CODSOFT_task_5_Credit_Card_Fraud_Detection.ipynb

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        "<a href=\"https://colab.research.google.com/github/shahjan49/CODSOFT/blob/main/CODSOFT_task_5_Credit_Card_Fraud_Detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "Loading Necessary Libraries."
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      "execution_count": null,
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        "id": "bNODnUHXEL4X"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as snr\n",
        "from sklearn.preprocessing import OneHotEncoder"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load data and show first 10 rows of data."
      ],
      "metadata": {
        "id": "JxNaOn6DGvCd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data=pd.read_csv(\"/content/creditcard.csv\")\n",
        "data.head(10)"
      ],
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          "height": 412
        },
        "id": "tGm7d7ZJGpEr",
        "outputId": "c5e222bd-efa6-402a-e17d-4b8848e78795"
      },
      "execution_count": null,
      "outputs": [
        {
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              "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
              "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
              "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
              "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
              "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
              "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
              "5   2.0 -0.425966  0.960523  1.141109 -0.168252  0.420987 -0.029728  0.476201   \n",
              "6   4.0  1.229658  0.141004  0.045371  1.202613  0.191881  0.272708 -0.005159   \n",
              "7   7.0 -0.644269  1.417964  1.074380 -0.492199  0.948934  0.428118  1.120631   \n",
              "8   7.0 -0.894286  0.286157 -0.113192 -0.271526  2.669599  3.721818  0.370145   \n",
              "9   9.0 -0.338262  1.119593  1.044367 -0.222187  0.499361 -0.246761  0.651583   \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",
              "5  0.260314 -0.568671  ... -0.208254 -0.559825 -0.026398 -0.371427 -0.232794   \n",
              "6  0.081213  0.464960  ... -0.167716 -0.270710 -0.154104 -0.780055  0.750137   \n",
              "7 -3.807864  0.615375  ...  1.943465 -1.015455  0.057504 -0.649709 -0.415267   \n",
              "8  0.851084 -0.392048  ... -0.073425 -0.268092 -0.204233  1.011592  0.373205   \n",
              "9  0.069539 -0.736727  ... -0.246914 -0.633753 -0.120794 -0.385050 -0.069733   \n",
              "\n",
              "        V26       V27       V28  Amount  Class  \n",
              "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
              "1  0.125895 -0.008983  0.014724    2.69      0  \n",
              "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
              "3 -0.221929  0.062723  0.061458  123.50      0  \n",
              "4  0.502292  0.219422  0.215153   69.99      0  \n",
              "5  0.105915  0.253844  0.081080    3.67      0  \n",
              "6 -0.257237  0.034507  0.005168    4.99      0  \n",
              "7 -0.051634 -1.206921 -1.085339   40.80      0  \n",
              "8 -0.384157  0.011747  0.142404   93.20      0  \n",
              "9  0.094199  0.246219  0.083076    3.68      0  \n",
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              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
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              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.359807</td>\n",
              "      <td>-0.072781</td>\n",
              "      <td>2.536347</td>\n",
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              "      <td>2.69</td>\n",
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              "      <th>2</th>\n",
              "      <td>1.0</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",
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              "      <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",
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              "      <td>1.792993</td>\n",
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              "      <td>0.377436</td>\n",
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              "      <td>0.647376</td>\n",
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              "      <td>0.062723</td>\n",
              "      <td>0.061458</td>\n",
              "      <td>123.50</td>\n",
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              "      <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",
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              "      <td>0.817739</td>\n",
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              "      <td>0.502292</td>\n",
              "      <td>0.219422</td>\n",
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              "      <td>69.99</td>\n",
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              "      <td>-0.568671</td>\n",
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              "      <td>0.105915</td>\n",
              "      <td>0.253844</td>\n",
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              "      <td>0.272708</td>\n",
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              "      <td>0.081213</td>\n",
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              "      <td>-0.270710</td>\n",
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              "      <td>0.750137</td>\n",
              "      <td>-0.257237</td>\n",
              "      <td>0.034507</td>\n",
              "      <td>0.005168</td>\n",
              "      <td>4.99</td>\n",
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              "      <td>-0.113192</td>\n",
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              "      <td>2.669599</td>\n",
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              "      <td>0.851084</td>\n",
              "      <td>-0.392048</td>\n",
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              "      <td>-0.073425</td>\n",
              "      <td>-0.268092</td>\n",
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              "      <td>1.011592</td>\n",
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              "\n",
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              "\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        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-230e5ffe-37b8-4b1b-b3c2-ab2f1cefbae8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calculate statistical values and round them up to 3 decimal places."
      ],
      "metadata": {
        "id": "-m2qOhpzHhjS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe().round(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "ukiYIFTiHR3K",
        "outputId": "7ddbce57-1b35-443a-f373-a64c8a76eed2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Time          V1          V2          V3          V4          V5  \\\n",
              "count  284807.000  284807.000  284807.000  284807.000  284807.000  284807.000   \n",
              "mean    94813.860       0.000       0.000      -0.000       0.000       0.000   \n",
              "std     47488.146       1.959       1.651       1.516       1.416       1.380   \n",
              "min         0.000     -56.408     -72.716     -48.326      -5.683    -113.743   \n",
              "25%     54201.500      -0.920      -0.599      -0.890      -0.849      -0.692   \n",
              "50%     84692.000       0.018       0.065       0.180      -0.020      -0.054   \n",
              "75%    139320.500       1.316       0.804       1.027       0.743       0.612   \n",
              "max    172792.000       2.455      22.058       9.383      16.875      34.802   \n",
              "\n",
              "               V6          V7          V8          V9  ...         V21  \\\n",
              "count  284807.000  284807.000  284807.000  284807.000  ...  284807.000   \n",
              "mean        0.000      -0.000       0.000      -0.000  ...       0.000   \n",
              "std         1.332       1.237       1.194       1.099  ...       0.735   \n",
              "min       -26.161     -43.557     -73.217     -13.434  ...     -34.830   \n",
              "25%        -0.768      -0.554      -0.209      -0.643  ...      -0.228   \n",
              "50%        -0.274       0.040       0.022      -0.051  ...      -0.029   \n",
              "75%         0.399       0.570       0.327       0.597  ...       0.186   \n",
              "max        73.302     120.589      20.007      15.595  ...      27.203   \n",
              "\n",
              "              V22         V23         V24         V25         V26         V27  \\\n",
              "count  284807.000  284807.000  284807.000  284807.000  284807.000  284807.000   \n",
              "mean       -0.000       0.000       0.000       0.000       0.000      -0.000   \n",
              "std         0.726       0.624       0.606       0.521       0.482       0.404   \n",
              "min       -10.933     -44.808      -2.837     -10.295      -2.605     -22.566   \n",
              "25%        -0.542      -0.162      -0.355      -0.317      -0.327      -0.071   \n",
              "50%         0.007      -0.011       0.041       0.017      -0.052       0.001   \n",
              "75%         0.529       0.148       0.440       0.351       0.241       0.091   \n",
              "max        10.503      22.528       4.585       7.520       3.517      31.612   \n",
              "\n",
              "              V28      Amount       Class  \n",
              "count  284807.000  284807.000  284807.000  \n",
              "mean       -0.000      88.350       0.002  \n",
              "std         0.330     250.120       0.042  \n",
              "min       -15.430       0.000       0.000  \n",
              "25%        -0.053       5.600       0.000  \n",
              "50%         0.011      22.000       0.000  \n",
              "75%         0.078      77.165       0.000  \n",
              "max        33.848   25691.160       1.000  \n",
              "\n",
              "[8 rows x 31 columns]"
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              "      <th></th>\n",
              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
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              "      <th>V25</th>\n",
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              "      <th>V28</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Class</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>284807.000</td>\n",
              "      <td>284807.000</td>\n",
              "      <td>284807.000</td>\n",
              "      <td>284807.000</td>\n",
              "      <td>284807.000</td>\n",
              "      <td>284807.000</td>\n",
              "      <td>284807.000</td>\n",
              "      <td>284807.000</td>\n",
              "      <td>284807.000</td>\n",
              "      <td>284807.000</td>\n",
              "      <td>...</td>\n",
              "      <td>284807.000</td>\n",
              "      <td>284807.000</td>\n",
              "      <td>284807.000</td>\n",
              "      <td>284807.000</td>\n",
              "      <td>284807.000</td>\n",
              "      <td>284807.000</td>\n",
              "      <td>284807.000</td>\n",
              "      <td>284807.000</td>\n",
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              "      <th>mean</th>\n",
              "      <td>94813.860</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-0.000</td>\n",
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              "      <td>-0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-0.000</td>\n",
              "      <td>-0.000</td>\n",
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              "      <th>std</th>\n",
              "      <td>47488.146</td>\n",
              "      <td>1.959</td>\n",
              "      <td>1.651</td>\n",
              "      <td>1.516</td>\n",
              "      <td>1.416</td>\n",
              "      <td>1.380</td>\n",
              "      <td>1.332</td>\n",
              "      <td>1.237</td>\n",
              "      <td>1.194</td>\n",
              "      <td>1.099</td>\n",
              "      <td>...</td>\n",
              "      <td>0.735</td>\n",
              "      <td>0.726</td>\n",
              "      <td>0.624</td>\n",
              "      <td>0.606</td>\n",
              "      <td>0.521</td>\n",
              "      <td>0.482</td>\n",
              "      <td>0.404</td>\n",
              "      <td>0.330</td>\n",
              "      <td>250.120</td>\n",
              "      <td>0.042</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000</td>\n",
              "      <td>-56.408</td>\n",
              "      <td>-72.716</td>\n",
              "      <td>-48.326</td>\n",
              "      <td>-5.683</td>\n",
              "      <td>-113.743</td>\n",
              "      <td>-26.161</td>\n",
              "      <td>-43.557</td>\n",
              "      <td>-73.217</td>\n",
              "      <td>-13.434</td>\n",
              "      <td>...</td>\n",
              "      <td>-34.830</td>\n",
              "      <td>-10.933</td>\n",
              "      <td>-44.808</td>\n",
              "      <td>-2.837</td>\n",
              "      <td>-10.295</td>\n",
              "      <td>-2.605</td>\n",
              "      <td>-22.566</td>\n",
              "      <td>-15.430</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>54201.500</td>\n",
              "      <td>-0.920</td>\n",
              "      <td>-0.599</td>\n",
              "      <td>-0.890</td>\n",
              "      <td>-0.849</td>\n",
              "      <td>-0.692</td>\n",
              "      <td>-0.768</td>\n",
              "      <td>-0.554</td>\n",
              "      <td>-0.209</td>\n",
              "      <td>-0.643</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.228</td>\n",
              "      <td>-0.542</td>\n",
              "      <td>-0.162</td>\n",
              "      <td>-0.355</td>\n",
              "      <td>-0.317</td>\n",
              "      <td>-0.327</td>\n",
              "      <td>-0.071</td>\n",
              "      <td>-0.053</td>\n",
              "      <td>5.600</td>\n",
              "      <td>0.000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>84692.000</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.065</td>\n",
              "      <td>0.180</td>\n",
              "      <td>-0.020</td>\n",
              "      <td>-0.054</td>\n",
              "      <td>-0.274</td>\n",
              "      <td>0.040</td>\n",
              "      <td>0.022</td>\n",
              "      <td>-0.051</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.029</td>\n",
              "      <td>0.007</td>\n",
              "      <td>-0.011</td>\n",
              "      <td>0.041</td>\n",
              "      <td>0.017</td>\n",
              "      <td>-0.052</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.011</td>\n",
              "      <td>22.000</td>\n",
              "      <td>0.000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>139320.500</td>\n",
              "      <td>1.316</td>\n",
              "      <td>0.804</td>\n",
              "      <td>1.027</td>\n",
              "      <td>0.743</td>\n",
              "      <td>0.612</td>\n",
              "      <td>0.399</td>\n",
              "      <td>0.570</td>\n",
              "      <td>0.327</td>\n",
              "      <td>0.597</td>\n",
              "      <td>...</td>\n",
              "      <td>0.186</td>\n",
              "      <td>0.529</td>\n",
              "      <td>0.148</td>\n",
              "      <td>0.440</td>\n",
              "      <td>0.351</td>\n",
              "      <td>0.241</td>\n",
              "      <td>0.091</td>\n",
              "      <td>0.078</td>\n",
              "      <td>77.165</td>\n",
              "      <td>0.000</td>\n",
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              "      <td>2.455</td>\n",
              "      <td>22.058</td>\n",
              "      <td>9.383</td>\n",
              "      <td>16.875</td>\n",
              "      <td>34.802</td>\n",
              "      <td>73.302</td>\n",
              "      <td>120.589</td>\n",
              "      <td>20.007</td>\n",
              "      <td>15.595</td>\n",
              "      <td>...</td>\n",
              "      <td>27.203</td>\n",
              "      <td>10.503</td>\n",
              "      <td>22.528</td>\n",
              "      <td>4.585</td>\n",
              "      <td>7.520</td>\n",
              "      <td>3.517</td>\n",
              "      <td>31.612</td>\n",
              "      <td>33.848</td>\n",
              "      <td>25691.160</td>\n",
              "      <td>1.000</td>\n",
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              "<p>8 rows × 31 columns</p>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Checking for null values and return their sum of numbers of true values\n",
        "in each column."
      ],
      "metadata": {
        "id": "rK_6k19tHtIe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ELby0IY_HpP1",
        "outputId": "e92d98b7-4fcf-48c1-ee82-acfaf6aa9faa"
      },
      "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        0\n",
              "V6        0\n",
              "V7        0\n",
              "V8        0\n",
              "V9        0\n",
              "V10       0\n",
              "V11       0\n",
              "V12       0\n",
              "V13       0\n",
              "V14       0\n",
              "V15       0\n",
              "V16       0\n",
              "V17       0\n",
              "V18       0\n",
              "V19       0\n",
              "V20       0\n",
              "V21       0\n",
              "V22       0\n",
              "V23       0\n",
              "V24       0\n",
              "V25       0\n",
              "V26       0\n",
              "V27       0\n",
              "V28       0\n",
              "Amount    0\n",
              "Class     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Extracting all information about data."
      ],
      "metadata": {
        "id": "yAA-CKw4r71j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ljNFxxNVr8W2",
        "outputId": "31482d86-d4ea-4852-81d7-d6eac9ed4033"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 284807 entries, 0 to 284806\n",
            "Data columns (total 31 columns):\n",
            " #   Column  Non-Null Count   Dtype  \n",
            "---  ------  --------------   -----  \n",
            " 0   Time    284807 non-null  float64\n",
            " 1   V1      284807 non-null  float64\n",
            " 2   V2      284807 non-null  float64\n",
            " 3   V3      284807 non-null  float64\n",
            " 4   V4      284807 non-null  float64\n",
            " 5   V5      284807 non-null  float64\n",
            " 6   V6      284807 non-null  float64\n",
            " 7   V7      284807 non-null  float64\n",
            " 8   V8      284807 non-null  float64\n",
            " 9   V9      284807 non-null  float64\n",
            " 10  V10     284807 non-null  float64\n",
            " 11  V11     284807 non-null  float64\n",
            " 12  V12     284807 non-null  float64\n",
            " 13  V13     284807 non-null  float64\n",
            " 14  V14     284807 non-null  float64\n",
            " 15  V15     284807 non-null  float64\n",
            " 16  V16     284807 non-null  float64\n",
            " 17  V17     284807 non-null  float64\n",
            " 18  V18     284807 non-null  float64\n",
            " 19  V19     284807 non-null  float64\n",
            " 20  V20     284807 non-null  float64\n",
            " 21  V21     284807 non-null  float64\n",
            " 22  V22     284807 non-null  float64\n",
            " 23  V23     284807 non-null  float64\n",
            " 24  V24     284807 non-null  float64\n",
            " 25  V25     284807 non-null  float64\n",
            " 26  V26     284807 non-null  float64\n",
            " 27  V27     284807 non-null  float64\n",
            " 28  V28     284807 non-null  float64\n",
            " 29  Amount  284807 non-null  float64\n",
            " 30  Class   284807 non-null  int64  \n",
            "dtypes: float64(30), int64(1)\n",
            "memory usage: 67.4 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Checking the shape of data."
      ],
      "metadata": {
        "id": "ezUFiRgAcnkF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_DCIGSiYcnLF",
        "outputId": "702283e4-b471-4b78-edeb-7771b96820f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(284807, 31)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Splitting the data into Independent and dependent set."
      ],
      "metadata": {
        "id": "Vj-YDyVXM_XT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x=data.drop(['Class'],axis=1)\n",
        "x.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "mwLTud9FMyGz",
        "outputId": "053601c1-947b-417e-e5a5-a0110ed15474"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
              "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
              "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
              "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
              "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
              "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
              "\n",
              "         V8        V9  ...       V20       V21       V22       V23       V24  \\\n",
              "0  0.098698  0.363787  ...  0.251412 -0.018307  0.277838 -0.110474  0.066928   \n",
              "1  0.085102 -0.255425  ... -0.069083 -0.225775 -0.638672  0.101288 -0.339846   \n",
              "2  0.247676 -1.514654  ...  0.524980  0.247998  0.771679  0.909412 -0.689281   \n",
              "3  0.377436 -1.387024  ... -0.208038 -0.108300  0.005274 -0.190321 -1.175575   \n",
              "4 -0.270533  0.817739  ...  0.408542 -0.009431  0.798278 -0.137458  0.141267   \n",
              "\n",
              "        V25       V26       V27       V28  Amount  \n",
              "0  0.128539 -0.189115  0.133558 -0.021053  149.62  \n",
              "1  0.167170  0.125895 -0.008983  0.014724    2.69  \n",
              "2 -0.327642 -0.139097 -0.055353 -0.059752  378.66  \n",
              "3  0.647376 -0.221929  0.062723  0.061458  123.50  \n",
              "4 -0.206010  0.502292  0.219422  0.215153   69.99  \n",
              "\n",
              "[5 rows x 30 columns]"
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              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.359807</td>\n",
              "      <td>-0.072781</td>\n",
              "      <td>2.536347</td>\n",
              "      <td>1.378155</td>\n",
              "      <td>-0.338321</td>\n",
              "      <td>0.462388</td>\n",
              "      <td>0.239599</td>\n",
              "      <td>0.098698</td>\n",
              "      <td>0.363787</td>\n",
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              "      <td>0.251412</td>\n",
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              "      <td>0.277838</td>\n",
              "      <td>-0.110474</td>\n",
              "      <td>0.066928</td>\n",
              "      <td>0.128539</td>\n",
              "      <td>-0.189115</td>\n",
              "      <td>0.133558</td>\n",
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              "      <td>149.62</td>\n",
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              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.191857</td>\n",
              "      <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>-0.069083</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>1.0</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>0.524980</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</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>-0.208038</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.0</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>0.408542</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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 30 columns</p>\n",
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              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2fa77ef7-3131-4420-b1d2-bc2a710fd904')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\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-2fa77ef7-3131-4420-b1d2-bc2a710fd904 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-2fa77ef7-3131-4420-b1d2-bc2a710fd904');\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-695b07b1-3f89-450a-ab2c-ea8f11edabb9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-695b07b1-3f89-450a-ab2c-ea8f11edabb9')\"\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",
              "     width=\"24px\">\n",
              "    <g>\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-695b07b1-3f89-450a-ab2c-ea8f11edabb9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y=data['Class']\n",
        "y.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w0ZtSjVmNZe7",
        "outputId": "040acb87-3177-4dfb-b8f5-77ec788062d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0\n",
              "1    0\n",
              "2    0\n",
              "3    0\n",
              "4    0\n",
              "Name: Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Devide the cleaned data into training and testing testing sets."
      ],
      "metadata": {
        "id": "wg3sGONJNxP7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test=train_test_split(x,y,train_size=0.8)"
      ],
      "metadata": {
        "id": "Qu6I5hnjNoEc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Import first Machine Learning Model 'K-Nearest Neighbor'."
      ],
      "metadata": {
        "id": "6pQlENP2OXV3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "neighbor=KNeighborsClassifier(n_neighbors=5)"
      ],
      "metadata": {
        "id": "raYJ4p1TOTP8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train the model"
      ],
      "metadata": {
        "id": "cZWUHEM2GsQk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "neighbor.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "s7z3DqwhGn2v",
        "outputId": "1ea5ffb5-b893-4b75-d75f-9bcca2fd3868"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Make predictions on model."
      ],
      "metadata": {
        "id": "s0EjYxsFc6vL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions=neighbor.predict(x_test)\n",
        "print(predictions)"
      ],
      "metadata": {
        "id": "UDQEJeYOG3bg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a467bf8d-0d1f-4182-d191-3befe2a55795"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 0 0 ... 0 0 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Check confusion metrics and check accuracy score."
      ],
      "metadata": {
        "id": "2HYAYwmeeEvC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "cm=confusion_matrix(y_test, predictions)\n",
        "ac=accuracy_score(y_test, predictions)\n",
        "print(cm)"
      ],
      "metadata": {
        "id": "F95VG_wLdDNx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "32970422-73c0-4818-eb2b-f910a9fe559e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[56869     1]\n",
            " [   85     7]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(ac)"
      ],
      "metadata": {
        "id": "IuowmBSwe6aA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2f8017a3-e5a2-4875-b0ea-82869f69ad27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9984902215512096\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creating second machine learning model 'Logistic Regression'."
      ],
      "metadata": {
        "id": "P56JKJ_wfBZc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "log=LogisticRegression()"
      ],
      "metadata": {
        "id": "r6gpI804e_Uf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train the model."
      ],
      "metadata": {
        "id": "SbseJsxQfffb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "log.fit(x_train, y_train)"
      ],
      "metadata": {
        "id": "bBkukQEKfdm-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 213
        },
        "outputId": "c902a68a-7d71-43c0-fca5-e948674a0af3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Make Predictions on model."
      ],
      "metadata": {
        "id": "BUrAy-q_fppA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "log_predictions=log.predict(x_test)\n",
        "print(log_predictions)"
      ],
      "metadata": {
        "id": "a68c_ryDfmKp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e91956ae-63f0-4051-8dca-423707b9bc63"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 0 0 ... 0 0 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Check confusion metrics and accuracy score."
      ],
      "metadata": {
        "id": "PH4wDVl-gQcA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "cm=confusion_matrix(y_test, log_predictions)\n",
        "ac=accuracy_score(y_test, log_predictions)\n",
        "print(cm)"
      ],
      "metadata": {
        "id": "4azh6We9f6Ke",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6c0988e4-ad14-4715-8b9c-65e61af01d9b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[56851    19]\n",
            " [   37    55]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(ac)"
      ],
      "metadata": {
        "id": "DFomFv8IgLSj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "645b3e0b-1c31-4eae-e4c4-bfe04c560653"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9990168884519505\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creating third machine machine learning model 'Decision Tree'."
      ],
      "metadata": {
        "id": "NfWweglTgZ6q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "tree=DecisionTreeClassifier()"
      ],
      "metadata": {
        "id": "1312cxy0hb5o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train the model."
      ],
      "metadata": {
        "id": "zzV70IPaj5OJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tree.fit(x_train, y_train)"
      ],
      "metadata": {
        "id": "RRrH1D0oi0TR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "6d56971a-b2d1-429d-c78c-12f5a0ce5709"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Make predictions on model."
      ],
      "metadata": {
        "id": "Tu3R5Y-3j8Ld"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tree_predictions=tree.predict(x_test)\n",
        "print(tree_predictions)"
      ],
      "metadata": {
        "id": "46EvKmZci5Id",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9752944c-0aa0-420b-c760-d3a640ac31f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 0 0 ... 0 0 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Check confusion metrics and accuracy score."
      ],
      "metadata": {
        "id": "RJwN7VHdkAYZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "cm=confusion_matrix(y_test, tree_predictions)\n",
        "ac=accuracy_score(y_test, tree_predictions)\n",
        "print(cm)"
      ],
      "metadata": {
        "id": "G7Peju7pjKOi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "532fe4a7-fecb-432f-b2c0-59d3f385724f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[56845    25]\n",
            " [   20    72]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(ac)"
      ],
      "metadata": {
        "id": "zhhOEB7Pjgbk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0d641953-def0-4194-b4f9-9390f517d989"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9992099996488887\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "snr.histplot(data=data, x='Time', y='Amount', bins=50)\n",
        "plt.title(\"Histogram of Time vs Amount\")\n",
        "plt.xlabel(\"Time(Sec)\")\n",
        "plt.ylabel(\"Amount($)\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "iArYh-R8Jql4",
        "outputId": "8d36fe26-b742-49bd-c101-0298e18ae076"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(8,6))\n",
        "snr.countplot(x='Class',data=data)\n",
        "plt.xlabel('Fraud Class')\n",
        "plt.ylabel(\"Count\")\n",
        "plt.title(\"Fraud Classification Count\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "njleLR8IqUk_",
        "outputId": "6a79f395-e192-4028-af65-b1f90c4717e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.hist(figsize=(25,20))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 935
        },
        "id": "NrKRk9Q_ttLi",
        "outputId": "c4fe0fe1-7824-47f1-b10c-00736f944d87"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x2000 with 36 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Uiim3uROjDXh"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}