208.302212     -0.264415     -0.039672      0.679463      0.163042   \n",
              "std    15846.338289      1.876993      1.667305      1.359385      1.360959   \n",
              "min        0.000000    -56.407510    -72.715728    -33.680984     -5.172595   \n",
              "25%    31867.750000     -1.028026     -0.603069      0.184406     -0.720243   \n",
              "50%    41579.000000     -0.261473      0.070422      0.762151      0.186247   \n",
              "75%    51578.500000      1.152808      0.726160      1.389214      1.039812   \n",
              "max    61596.000000      1.960497     18.902453      4.226108     16.715537   \n",
              "\n",
              "                 V5            V6            V7            V8            V9  \\\n",
              "count  87239.000000  87239.000000  87239.000000  87239.000000  87239.000000   \n",
              "mean      -0.277593      0.094179     -0.110135      0.054041     -0.025376   \n",
              "std        1.372054      1.302524      1.233574      1.217704      1.131867   \n",
              "min      -42.147898    -26.160506    -31.764946    -73.216718     -9.283925   \n",
              "25%       -0.897262     -0.645804     -0.602405     -0.140723     -0.691868   \n",
              "50%       -0.312307     -0.155825     -0.071350      0.070699     -0.100498   \n",
              "75%        0.256678      0.488780      0.417652      0.353244      0.595250   \n",
              "max       34.801666     22.529298     36.677268     20.007208     10.392889   \n",
              "\n",
              "       ...           V21           V22           V23           V24  \\\n",
              "count  ...  87239.000000  87239.000000  87239.000000  87239.000000   \n",
              "mean   ...     -0.029472     -0.106031     -0.037054      0.009686   \n",
              "std    ...      0.729483      0.635050      0.624121      0.594797   \n",
              "min    ...    -34.830382    -10.933144    -26.751119     -2.836627   \n",
              "25%    ...     -0.223746     -0.525484     -0.177939     -0.321974   \n",
              "50%    ...     -0.057635     -0.081403     -0.049749      0.065023   \n",
              "75%    ...      0.118528      0.309661      0.080971      0.406151   \n",
              "max    ...     22.614889     10.503090     18.946734      4.014444   \n",
              "\n",
              "                V25           V26           V27           V28        Amount  \\\n",
              "count  87239.000000  87239.000000  87239.000000  87239.000000  87239.000000   \n",
              "mean       0.132652      0.026722      0.000801      0.002105     98.400990   \n",
              "std        0.441065      0.496992      0.390442      0.327910    267.389535   \n",
              "min       -7.495741     -2.534330     -9.390980     -9.617915      0.000000   \n",
              "25%       -0.131957     -0.326135     -0.063559     -0.005877      7.680000   \n",
              "50%        0.171178     -0.071981      0.009300      0.023036     26.990000   \n",
              "75%        0.420203      0.306278      0.082638      0.076227     89.900000   \n",
              "max        5.525093      3.517346     12.152401     33.847808  19656.530000   \n",
              "\n",
              "              Class  \n",
              "count  87239.000000  \n",
              "mean       0.002350  \n",
              "std        0.048419  \n",
              "min        0.000000  \n",
              "25%        0.000000  \n",
              "50%        0.000000  \n",
              "75%        0.000000  \n",
              "max        1.000000  \n",
              "\n",
              "[8 rows x 31 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>...</th>\n",
              "      <th>V21</th>\n",
              "      <th>V22</th>\n",
              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
              "      <th>V25</th>\n",
              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>87240.000000</td>\n",
              "      <td>87240.000000</td>\n",
              "      <td>87240.000000</td>\n",
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              "      <td>87240.000000</td>\n",
              "      <td>87239.000000</td>\n",
              "      <td>87239.000000</td>\n",
              "      <td>87239.000000</td>\n",
              "      <td>87239.000000</td>\n",
              "      <td>87239.000000</td>\n",
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              "      <td>87239.000000</td>\n",
              "      <td>87239.000000</td>\n",
              "      <td>87239.000000</td>\n",
              "      <td>87239.000000</td>\n",
              "      <td>87239.000000</td>\n",
              "      <td>87239.000000</td>\n",
              "      <td>87239.000000</td>\n",
              "      <td>87239.000000</td>\n",
              "      <td>87239.000000</td>\n",
              "      <td>87239.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>39208.302212</td>\n",
              "      <td>-0.264415</td>\n",
              "      <td>-0.039672</td>\n",
              "      <td>0.679463</td>\n",
              "      <td>0.163042</td>\n",
              "      <td>-0.277593</td>\n",
              "      <td>0.094179</td>\n",
              "      <td>-0.110135</td>\n",
              "      <td>0.054041</td>\n",
              "      <td>-0.025376</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.029472</td>\n",
              "      <td>-0.106031</td>\n",
              "      <td>-0.037054</td>\n",
              "      <td>0.009686</td>\n",
              "      <td>0.132652</td>\n",
              "      <td>0.026722</td>\n",
              "      <td>0.000801</td>\n",
              "      <td>0.002105</td>\n",
              "      <td>98.400990</td>\n",
              "      <td>0.002350</td>\n",
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              "      <th>std</th>\n",
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              "      <td>1.876993</td>\n",
              "      <td>1.667305</td>\n",
              "      <td>1.359385</td>\n",
              "      <td>1.360959</td>\n",
              "      <td>1.372054</td>\n",
              "      <td>1.302524</td>\n",
              "      <td>1.233574</td>\n",
              "      <td>1.217704</td>\n",
              "      <td>1.131867</td>\n",
              "      <td>...</td>\n",
              "      <td>0.729483</td>\n",
              "      <td>0.635050</td>\n",
              "      <td>0.624121</td>\n",
              "      <td>0.594797</td>\n",
              "      <td>0.441065</td>\n",
              "      <td>0.496992</td>\n",
              "      <td>0.390442</td>\n",
              "      <td>0.327910</td>\n",
              "      <td>267.389535</td>\n",
              "      <td>0.048419</td>\n",
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              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
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              "      <td>-72.715728</td>\n",
              "      <td>-33.680984</td>\n",
              "      <td>-5.172595</td>\n",
              "      <td>-42.147898</td>\n",
              "      <td>-26.160506</td>\n",
              "      <td>-31.764946</td>\n",
              "      <td>-73.216718</td>\n",
              "      <td>-9.283925</td>\n",
              "      <td>...</td>\n",
              "      <td>-34.830382</td>\n",
              "      <td>-10.933144</td>\n",
              "      <td>-26.751119</td>\n",
              "      <td>-2.836627</td>\n",
              "      <td>-7.495741</td>\n",
              "      <td>-2.534330</td>\n",
              "      <td>-9.390980</td>\n",
              "      <td>-9.617915</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>31867.750000</td>\n",
              "      <td>-1.028026</td>\n",
              "      <td>-0.603069</td>\n",
              "      <td>0.184406</td>\n",
              "      <td>-0.720243</td>\n",
              "      <td>-0.897262</td>\n",
              "      <td>-0.645804</td>\n",
              "      <td>-0.602405</td>\n",
              "      <td>-0.140723</td>\n",
              "      <td>-0.691868</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.223746</td>\n",
              "      <td>-0.525484</td>\n",
              "      <td>-0.177939</td>\n",
              "      <td>-0.321974</td>\n",
              "      <td>-0.131957</td>\n",
              "      <td>-0.326135</td>\n",
              "      <td>-0.063559</td>\n",
              "      <td>-0.005877</td>\n",
              "      <td>7.680000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>41579.000000</td>\n",
              "      <td>-0.261473</td>\n",
              "      <td>0.070422</td>\n",
              "      <td>0.762151</td>\n",
              "      <td>0.186247</td>\n",
              "      <td>-0.312307</td>\n",
              "      <td>-0.155825</td>\n",
              "      <td>-0.071350</td>\n",
              "      <td>0.070699</td>\n",
              "      <td>-0.100498</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.057635</td>\n",
              "      <td>-0.081403</td>\n",
              "      <td>-0.049749</td>\n",
              "      <td>0.065023</td>\n",
              "      <td>0.171178</td>\n",
              "      <td>-0.071981</td>\n",
              "      <td>0.009300</td>\n",
              "      <td>0.023036</td>\n",
              "      <td>26.990000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>51578.500000</td>\n",
              "      <td>1.152808</td>\n",
              "      <td>0.726160</td>\n",
              "      <td>1.389214</td>\n",
              "      <td>1.039812</td>\n",
              "      <td>0.256678</td>\n",
              "      <td>0.488780</td>\n",
              "      <td>0.417652</td>\n",
              "      <td>0.353244</td>\n",
              "      <td>0.595250</td>\n",
              "      <td>...</td>\n",
              "      <td>0.118528</td>\n",
              "      <td>0.309661</td>\n",
              "      <td>0.080971</td>\n",
              "      <td>0.406151</td>\n",
              "      <td>0.420203</td>\n",
              "      <td>0.306278</td>\n",
              "      <td>0.082638</td>\n",
              "      <td>0.076227</td>\n",
              "      <td>89.900000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>18.902453</td>\n",
              "      <td>4.226108</td>\n",
              "      <td>16.715537</td>\n",
              "      <td>34.801666</td>\n",
              "      <td>22.529298</td>\n",
              "      <td>36.677268</td>\n",
              "      <td>20.007208</td>\n",
              "      <td>10.392889</td>\n",
              "      <td>...</td>\n",
              "      <td>22.614889</td>\n",
              "      <td>10.503090</td>\n",
              "      <td>18.946734</td>\n",
              "      <td>4.014444</td>\n",
              "      <td>5.525093</td>\n",
              "      <td>3.517346</td>\n",
              "      <td>12.152401</td>\n",
              "      <td>33.847808</td>\n",
              "      <td>19656.530000</td>\n",
              "      <td>1.000000</td>\n",
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              "<p>8 rows × 31 columns</p>\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",
              "    }\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-2b923fb4-f670-4572-83b1-ab2511fdce24 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"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KfsFxAB4umIG",
        "outputId": "1d5999f3-973c-4875-8b46-0c0bf631b7e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Time      0\n",
              "V1        0\n",
              "V2        0\n",
              "V3        0\n",
              "V4        0\n",
              "V5        1\n",
              "V6        1\n",
              "V7        1\n",
              "V8        1\n",
              "V9        1\n",
              "V10       1\n",
              "V11       1\n",
              "V12       1\n",
              "V13       1\n",
              "V14       1\n",
              "V15       1\n",
              "V16       1\n",
              "V17       1\n",
              "V18       1\n",
              "V19       1\n",
              "V20       1\n",
              "V21       1\n",
              "V22       1\n",
              "V23       1\n",
              "V24       1\n",
              "V25       1\n",
              "V26       1\n",
              "V27       1\n",
              "V28       1\n",
              "Amount    1\n",
              "Class     1\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ko1j3K6Uut5b",
        "outputId": "e46b7148-9cc2-40b4-e17b-710497b51107"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "319"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop_duplicates(inplace=True)"
      ],
      "metadata": {
        "id": "SSoamfCbux9S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M3O6x6Fsu1mt",
        "outputId": "deb52186-d800-4bdd-f180-89d21f681f70"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(87240, 31)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Class'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hfXFjUTlu5CH",
        "outputId": "4de16f37-0bf5-4ae8-d59d-438c249c54b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.,  1., nan])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Class'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pwV7eSTtu9fw",
        "outputId": "f64d3f49-81cb-4b4e-d977-b58044d69ebb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Class\n",
              "0.0    87034\n",
              "1.0      205\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fraud = df[df['Class'] == 1]\n",
        "normal = df[df['Class'] == 0]\n",
        "normal_percentage = len(normal)/(len(fraud)+len(normal))\n",
        "fraud_percentage = len(fraud)/(len(fraud)+len(normal))\n",
        "print('Percentage of fraud transactions = ', round(fraud_percentage * 100, 3))\n",
        "print('Percentage of normal transactions = ', round(normal_percentage * 100, 3))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A0U2uskzvCzN",
        "outputId": "9f37e1a2-26f5-4d86-e1cf-3a6906ef15aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of fraud transactions =  0.235\n",
            "Percentage of normal transactions =  99.765\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(9,7))\n",
        "sns.countplot(data=df,x='Class',palette=['blue', 'red'])\n",
        "plt.title(\"Number of Normal and Fraud Transactions\");"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "id": "M97edxamv4dH",
        "outputId": "40c81914-82c2-4962-ccb9-5779d7c3eb69"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-19-2db7daf10a38>:2: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.countplot(data=df,x='Class',palette=['blue', 'red'])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "sns.FacetGrid(df, hue=\"Class\", height=6,palette=['blue','red']).map(plt.scatter, \"Time\", \"Amount\").add_legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "id": "GH0KCQLhvG73",
        "outputId": "2c187268-cfe7-4864-8d8e-17bc22f2fd26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 672x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,7))\n",
        "sns.heatmap(data=df.corr(),cmap='mako')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "id": "UNW65igUw7UC",
        "outputId": "baf411f8-4e02-4436-9934-ec524bc88813"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop('Class',axis=1)\n",
        "y = df['Class']"
      ],
      "metadata": {
        "id": "W9yL-3tjxGfB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
      ],
      "metadata": {
        "id": "YullUkJ8xM0O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qwkw7de2IaEa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "26cCztFmIaBA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def model_train_test(model,X_train,y_train,X_test,y_test):\n",
        "  model.fit(X_train,y_train)\n",
        "  prediction = model.predict(X_test)\n",
        "  print('Accuracy = {}'.format(accuracy_score(y_test,prediction)))\n",
        "  print(classification_report(y_test,prediction))\n",
        "  matrix = confusion_matrix(y_test,prediction)\n",
        "  dis = ConfusionMatrixDisplay(matrix)\n",
        "  dis.plot()\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "lWPel-0ZxZja"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf_model = RandomForestClassifier()"
      ],
      "metadata": {
        "id": "fHRYgnTUx8Dr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_train_test(rf_model,X_train,y_train,X_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "collapsed": true,
        "id": "kofFcUbFyBNu",
        "outputId": "aaf11381-7568-4999-de84-e4db7aecba09"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-41-1e6cfde5fa19>:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  model.fit(X_train,y_train)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "y_test type: <class 'numpy.ndarray'>\n",
            "Prediction type: <class 'numpy.float64'>\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "Classification metrics can't handle a mix of continuous and binary targets",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-43-eb8cbd3c165b>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel_train_test\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrf_model\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-41-1e6cfde5fa19>\u001b[0m in \u001b[0;36mmodel_train_test\u001b[0;34m(model, X_train, y_train, X_test, y_test)\u001b[0m\n\u001b[1;32m      4\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'y_test type: {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Prediction type: {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprediction\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Accuracy = {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maccuracy_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mprediction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclassification_report\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mprediction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m   \u001b[0mmatrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconfusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mprediction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    190\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    191\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 192\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    193\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mInvalidParameterError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    194\u001b[0m                 \u001b[0;31m# When the function is just a wrapper around an estimator, we allow\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36maccuracy_score\u001b[0;34m(y_true, y_pred, normalize, sample_weight)\u001b[0m\n\u001b[1;32m    219\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    220\u001b[0m     \u001b[0;31m# Compute accuracy for each possible representation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 221\u001b[0;31m     \u001b[0my_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    222\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0my_type\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"multilabel\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36m_check_targets\u001b[0;34m(y_true, y_pred)\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m         raise ValueError(\n\u001b[0m\u001b[1;32m     96\u001b[0m             \"Classification metrics can't handle a mix of {0} and {1} targets\".format(\n\u001b[1;32m     97\u001b[0m                 \u001b[0mtype_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtype_pred\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Classification metrics can't handle a mix of continuous and binary targets"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Decision_tree = DecisionTreeClassifier()"
      ],
      "metadata": {
        "id": "hoeOBeZl35pT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_train_test(Decision_tree,X_train,y_train,X_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "collapsed": true,
        "id": "clcYqstc4A4l",
        "outputId": "28cae1a7-311b-4067-f889-0770d4cdb74b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "y_test type: <class 'numpy.ndarray'>\n",
            "Prediction type: <class 'numpy.float64'>\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "Classification metrics can't handle a mix of continuous and binary targets",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-45-71400fb2b530>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel_train_test\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDecision_tree\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-41-1e6cfde5fa19>\u001b[0m in \u001b[0;36mmodel_train_test\u001b[0;34m(model, X_train, y_train, X_test, y_test)\u001b[0m\n\u001b[1;32m      4\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'y_test type: {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Prediction type: {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprediction\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Accuracy = {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maccuracy_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mprediction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclassification_report\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mprediction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m   \u001b[0mmatrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconfusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mprediction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    190\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    191\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 192\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    193\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mInvalidParameterError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    194\u001b[0m                 \u001b[0;31m# When the function is just a wrapper around an estimator, we allow\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36maccuracy_score\u001b[0;34m(y_true, y_pred, normalize, sample_weight)\u001b[0m\n\u001b[1;32m    219\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    220\u001b[0m     \u001b[0;31m# Compute accuracy for each possible representation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 221\u001b[0;31m     \u001b[0my_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    222\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0my_type\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"multilabel\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36m_check_targets\u001b[0;34m(y_true, y_pred)\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m         raise ValueError(\n\u001b[0m\u001b[1;32m     96\u001b[0m             \"Classification metrics can't handle a mix of {0} and {1} targets\".format(\n\u001b[1;32m     97\u001b[0m                 \u001b[0mtype_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtype_pred\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Classification metrics can't handle a mix of continuous and binary targets"
          ]
        }
      ]
    }
  ]
}