BreastCancerDiagnosis.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/abhishekalandikar/BreastCancerDiagnosis/blob/main/BreastCancerDiagnosis.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": [
        "Importing the required libraries"
      ],
      "metadata": {
        "id": "xmojXOcwMDZ9"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B6ncteNwv-ih"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Loading the dataset"
      ],
      "metadata": {
        "id": "VoD_2AEoMHfY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('data.csv')\n",
        "print(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MAEgTIq7wSvZ",
        "outputId": "8d8679ef-f868-46fa-e4a5-216aad8b8463"
      },
      "execution_count": null,
      "outputs": [
        {
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          "text": [
            "           id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
            "0      842302         M        17.99         10.38          122.80     1001.0   \n",
            "1      842517         M        20.57         17.77          132.90     1326.0   \n",
            "2    84300903         M        19.69         21.25          130.00     1203.0   \n",
            "3    84348301         M        11.42         20.38           77.58      386.1   \n",
            "4    84358402         M        20.29         14.34          135.10     1297.0   \n",
            "..        ...       ...          ...           ...             ...        ...   \n",
            "564    926424         M        21.56         22.39          142.00     1479.0   \n",
            "565    926682         M        20.13         28.25          131.20     1261.0   \n",
            "566    926954         M        16.60         28.08          108.30      858.1   \n",
            "567    927241         M        20.60         29.33          140.10     1265.0   \n",
            "568     92751         B         7.76         24.54           47.92      181.0   \n",
            "\n",
            "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
            "0            0.11840           0.27760         0.30010              0.14710   \n",
            "1            0.08474           0.07864         0.08690              0.07017   \n",
            "2            0.10960           0.15990         0.19740              0.12790   \n",
            "3            0.14250           0.28390         0.24140              0.10520   \n",
            "4            0.10030           0.13280         0.19800              0.10430   \n",
            "..               ...               ...             ...                  ...   \n",
            "564          0.11100           0.11590         0.24390              0.13890   \n",
            "565          0.09780           0.10340         0.14400              0.09791   \n",
            "566          0.08455           0.10230         0.09251              0.05302   \n",
            "567          0.11780           0.27700         0.35140              0.15200   \n",
            "568          0.05263           0.04362         0.00000              0.00000   \n",
            "\n",
            "     ...  radius_worst  texture_worst  perimeter_worst  area_worst  \\\n",
            "0    ...        25.380          17.33           184.60      2019.0   \n",
            "1    ...        24.990          23.41           158.80      1956.0   \n",
            "2    ...        23.570          25.53           152.50      1709.0   \n",
            "3    ...        14.910          26.50            98.87       567.7   \n",
            "4    ...        22.540          16.67           152.20      1575.0   \n",
            "..   ...           ...            ...              ...         ...   \n",
            "564  ...        25.450          26.40           166.10      2027.0   \n",
            "565  ...        23.690          38.25           155.00      1731.0   \n",
            "566  ...        18.980          34.12           126.70      1124.0   \n",
            "567  ...        25.740          39.42           184.60      1821.0   \n",
            "568  ...         9.456          30.37            59.16       268.6   \n",
            "\n",
            "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
            "0             0.16220            0.66560           0.7119   \n",
            "1             0.12380            0.18660           0.2416   \n",
            "2             0.14440            0.42450           0.4504   \n",
            "3             0.20980            0.86630           0.6869   \n",
            "4             0.13740            0.20500           0.4000   \n",
            "..                ...                ...              ...   \n",
            "564           0.14100            0.21130           0.4107   \n",
            "565           0.11660            0.19220           0.3215   \n",
            "566           0.11390            0.30940           0.3403   \n",
            "567           0.16500            0.86810           0.9387   \n",
            "568           0.08996            0.06444           0.0000   \n",
            "\n",
            "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
            "0                  0.2654          0.4601                  0.11890  \n",
            "1                  0.1860          0.2750                  0.08902  \n",
            "2                  0.2430          0.3613                  0.08758  \n",
            "3                  0.2575          0.6638                  0.17300  \n",
            "4                  0.1625          0.2364                  0.07678  \n",
            "..                    ...             ...                      ...  \n",
            "564                0.2216          0.2060                  0.07115  \n",
            "565                0.1628          0.2572                  0.06637  \n",
            "566                0.1418          0.2218                  0.07820  \n",
            "567                0.2650          0.4087                  0.12400  \n",
            "568                0.0000          0.2871                  0.07039  \n",
            "\n",
            "[569 rows x 32 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(data.shape)     #Records and columns"
      ],
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          "text": [
            "(569, 32)\n"
          ]
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      ]
    },
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      "cell_type": "code",
      "source": [
        "data.head()"
      ],
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              "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
              "0    842302         M        17.99         10.38          122.80     1001.0   \n",
              "1    842517         M        20.57         17.77          132.90     1326.0   \n",
              "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
              "3  84348301         M        11.42         20.38           77.58      386.1   \n",
              "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
              "\n",
              "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
              "0          0.11840           0.27760          0.3001              0.14710   \n",
              "1          0.08474           0.07864          0.0869              0.07017   \n",
              "2          0.10960           0.15990          0.1974              0.12790   \n",
              "3          0.14250           0.28390          0.2414              0.10520   \n",
              "4          0.10030           0.13280          0.1980              0.10430   \n",
              "\n",
              "   ...  radius_worst  texture_worst  perimeter_worst  area_worst  \\\n",
              "0  ...         25.38          17.33           184.60      2019.0   \n",
              "1  ...         24.99          23.41           158.80      1956.0   \n",
              "2  ...         23.57          25.53           152.50      1709.0   \n",
              "3  ...         14.91          26.50            98.87       567.7   \n",
              "4  ...         22.54          16.67           152.20      1575.0   \n",
              "\n",
              "   smoothness_worst  compactness_worst  concavity_worst  concave points_worst  \\\n",
              "0            0.1622             0.6656           0.7119                0.2654   \n",
              "1            0.1238             0.1866           0.2416                0.1860   \n",
              "2            0.1444             0.4245           0.4504                0.2430   \n",
              "3            0.2098             0.8663           0.6869                0.2575   \n",
              "4            0.1374             0.2050           0.4000                0.1625   \n",
              "\n",
              "   symmetry_worst  fractal_dimension_worst  \n",
              "0          0.4601                  0.11890  \n",
              "1          0.2750                  0.08902  \n",
              "2          0.3613                  0.08758  \n",
              "3          0.6638                  0.17300  \n",
              "4          0.2364                  0.07678  \n",
              "\n",
              "[5 rows x 32 columns]"
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
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              "      <th>perimeter_worst</th>\n",
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              "      <th>concavity_worst</th>\n",
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              "      <td>842302</td>\n",
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              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
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              "      <td>0.1622</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>842517</td>\n",
              "      <td>M</td>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.0869</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>...</td>\n",
              "      <td>24.99</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
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              "      <th>2</th>\n",
              "      <td>84300903</td>\n",
              "      <td>M</td>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.1974</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>...</td>\n",
              "      <td>23.57</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>84348301</td>\n",
              "      <td>M</td>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
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              "      <td>0.2414</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>...</td>\n",
              "      <td>14.91</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.2098</td>\n",
              "      <td>0.8663</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>84358402</td>\n",
              "      <td>M</td>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.1980</td>\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "data.describe()   #Statistical view of our dataset"
      ],
      "metadata": {
        "colab": {
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          "height": 411
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        "id": "7-lKyc7gxe2G",
        "outputId": "0269d896-4682-4cdb-f7ac-7c9ed452b53c"
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          "data": {
            "text/plain": [
              "                 id  radius_mean  texture_mean  perimeter_mean    area_mean  \\\n",
              "count  5.690000e+02   569.000000    569.000000      569.000000   569.000000   \n",
              "mean   3.037183e+07    14.127292     19.289649       91.969033   654.889104   \n",
              "std    1.250206e+08     3.524049      4.301036       24.298981   351.914129   \n",
              "min    8.670000e+03     6.981000      9.710000       43.790000   143.500000   \n",
              "25%    8.692180e+05    11.700000     16.170000       75.170000   420.300000   \n",
              "50%    9.060240e+05    13.370000     18.840000       86.240000   551.100000   \n",
              "75%    8.813129e+06    15.780000     21.800000      104.100000   782.700000   \n",
              "max    9.113205e+08    28.110000     39.280000      188.500000  2501.000000   \n",
              "\n",
              "       smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
              "count       569.000000        569.000000      569.000000           569.000000   \n",
              "mean          0.096360          0.104341        0.088799             0.048919   \n",
              "std           0.014064          0.052813        0.079720             0.038803   \n",
              "min           0.052630          0.019380        0.000000             0.000000   \n",
              "25%           0.086370          0.064920        0.029560             0.020310   \n",
              "50%           0.095870          0.092630        0.061540             0.033500   \n",
              "75%           0.105300          0.130400        0.130700             0.074000   \n",
              "max           0.163400          0.345400        0.426800             0.201200   \n",
              "\n",
              "       symmetry_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
              "count     569.000000  ...    569.000000     569.000000       569.000000   \n",
              "mean        0.181162  ...     16.269190      25.677223       107.261213   \n",
              "std         0.027414  ...      4.833242       6.146258        33.602542   \n",
              "min         0.106000  ...      7.930000      12.020000        50.410000   \n",
              "25%         0.161900  ...     13.010000      21.080000        84.110000   \n",
              "50%         0.179200  ...     14.970000      25.410000        97.660000   \n",
              "75%         0.195700  ...     18.790000      29.720000       125.400000   \n",
              "max         0.304000  ...     36.040000      49.540000       251.200000   \n",
              "\n",
              "        area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
              "count   569.000000        569.000000         569.000000       569.000000   \n",
              "mean    880.583128          0.132369           0.254265         0.272188   \n",
              "std     569.356993          0.022832           0.157336         0.208624   \n",
              "min     185.200000          0.071170           0.027290         0.000000   \n",
              "25%     515.300000          0.116600           0.147200         0.114500   \n",
              "50%     686.500000          0.131300           0.211900         0.226700   \n",
              "75%    1084.000000          0.146000           0.339100         0.382900   \n",
              "max    4254.000000          0.222600           1.058000         1.252000   \n",
              "\n",
              "       concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
              "count            569.000000      569.000000               569.000000  \n",
              "mean               0.114606        0.290076                 0.083946  \n",
              "std                0.065732        0.061867                 0.018061  \n",
              "min                0.000000        0.156500                 0.055040  \n",
              "25%                0.064930        0.250400                 0.071460  \n",
              "50%                0.099930        0.282200                 0.080040  \n",
              "75%                0.161400        0.317900                 0.092080  \n",
              "max                0.291000        0.663800                 0.207500  \n",
              "\n",
              "[8 rows x 31 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
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              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
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              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
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              "      <th>count</th>\n",
              "      <td>5.690000e+02</td>\n",
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              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
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              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.037183e+07</td>\n",
              "      <td>14.127292</td>\n",
              "      <td>19.289649</td>\n",
              "      <td>91.969033</td>\n",
              "      <td>654.889104</td>\n",
              "      <td>0.096360</td>\n",
              "      <td>0.104341</td>\n",
              "      <td>0.088799</td>\n",
              "      <td>0.048919</td>\n",
              "      <td>0.181162</td>\n",
              "      <td>...</td>\n",
              "      <td>16.269190</td>\n",
              "      <td>25.677223</td>\n",
              "      <td>107.261213</td>\n",
              "      <td>880.583128</td>\n",
              "      <td>0.132369</td>\n",
              "      <td>0.254265</td>\n",
              "      <td>0.272188</td>\n",
              "      <td>0.114606</td>\n",
              "      <td>0.290076</td>\n",
              "      <td>0.083946</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.250206e+08</td>\n",
              "      <td>3.524049</td>\n",
              "      <td>4.301036</td>\n",
              "      <td>24.298981</td>\n",
              "      <td>351.914129</td>\n",
              "      <td>0.014064</td>\n",
              "      <td>0.052813</td>\n",
              "      <td>0.079720</td>\n",
              "      <td>0.038803</td>\n",
              "      <td>0.027414</td>\n",
              "      <td>...</td>\n",
              "      <td>4.833242</td>\n",
              "      <td>6.146258</td>\n",
              "      <td>33.602542</td>\n",
              "      <td>569.356993</td>\n",
              "      <td>0.022832</td>\n",
              "      <td>0.157336</td>\n",
              "      <td>0.208624</td>\n",
              "      <td>0.065732</td>\n",
              "      <td>0.061867</td>\n",
              "      <td>0.018061</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>8.670000e+03</td>\n",
              "      <td>6.981000</td>\n",
              "      <td>9.710000</td>\n",
              "      <td>43.790000</td>\n",
              "      <td>143.500000</td>\n",
              "      <td>0.052630</td>\n",
              "      <td>0.019380</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.106000</td>\n",
              "      <td>...</td>\n",
              "      <td>7.930000</td>\n",
              "      <td>12.020000</td>\n",
              "      <td>50.410000</td>\n",
              "      <td>185.200000</td>\n",
              "      <td>0.071170</td>\n",
              "      <td>0.027290</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.156500</td>\n",
              "      <td>0.055040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>8.692180e+05</td>\n",
              "      <td>11.700000</td>\n",
              "      <td>16.170000</td>\n",
              "      <td>75.170000</td>\n",
              "      <td>420.300000</td>\n",
              "      <td>0.086370</td>\n",
              "      <td>0.064920</td>\n",
              "      <td>0.029560</td>\n",
              "      <td>0.020310</td>\n",
              "      <td>0.161900</td>\n",
              "      <td>...</td>\n",
              "      <td>13.010000</td>\n",
              "      <td>21.080000</td>\n",
              "      <td>84.110000</td>\n",
              "      <td>515.300000</td>\n",
              "      <td>0.116600</td>\n",
              "      <td>0.147200</td>\n",
              "      <td>0.114500</td>\n",
              "      <td>0.064930</td>\n",
              "      <td>0.250400</td>\n",
              "      <td>0.071460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>9.060240e+05</td>\n",
              "      <td>13.370000</td>\n",
              "      <td>18.840000</td>\n",
              "      <td>86.240000</td>\n",
              "      <td>551.100000</td>\n",
              "      <td>0.095870</td>\n",
              "      <td>0.092630</td>\n",
              "      <td>0.061540</td>\n",
              "      <td>0.033500</td>\n",
              "      <td>0.179200</td>\n",
              "      <td>...</td>\n",
              "      <td>14.970000</td>\n",
              "      <td>25.410000</td>\n",
              "      <td>97.660000</td>\n",
              "      <td>686.500000</td>\n",
              "      <td>0.131300</td>\n",
              "      <td>0.211900</td>\n",
              "      <td>0.226700</td>\n",
              "      <td>0.099930</td>\n",
              "      <td>0.282200</td>\n",
              "      <td>0.080040</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>8.813129e+06</td>\n",
              "      <td>15.780000</td>\n",
              "      <td>21.800000</td>\n",
              "      <td>104.100000</td>\n",
              "      <td>782.700000</td>\n",
              "      <td>0.105300</td>\n",
              "      <td>0.130400</td>\n",
              "      <td>0.130700</td>\n",
              "      <td>0.074000</td>\n",
              "      <td>0.195700</td>\n",
              "      <td>...</td>\n",
              "      <td>18.790000</td>\n",
              "      <td>29.720000</td>\n",
              "      <td>125.400000</td>\n",
              "      <td>1084.000000</td>\n",
              "      <td>0.146000</td>\n",
              "      <td>0.339100</td>\n",
              "      <td>0.382900</td>\n",
              "      <td>0.161400</td>\n",
              "      <td>0.317900</td>\n",
              "      <td>0.092080</td>\n",
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              "      <th>max</th>\n",
              "      <td>9.113205e+08</td>\n",
              "      <td>28.110000</td>\n",
              "      <td>39.280000</td>\n",
              "      <td>188.500000</td>\n",
              "      <td>2501.000000</td>\n",
              "      <td>0.163400</td>\n",
              "      <td>0.345400</td>\n",
              "      <td>0.426800</td>\n",
              "      <td>0.201200</td>\n",
              "      <td>0.304000</td>\n",
              "      <td>...</td>\n",
              "      <td>36.040000</td>\n",
              "      <td>49.540000</td>\n",
              "      <td>251.200000</td>\n",
              "      <td>4254.000000</td>\n",
              "      <td>0.222600</td>\n",
              "      <td>1.058000</td>\n",
              "      <td>1.252000</td>\n",
              "      <td>0.291000</td>\n",
              "      <td>0.663800</td>\n",
              "      <td>0.207500</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 31 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()           # Getting the information of our dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oBESpOWQyTLQ",
        "outputId": "992e2092-ab8b-40e6-f0a2-2216c14b7594"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 569 entries, 0 to 568\n",
            "Data columns (total 32 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   id                       569 non-null    int64  \n",
            " 1   diagnosis                569 non-null    object \n",
            " 2   radius_mean              569 non-null    float64\n",
            " 3   texture_mean             569 non-null    float64\n",
            " 4   perimeter_mean           569 non-null    float64\n",
            " 5   area_mean                569 non-null    float64\n",
            " 6   smoothness_mean          569 non-null    float64\n",
            " 7   compactness_mean         569 non-null    float64\n",
            " 8   concavity_mean           569 non-null    float64\n",
            " 9   concave points_mean      569 non-null    float64\n",
            " 10  symmetry_mean            569 non-null    float64\n",
            " 11  fractal_dimension_mean   569 non-null    float64\n",
            " 12  radius_se                569 non-null    float64\n",
            " 13  texture_se               569 non-null    float64\n",
            " 14  perimeter_se             569 non-null    float64\n",
            " 15  area_se                  569 non-null    float64\n",
            " 16  smoothness_se            569 non-null    float64\n",
            " 17  compactness_se           569 non-null    float64\n",
            " 18  concavity_se             569 non-null    float64\n",
            " 19  concave points_se        569 non-null    float64\n",
            " 20  symmetry_se              569 non-null    float64\n",
            " 21  fractal_dimension_se     569 non-null    float64\n",
            " 22  radius_worst             569 non-null    float64\n",
            " 23  texture_worst            569 non-null    float64\n",
            " 24  perimeter_worst          569 non-null    float64\n",
            " 25  area_worst               569 non-null    float64\n",
            " 26  smoothness_worst         569 non-null    float64\n",
            " 27  compactness_worst        569 non-null    float64\n",
            " 28  concavity_worst          569 non-null    float64\n",
            " 29  concave points_worst     569 non-null    float64\n",
            " 30  symmetry_worst           569 non-null    float64\n",
            " 31  fractal_dimension_worst  569 non-null    float64\n",
            "dtypes: float64(30), int64(1), object(1)\n",
            "memory usage: 142.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()                    #Checking the null values\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QD6EFoWjyXpJ",
        "outputId": "d21331cc-21ec-4194-baf5-19923418d26e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "diagnosis                  0\n",
              "radius_mean                0\n",
              "texture_mean               0\n",
              "perimeter_mean             0\n",
              "area_mean                  0\n",
              "smoothness_mean            0\n",
              "compactness_mean           0\n",
              "concavity_mean             0\n",
              "concave points_mean        0\n",
              "symmetry_mean              0\n",
              "fractal_dimension_mean     0\n",
              "radius_se                  0\n",
              "texture_se                 0\n",
              "perimeter_se               0\n",
              "area_se                    0\n",
              "smoothness_se              0\n",
              "compactness_se             0\n",
              "concavity_se               0\n",
              "concave points_se          0\n",
              "symmetry_se                0\n",
              "fractal_dimension_se       0\n",
              "radius_worst               0\n",
              "texture_worst              0\n",
              "perimeter_worst            0\n",
              "area_worst                 0\n",
              "smoothness_worst           0\n",
              "compactness_worst          0\n",
              "concavity_worst            0\n",
              "concave points_worst       0\n",
              "symmetry_worst             0\n",
              "fractal_dimension_worst    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(data.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3G0I4K4-zK_I",
        "outputId": "210158e4-8b4d-4fc8-9790-86f76e7dec93"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(569, 31)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['diagnosis'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ES-uAauTz39H",
        "outputId": "076f0587-22d5-4a6f-b80e-9d108a556bdd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "B    357\n",
              "M    212\n",
              "Name: diagnosis, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "EDA part"
      ],
      "metadata": {
        "id": "j9gLidlGM9fx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Here malign have greater radius than benign\n",
        "fig = plt.figure(figsize = (5,5))\n",
        "sns.barplot(x = 'diagnosis', y = 'radius_mean', data = data)\n",
        "\n",
        "plt.title('Diagnosis vs radius_mean')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "Rv33zmfR19yj",
        "outputId": "adf203c4-b6e7-413b-98b5-902db9d7608a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Diagnosis vs radius_mean')"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#here the graph tell us that malign have greater texture than benign\n",
        "fig = plt.figure(figsize = (5,5))\n",
        "sns.barplot(x = 'diagnosis', y = 'texture_mean', data = data)\n",
        "\n",
        "plt.title('Diagnosis vs texture_mean')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "MlOXZoxe2m6f",
        "outputId": "d08314a1-0523-49a8-e045-b51ae786c046"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Diagnosis vs texture_mean')"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#malign have greater perimeter than benign\n",
        "fig = plt.figure(figsize = (5,5))\n",
        "sns.barplot(x = 'diagnosis', y = 'perimeter_mean', data = data)\n",
        "\n",
        "plt.title('Diagnosis vs perimeter_mean')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "K2CTS3-I29PS",
        "outputId": "88fffc00-b894-434f-a4b5-21cb4176c3b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Diagnosis vs perimeter_mean')"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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bt9c///lPZWVlafbs2YqIiNDJkydt4yIjI/Xss8/qjTfeUIcOHdSmTRvt3r1bq1evVrly5RxaeyhfvrwSExM1btw4tWnTRh06dNCBAwf09ttv66677lLPnj0l/fmPwODBg/XII4/otttu0+XLl/Xuu+/a/rgVpWbNmmrZsqUaNGigsLAwffvtt7ZTS65Ft27dNHr0aPn5+alfv352BzWdP39et956qx5++GHVrVtXgYGB2rBhg3bs2KE33njjmuY/cuSI7bPctm2b3nvvPT322GOqW7eupD/XziZMmKDExEQdPXpUnTp1UlBQkI4cOaKPP/5YAwYM0HPPPXdNr+VMZupq0KCBlixZouHDh+uuu+5SYGCg2rdvr8cff1xLly7VwIEDtXnzZjVp0kR5eXnav3+/li5dqrVr19pd+MNVXnnlFW3evFmNGjVS//79VbNmTaWnp2vXrl3asGGD0tPTJf15JaXly5froYceUrt27XTkyBHNnDlTNWvWVFZWlovfRSlzsw/vRfGTf0pI/sPHx8eIiooy7rvvPuPNN980MjMzCzynsFNVVqxYYdSpU8fw8/MzKlWqZLz66qvG3LlzDUnGkSNHbOMuX75sjBo1yoiKijL8/f2NVq1aGfv27TPCw8ONgQMHFqjr76fQ5J8isXnzZrv2t956y6hevbrh7e1tREZGGk899ZRx9uxZW/8vv/xi9O3b16hSpYrh5+dnhIWFGffee6+xYcMGu3n+fqrKhAkTjLvvvtsIDQ01/P39jerVqxsTJ060Oz3jSg4ePGj7bL/88ku7vuzsbGPEiBFG3bp1jaCgICMgIMCoW7eu8fbbb1913vzvwU8//WQ8/PDDRlBQkFG2bFlj8ODBxh9//FFg/LJly4ymTZsaAQEBRkBAgFG9enVj0KBBxoEDB2xjWrRoYdxxxx2Fvl5Rp6r8/dSfor5v+fWePn3adF1ZWVnGY489ZoSGhhqS7E5bycnJMV599VXjjjvuMHx9fY2yZcsaDRo0MMaNG2dkZGTYxkly6JSg/Pde2OdSsWLFQk9jKuy10tLSjEGDBhmxsbGGt7e3ERUVZbRu3dqYNWuWbYzVajVefvllo2LFioavr69Rv359Y9WqVUbv3r3t3nP+qSqvvfZaoa89ZswYh94n/odr26JYOHfunMqWLasJEyboxRdfdHU5xUL+hSFOnz6tcuXKubocoERhnyfczh9//FGgLf+OGdzyCoA7YJ8n3M6SJUs0f/58PfDAAwoMDNSXX36p999/X/fff7+aNGni6vJQiqSnp9vuFlMYT0/PIs8hRclGeMLt1KlTR15eXpo0aZIyMzNtBxFd6VQR4Ebo3LmztmzZUmR/xYoVuQ1aKcU+TwAows6dO694frG/vz9bQ0opwhMAAJM4YAgAAJPY56k/ry154sQJBQUFue0lvAAAN5ZhGDp//rxiYmLsLlZSGMJT0okTJwpcIBoAUDqlpKQUeSOIfISnZLube0pKioKDg11cDQDAFTIzMxUbG2vLhCshPPW/OzIEBwcTngBQyl3L7jsOGAIAwCTCEwAAkwhPAABMIjwBADCJ8AQAwCSXhufnn3+u9u3bKyYmRhaLRZ988omtLzc3V88//7xq166tgIAAxcTEqFevXjpx4oTdHOnp6erRo4eCg4MVGhqqfv36cUd1AMAN5dLwvHDhgurWravp06cX6Lt48aJ27dqlUaNGadeuXVq+fLkOHDigDh062I3r0aOHfvzxR61fv16rVq3S559/rgEDBtystwAAKIXc5sLwFotFH3/8sTp16lTkmB07dujuu+/WsWPHVKFCBe3bt081a9bUjh071LBhQ0nSmjVr9MADD+jXX39VTEzMNb12ZmamQkJClJGRwXmeAFBKmcmCYrXPMyMjQxaLRaGhoZKkbdu2KTQ01BackhQfHy8PDw9t3769yHmys7OVmZlp9wAA4FoVm/C8dOmSnn/+eT366KO2/whSU1MVERFhN87Ly0thYWFKTU0tcq6kpCSFhITYHlzXFgBgRrEIz9zcXHXt2lWGYWjGjBnXPV9iYqIyMjJsj5SUFCdUCQAoLdz+2rb5wXns2DFt2rTJbjt0VFSUTp06ZTf+8uXLSk9PV1RUVJFz+vr6ytfX94bVDAAo2dx6zTM/OA8ePKgNGzYoPDzcrr9x48Y6d+6cdu7caWvbtGmTrFarGjVqdLPLBQCUEi5d88zKytKhQ4dsy0eOHNH333+vsLAwRUdH6+GHH9auXbu0atUq5eXl2fZjhoWFycfHRzVq1FCbNm3Uv39/zZw5U7m5uRo8eLC6d+9+zUfawvWeffZZnT59WpJUvnx5vfnmmy6uCACuzKXh+e233+ree++1LQ8fPlyS1Lt3b40dO1YrVqyQJNWrV8/ueZs3b1bLli0lSYsWLdLgwYPVunVreXh4qEuXLpo6depNqR/Ocfr0aaWlpbm6DAC4Zi4Nz5YtW+pKp5leyymoYWFhWrx4sTPLAgDgitx6nycAAO6I8AQAwCTCEwAAkwhPAABMIjwBADCJ8AQAwCTCEwAAkwhPAABMcvsLwxdHDUYsdHUJxUrw2Szbf3Enz2bx+Zmw87Veri4BKJVY8wQAwCTCEwAAkwhPAABMIjwBADCJ8AQAwCTCEwAAkwhPAABMIjwBADCJ8AQAwCSuMASXs3oHFPo1ALgrwhMul3V7W1eXAACmsNkWAACTCE8AAEwiPAEAMInwBADAJMITAACTCE8AAEwiPAEAMInwBADAJMITAACTCE8AAEwiPAEAMInwBADAJMITAACTCE8AAEwiPAEAMInwBADAJMITAACTCE8AAEwiPAEAMInwBADAJMITAACTCE8AAEwiPAEAMInwBADAJMITAACTCE8AAEwiPAEAMInwBADAJMITAACTCE8AAEwiPAEAMMml4fn555+rffv2iomJkcVi0SeffGLXbxiGRo8erejoaPn7+ys+Pl4HDx60G5Oenq4ePXooODhYoaGh6tevn7Kysm7iuwAAlDYuDc8LFy6obt26mj59eqH9kyZN0tSpUzVz5kxt375dAQEBSkhI0KVLl2xjevTooR9//FHr16/XqlWr9Pnnn2vAgAE36y0AAEohL1e+eNu2bdW2bdtC+wzD0JQpU/TSSy+pY8eOkqSFCxcqMjJSn3zyibp37659+/ZpzZo12rFjhxo2bChJmjZtmh544AG9/vrriomJuWnvBQBQerjtPs8jR44oNTVV8fHxtraQkBA1atRI27ZtkyRt27ZNoaGhtuCUpPj4eHl4eGj79u1Fzp2dna3MzEy7BwAA18ptwzM1NVWSFBkZadceGRlp60tNTVVERIRdv5eXl8LCwmxjCpOUlKSQkBDbIzY21snVAwBKMrcNzxspMTFRGRkZtkdKSoqrSwIAFCNuG55RUVGSpLS0NLv2tLQ0W19UVJROnTpl13/58mWlp6fbxhTG19dXwcHBdg8AAK6V24ZnXFycoqKitHHjRltbZmamtm/frsaNG0uSGjdurHPnzmnnzp22MZs2bZLValWjRo1ues0AgNLBpUfbZmVl6dChQ7blI0eO6Pvvv1dYWJgqVKigoUOHasKECapWrZri4uI0atQoxcTEqFOnTpKkGjVqqE2bNurfv79mzpyp3NxcDR48WN27d+dIWwDADePS8Pz2229177332paHDx8uSerdu7fmz5+vkSNH6sKFCxowYIDOnTunpk2bas2aNfLz87M9Z9GiRRo8eLBat24tDw8PdenSRVOnTr3p7wUAUHpYDMMwXF2Eq2VmZiokJEQZGRlO2f/ZYMRCJ1QFXN3O13q5ugSgxDCTBW67zxMAAHdFeAIAYBLhCQCASYQnAAAmEZ4AAJhEeAIAYBLhCQCASYQnAAAmEZ4AAJhEeAIAYBLhCQCASYQnAAAmEZ4AAJhEeAIAYBLhCQCASYQnAAAmEZ4AAJhEeAIAYBLhCQCASYQnAAAmEZ4AAJhEeAIAYBLhCQCASYQnAAAmEZ4AAJhEeAIAYBLhCQCASYQnAAAmEZ4AAJhEeAIAYBLhCQCASYQnAAAmEZ4AAJhEeAIAYBLhCQCASYQnAAAmEZ4AAJhEeAIAYBLhCQCASYQnAAAmEZ4AAJhEeAIAYBLhCQCASYQnAAAmEZ4AAJhEeAIAYBLhCQCASYQnAAAmEZ4AAJhEeAIAYBLhCQCASW4dnnl5eRo1apTi4uLk7++vKlWq6N///rcMw7CNMQxDo0ePVnR0tPz9/RUfH6+DBw+6sGoAQEnn1uH56quvasaMGXrrrbe0b98+vfrqq5o0aZKmTZtmGzNp0iRNnTpVM2fO1Pbt2xUQEKCEhARdunTJhZUDAEoyL1cXcCVfffWVOnbsqHbt2kmSKlWqpPfff1/ffPONpD/XOqdMmaKXXnpJHTt2lCQtXLhQkZGR+uSTT9S9e3eX1Q4AKLnces3zH//4hzZu3Kiff/5ZkrR79259+eWXatu2rSTpyJEjSk1NVXx8vO05ISEhatSokbZt21bkvNnZ2crMzLR7AABwrdx6zfOFF15QZmamqlevLk9PT+Xl5WnixInq0aOHJCk1NVWSFBkZafe8yMhIW19hkpKSNG7cuBtXOACY8Oyzz+r06dOSpPLly+vNN990cUW4Grde81y6dKkWLVqkxYsXa9euXVqwYIFef/11LViw4LrmTUxMVEZGhu2RkpLipIoBwLzTp08rLS1NaWlpthCFe3PrNc8RI0bohRdesO27rF27to4dO6akpCT17t1bUVFRkqS0tDRFR0fbnpeWlqZ69eoVOa+vr698fX1vaO0AgJLLoTXPtLQ0Pf7444qJiZGXl5c8PT3tHs5y8eJFeXjYl+jp6Smr1SpJiouLU1RUlDZu3Gjrz8zM1Pbt29W4cWOn1QEAwF85tOb5xBNP6Pjx4xo1apSio6NlsVicXZckqX379po4caIqVKigO+64Q999952Sk5PVt29fSZLFYtHQoUM1YcIEVatWTXFxcRo1apRiYmLUqVOnG1ITAAAOheeXX36pL7744oqbRp1h2rRpGjVqlJ5++mmdOnVKMTEx+uc//6nRo0fbxowcOVIXLlzQgAEDdO7cOTVt2lRr1qyRn5/fDa0NAFB6ORSesbGxdlf5uVGCgoI0ZcoUTZkypcgxFotF48eP1/jx4294PQCu3fHxtV1dQrFx+Vy4JM///+sTfHYmVRi956a/pkP7PKdMmaIXXnhBR48edXI5AAC4P4fWPLt166aLFy+qSpUqKlOmjLy9ve3609PTnVIcAADuyKHwvNJmVAAASjqHwrN3797OrgMAgGLjui+ScOnSJeXk5Ni1BQcHX++0AFBqhPnmFfo13JdD4XnhwgU9//zzWrp0qc6cOVOgPy+Pbz4AXKt/1T/n6hJgkkNH244cOVKbNm3SjBkz5Ovrq3feeUfjxo1TTEyMFi5c6OwaAQBwKw6tea5cuVILFy5Uy5Yt1adPHzVr1kxVq1ZVxYoVtWjRIttdTwAAKIkcWvNMT09X5cqVJf25fzP/1JSmTZvq888/d151AAC4IYfCs3Llyjpy5IgkqXr16lq6dKmkP9dIQ0NDnVYcAADuyKHw7NOnj3bv3i3pzxtWT58+XX5+fho2bJhGjBjh1AIBAHA3Du3zHDZsmO3r+Ph47d+/Xzt37lTVqlVVp04dpxUHAIA7csp5nhUrVlTFihWdUQ8AAG7Poc22eXl5+ve//61bbrlFgYGB+uWXXyRJo0aN0pw5c5xaIAAA7sah8Jw4caLmz5+vSZMmycfHx9Zeq1YtvfPOO04rDgAAd+RQeC5cuFCzZs1Sjx495OnpaWuvW7eu9u/f77TiAABwRw6F52+//aaqVasWaLdarcrNzb3uogAAcGcOhWfNmjX1xRdfFGj/6KOPVL9+/esuCgAAd+bQ0bajR49W79699dtvv8lqtWr58uU6cOCAFi5cqFWrVjm7RgAA3IpDa54dO3bUypUrtWHDBgUEBGj06NHat2+fVq5cqfvuu8/ZNQIA4FYcPs+zWbNmWr9+vTNrAQCgWLjuiyRkZWXJarXatXEzbABASebQZtsjR46oXbt2CggIUEhIiMqWLauyZcsqNDRUZcuWdXaNAAC4FYfWPHv27CnDMDR37lxFRkbKYrE4uy4AANyWQ+G5e/du7dy5U7fffruz6wEAwO05tNn2rrvuUkpKirNrAQCgWHBozfOdd97RwIED9dtvv6lWrVry9va26+e2ZACAksyh8Dx9+rQOHz6sPn362NosFosMw5DFYlFeXp7TCgQAwN04FJ59+/ZV/fr19f7773PAEACg1HEoPI8dO6YVK1YUenF4AABKOocOGGrVqpV2797t7FoAACgWHFrzbN++vYYNG6Y9e/aodu3aBQ4Y6tChg1OKAwDAHTkUngMHDpQkjR8/vkAfBwwBAEo6h8Lz79eyBQCgNHFon+e1ql27NhdTAACUODc0PI8eParc3Nwb+RIAANx0NzQ8AQAoiQhPAABMIjwBADCJ8AQAwCTCEwAAk0yHZ25urlq3bq2DBw9edex//vMfRUZGOlQYAADuyvRFEry9vfXDDz9c09jHHnvMdEEAALg7hzbb9uzZU3PmzHF2LQAAFAsOXZ7v8uXLmjt3rjZs2KAGDRooICDArj85OdkpxQEA4I4cCs+9e/fqzjvvlCT9/PPPdn3cGBsAUNI5FJ6bN292dh0AABQb13WqyqFDh7R27Vr98ccfkiTDMJxSFAAA7syh8Dxz5oxat26t2267TQ888IBOnjwpSerXr5/+3//7f04tEAAAd+NQeA4bNkze3t46fvy4ypQpY2vv1q2b1qxZ47TiAABwRw7t81y3bp3Wrl2rW2+91a69WrVqOnbsmFMKAwDAXTm05nnhwgW7Nc586enp8vX1ve6iAABwZw6FZ7NmzbRw4ULbssVikdVq1aRJk3Tvvfc6rThJ+u2339SzZ0+Fh4fL399ftWvX1rfffmvrNwxDo0ePVnR0tPz9/RUfH39Nlw4EAMBRDm22nTRpklq3bq1vv/1WOTk5GjlypH788Uelp6dr69atTivu7NmzatKkie69916tXr1a5cuX18GDB1W2bFm7WqZOnaoFCxYoLi5Oo0aNUkJCgn766Sf5+fk5rRYAAPI5FJ61atXSzz//rLfeektBQUHKyspS586dNWjQIEVHRzutuFdffVWxsbGaN2+erS0uLs72tWEYmjJlil566SV17NhRkrRw4UJFRkbqk08+Uffu3QudNzs7W9nZ2bblzMxMp9UMACj5HNpse/z4cQUHB+vFF1/U0qVL9d///lcTJkxQdHS0jh8/7rTiVqxYoYYNG+qRRx5RRESE6tevr9mzZ9v6jxw5otTUVMXHx9vaQkJC1KhRI23btq3IeZOSkhQSEmJ7xMbGOq1mAEDJ51B4xsXF6fTp0wXaz5w5Y7dmeL1++eUXzZgxQ9WqVdPatWv11FNP6ZlnntGCBQskSampqZJU4LZnkZGRtr7CJCYmKiMjw/ZISUlxWs0AgJLPoc22hmEUeg3brKwsp+5ntFqtatiwoV5++WVJUv369bV3717NnDlTvXv3dnheX19fjgoGADjMVHgOHz5c0p9H144aNcrudJW8vDxt375d9erVc1px0dHRqlmzpl1bjRo1tGzZMklSVFSUJCktLc1uX2taWppT6wAA4K9Mhed3330n6c81zz179sjHx8fW5+Pjo7p16+q5555zWnFNmjTRgQMH7Np+/vlnVaxYUdKfm4+joqK0ceNGW1hmZmZq+/bteuqpp5xWBwAAf2UqPPPvptKnTx+9+eabCg4OviFF5Rs2bJj+8Y9/6OWXX1bXrl31zTffaNasWZo1a5akP9eAhw4dqgkTJqhatWq2U1ViYmLUqVOnG1obAKD0cmifZ/6pI4cOHdLhw4fVvHlz+fv7F7kv1FF33XWXPv74YyUmJmr8+PGKi4vTlClT1KNHD9uYkSNH6sKFCxowYIDOnTunpk2bas2aNZzjCQC4YRwKz/T0dD3yyCPavHmzLBaLDh48qMqVK6tfv34qW7as3njjDacV+OCDD+rBBx8sst9isWj8+PEaP368014TAIArcehUlaFDh3JXFQBAqcVdVQAAMIm7qgAAYJLb31UFAAB349Z3VQEAwB05tOaZf1eVpk2bqmPHjrpw4YI6d+6s7777TlWqVHF2jQAAuBWH1jylP+9e8uKLLzqzFgAAigWHw/PSpUv64YcfdOrUKVmtVru+Dh06XHdhAAC4K4fCc82aNerVq5d+//33An0Wi0V5eXnXXRgAAO7KoX2eQ4YM0SOPPKKTJ0/KarXaPQhOAEBJ51B4pqWlafjw4QVuQg0AQGngUHg+/PDD+uyzz5xcCgAAxYND+zzfeustPfLII/riiy9Uu3ZteXt72/U/88wzTikOAAB35FB4vv/++1q3bp38/Pz02Wef2d2GzGKxEJ4AgBLNofB88cUXNW7cOL3wwgvy8HBoyy8AAMWWQ8mXk5Ojbt26EZwAgFLJofTr3bu3lixZ4uxaAAAoFhzabJuXl6dJkyZp7dq1qlOnToEDhpKTk51SHAAA7sih8NyzZ4/q168vSdq7d69d318PHgIAoCRyKDw3b97s7DoAACg2OOIHAACTrnnNs3Pnzpo/f76Cg4PVuXPnK45dvnz5dRcGAIC7uubwDAkJse3PDAkJuWEFAQDg7q45POfNmydJMgxD48aNU/ny5eXv73/DCgMAwF2Z3udpGIaqVq2qX3/99UbUAwCA2zMdnh4eHqpWrZrOnDlzI+oBAMDtOXS07SuvvKIRI0YUOMcTAIDSwKHzPHv16qWLFy+qbt268vHxKbDvMz093SnFAQDgjhwKzylTpji5DAAAig+HwrN3797OrgMAgGLD4SsMHT58WC+99JIeffRRnTp1SpK0evVq/fjjj04rDgAAd+RQeG7ZskW1a9fW9u3btXz5cmVlZUmSdu/erTFjxji1QAAA3I1D4fnCCy9owoQJWr9+vXx8fGztrVq10tdff+204gAAcEcOheeePXv00EMPFWiPiIjQ77//ft1FAQDgzhwKz9DQUJ08ebJA+3fffadbbrnluosCAMCdORSe3bt31/PPP6/U1FRZLBZZrVZt3bpVzz33nHr16uXsGgEAcCsOhefLL7+s6tWrKzY2VllZWapZs6aaNWumf/zjH3rppZecXSMAAG7FofM8fXx8NHv2bI0ePVp79uzRhQsXVL9+fVWtWtXZ9QEA4HYcCk9JmjNnjiZPnqyDBw9KkqpVq6ahQ4fqySefdFpxAAC4I4fCc/To0UpOTtaQIUPUuHFjSdK2bds0bNgwHT9+XOPHj3dqkQAAuBOHwnPGjBmaPXu2Hn30UVtbhw4dVKdOHQ0ZMoTwBACUaA4dMJSbm6uGDRsWaG/QoIEuX7583UUBAODOHArPxx9/XDNmzCjQPmvWLPXo0eO6iwIAwJ1d1wFD69at0z333CNJ2r59u44fP65evXpp+PDhtnHJycnXXyUAAG7EofDcu3ev7rzzTkl/3l1FksqVK6dy5cpp7969tnEWi8UJJQIA4F4cCs/Nmzc7uw4AAIoNh+/nCQBAaUV4AgBgEuEJAIBJhCcAACYRngAAmFSswvOVV16RxWLR0KFDbW2XLl3SoEGDFB4ersDAQHXp0kVpaWmuKxIAUOIVm/DcsWOH/vOf/6hOnTp27cOGDdPKlSv14YcfasuWLTpx4oQ6d+7soioBAKVBsQjPrKws9ejRQ7Nnz1bZsmVt7RkZGZozZ46Sk5PVqlUrNWjQQPPmzdNXX32lr7/+2oUVAwBKsmIRnoMGDVK7du0UHx9v175z507l5ubatVevXl0VKlTQtm3bipwvOztbmZmZdg8AAK6Vw9e2vVk++OAD7dq1Szt27CjQl5qaKh8fH4WGhtq1R0ZGKjU1tcg5k5KSNG7cOGeXCgAoJdx6zTMlJUXPPvusFi1aJD8/P6fNm5iYqIyMDNsjJSXFaXMDAEo+tw7PnTt36tSpU7rzzjvl5eUlLy8vbdmyRVOnTpWXl5ciIyOVk5Ojc+fO2T0vLS1NUVFRRc7r6+ur4OBguwcAANfKrTfbtm7dWnv27LFr69Onj6pXr67nn39esbGx8vb21saNG9WlSxdJ0oEDB3T8+HE1btzYFSUDAEoBtw7PoKAg1apVy64tICBA4eHhtvZ+/fpp+PDhCgsLU3BwsIYMGaLGjRvb7jMKAICzuXV4XovJkyfLw8NDXbp0UXZ2thISEvT222+7uiwAQAlW7MLzs88+s1v28/PT9OnTNX36dNcUBAAoddz6gCEAANwR4QkAgEmEJwAAJhGeAACYRHgCAGAS4QkAgEmEJwAAJhGeAACYRHgCAGAS4QkAgEmEJwAAJhGeAACYRHgCAGAS4QkAgEmEJwAAJhGeAACYRHgCAGAS4QkAgEmEJwAAJhGeAACYRHgCAGAS4QkAgEmEJwAAJhGeAACYRHgCAGAS4QkAgEmEJwAAJhGeAACYRHgCAGAS4QkAgEmEJwAAJhGeAACYRHgCAGAS4QkAgEmEJwAAJhGeAACYRHgCAGAS4QkAgEmEJwAAJhGeAACYRHgCAGAS4QkAgEmEJwAAJhGeAACYRHgCAGAS4QkAgEmEJwAAJhGeAACYRHgCAGAS4QkAgEmEJwAAJrl9eCYlJemuu+5SUFCQIiIi1KlTJx04cMBuzKVLlzRo0CCFh4crMDBQXbp0UVpamosqBgCUdG4fnlu2bNGgQYP09ddfa/369crNzdX999+vCxcu2MYMGzZMK1eu1IcffqgtW7boxIkT6ty5swurBgCUZF6uLuBq1qxZY7c8f/58RUREaOfOnWrevLkyMjI0Z84cLV68WK1atZIkzZs3TzVq1NDXX3+te+65xxVlAwBKMLdf8/y7jIwMSVJYWJgkaefOncrNzVV8fLxtTPXq1VWhQgVt27at0Dmys7OVmZlp9wAA4FoVq/C0Wq0aOnSomjRpolq1akmSUlNT5ePjo9DQULuxkZGRSk1NLXSepKQkhYSE2B6xsbE3unQAQAlSrMJz0KBB2rt3rz744IPrmicxMVEZGRm2R0pKipMqBACUBm6/zzPf4MGDtWrVKn3++ee69dZbbe1RUVHKycnRuXPn7NY+09LSFBUVVehcvr6+8vX1vdElAwBKKLdf8zQMQ4MHD9bHH3+sTZs2KS4uzq6/QYMG8vb21saNG21tBw4c0PHjx9W4ceObXS4AoBRw+zXPQYMGafHixfr0008VFBRk248ZEhIif39/hYSEqF+/fho+fLjCwsIUHBysIUOGqHHjxhxpCwC4Idw+PGfMmCFJatmypV37vHnz9MQTT0iSJk+eLA8PD3Xp0kXZ2dlKSEjQ22+/fZMrBQCUFm4fnoZhXHWMn5+fpk+frunTp9+EigAApZ3b7/MEAMDdEJ4AAJhEeAIAYBLhCQCASYQnAAAmEZ4AAJhEeAIAYBLhCQCASYQnAAAmEZ4AAJhEeAIAYBLhCQCASYQnAAAmEZ4AAJhEeAIAYBLhCQCASYQnAAAmEZ4AAJhEeAIAYBLhCQCASYQnAAAmEZ4AAJhEeAIAYBLhCQCASYQnAAAmEZ4AAJhEeAIAYBLhCQCASYQnAAAmEZ4AAJhEeAIAYBLhCQCASYQnAAAmEZ4AAJhEeAIAYBLhCQCASYQnAAAmEZ4AAJhEeAIAYBLhCQCASYQnAAAmEZ4AAJhEeAIAYBLhCQCASYQnAAAmEZ4AAJhEeAIAYBLhCQCASYQnAAAmEZ4AAJhEeAIAYFKJCc/p06erUqVK8vPzU6NGjfTNN9+4uiQAQAlVIsJzyZIlGj58uMaMGaNdu3apbt26SkhI0KlTp1xdGgCgBCoR4ZmcnKz+/furT58+qlmzpmbOnKkyZcpo7ty5ri4NAFACebm6gOuVk5OjnTt3KjEx0dbm4eGh+Ph4bdu2rdDnZGdnKzs727ackZEhScrMzHRKTXnZfzhlHuBqnPUzeyOcv5Tn6hJQSjjr9yB/HsMwrjq22Ifn77//rry8PEVGRtq1R0ZGav/+/YU+JykpSePGjSvQHhsbe0NqBG6UkGkDXV0C4HpJIU6d7vz58woJufKcxT48HZGYmKjhw4fblq1Wq9LT0xUeHi6LxeLCykqvzMxMxcbGKiUlRcHBwa4uB7jp+B1wPcMwdP78ecXExFx1bLEPz3LlysnT01NpaWl27WlpaYqKiir0Ob6+vvL19bVrCw0NvVElwoTg4GD+cKBU43fAta62xpmv2B8w5OPjowYNGmjjxo22NqvVqo0bN6px48YurAwAUFIV+zVPSRo+fLh69+6thg0b6u6779aUKVN04cIF9enTx9WlAQBKoBIRnt26ddPp06c1evRopaamql69elqzZk2Bg4jgvnx9fTVmzJgCm9OB0oLfgeLFYlzLMbkAAMCm2O/zBADgZiM8AQAwifAEAMAkwhMAAJMIT7jEE088IYvFooEDC15ebtCgQbJYLHriiSdufmHATZb/u5D/CA8PV5s2bfTDDz+4ujRcAeEJl4mNjdUHH3ygP/7434X0L126pMWLF6tChQourAy4udq0aaOTJ0/q5MmT2rhxo7y8vPTggw+6uixcAeEJl7nzzjsVGxur5cuX29qWL1+uChUqqH79+i6sDLi5fH19FRUVpaioKNWrV08vvPCCUlJSdPr0aVeXhiIQnnCpvn37at68ebbluXPncmUolGpZWVl67733VLVqVYWHh7u6HBSB8IRL9ezZU19++aWOHTumY8eOaevWrerZs6erywJuqlWrVikwMFCBgYEKCgrSihUrtGTJEnl48CfaXZWIy/Oh+CpfvrzatWun+fPnyzAMtWvXTuXKlXN1WcBNde+992rGjBmSpLNnz+rtt99W27Zt9c0336hixYourg6FITzhcn379tXgwYMlSdOnT3dxNcDNFxAQoKpVq9qW33nnHYWEhGj27NmaMGGCCytDUQhPuFybNm2Uk5Mji8WihIQEV5cDuJzFYpGHh4fdkehwL4QnXM7T01P79u2zfQ2UNtnZ2UpNTZX052bbt956S1lZWWrfvr2LK0NRCE+4heDgYFeXALjMmjVrFB0dLUkKCgpS9erV9eGHH6ply5auLQxF4pZkAACYxHHQAACYRHgCAGAS4QkAgEmEJwAAJhGeAACYRHgCAGAS4QkAgEmEJwAAJhGegJtr2bKlhg4dKkmqVKmSpkyZ4tJ6zDp69KgsFou+//57V5cCOA2X5wOKkR07diggIMDVZZgSGxurkydPcqs5lCiEJ1CMlC9f3tUlmObp6amoqChXlwE4FZttATdy4cIF9erVS4GBgYqOjtYbb7xh1//3zbbJycmqXbu2AgICFBsbq6efflpZWVl2z5k9e7ZiY2NVpkwZPfTQQ0pOTlZoaKitf+zYsapXr57effddVapUSSEhIerevbvOnz9vG5Odna1nnnlGERER8vPzU9OmTbVjxw5b/9mzZ9WjRw+VL19e/v7+qlatmubNmyep4GbbK40FigvCE3AjI0aM0JYtW/Tpp59q3bp1+uyzz7Rr164ix3t4eGjq1Kn68ccftWDBAm3atEkjR4609W/dulUDBw7Us88+q++//1733XefJk6cWGCew4cP65NPPtGqVau0atUqbdmyRa+88oqtf+TIkVq2bJkWLFigXbt2qWrVqkpISFB6erokadSoUfrpp5+0evVq7du3TzNmzChyM62ZsYDbMgC4hfPnzxs+Pj7G0qVLbW1nzpwx/P39jWeffdYwDMOoWLGiMXny5CLn+PDDD43w8HDbcrdu3Yx27drZjenRo4cREhJiWx4zZoxRpkwZIzMz09Y2YsQIo1GjRoZhGEZWVpbh7e1tLFq0yNafk5NjxMTEGJMmTTIMwzDat29v9OnTp9Cajhw5Ykgyvvvuu6uOBYoL1jwBN3H48GHl5OSoUaNGtrawsDDdfvvtRT5nw4YNat26tW655RYFBQXp8ccf15kzZ3Tx4kVJ0oEDB3T33XfbPefvy9Kfm4ODgoJsy9HR0Tp16pStrtzcXDVp0sTW7+3trbvvvtt2E/OnnnpKH3zwgerVq6eRI0fqq6++KrJmM2MBd0V4AsXU0aNH9eCDD6pOnTpatmyZdu7cqenTp0uScnJyTM3l7e1tt2yxWGS1Wq/5+W3bttWxY8c0bNgwnThxQq1bt9Zzzz133WMBd0V4Am6iSpUq8vb21vbt221tZ8+e1c8//1zo+J07d8pqteqNN97QPffco9tuu00nTpywG3P77bfbHdgjqcDytdTl4+OjrVu32tpyc3O1Y8cO1axZ09ZWvnx59e7dW++9956mTJmiWbNmFTmnmbGAO+JUFcBNBAYGql+/fhoxYoTCw8MVERGhF198UR4ehf+PW7VqVeXm5mratGlq3769tm7dqpkzZ9qNGTJkiJo3b67k5GS1b99emzZt0urVq2WxWK65roCAAD311FMaMWKEwsLCVKFCBU2aNEkXL15Uv379JEmjR49WgwYNdMcddyg7O1urVq1SjRo1Cp3PzFjAXbHmCbiR1157Tc2aNVP79u0VHx+vpk2bqkGDBoWOrVu3rpKTk/Xqq6+qVq1aWrRokZKSkuzGNGnSRDNnzlRycrLq1q2rNWvWaNiwYfLz8zNV1yuvvKIuXbro8ccf15133qlDhw5p7dq1Klu2rCTJx8dHiYmJqlOnjpo3by5PT0998MEHhc5lZizgriyGYRiuLgLAzdO/f3/t379fX3zxhatLAYotNtsCJdzrr7+u++67TwEBAVq9erUWLFigt99+29VlAcUaa55ACde1a1d99tlnOn/+vCpXrqwhQ4Zo4MCBri4LKNYITwAATOKAIQAATCI8AQAwifAEAMAkwhMAAJMITwAATCI8AQAwifAEAMAkwhMAAJP+P29a1+2VIDUTAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Here malign have greater area than benign\n",
        "fig = plt.figure(figsize = (5,5))\n",
        "sns.barplot(x = 'diagnosis', y = 'area_mean', data = data)\n",
        "\n",
        "plt.title('Diagnosis vs area_mean')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "iES4qqbL3Nfl",
        "outputId": "d1083d96-ede5-4385-f721-02dfdbfb33a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Diagnosis vs area_mean')"
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Here malign have greater smoothness than benign\n",
        "fig = plt.figure(figsize = (5,5))\n",
        "sns.barplot(x = 'diagnosis', y = 'smoothness_mean', data = data)\n",
        "\n",
        "plt.title('Diagnosis vs smoothness_mean')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "Qf4McNkO3h1l",
        "outputId": "0c377c06-5e42-47a4-a8ab-44fa7954a7f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Diagnosis vs smoothness_mean')"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Here malign have greater compactness than benign\n",
        "fig = plt.figure(figsize = (5,5))\n",
        "sns.barplot(x = 'diagnosis', y = 'compactness_mean', data = data)\n",
        "\n",
        "plt.title('Diagnosis vs compactness_mean')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "5RaSpGgS3vSq",
        "outputId": "46e66d5b-7f49-443c-bfa5-44f10e9d23b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Diagnosis vs compactness_mean')"
            ]
          },
          "metadata": {},
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Here malign have greater concavity than benign\n",
        "fig = plt.figure(figsize = (5,5))\n",
        "sns.barplot(x = 'diagnosis', y = 'concavity_mean', data = data)\n",
        "\n",
        "plt.title('Diagnosis vs concavity_mean')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "jvdNMWV84DTu",
        "outputId": "43e2454d-3605-49a1-9f05-76c0c5b72dd9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Diagnosis vs concavity_mean')"
            ]
          },
          "metadata": {},
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Here malign have greater cconcave points than benign\n",
        "fig = plt.figure(figsize = (5,5))\n",
        "sns.barplot(x = 'diagnosis', y = 'concave points_mean', data = data)\n",
        "\n",
        "plt.title('Diagnosis vs concave points_mean')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "3FWx0krO4TOr",
        "outputId": "2894f0b6-9e93-4412-e4b2-1f1c848ec6f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Diagnosis vs concave points_mean')"
            ]
          },
          "metadata": {},
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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x5uXLl9G8eXOpvbi4GBkZGQgODlbr7+3tDW9vb0yfPh3Hjh3Dq6++isTERHzyySdPfY0mTZogIiICERERePToEbp164ZZs2bh73//e5W1hYWFYfbs2diyZQvs7OyQn5+vNv1WwcHBAePGjcO4ceOQnZ2Ndu3a4dNPP0WfPn00jmtjYwMLCwuNZ6P+lYuLCy5fvlyp/dKlS9JybbRo0QIAcP78+Ur7tcKDBw+QkpKC2bNnY+bMmVL706bJw8LCsG7dOqSkpODixYsQQqiFacX/UxMTk6e+ZnVoev2ff/4Zrq6uANTfR0+6dOkSrK2tYW5u/tyvX+FZ16V27NgRHTt2xKeffooNGzbg7bffxsaNG5/5XtMFGxsbNGzYEGVlZc/c15s3b0bz5s2xdetWtW2qmC0gzXhkWkcdOHAAH3/8Mdzc3PD2228/tV/FX51//SszLy8Pa9asUevXs2dP1KtXr9IlMwkJCc9dY3BwMExNTfHll1+qvf6//vUv5OXloV+/fgCA/Px8lJaWqq3r7e0NIyOjKqfS/no5B/DH51EtW7as1vRb27Zt4e3tjaSkJCQlJcHBwQHdunWTlpeVlVWaErO1tYWjo2OV4xsZGSE0NBQ//vgjTp06VWl5xX7o27cvTpw4gdTUVGlZYWEhVqxYAVdXV3h4eDxzG/6qXbt2cHNzw6JFiypddlTxmpreCwCwaNEijWMGBwejSZMm0j4KCAhQ+0PL1tYW3bt3x1dffYW7d+9WWl/TZVKabNu2Te2z1RMnTuD48ePSHywODg7w8/PDunXr1Lbt/Pnz2Lt3L/r27Vut13mWikB+cv89ePCg0j7z8/MDgOea6n0exsbGePPNN7FlyxaNf6j9dV9r+v98/PhxtfcaVcYj0zpg165duHTpEkpLS5GVlYUDBw5g3759cHFxwfbt26u829Hrr78OU1NTDBgwAP/4xz/w6NEjrFy5Era2tmr/ANrZ2SEqKgoLFizAwIED0bt3b5w9exa7du2CtbX1c93v18bGBrGxsZg9ezZ69+6NgQMH4vLly1i2bBk6dOiA4cOHA/jjD4MJEyZg8ODBaN26NUpLS/HNN99I/4A8jYeHB7p37w5/f380adIEp06dwubNmzFhwoRq1RcWFoaZM2dCpVIhMjJS7SSpgoICNGvWDG+99RZ8fX3RoEED7N+/HydPnsSCBQuqHPezzz7D3r17ERQUhDFjxqBt27a4e/cuNm3ahCNHjqBRo0b44IMP8O9//xt9+vTBpEmT0KRJE6xbtw4ZGRnYsmWLWi3VYWRkhOXLl2PAgAHw8/NDREQEHBwccOnSJfzvf//Dnj17YGFhgW7dumHevHkoKSlB06ZNsXfvXmRkZGgc08TEBH/729+wceNGFBYW4vPPP6/UZ+nSpejSpQu8vb0xevRoNG/eHFlZWUhNTcWvv/6Ks2fPPrP2li1bokuXLnj33XdRVFSERYsWwcrKClOnTpX6zJ8/H3369EGnTp0QGRmJ3377DUuWLIGlpSVmzZql1b56Gj8/PxgbG2Pu3LnIy8uDUqlEjx49sGHDBixbtgyDBg1CixYtUFBQgJUrV8LCwkJnQV4dc+bMwcGDBxEYGIjRo0fDw8MD9+/fR3p6Ovbv34/79+8DAPr374+tW7di0KBB6NevHzIyMpCYmAgPDw+tp6XrlBd/AjG9KBWn+Vc8TE1Nhb29vejVq5dYvHix2jVoFTRdGrN9+3bh4+MjVCqVdD3f6tWrK11CUFpaKmbMmCHs7e2FmZmZ6NGjh7h48aKwsrISY8eOrVTXk5cKVFzG8eSlBQkJCcLd3V2YmJgIOzs78e6776pdvnHt2jUxatQo0aJFC6FSqUSTJk3Ea6+9Jvbv3682zpOXxnzyySciICBANGrUSJiZmQl3d3fx6aefVvv6uCtXrkj79siRI2rLioqKRExMjPD19RUNGzYU5ubmwtfXVyxbtqxaY9+4cUOEh4cLGxsboVQqRfPmzcX48ePVLlm4evWqeOutt0SjRo2ESqUSAQEBIjk5WW2cin365GVDFZc1/PXSCSGEOHLkiOjVq5dUs4+Pj1iyZIm0/NdffxWDBg0SjRo1EpaWlmLw4MHizp07AoCIi4urtB379u0TAIRCoRC3bt3SuK1Xr14V4eHhwt7eXpiYmIimTZuK/v37i82bN1e5jyq2Yf78+WLBggXCyclJKJVK0bVrV3H27NlK/ffv3y9effVVYWZmJiwsLMSAAQPEhQsX1Po87dIYTZdQBQUFiaCgILW2lStXiubNmwtjY2PpvZyeni6GDRsmnJ2dhVKpFLa2tqJ///7i1KlTVW6fptf76zW6z6oPQKVLs7KyssT48eOFk5OTMDExEfb29qJnz55ixYoVUp/y8nLx2WefCRcXF6FUKsUrr7wikpOTxYgRI4SLi4vU76/7X9Nra3o/vMx4b16qUQ8fPkTjxo3xySef4MMPP9R3OfQSuX79Otzc3DB//nz885//1Hc5VMfxM1PSmd9++61SW8XnabzxNxG9zPiZKelMUlIS1q5di759+6JBgwY4cuQI/v3vf+P111+XTrsnInX3799Xu+fyk4yNjWFjY/MCK6LnwTAlnfHx8UG9evUwb9485OfnSyclVXVpClFd97e//U3jlwJUcHFx4XekGgB+ZkpEpEdpaWlVXottZmbGmR0DwDAlIiKSiScgERERycTPTDUoLy/HnTt30LBhw+e62QAREb0chBAoKCiAo6NjlTdDYZhqcOfOnUrfwkBERHXXrVu30KxZs6cuZ5hqUPGVZLdu3YKFhYWeqyEiIn3Jz8+Hk5PTM7+qkmGqQcXUroWFBcOUiIie+ZEfT0AiIiKSiWFKREQkE8OUiIhIJoYpERGRTAxTIiIimRimREREMjFMiYiIZGKYEhERycQwJSIikolhSkREJBPDlIiISCaGKRERkUwMUyIiIpn4rTFU60RFRSEnJwcAYGNjg8WLF+u5IiKiqjFMqdbJyclBVlaWvssgIqo2TvMSERHJxDAlIiKSiWFKREQkE8OUiIhIJoYpERGRTAxTIiIimRimREREMjFMiYiIZGKYEhERycQwJSIikolhSkREJBPDlIiISCaGKRERkUwMUyIiIpkYpkRERDIxTImIiGRimBIREcnEMCUiIpKJYUpERCRTPX0XUFf4x3yt7xIMhsWDR9JfeXcfPOK+01La/HB9l0BU5/DIlIiISCaGKRERkUwMUyIiIpkYpkRERDIxTImIiGRimBIREcnEMCUiIpKJYUpERCQTw5SIiEgmhikREZFMDFMiIiKZGKZEREQyMUyJiIhkYpgSERHJxDAlIiKSiWFKREQkE8OUiIhIJoYpERGRTPX0XQDRk8pNzDX+TERUWzFMqdZ51KaPvksgItIKp3mJiIhkYpgSERHJxDAlIiKSiWFKREQkE8OUiIhIJoYpERGRTLUiTJcuXQpXV1eoVCoEBgbixIkTVfbftGkT3N3doVKp4O3tjZ07d6otf/ToESZMmIBmzZrBzMwMHh4eSExMrMlNICKiOkzvYZqUlITo6GjExcUhPT0dvr6+CAkJQXZ2tsb+x44dw7BhwxAZGYnTp08jNDQUoaGhOH/+vNQnOjoau3fvxvr163Hx4kVMnjwZEyZMwPbt21/UZhERUR2i9zBduHAhRo8ejYiICOkIsn79+li9erXG/osXL0bv3r0RExODtm3b4uOPP0a7du2QkJAg9Tl27BhGjBiB7t27w9XVFWPGjIGvr+8zj3iJiIieh17DtLi4GGlpaQgODpbajIyMEBwcjNTUVI3rpKamqvUHgJCQELX+nTt3xvbt23H79m0IIXDw4EH8/PPPeP311zWOWVRUhPz8fLUHERFRdek1THNzc1FWVgY7Ozu1djs7O2RmZmpcJzMz85n9lyxZAg8PDzRr1gympqbo3bs3li5dim7dumkcMz4+HpaWltLDyclJ5pYREVFdovdp3pqwZMkS/PTTT9i+fTvS0tKwYMECjB8/Hvv379fYPzY2Fnl5edLj1q1bL7hiIiIyZHq90b21tTWMjY2RlZWl1p6VlQV7e3uN69jb21fZ/7fffsO0adPw/fffo1+/fgAAHx8fnDlzBp9//nmlKWIAUCqVUCqVutgkIiKqg/R6ZGpqagp/f3+kpKRIbeXl5UhJSUGnTp00rtOpUye1/gCwb98+qX9JSQlKSkpgZKS+acbGxigvL9fxFhAREdWCr2CLjo7GiBEj0L59ewQEBGDRokUoLCxEREQEACA8PBxNmzZFfHw8ACAqKgpBQUFYsGAB+vXrh40bN+LUqVNYsWIFAMDCwgJBQUGIiYmBmZkZXFxccPjwYXz99ddYuHCh3raTiIheXnoP07CwMOTk5GDmzJnIzMyEn58fdu/eLZ1kdPPmTbWjzM6dO2PDhg2YPn06pk2bhlatWmHbtm3w8vKS+mzcuBGxsbF4++23cf/+fbi4uODTTz/F2LFjX/j2ERHRy08hhBD6LqK2yc/Ph6WlJfLy8mBhYaGTMf1jvtbJOETPkjY/XN8lEL00qpsHL+XZvERERC8Sw5SIiEgmhikREZFMDFMiIiKZGKZEREQyMUyJiIhkYpgSERHJxDAlIiKSiWFKREQkE8OUiIhIJoYpERGRTAxTIiIimRimREREMjFMiYiIZGKYEhERycQwJSIikolhSkREJBPDlIiISCaGKRERkUwMUyIiIpkYpkRERDIxTImIiGRimBIREcnEMCUiIpKJYUpERCQTw5SIiEgmhikREZFMDFMiIiKZGKZEREQyMUyJiIhkYpgSERHJxDAlIiKSiWFKREQkE8OUiIhIJoYpERGRTAxTIiIimRimREREMjFMiYiIZGKYEhERycQwJSIikolhSkREJBPDlIiISCaGKRERkUz1nnfF4uJiZGdno7y8XK3d2dlZdlFERESGROswvXLlCkaNGoVjx46ptQshoFAoUFZWprPiiIiIDIHWYTpy5EjUq1cPycnJcHBwgEKhqIm6iIiIDIbWYXrmzBmkpaXB3d29JuohIiIyOFqfgOTh4YHc3NyaqIWIiMggaR2mc+fOxdSpU3Ho0CHcu3cP+fn5ag8iIqK6Rutp3uDgYABAz5491dp5AhIREdVVWofpwYMHa6IOIiIig6V1mAYFBdVEHURERAbruW/a8PjxY9y8eRPFxcVq7T4+PrKLIiIiMiRah2lOTg4iIiKwa9cujcv5mSkREdU1Wp/NO3nyZDx8+BDHjx+HmZkZdu/ejXXr1qFVq1bYvn17TdRIRERUq2l9ZHrgwAH88MMPaN++PYyMjODi4oJevXrBwsIC8fHx6NevX03USUREVGtpfWRaWFgIW1tbAEDjxo2Rk5MDAPD29kZ6erpuqyMiIjIAWodpmzZtcPnyZQCAr68vvvrqK9y+fRuJiYlwcHDQeYFERES1ndbTvFFRUbh79y4AIC4uDr1798a3334LU1NTrF27Vtf1ERER1Xpah+nw4cOln/39/XHjxg1cunQJzs7OsLa21mlxREREhkDrad4KxcXFuHz5MkxNTdGuXTsGKRER1Vlah+njx48RGRmJ+vXrw9PTEzdv3gQATJw4EXPmzNF5gURERLWd1mEaGxuLs2fP4tChQ1CpVFJ7cHAwkpKSdFocERGRIdD6M9Nt27YhKSkJHTt2hEKhkNo9PT1x9epVnRZHRERkCLQ+Ms3JyZGuM/2rwsJCtXAlIiKqK7QO0/bt22PHjh3S84oAXbVqFTp16qS7yoiIiAyE1tO8n332Gfr06YMLFy6gtLQUixcvxoULF3Ds2DEcPny4JmokIiKq1bQ+Mu3SpQvOnDmD0tJSeHt7Y+/evbC1tUVqair8/f1rokYiIqJa7bmuM23RogVWrlyJEydO4MKFC1i/fj28vb2fu4ilS5fC1dUVKpUKgYGBOHHiRJX9N23aBHd3d6hUKnh7e2Pnzp2V+ly8eBEDBw6EpaUlzM3N0aFDB+kyHiIiIl167ps2ZGdn4/z58zh37pzaQ1tJSUmIjo5GXFwc0tPT4evri5CQEGRnZ2vsf+zYMQwbNgyRkZE4ffo0QkNDERoaivPnz0t9rl69ii5dusDd3R2HDh3CuXPnMGPGDLVLeYiIiHRFIYQQ2qyQlpaGESNG4OLFi3hyVYVCofWXgwcGBqJDhw5ISEgAAJSXl8PJyQkTJ07EBx98UKl/WFgYCgsLkZycLLV17NgRfn5+SExMBAAMHToUJiYm+Oabb7SqpUJ+fj4sLS2Rl5cHCwuL5xrjSf4xX+tkHKJnSZsfru8SiF4a1c0DrY9MR40ahdatW+PYsWO4du0aMjIypMe1a9e0Gqu4uBhpaWkIDg7+syAjIwQHByM1NVXjOqmpqWr9ASAkJETqX15ejh07dqB169YICQmBra0tAgMDsW3btqfWUVRUhPz8fLUHERFRdWkdpteuXcO8efMQGBgIV1dXuLi4qD20kZubi7KyMtjZ2am129nZITMzU+M6mZmZVfbPzs7Go0ePMGfOHPTu3Rt79+7FoEGD8Le//e2pZxvHx8fD0tJSejg5OWm1HUREVLdpHaY9e/bE2bNna6IWnSgvLwcAvPHGG5gyZQr8/PzwwQcfoH///tI08JNiY2ORl5cnPW7duvUiSyYiIgOn9XWmq1atwogRI3D+/Hl4eXnBxMREbfnAgQOrPZa1tTWMjY2RlZWl1p6VlQV7e3uN69jb21fZ39raGvXq1YOHh4dan7Zt2+LIkSMax1QqlVAqldWum4iI6K+0DtPU1FQcPXoUu3btqrRM2xOQTE1N4e/vj5SUFISGhgL448gyJSUFEyZM0LhOp06dkJKSgsmTJ0tt+/btk+6+ZGpqig4dOuDy5ctq6/38889aT0MTERFVh9bTvBMnTsTw4cNx9+5dlJeXqz20PZMXAKKjo7Fy5UqsW7cOFy9exLvvvovCwkJEREQAAMLDwxEbGyv1j4qKwu7du7FgwQJcunQJs2bNwqlTp9TCNyYmBklJSVi5ciV++eUXJCQk4Mcff8S4ceO0ro+IiOhZtD4yvXfvHqZMmVLpJKDnFRYWhpycHMycOROZmZnw8/PD7t27pfFv3rwJI6M/M79z587YsGEDpk+fjmnTpqFVq1bYtm0bvLy8pD6DBg1CYmIi4uPjMWnSJLRp0wZbtmxBly5ddFIzERHRX2l9nemIESPQtWtX/P3vf6+pmvSO15mSIeN1pkS6U9080PrItHXr1oiNjcWRI0fg7e1d6QSkSZMmaV8tERGRAXuus3kbNGiAw4cPV7puU6FQMEyJiKjO0TpMMzIyaqIOIiIig/XcN7p/FgsLC61vL0hERGSIaixMtTyviYiIyGDVWJgSERHVFQxTIiIimRimREREMtVYmCoUipoamoiIqFbhCUhEREQyyQ7TsrIynDlzBg8ePFBr37VrF5o2bSp3eCIiolpP6zCdPHky/vWvfwH4I0iDgoLQrl07ODk54dChQ1K/Ll268DtCiYioTtA6TDdv3gxfX18AwI8//oiMjAxcunQJU6ZMwYcffqjzAomIiGo7rcM0NzcX9vb2AICdO3di8ODBaN26NUaNGoX//ve/Oi+QiIiottM6TO3s7HDhwgWUlZVh9+7d6NWrFwDg8ePHMDY21nmBREREtZ3WN7qPiIjAkCFD4ODgAIVCgeDgYADA8ePH4e7urvMCiYiIajutw3TWrFnw8vLCrVu3MHjwYOkkI2NjY3zwwQc6L5CIiKi20zpMv/76a4SFhVU6U3fYsGHYuHGjzgojIiIyFFp/ZhoREYG8vLxK7QUFBYiIiNBJUURERIZE6zAVQmi8VeCvv/4KS0tLnRRFRERkSKo9zfvKK69AoVBAoVCgZ8+eqFfvz1XLysqQkZGB3r1710iRREREtVm1wzQ0NBQAcObMGYSEhKBBgwbSMlNTU7i6uuLNN9/UeYFERES1XbXDNC4uDgDg6uqKsLAwqFSqGiuKiIjIkGh9Nu+IESMAAMXFxcjOzkZ5ebnacmdnZ91URkREZCC0DtMrV65g1KhROHbsmFp7xYlJZWVlOiuOiIjIEGgdpiNHjkS9evWQnJws3QWJiIioLtM6TM+cOYO0tDTeOpCIiOj/0/o6Uw8PD+Tm5tZELURERAZJ6zCdO3cupk6dikOHDuHevXvIz89XexAREdU1Wk/zVnxLTM+ePdXaeQISERHVVVqH6cGDB2uiDiIiIoOldZgGBQXVRB1EREQGq1pheu7cOXh5ecHIyAjnzp2rsq+Pj49OCiMiIjIU1QpTPz8/ZGZmwtbWFn5+flAoFBBCVOrHz0yJiKguqlaYZmRkwMbGRvqZiIiI/lStMHVxcdH4MxERET3HCUgAcPXqVSxatAgXL14E8MeNHKKiotCiRQudFkdERGQItL5pw549e+Dh4YETJ07Ax8cHPj4+OH78ODw9PbFv376aqJGIiKhW0/rI9IMPPsCUKVMwZ86cSu3vv/8+evXqpbPiiIiIDIHWR6YXL15EZGRkpfZRo0bhwoULOimKiIjIkGgdpjY2Njhz5kyl9jNnzsDW1lYXNRERERkUrad5R48ejTFjxuDatWvo3LkzAODo0aOYO3cuoqOjdV4gERFRbad1mM6YMQMNGzbEggULEBsbCwBwdHTErFmzMGnSJJ0XSEREVNtpHaYKhQJTpkzBlClTUFBQAABo2LChzgsjIiIyFM91nSkAZGdn4/LlywAAd3d36Q5JREREdY3WJyAVFBTgnXfegaOjI4KCghAUFARHR0cMHz4ceXl5NVEjERFRraZ1mP7973/H8ePHsWPHDjx8+BAPHz5EcnIyTp06hX/84x81USMREVGtpvU0b3JyMvbs2YMuXbpIbSEhIVi5ciV69+6t0+KIiIgMgdZHplZWVrC0tKzUbmlpicaNG+ukKCIiIkOidZhOnz4d0dHRyMzMlNoyMzMRExODGTNm6LQ4IiIiQ6D1NO/y5cvxyy+/wNnZGc7OzgCAmzdvQqlUIicnB1999ZXUNz09XXeVEhER1VJah2loaGgNlEFERGS4tA7TuLi4mqiDiIjIYGn9mSkRERGpY5gSERHJxDAlIiKSiWFKREQk03OHaXFxMS5fvozS0lJd1kNERGRwtA7Tx48fIzIyEvXr14enpydu3rwJAJg4cSLmzJmj8wKJiIhqO63DNDY2FmfPnsWhQ4egUqmk9uDgYCQlJem0OCIiIkOg9XWm27ZtQ1JSEjp27AiFQiG1e3p64urVqzotjoiIyBBofWSak5MDW1vbSu2FhYVq4UpERFRXaB2m7du3x44dO6TnFQG6atUqdOrUSXeVERERGQitp3k/++wz9OnTBxcuXEBpaSkWL16MCxcu4NixYzh8+HBN1EhERFSraX1k2qVLF5w5cwalpaXw9vbG3r17YWtri9TUVPj7+9dEjURERLWa1kemANCiRQusXLlS17UQEREZJK2PTIODg7F27Vrk5+fXRD1EREQGR+sw9fT0RGxsLOzt7TF48GD88MMPKCkpqYnaiIiIDILWYbp48WLcvn0b27Ztg7m5OcLDw2FnZ4cxY8bwBCQiIqqTnuvevEZGRnj99dexdu1aZGVl4auvvsKJEyfQo0eP5ypi6dKlcHV1hUqlQmBgIE6cOFFl/02bNsHd3R0qlQre3t7YuXPnU/uOHTsWCoUCixYteq7aiIiInkXWt8ZkZmYiMTERc+fOxblz59ChQwetx0hKSkJ0dDTi4uKQnp4OX19fhISEIDs7W2P/Y8eOYdiwYYiMjMTp06cRGhqK0NBQnD9/vlLf77//Hj/99BMcHR21rouIiKi6tA7T/Px8rFmzBr169YKTkxOWL1+OgQMH4sqVK/jpp5+0LmDhwoUYPXo0IiIi4OHhgcTERNSvXx+rV6/W2H/x4sXo3bs3YmJi0LZtW3z88cdo164dEhIS1Prdvn0bEydOxLfffgsTE5MqaygqKkJ+fr7ag4iIqLq0DlM7Ozt8+OGH8PLyQmpqKi5fvoyZM2eiRYsWWr94cXEx0tLSEBwc/GdBRkYIDg5GamqqxnVSU1PV+gNASEiIWv/y8nK88847iImJgaen5zPriI+Ph6WlpfRwcnLSeluIiKju0vo60+3bt6Nnz54wMpL/veK5ubkoKyuDnZ2dWrudnR0uXbqkcZ3MzEyN/TMzM6Xnc+fORb169TBp0qRq1REbG4vo6GjpeX5+PgOViIiqTesw7dWrV03UoTNpaWlYvHgx0tPTq33jfaVSCaVSWcOVERHRy+q57oC0efNmfPfdd7h58yaKi4vVlqWnp1d7HGtraxgbGyMrK0utPSsrC/b29hrXsbe3r7L/f/7zH2RnZ8PZ2VlaXlZWhvfeew+LFi3C9evXq10fERFRdWg9V/vll18iIiICdnZ2OH36NAICAmBlZYVr166hT58+Wo1lamoKf39/pKSkSG3l5eVISUl56jfQdOrUSa0/AOzbt0/q/8477+DcuXM4c+aM9HB0dERMTAz27Nmj5dYSERE9m9ZHpsuWLcOKFSswbNgwrF27FlOnTkXz5s0xc+ZM3L9/X+sCoqOjMWLECLRv3x4BAQFYtGgRCgsLERERAQAIDw9H06ZNER8fDwCIiopCUFAQFixYgH79+mHjxo04deoUVqxYAQCwsrKClZWV2muYmJjA3t4ebdq00bo+IiKiZ9E6TG/evInOnTsDAMzMzFBQUADgjyPCjh07VrpE5VnCwsKQk5ODmTNnIjMzE35+fti9e7d0ktHNmzfVTnbq3LkzNmzYgOnTp2PatGlo1aoVtm3bBi8vL203hYiISCe0DlN7e3vcv38fLi4ucHZ2xk8//QRfX19kZGRACPFcRUyYMAETJkzQuOzQoUOV2gYPHozBgwdXe3x+TkpERDVJ689Me/Toge3btwMAIiIiMGXKFPTq1QthYWEYNGiQzgskIiKq7bQ+Ml2xYgXKy8sBAOPHj4eVlRWOHTuGgQMH4h//+IfOCyQiIqrttA5TIyMjtc8whw4diqFDh+q0KCIiIkOi9TTvmjVrsGnTpkrtmzZtwrp163RSFBERkSHROkzj4+NhbW1dqd3W1hafffaZTooiIiIyJFqH6c2bN+Hm5lap3cXFBTdv3tRJUURERIZE6zC1tbXFuXPnKrWfPXu20s0SiIiI6gKtw3TYsGGYNGkSDh48iLKyMpSVleHAgQOIioriiUhERFQnaX0278cff4zr16+jZ8+eqFfvj9XLy8sRHh7Oz0yJiKhO0jpMTU1NkZSUhI8//hhnz56FmZkZvL294eLiUhP1ERER1XrP9RVsANC6dWu0bt1al7UQEREZJK3DtKysDGvXrkVKSgqys7OluyFVOHDggM6KIyIiMgRah2lUVBTWrl2Lfv36wcvLCwqFoibqIiIiMhhah+nGjRvx3XffoW/fvjVRDxERkcHR+tIYU1NTtGzZsiZqISIiMkhah+l7772HxYsXP/d3lxIREb1stJ7mPXLkCA4ePIhdu3bB09MTJiYmasu3bt2qs+KIiIgMgdZh2qhRI34JOBER0V9oHaZr1qypiTqIiIgM1nPftCEnJweXL18GALRp0wY2NjY6K4qIiMiQaH0CUmFhIUaNGgUHBwd069YN3bp1g6OjIyIjI/H48eOaqJGIiKhW0zpMo6OjcfjwYfz44494+PAhHj58iB9++AGHDx/Ge++9VxM1EhER1WpaT/Nu2bIFmzdvRvfu3aW2vn37wszMDEOGDMHy5ct1WR8REVGtp/WR6ePHj2FnZ1ep3dbWltO8RERUJ2kdpp06dUJcXBx+//13qe23337D7Nmz0alTJ50WR0REZAi0nuZdvHgxQkJC0KxZM/j6+gIAzp49C5VKhT179ui8QCIiotpO6zD18vLClStX8O233+LSpUsAgGHDhuHtt9+GmZmZzgskIiKq7Z7rOtP69etj9OjRuq6FiIjIIGn9mWl8fDxWr15dqX316tWYO3euTooiIiIyJFqH6VdffQV3d/dK7Z6enkhMTNRJUURERIZE6zDNzMyEg4NDpXYbGxvcvXtXJ0UREREZEq3D1MnJCUePHq3UfvToUTg6OuqkKCIiIkOi9QlIo0ePxuTJk1FSUoIePXoAAFJSUjB16lTeTpCIiOokrcM0JiYG9+7dw7hx41BcXAwAUKlUeP/99xEbG6vzAomIiGo7rcNUoVBg7ty5mDFjBi5evAgzMzO0atUKSqWyJuojIiKq9Z77+0wbNGiADh066LIWIiIig6T1CUhERESkjmFKREQkE8OUiIhIJoYpERGRTM99AhIREdWcqKgo5OTkAPjjDnOLFy/Wc0VUFYYpEVEtlJOTg6ysLH2XQdXEaV4iIiKZGKZEREQyMUyJiIhkYpgSERHJxDAlIiKSiWFKREQkE8OUiIhIJl5nSkQvxM2PvPVdgkEpfWgFwPj//3yH+08LzjP/+8Jfk0emREREMjFMiYiIZGKYEhERycQwJSIikolhSkREJBPDlIiISCaGKRERkUy8zpSIqBZqoizT+DPVTgxTIqJaaNorD/VdAmmB07xEREQyMUyJiIhkYpgSERHJxDAlIiKSiWFKREQkE8OUiIhIJoYpERGRTAxTIiIimRimREREMtWKMF26dClcXV2hUqkQGBiIEydOVNl/06ZNcHd3h0qlgre3N3bu3CktKykpwfvvvw9vb2+Ym5vD0dER4eHhuHPnTk1vBhER1VF6D9OkpCRER0cjLi4O6enp8PX1RUhICLKzszX2P3bsGIYNG4bIyEicPn0aoaGhCA0Nxfnz5wEAjx8/Rnp6OmbMmIH09HRs3boVly9fxsCBA1/kZhERUR2iEEIIfRYQGBiIDh06ICEhAQBQXl4OJycnTJw4ER988EGl/mFhYSgsLERycrLU1rFjR/j5+SExMVHja5w8eRIBAQG4ceMGnJ2dn1lTfn4+LC0tkZeXBwsLi+fcMnX+MV/rZByiZ0mbH67vEjS6+ZG3vkugOsJ55n91NlZ180CvR6bFxcVIS0tDcHCw1GZkZITg4GCkpqZqXCc1NVWtPwCEhIQ8tT8A5OXlQaFQoFGjRhqXFxUVIT8/X+1BRERUXXoN09zcXJSVlcHOzk6t3c7ODpmZmRrXyczM1Kr/77//jvfffx/Dhg176l8V8fHxsLS0lB5OTk7PsTVERFRX6f0z05pUUlKCIUOGQAiB5cuXP7VfbGws8vLypMetW7deYJVERGTo9Pp9ptbW1jA2NkZWVpZae1ZWFuzt7TWuY29vX63+FUF648YNHDhwoMq5bqVSCaVS+ZxbQUREdZ1ej0xNTU3h7++PlJQUqa28vBwpKSno1KmTxnU6deqk1h8A9u3bp9a/IkivXLmC/fv3w8rKqmY2gIiICHo+MgWA6OhojBgxAu3bt0dAQAAWLVqEwsJCREREAADCw8PRtGlTxMfHAwCioqIQFBSEBQsWoF+/fti4cSNOnTqFFStWAPgjSN966y2kp6cjOTkZZWVl0uepTZo0gampqX42lIiIXlp6D9OwsDDk5ORg5syZyMzMhJ+fH3bv3i2dZHTz5k0YGf15AN25c2ds2LAB06dPx7Rp09CqVSts27YNXl5eAIDbt29j+/btAAA/Pz+11zp48CC6d+/+QraLiIjqDr1fZ1ob8TpTMmS8zpTqujp3nSkREdHLgGFKREQkE8OUiIhIJoYpERGRTAxTIiIimRimREREMjFMiYiIZGKYEhERycQwJSIikolhSkREJBPDlIiISCaGKRERkUwMUyIiIpkYpkRERDIxTImIiGRimBIREcnEMCUiIpKJYUpERCQTw5SIiEgmhikREZFMDFMiIiKZGKZEREQyMUyJiIhkYpgSERHJxDAlIiKSiWFKREQkE8OUiIhIJoYpERGRTAxTIiIimRimREREMjFMiYiIZGKYEhERycQwJSIikolhSkREJBPDlIiISCaGKRERkUwMUyIiIpkYpkRERDIxTImIiGRimBIREcnEMCUiIpKJYUpERCQTw5SIiEgmhikREZFMDFMiIiKZGKZEREQyMUyJiIhkYpgSERHJxDAlIiKSiWFKREQkE8OUiIhIJoYpERGRTAxTIiIimRimREREMjFMiYiIZGKYEhERycQwJSIikolhSkREJBPDlIiISCaGKRERkUwMUyIiIpkYpkRERDIxTImIiGRimBIREcnEMCUiIpKJYUpERCQTw5SIiEgmhikREZFMtSJMly5dCldXV6hUKgQGBuLEiRNV9t+0aRPc3d2hUqng7e2NnTt3qi0XQmDmzJlwcHCAmZkZgoODceXKlZrcBCIiqsP0HqZJSUmIjo5GXFwc0tPT4evri5CQEGRnZ2vsf+zYMQwbNgyRkZE4ffo0QkNDERoaivPnz0t95s2bhy+//BKJiYk4fvw4zM3NERISgt9///1FbRYREdUheg/ThQsXYvTo0YiIiICHhwcSExNRv359rF69WmP/xYsXo3fv3oiJiUHbtm3x8ccfo127dkhISADwx1HpokWLMH36dLzxxhvw8fHB119/jTt37mDbtm0vcMuIiKiuqKfPFy8uLkZaWhpiY2OlNiMjIwQHByM1NVXjOqmpqYiOjlZrCwkJkYIyIyMDmZmZCA4OlpZbWloiMDAQqampGDp0aKUxi4qKUFRUJD3Py8sDAOTn5z/3tj2prOg3nY1FVBVdvm91qeD3Mn2XQHWELn8HKsYSQlTZT69hmpubi7KyMtjZ2am129nZ4dKlSxrXyczM1Ng/MzNTWl7R9rQ+T4qPj8fs2bMrtTs5OVVvQ4hqEcslY/VdApF+xVvqfMiCggJYWj59XL2GaW0RGxurdrRbXl6O+/fvw8rKCgqFQo+V1V35+flwcnLCrVu3YGFhoe9yiPSCvwf6J4RAQUEBHB0dq+yn1zC1traGsbExsrKy1NqzsrJgb2+vcR17e/sq+1f8NysrCw4ODmp9/Pz8NI6pVCqhVCrV2ho1aqTNplANsbCw4D8iVOfx90C/qjoiraDXE5BMTU3h7++PlJQUqa28vBwpKSno1KmTxnU6deqk1h8A9u3bJ/V3c3ODvb29Wp/8/HwcP378qWMSERHJofdp3ujoaIwYMQLt27dHQEAAFi1ahMLCQkRERAAAwsPD0bRpU8THxwMAoqKiEBQUhAULFqBfv37YuHEjTp06hRUrVgAAFAoFJk+ejE8++QStWrWCm5sbZsyYAUdHR4SGhuprM4mI6CWm9zANCwtDTk4OZs6ciczMTPj5+WH37t3SCUQ3b96EkdGfB9CdO3fGhg0bMH36dEybNg2tWrXCtm3b4OXlJfWZOnUqCgsLMWbMGDx8+BBdunTB7t27oVKpXvj20fNRKpWIi4urNP1OVJfw98BwKMSzzvclIiKiKun9pg1ERESGjmFKREQkE8OUiIhIJoYpERGRTAxTqhVGjhwJhUKBsWMr3wpv/PjxUCgUGDly5IsvjOgFq/hdqHhYWVmhd+/eOHfunL5LoyowTKnWcHJywsaNG/Hbb39+KcDvv/+ODRs2wNnZWY+VEb1YvXv3xt27d3H37l2kpKSgXr166N+/v77LoiowTKnWaNeuHZycnLB161apbevWrXB2dsYrr7yix8qIXiylUgl7e3vY29vDz88PH3zwAW7duoWcnBx9l0ZPwTClWmXUqFFYs2aN9Hz16tXS3bCI6qJHjx5h/fr1aNmyJaysrPRdDj0Fw5RqleHDh+PIkSO4ceMGbty4gaNHj2L48OH6LovohUpOTkaDBg3QoEEDNGzYENu3b0dSUpLa3eCodtH77QSJ/srGxgb9+vXD2rVrIYRAv379YG1tre+yiF6o1157DcuXLwcAPHjwAMuWLUOfPn1w4sQJuLi46Lk60oRhSrXOqFGjMGHCBADA0qVL9VwN0Ytnbm6Oli1bSs9XrVoFS0tLrFy5Ep988okeK6OnYZhSrdO7d28UFxdDoVAgJCRE3+UQ6Z1CoYCRkZHame5UuzBMqdYxNjbGxYsXpZ+J6pqioiJkZmYC+GOaNyEhAY8ePcKAAQP0XBk9DcOUaiULCwt9l0CkN7t374aDgwMAoGHDhnB3d8emTZvQvXt3/RZGT8WvYCMiIpKJ51kTERHJxDAlIiKSiWFKREQkE8OUiIhIJoYpERGRTAxTIiIimRimREREMjFMiYiIZGKYEhmg7t27Y/LkyQAAV1dXLFq0SK/1aOv69etQKBQ4c+aMvksh0gneTpDIwJ08eRLm5ub6LkMrTk5OuHv3Lr9ej14aDFMiA2djY6PvErRmbGwMe3t7fZdBpDOc5iWq5QoLCxEeHo4GDRrAwcEBCxYsUFv+5DTvwoUL4e3tDXNzczg5OWHcuHF49OiR2jorV66Ek5MT6tevj0GDBmHhwoVo1KiRtHzWrFnw8/PDN998A1dXV1haWmLo0KEoKCiQ+hQVFWHSpEmwtbWFSqVCly5dcPLkSWn5gwcP8Pbbb8PGxgZmZmZo1aoV1qxZA6DyNG9VfYkMAcOUqJaLiYnB4cOH8cMPP2Dv3r04dOgQ0tPTn9rfyMgIX375Jf73v/9h3bp1OHDgAKZOnSotP3r0KMaOHYuoqCicOXMGvXr1wqefflppnKtXr2Lbtm1ITk5GcnIyDh8+jDlz5kjLp06dii1btmDdunVIT09Hy5YtERISgvv37wMAZsyYgQsXLmDXrl24ePEili9f/tRpXW36EtVKgohqrYKCAmFqaiq+++47qe3evXvCzMxMREVFCSGEcHFxEV988cVTx9i0aZOwsrKSnoeFhYl+/fqp9Xn77beFpaWl9DwuLk7Ur19f5OfnS20xMTEiMDBQCCHEo0ePhImJifj222+l5cXFxcLR0VHMmzdPCCHEgAEDREREhMaaMjIyBABx+vTpZ/YlMgQ8MiWqxa5evYri4mIEBgZKbU2aNEGbNm2eus7+/fvRs2dPNG3aFA0bNsQ777yDe/fu4fHjxwCAy5cvIyAgQG2dJ58Df0wfN2zYUHru4OCA7Oxsqa6SkhK8+uqr0nITExMEBARIX+z+7rvvYuPGjfDz88PUqVNx7Nixp9asTV+i2ohhSvQSuX79Ovr37w8fHx9s2bIFaWlpWLp0KQCguLhYq7FMTEzUnisUCpSXl1d7/T59+uDGjRuYMmUK7ty5g549e+Kf//yn7L5EtRHDlKgWa9GiBUxMTHD8+HGp7cGDB/j555819k9LS0N5eTkWLFiAjh07onXr1rhz545anzZt2qidKASg0vPq1GVqaoqjR49KbSUlJTh58iQ8PDykNhsbG4wYMQLr16/HokWLsGLFiqeOqU1fotqGl8YQ1WINGjRAZGQkYmJiYGVlBVtbW3z44YcwMtL8d3DLli1RUlKCJUuWYMCAATh69CgSExPV+kycOBHdunXDwoULMWDAABw4cAC7du2CQqGodl3m5uZ49913ERMTgyZNmsDZ2Rnz5s3D48ePERkZCQCYOXMm/P394enpiaKiIiQnJ6Nt27Yax9OmL1FtxCNTolpu/vz56Nq1KwYMGIDg4GB06dIF/v7+Gvv6+vpi4cKFmDt3Lry8vPDtt98iPj5erc+rr76KxMRELFy4EL6+vti9ezemTJkClUqlVV1z5szBm2++iXfeeQft2rXDL7/8gj179qBx48YAAFNTU8TGxsLHxwfdunWDsbExNm7cqHEsbfoS1UYKIYTQdxFEpF+jR4/GpUuX8J///EffpRAZJE7zEtVBn3/+OXr16gVzc3Ps2rUL69atw7Jly/RdFpHB4pEpUR00ZMgQHDp0CAUFBWjevDkmTpyIsWPH6rssIoPFMCUiIpKJJyARERHJxDAlIiKSiWFKREQkE8OUiIhIJoYpERGRTAxTIiIimRimREREMjFMiYiIZPp/GhNNes/cMKgAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Here malign have  slightly greater symmetry than benign\n",
        "fig = plt.figure(figsize = (5,5))\n",
        "sns.barplot(x = 'diagnosis', y = 'symmetry_mean', data = data)\n",
        "\n",
        "plt.title('Diagnosis vs symmetry_mean')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "Pf19NkE44ah2",
        "outputId": "93e4d75e-c4c0-4032-ad9c-ea3611838d94"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Diagnosis vs symmetry_mean')"
            ]
          },
          "metadata": {},
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Here malign cells have almost same equal size as benign cells\n",
        "fig = plt.figure(figsize = (5,5))\n",
        "sns.barplot(x = 'diagnosis', y = 'fractal_dimension_mean', data = data)\n",
        "\n",
        "plt.title('Diagnosis vs fractal_dimension_mean')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "z9VNfDOY4jI5",
        "outputId": "1386afc6-c81a-4c7c-a076-c72714caf48e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Diagnosis vs fractal_dimension_mean')"
            ]
          },
          "metadata": {},
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Heatmap of dataset in order to find correlation among features(columns)\n",
        "f, ax = plt.subplots(figsize=(20, 15))\n",
        "corr = data.corr()\n",
        "sns.heatmap(corr, mask=np.zeros_like(corr, dtype=np.bool), cmap=sns.diverging_palette(220, 10, as_cmap=True),\n",
        "            square=True, ax=ax)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "drs5V3Mk5D1k",
        "outputId": "5155f89f-ed48-4dbb-9f41-7370ea37df92"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-53-ce85fda8b38b>:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  corr = data.corr()\n",
            "<ipython-input-53-ce85fda8b38b>:3: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  sns.heatmap(corr, mask=np.zeros_like(corr, dtype=np.bool), cmap=sns.diverging_palette(220, 10, as_cmap=True),\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# importing label encoder\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "# performing label encoding\n",
        "le = LabelEncoder()\n",
        "data['diagnosis'] = le.fit_transform(data['diagnosis'])\n",
        "data['diagnosis'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1bSb0ocj58KZ",
        "outputId": "69e0b6bd-4132-4d05-f25b-1ba7e46c0a6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    357\n",
              "1    212\n",
              "Name: diagnosis, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Identifying indepedent (x) and dependent (y)\n",
        "\n",
        "x = data.iloc[:,1:]\n",
        "y = data.iloc[:,0]\n",
        "\n",
        "print(x.shape)\n",
        "print(y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zBOBJYko65hY",
        "outputId": "4beb23cd-cefd-45a2-a92d-21228d2251c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(569, 30)\n",
            "(569,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Train test splitting\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.25, random_state = 42)\n",
        "\n",
        "print(x_train.shape)\n",
        "print(y_train.shape)\n",
        "print(x_test.shape)\n",
        "print(y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tdM8-hQ7AJ1J",
        "outputId": "880ae989-cdb6-4de5-9caf-d169323d8add"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(426, 30)\n",
            "(426,)\n",
            "(143, 30)\n",
            "(143,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# standard scaling\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "sc = StandardScaler()\n",
        "\n",
        "x_train = sc.fit_transform(x_train)\n",
        "x_test = sc.fit_transform(x_test)\n"
      ],
      "metadata": {
        "id": "Kfk9b-8_Aeyw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Decision Tree"
      ],
      "metadata": {
        "id": "k5ulRaBsCFlr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "\n",
        "# creating a model\n",
        "model = DecisionTreeClassifier()\n",
        "\n",
        "# feeding the training set into the model\n",
        "model.fit(x_train, y_train)\n",
        "\n",
        "# predicting the test set results\n",
        "y_pred = model.predict(x_test)\n",
        "\n",
        "# Calculating the accuracies\n",
        "print(\"Training accuracy :\", model.score(x_train, y_train))\n",
        "print(\"Testing accuarcy :\", model.score(x_test, y_test))\n",
        "\n",
        "# classification report\n",
        "cr = classification_report(y_test, y_pred)\n",
        "print(cr)\n",
        "\n",
        "# confusion matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8_u9airOA534",
        "outputId": "c311edf3-3e7b-4e10-f65f-f2c67cdfae33"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy : 1.0\n",
            "Testing accuarcy : 0.9440559440559441\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.98      0.96        89\n",
            "           1       0.96      0.89      0.92        54\n",
            "\n",
            "    accuracy                           0.94       143\n",
            "   macro avg       0.95      0.93      0.94       143\n",
            "weighted avg       0.94      0.94      0.94       143\n",
            "\n",
            "[[87  2]\n",
            " [ 6 48]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Random Forest"
      ],
      "metadata": {
        "id": "ErBXwOp_N65L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "# creating a model\n",
        "model = RandomForestClassifier()\n",
        "\n",
        "# feeding the training set into the model\n",
        "model.fit(x_train, y_train)\n",
        "\n",
        "# predicting the test set results\n",
        "y_pred = model.predict(x_test)\n",
        "\n",
        "# Calculating the accuracies\n",
        "print(\"Training accuracy :\", model.score(x_train, y_train))\n",
        "print(\"Testing accuarcy :\", model.score(x_test, y_test))\n",
        "\n",
        "# classification report\n",
        "cr = classification_report(y_test, y_pred)\n",
        "print(cr)\n",
        "\n",
        "# confusion matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JAU9a_edCKRc",
        "outputId": "8eb799ba-7f5b-432d-aa8b-164c0d3afa24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy : 1.0\n",
            "Testing accuarcy : 0.965034965034965\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.99      0.97        89\n",
            "           1       0.98      0.93      0.95        54\n",
            "\n",
            "    accuracy                           0.97       143\n",
            "   macro avg       0.97      0.96      0.96       143\n",
            "weighted avg       0.97      0.97      0.96       143\n",
            "\n",
            "[[88  1]\n",
            " [ 4 50]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Logistics Regression"
      ],
      "metadata": {
        "id": "SphQ4p1sGy_v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "\n",
        "from sklearn.model_selection import GridSearchCV, cross_val_score\n",
        "\n",
        "# creating the model\n",
        "model = LogisticRegression()\n",
        "\n",
        "# feeding the training data into the model\n",
        "model.fit(x_train, y_train)\n",
        "\n",
        "# predicting the test set results\n",
        "y_pred = model.predict(x_test)\n",
        "\n",
        "# Calculating the accuracies\n",
        "print(\"Training accuracy :\", model.score(x_train, y_train))\n",
        "print(\"Testing accuarcy :\", model.score(x_test, y_test))\n",
        "\n",
        "# classification report\n",
        "cr = classification_report(y_test, y_pred)\n",
        "print(cr)\n",
        "\n",
        "# confusion matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "afFT8LqcCEaO",
        "outputId": "a1445bb3-1717-45ae-a6c6-a841ca421fd4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy : 0.9859154929577465\n",
            "Testing accuarcy : 0.986013986013986\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      1.00      0.99        89\n",
            "           1       1.00      0.96      0.98        54\n",
            "\n",
            "    accuracy                           0.99       143\n",
            "   macro avg       0.99      0.98      0.99       143\n",
            "weighted avg       0.99      0.99      0.99       143\n",
            "\n",
            "[[89  0]\n",
            " [ 2 52]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Support Vector Machine"
      ],
      "metadata": {
        "id": "wj2uYPmeHJTC"
      }
    },
    {
      "metadata": {
        "id": "CSWge30_7YEF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6a9458aa-9d6b-4347-e9ec-aea5cb85d560"
      },
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "\n",
        "# creating a model\n",
        "model = SVC()\n",
        "\n",
        "# feeding the training data into the model\n",
        "model.fit(x_train, y_train)\n",
        "\n",
        "# predicting the test set results\n",
        "y_pred = model.predict(x_test)\n",
        "\n",
        "# Calculating the accuracies\n",
        "print(\"Training accuracy :\", model.score(x_train, y_train))\n",
        "print(\"Testing accuarcy :\", model.score(x_test, y_test))\n",
        "\n",
        "# classification report\n",
        "cr = classification_report(y_test, y_pred)\n",
        "print(cr)\n",
        "\n",
        "# confusion matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy : 0.9882629107981221\n",
            "Testing accuarcy : 0.972027972027972\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.99      0.98        89\n",
            "           1       0.98      0.94      0.96        54\n",
            "\n",
            "    accuracy                           0.97       143\n",
            "   macro avg       0.97      0.97      0.97       143\n",
            "weighted avg       0.97      0.97      0.97       143\n",
            "\n",
            "[[88  1]\n",
            " [ 3 51]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# using grid search to find the best parameters for svm\n",
        "\n",
        "param = {\n",
        "    'C': [0.8,0.9,1,1.1,1.2,1.3,1.4],\n",
        "    'kernel':['linear', 'rbf'],\n",
        "    'gamma' :[0.1,0.8,0.9,1,1.1,1.2,1.3,1.4]\n",
        "}\n",
        "grid_svc = GridSearchCV(model, param_grid = param, scoring = 'accuracy', cv = 10)\n"
      ],
      "metadata": {
        "id": "WJui2A2IHQdu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "grid_svc.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "id": "hVmVtYwBIMnX",
        "outputId": "9adc6895-7c28-4e26-ea25-cb416aad3884"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=10, estimator=SVC(),\n",
              "             param_grid={'C': [0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4],\n",
              "                         'gamma': [0.1, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4],\n",
              "                         'kernel': ['linear', 'rbf']},\n",
              "             scoring='accuracy')"
            ],
            "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>GridSearchCV(cv=10, estimator=SVC(),\n",
              "             param_grid={&#x27;C&#x27;: [0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4],\n",
              "                         &#x27;gamma&#x27;: [0.1, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4],\n",
              "                         &#x27;kernel&#x27;: [&#x27;linear&#x27;, &#x27;rbf&#x27;]},\n",
              "             scoring=&#x27;accuracy&#x27;)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=SVC(),\n",
              "             param_grid={&#x27;C&#x27;: [0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4],\n",
              "                         &#x27;gamma&#x27;: [0.1, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4],\n",
              "                         &#x27;kernel&#x27;: [&#x27;linear&#x27;, &#x27;rbf&#x27;]},\n",
              "             scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div><div class=\"sk-serial\"><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\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grid_svc.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TlSSgNKaITrl",
        "outputId": "e3d1092b-1bb2-4275-ed81-62247f7e43f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'C': 1.3, 'gamma': 0.1, 'kernel': 'linear'}"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model2 = SVC(C = 1.3, gamma = 0.1, kernel = 'linear')\n",
        "model2.fit(x_train, y_train)\n",
        "y_pred = model2.predict(x_test)\n",
        "\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "print(\"Testing accuarcy :\", model.score(x_test, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IYxtKScAIeA2",
        "outputId": "56fffd4f-e63b-4fe1-f25c-8e901c64c688"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.99      0.98        89\n",
            "           1       0.98      0.94      0.96        54\n",
            "\n",
            "    accuracy                           0.97       143\n",
            "   macro avg       0.97      0.97      0.97       143\n",
            "weighted avg       0.97      0.97      0.97       143\n",
            "\n",
            "Testing accuarcy : 0.972027972027972\n"
          ]
        }
      ]
    }
  ]
}