Breast_Cancer_Detection.ipynb

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    "colab": {
      "name": "Breast Cancer Detection.ipynb",
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        "id": "fKrOkg623SiO"
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      "source": [
        "# Breast Cancer Detection\n",
        "#description: this program detaset breast cancer, based off of data."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7410xgGJWukC",
        "outputId": "3eeca9ce-f98c-43bf-820c-faba3321d252"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import  matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "fNIh_-5U-AGy"
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      "execution_count": 80,
      "outputs": []
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      "source": [
        "#load the data\n",
        "# https://www.kaggle.com/uciml/breast-cancer-wisconsin-data\n",
        "df = pd.read_csv('/content/drive/MyDrive/data.csv')\n",
        "df.head()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
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        "id": "vcVybtCPXVgW",
        "outputId": "0187f5fe-c201-40aa-d1bb-edf798853e9c"
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      "outputs": [
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              "      <th>radius_worst</th>\n",
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              "      <td>153.40</td>\n",
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              "      <td>132.90</td>\n",
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              "      <td>0.7339</td>\n",
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              "      <td>0.01860</td>\n",
              "      <td>0.01340</td>\n",
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              "      <td>23.41</td>\n",
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              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
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              "      <td>1203.0</td>\n",
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              "      <td>0.15990</td>\n",
              "      <td>0.1974</td>\n",
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              "      <td>0.2069</td>\n",
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              "      <td>0.7869</td>\n",
              "      <td>4.585</td>\n",
              "      <td>94.03</td>\n",
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              "      <td>0.02058</td>\n",
              "      <td>0.02250</td>\n",
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              "      <td>26.50</td>\n",
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              "      <td>567.7</td>\n",
              "      <td>0.2098</td>\n",
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              "      <td>0.6638</td>\n",
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              "      <th>4</th>\n",
              "      <td>84358402</td>\n",
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              "      <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",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>0.7572</td>\n",
              "      <td>0.7813</td>\n",
              "      <td>5.438</td>\n",
              "      <td>94.44</td>\n",
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            "text/plain": [
              "         id diagnosis  ...  fractal_dimension_worst  Unnamed: 32\n",
              "0    842302         M  ...                  0.11890          NaN\n",
              "1    842517         M  ...                  0.08902          NaN\n",
              "2  84300903         M  ...                  0.08758          NaN\n",
              "3  84348301         M  ...                  0.17300          NaN\n",
              "4  84358402         M  ...                  0.07678          NaN\n",
              "\n",
              "[5 rows x 33 columns]"
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          "metadata": {},
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        }
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    {
      "cell_type": "code",
      "source": [
        "#count the number of rows and columns in the dataset\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bX7mJiDCTSxp",
        "outputId": "cc9d5ff8-ae33-441c-af7a-d3136a44c2cf"
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      "outputs": [
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              "(569, 33)"
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "#count the number of empty(Nan,NaN, na) values in each column\n",
        "df.isna().sum()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mEMdVINMUSpU",
        "outputId": "1a66294f-1393-4713-e94e-ccc1604e3889"
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      "execution_count": 83,
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              "id                           0\n",
              "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",
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              "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",
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              "fractal_dimension_se         0\n",
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              "perimeter_worst              0\n",
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              "fractal_dimension_worst      0\n",
              "Unnamed: 32                569\n",
              "dtype: int64"
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          "execution_count": 83
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      "cell_type": "code",
      "source": [
        "#drop the columns with all missing values\n",
        "df= df.drop(\"Unnamed: 32\",axis='columns')\n",
        "df\n",
        "# ards.drop(['Car_Model','BMW X5'],axis='columns')"
      ],
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              "      <th>smoothness_se</th>\n",
              "      <th>compactness_se</th>\n",
              "      <th>concavity_se</th>\n",
              "      <th>concave points_se</th>\n",
              "      <th>symmetry_se</th>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>842302</td>\n",
              "      <td>M</td>\n",
              "      <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",
              "      <td>0.27760</td>\n",
              "      <td>0.30010</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
              "      <td>1.0950</td>\n",
              "      <td>0.9053</td>\n",
              "      <td>8.589</td>\n",
              "      <td>153.40</td>\n",
              "      <td>0.006399</td>\n",
              "      <td>0.04904</td>\n",
              "      <td>0.05373</td>\n",
              "      <td>0.01587</td>\n",
              "      <td>0.03003</td>\n",
              "      <td>0.006193</td>\n",
              "      <td>25.380</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.16220</td>\n",
              "      <td>0.66560</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "    </tr>\n",
              "    <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>0.08690</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>0.5435</td>\n",
              "      <td>0.7339</td>\n",
              "      <td>3.398</td>\n",
              "      <td>74.08</td>\n",
              "      <td>0.005225</td>\n",
              "      <td>0.01308</td>\n",
              "      <td>0.01860</td>\n",
              "      <td>0.01340</td>\n",
              "      <td>0.01389</td>\n",
              "      <td>0.003532</td>\n",
              "      <td>24.990</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.12380</td>\n",
              "      <td>0.18660</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <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>0.19740</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>0.7456</td>\n",
              "      <td>0.7869</td>\n",
              "      <td>4.585</td>\n",
              "      <td>94.03</td>\n",
              "      <td>0.006150</td>\n",
              "      <td>0.04006</td>\n",
              "      <td>0.03832</td>\n",
              "      <td>0.02058</td>\n",
              "      <td>0.02250</td>\n",
              "      <td>0.004571</td>\n",
              "      <td>23.570</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.14440</td>\n",
              "      <td>0.42450</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "    </tr>\n",
              "    <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",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.24140</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>0.4956</td>\n",
              "      <td>1.1560</td>\n",
              "      <td>3.445</td>\n",
              "      <td>27.23</td>\n",
              "      <td>0.009110</td>\n",
              "      <td>0.07458</td>\n",
              "      <td>0.05661</td>\n",
              "      <td>0.01867</td>\n",
              "      <td>0.05963</td>\n",
              "      <td>0.009208</td>\n",
              "      <td>14.910</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.20980</td>\n",
              "      <td>0.86630</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "    </tr>\n",
              "    <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>0.19800</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>0.7572</td>\n",
              "      <td>0.7813</td>\n",
              "      <td>5.438</td>\n",
              "      <td>94.44</td>\n",
              "      <td>0.011490</td>\n",
              "      <td>0.02461</td>\n",
              "      <td>0.05688</td>\n",
              "      <td>0.01885</td>\n",
              "      <td>0.01756</td>\n",
              "      <td>0.005115</td>\n",
              "      <td>22.540</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.13740</td>\n",
              "      <td>0.20500</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>564</th>\n",
              "      <td>926424</td>\n",
              "      <td>M</td>\n",
              "      <td>21.56</td>\n",
              "      <td>22.39</td>\n",
              "      <td>142.00</td>\n",
              "      <td>1479.0</td>\n",
              "      <td>0.11100</td>\n",
              "      <td>0.11590</td>\n",
              "      <td>0.24390</td>\n",
              "      <td>0.13890</td>\n",
              "      <td>0.1726</td>\n",
              "      <td>0.05623</td>\n",
              "      <td>1.1760</td>\n",
              "      <td>1.2560</td>\n",
              "      <td>7.673</td>\n",
              "      <td>158.70</td>\n",
              "      <td>0.010300</td>\n",
              "      <td>0.02891</td>\n",
              "      <td>0.05198</td>\n",
              "      <td>0.02454</td>\n",
              "      <td>0.01114</td>\n",
              "      <td>0.004239</td>\n",
              "      <td>25.450</td>\n",
              "      <td>26.40</td>\n",
              "      <td>166.10</td>\n",
              "      <td>2027.0</td>\n",
              "      <td>0.14100</td>\n",
              "      <td>0.21130</td>\n",
              "      <td>0.4107</td>\n",
              "      <td>0.2216</td>\n",
              "      <td>0.2060</td>\n",
              "      <td>0.07115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>565</th>\n",
              "      <td>926682</td>\n",
              "      <td>M</td>\n",
              "      <td>20.13</td>\n",
              "      <td>28.25</td>\n",
              "      <td>131.20</td>\n",
              "      <td>1261.0</td>\n",
              "      <td>0.09780</td>\n",
              "      <td>0.10340</td>\n",
              "      <td>0.14400</td>\n",
              "      <td>0.09791</td>\n",
              "      <td>0.1752</td>\n",
              "      <td>0.05533</td>\n",
              "      <td>0.7655</td>\n",
              "      <td>2.4630</td>\n",
              "      <td>5.203</td>\n",
              "      <td>99.04</td>\n",
              "      <td>0.005769</td>\n",
              "      <td>0.02423</td>\n",
              "      <td>0.03950</td>\n",
              "      <td>0.01678</td>\n",
              "      <td>0.01898</td>\n",
              "      <td>0.002498</td>\n",
              "      <td>23.690</td>\n",
              "      <td>38.25</td>\n",
              "      <td>155.00</td>\n",
              "      <td>1731.0</td>\n",
              "      <td>0.11660</td>\n",
              "      <td>0.19220</td>\n",
              "      <td>0.3215</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>0.2572</td>\n",
              "      <td>0.06637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>566</th>\n",
              "      <td>926954</td>\n",
              "      <td>M</td>\n",
              "      <td>16.60</td>\n",
              "      <td>28.08</td>\n",
              "      <td>108.30</td>\n",
              "      <td>858.1</td>\n",
              "      <td>0.08455</td>\n",
              "      <td>0.10230</td>\n",
              "      <td>0.09251</td>\n",
              "      <td>0.05302</td>\n",
              "      <td>0.1590</td>\n",
              "      <td>0.05648</td>\n",
              "      <td>0.4564</td>\n",
              "      <td>1.0750</td>\n",
              "      <td>3.425</td>\n",
              "      <td>48.55</td>\n",
              "      <td>0.005903</td>\n",
              "      <td>0.03731</td>\n",
              "      <td>0.04730</td>\n",
              "      <td>0.01557</td>\n",
              "      <td>0.01318</td>\n",
              "      <td>0.003892</td>\n",
              "      <td>18.980</td>\n",
              "      <td>34.12</td>\n",
              "      <td>126.70</td>\n",
              "      <td>1124.0</td>\n",
              "      <td>0.11390</td>\n",
              "      <td>0.30940</td>\n",
              "      <td>0.3403</td>\n",
              "      <td>0.1418</td>\n",
              "      <td>0.2218</td>\n",
              "      <td>0.07820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>567</th>\n",
              "      <td>927241</td>\n",
              "      <td>M</td>\n",
              "      <td>20.60</td>\n",
              "      <td>29.33</td>\n",
              "      <td>140.10</td>\n",
              "      <td>1265.0</td>\n",
              "      <td>0.11780</td>\n",
              "      <td>0.27700</td>\n",
              "      <td>0.35140</td>\n",
              "      <td>0.15200</td>\n",
              "      <td>0.2397</td>\n",
              "      <td>0.07016</td>\n",
              "      <td>0.7260</td>\n",
              "      <td>1.5950</td>\n",
              "      <td>5.772</td>\n",
              "      <td>86.22</td>\n",
              "      <td>0.006522</td>\n",
              "      <td>0.06158</td>\n",
              "      <td>0.07117</td>\n",
              "      <td>0.01664</td>\n",
              "      <td>0.02324</td>\n",
              "      <td>0.006185</td>\n",
              "      <td>25.740</td>\n",
              "      <td>39.42</td>\n",
              "      <td>184.60</td>\n",
              "      <td>1821.0</td>\n",
              "      <td>0.16500</td>\n",
              "      <td>0.86810</td>\n",
              "      <td>0.9387</td>\n",
              "      <td>0.2650</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.12400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568</th>\n",
              "      <td>92751</td>\n",
              "      <td>B</td>\n",
              "      <td>7.76</td>\n",
              "      <td>24.54</td>\n",
              "      <td>47.92</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.05263</td>\n",
              "      <td>0.04362</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.1587</td>\n",
              "      <td>0.05884</td>\n",
              "      <td>0.3857</td>\n",
              "      <td>1.4280</td>\n",
              "      <td>2.548</td>\n",
              "      <td>19.15</td>\n",
              "      <td>0.007189</td>\n",
              "      <td>0.00466</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.02676</td>\n",
              "      <td>0.002783</td>\n",
              "      <td>9.456</td>\n",
              "      <td>30.37</td>\n",
              "      <td>59.16</td>\n",
              "      <td>268.6</td>\n",
              "      <td>0.08996</td>\n",
              "      <td>0.06444</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.2871</td>\n",
              "      <td>0.07039</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>569 rows × 32 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-27a8c129-d3ac-4f80-8a82-0c3d41fdeec9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "          const element = document.querySelector('#df-27a8c129-d3ac-4f80-8a82-0c3d41fdeec9');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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              "          if (!dataTable) return;\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "           id diagnosis  ...  symmetry_worst  fractal_dimension_worst\n",
              "0      842302         M  ...          0.4601                  0.11890\n",
              "1      842517         M  ...          0.2750                  0.08902\n",
              "2    84300903         M  ...          0.3613                  0.08758\n",
              "3    84348301         M  ...          0.6638                  0.17300\n",
              "4    84358402         M  ...          0.2364                  0.07678\n",
              "..        ...       ...  ...             ...                      ...\n",
              "564    926424         M  ...          0.2060                  0.07115\n",
              "565    926682         M  ...          0.2572                  0.06637\n",
              "566    926954         M  ...          0.2218                  0.07820\n",
              "567    927241         M  ...          0.4087                  0.12400\n",
              "568     92751         B  ...          0.2871                  0.07039\n",
              "\n",
              "[569 rows x 32 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#get the now cout of the number of rows and columns\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ojyUEua2Ait_",
        "outputId": "5ca15edf-82f1-4ce6-ef95-921826327353"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 32)"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# get a count of the number of Malognant (M) or benign(B) cells\n",
        "df['diagnosis'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QZm2yW1IAxL6",
        "outputId": "df22b564-c59e-42bc-eb6a-09bf6422a5b0"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "B    357\n",
              "M    212\n",
              "Name: diagnosis, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Visualize the count\n",
        "sns.countplot(df['diagnosis'], label='count')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "EDxtmhigB-nd",
        "outputId": "3bb30f7c-35ad-435c-801e-b4af52435c13"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f10e69cce50>"
            ]
          },
          "metadata": {},
          "execution_count": 87
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#look the data types to see which columns need to be encoded\n",
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "huQ7IHHfCdDn",
        "outputId": "ad479044-c0f0-41cc-ee7d-79f84d3076fb"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                           int64\n",
              "diagnosis                   object\n",
              "radius_mean                float64\n",
              "texture_mean               float64\n",
              "perimeter_mean             float64\n",
              "area_mean                  float64\n",
              "smoothness_mean            float64\n",
              "compactness_mean           float64\n",
              "concavity_mean             float64\n",
              "concave points_mean        float64\n",
              "symmetry_mean              float64\n",
              "fractal_dimension_mean     float64\n",
              "radius_se                  float64\n",
              "texture_se                 float64\n",
              "perimeter_se               float64\n",
              "area_se                    float64\n",
              "smoothness_se              float64\n",
              "compactness_se             float64\n",
              "concavity_se               float64\n",
              "concave points_se          float64\n",
              "symmetry_se                float64\n",
              "fractal_dimension_se       float64\n",
              "radius_worst               float64\n",
              "texture_worst              float64\n",
              "perimeter_worst            float64\n",
              "area_worst                 float64\n",
              "smoothness_worst           float64\n",
              "compactness_worst          float64\n",
              "concavity_worst            float64\n",
              "concave points_worst       float64\n",
              "symmetry_worst             float64\n",
              "fractal_dimension_worst    float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#encode the categorical data values\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "labelencoder_Y = LabelEncoder()\n",
        "df.iloc[:,1] = labelencoder_Y.fit_transform(df.iloc[:,1].values)\n"
      ],
      "metadata": {
        "id": "jucijwpkKvT2"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#create a pair plot\n",
        "sns.pairplot(df.iloc[:,1:5],hue ='diagnosis')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "id": "J2Hy1UtaLeRA",
        "outputId": "c2e9a9ba-f75b-475d-90f6-c9f5a7d718a1"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f10e68ba210>"
            ]
          },
          "metadata": {},
          "execution_count": 90
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 595.5x540 with 12 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# print firt five rows of the dataset\n",
        "df.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "-rJgvkB1OV8R",
        "outputId": "c1bc47c7-e5eb-42ca-fb53-009cd36da8cf"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-a9ea0123-7a45-46cc-a1de-fd881953c6f2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>diagnosis</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",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <th>radius_se</th>\n",
              "      <th>texture_se</th>\n",
              "      <th>perimeter_se</th>\n",
              "      <th>area_se</th>\n",
              "      <th>smoothness_se</th>\n",
              "      <th>compactness_se</th>\n",
              "      <th>concavity_se</th>\n",
              "      <th>concave points_se</th>\n",
              "      <th>symmetry_se</th>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>842302</td>\n",
              "      <td>1</td>\n",
              "      <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",
              "      <td>0.27760</td>\n",
              "      <td>0.3001</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
              "      <td>1.0950</td>\n",
              "      <td>0.9053</td>\n",
              "      <td>8.589</td>\n",
              "      <td>153.40</td>\n",
              "      <td>0.006399</td>\n",
              "      <td>0.04904</td>\n",
              "      <td>0.05373</td>\n",
              "      <td>0.01587</td>\n",
              "      <td>0.03003</td>\n",
              "      <td>0.006193</td>\n",
              "      <td>25.38</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>842517</td>\n",
              "      <td>1</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>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>0.5435</td>\n",
              "      <td>0.7339</td>\n",
              "      <td>3.398</td>\n",
              "      <td>74.08</td>\n",
              "      <td>0.005225</td>\n",
              "      <td>0.01308</td>\n",
              "      <td>0.01860</td>\n",
              "      <td>0.01340</td>\n",
              "      <td>0.01389</td>\n",
              "      <td>0.003532</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",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>84300903</td>\n",
              "      <td>1</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>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>0.7456</td>\n",
              "      <td>0.7869</td>\n",
              "      <td>4.585</td>\n",
              "      <td>94.03</td>\n",
              "      <td>0.006150</td>\n",
              "      <td>0.04006</td>\n",
              "      <td>0.03832</td>\n",
              "      <td>0.02058</td>\n",
              "      <td>0.02250</td>\n",
              "      <td>0.004571</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>84348301</td>\n",
              "      <td>1</td>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.2414</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>0.4956</td>\n",
              "      <td>1.1560</td>\n",
              "      <td>3.445</td>\n",
              "      <td>27.23</td>\n",
              "      <td>0.009110</td>\n",
              "      <td>0.07458</td>\n",
              "      <td>0.05661</td>\n",
              "      <td>0.01867</td>\n",
              "      <td>0.05963</td>\n",
              "      <td>0.009208</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>84358402</td>\n",
              "      <td>1</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",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>0.7572</td>\n",
              "      <td>0.7813</td>\n",
              "      <td>5.438</td>\n",
              "      <td>94.44</td>\n",
              "      <td>0.011490</td>\n",
              "      <td>0.02461</td>\n",
              "      <td>0.05688</td>\n",
              "      <td>0.01885</td>\n",
              "      <td>0.01756</td>\n",
              "      <td>0.005115</td>\n",
              "      <td>22.54</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.1374</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a9ea0123-7a45-46cc-a1de-fd881953c6f2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a9ea0123-7a45-46cc-a1de-fd881953c6f2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a9ea0123-7a45-46cc-a1de-fd881953c6f2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         id  diagnosis  ...  symmetry_worst  fractal_dimension_worst\n",
              "0    842302          1  ...          0.4601                  0.11890\n",
              "1    842517          1  ...          0.2750                  0.08902\n",
              "2  84300903          1  ...          0.3613                  0.08758\n",
              "3  84348301          1  ...          0.6638                  0.17300\n",
              "4  84358402          1  ...          0.2364                  0.07678\n",
              "\n",
              "[5 rows x 32 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# se the correlation of the columns\n",
        "df.iloc[:,1:12].corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "4lFvqN93Pukx",
        "outputId": "a4902447-3228-436d-bbc7-da5cc6ec7d5b"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-6c6bbcd1-8de0-42de-b025-f323597486a0\">\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>diagnosis</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",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>diagnosis</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.730029</td>\n",
              "      <td>0.415185</td>\n",
              "      <td>0.742636</td>\n",
              "      <td>0.708984</td>\n",
              "      <td>0.358560</td>\n",
              "      <td>0.596534</td>\n",
              "      <td>0.696360</td>\n",
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              "      <td>0.330499</td>\n",
              "      <td>-0.012838</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>radius_mean</th>\n",
              "      <td>0.730029</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.323782</td>\n",
              "      <td>0.997855</td>\n",
              "      <td>0.987357</td>\n",
              "      <td>0.170581</td>\n",
              "      <td>0.506124</td>\n",
              "      <td>0.676764</td>\n",
              "      <td>0.822529</td>\n",
              "      <td>0.147741</td>\n",
              "      <td>-0.311631</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>texture_mean</th>\n",
              "      <td>0.415185</td>\n",
              "      <td>0.323782</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.329533</td>\n",
              "      <td>0.321086</td>\n",
              "      <td>-0.023389</td>\n",
              "      <td>0.236702</td>\n",
              "      <td>0.302418</td>\n",
              "      <td>0.293464</td>\n",
              "      <td>0.071401</td>\n",
              "      <td>-0.076437</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>perimeter_mean</th>\n",
              "      <td>0.742636</td>\n",
              "      <td>0.997855</td>\n",
              "      <td>0.329533</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.986507</td>\n",
              "      <td>0.207278</td>\n",
              "      <td>0.556936</td>\n",
              "      <td>0.716136</td>\n",
              "      <td>0.850977</td>\n",
              "      <td>0.183027</td>\n",
              "      <td>-0.261477</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>area_mean</th>\n",
              "      <td>0.708984</td>\n",
              "      <td>0.987357</td>\n",
              "      <td>0.321086</td>\n",
              "      <td>0.986507</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.177028</td>\n",
              "      <td>0.498502</td>\n",
              "      <td>0.685983</td>\n",
              "      <td>0.823269</td>\n",
              "      <td>0.151293</td>\n",
              "      <td>-0.283110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoothness_mean</th>\n",
              "      <td>0.358560</td>\n",
              "      <td>0.170581</td>\n",
              "      <td>-0.023389</td>\n",
              "      <td>0.207278</td>\n",
              "      <td>0.177028</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.659123</td>\n",
              "      <td>0.521984</td>\n",
              "      <td>0.553695</td>\n",
              "      <td>0.557775</td>\n",
              "      <td>0.584792</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>compactness_mean</th>\n",
              "      <td>0.596534</td>\n",
              "      <td>0.506124</td>\n",
              "      <td>0.236702</td>\n",
              "      <td>0.556936</td>\n",
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              "      <td>1.000000</td>\n",
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              "      <td>0.831135</td>\n",
              "      <td>0.602641</td>\n",
              "      <td>0.565369</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concavity_mean</th>\n",
              "      <td>0.696360</td>\n",
              "      <td>0.676764</td>\n",
              "      <td>0.302418</td>\n",
              "      <td>0.716136</td>\n",
              "      <td>0.685983</td>\n",
              "      <td>0.521984</td>\n",
              "      <td>0.883121</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.921391</td>\n",
              "      <td>0.500667</td>\n",
              "      <td>0.336783</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concave points_mean</th>\n",
              "      <td>0.776614</td>\n",
              "      <td>0.822529</td>\n",
              "      <td>0.293464</td>\n",
              "      <td>0.850977</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>0.462497</td>\n",
              "      <td>0.166917</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>symmetry_mean</th>\n",
              "      <td>0.330499</td>\n",
              "      <td>0.147741</td>\n",
              "      <td>0.071401</td>\n",
              "      <td>0.183027</td>\n",
              "      <td>0.151293</td>\n",
              "      <td>0.557775</td>\n",
              "      <td>0.602641</td>\n",
              "      <td>0.500667</td>\n",
              "      <td>0.462497</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.479921</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <td>-0.012838</td>\n",
              "      <td>-0.311631</td>\n",
              "      <td>-0.076437</td>\n",
              "      <td>-0.261477</td>\n",
              "      <td>-0.283110</td>\n",
              "      <td>0.584792</td>\n",
              "      <td>0.565369</td>\n",
              "      <td>0.336783</td>\n",
              "      <td>0.166917</td>\n",
              "      <td>0.479921</td>\n",
              "      <td>1.000000</td>\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                        diagnosis  ...  fractal_dimension_mean\n",
              "diagnosis                1.000000  ...               -0.012838\n",
              "radius_mean              0.730029  ...               -0.311631\n",
              "texture_mean             0.415185  ...               -0.076437\n",
              "perimeter_mean           0.742636  ...               -0.261477\n",
              "area_mean                0.708984  ...               -0.283110\n",
              "smoothness_mean          0.358560  ...                0.584792\n",
              "compactness_mean         0.596534  ...                0.565369\n",
              "concavity_mean           0.696360  ...                0.336783\n",
              "concave points_mean      0.776614  ...                0.166917\n",
              "symmetry_mean            0.330499  ...                0.479921\n",
              "fractal_dimension_mean  -0.012838  ...                1.000000\n",
              "\n",
              "[11 rows x 11 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#visualize the correlation\n",
        "plt.figure(figsize=(10,10))\n",
        "sns.heatmap(df.iloc[:,1:12].corr(), annot =True, fmt = '.0%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 725
        },
        "id": "UE6YKrOmQE7-",
        "outputId": "cf03bbbe-958f-4db2-fd08-744878b50819"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f10e647b510>"
            ]
          },
          "metadata": {},
          "execution_count": 93
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#split the datset into independent (X) and dependent (y) dataset\n",
        "X = df.iloc[:,2:31].values\n",
        "Y = df.iloc[:,1].values\n"
      ],
      "metadata": {
        "id": "9TcIopSiQlAu"
      },
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#split the dataset into 75% training and 25% testing\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,Y_train,Y_test = train_test_split(X, Y, test_size= 0.25, random_state = 0) \n"
      ],
      "metadata": {
        "id": "vr3XbVACSsvC"
      },
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Scale the data(Feature Scaling)\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.fit_transform(X_test)\n"
      ],
      "metadata": {
        "id": "9iEPGGpJUHOK"
      },
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from pandas.core.common import random_state\n",
        "#create a function for the model\n",
        "def models(X_train, Y_train):\n",
        "\n",
        "  #Logistic Regression\n",
        "  from sklearn.linear_model import LogisticRegression\n",
        "  log = LogisticRegression(random_state=0)\n",
        "  log.fit(X_train, Y_train)\n",
        "\n",
        "  #decision Tree\n",
        "  from sklearn.tree import DecisionTreeClassifier\n",
        "  tree = DecisionTreeClassifier(criterion = 'entropy', random_state=0)\n",
        "  tree.fit(X_train, Y_train)\n",
        "\n",
        "  #random forest classifier\n",
        "  from sklearn.ensemble import RandomForestClassifier\n",
        "  forest = RandomForestClassifier(n_estimators =10, criterion = 'entropy', random_state = 0)\n",
        "  forest.fit(X_train, Y_train)\n",
        "\n",
        "  #print the model accuracy on the training data\n",
        "  print('[0]Logistic Regression Training Accueacy:', log.score(X_train,Y_train))\n",
        "  print('[1]Decision Tree Classifier Training Accueacy:', tree.score(X_train,Y_train))\n",
        "  print('[2] Randm Forest Classifier Training Accueacy:', forest.score(X_train,Y_train))\n",
        "\n",
        "  return log,tree,forest"
      ],
      "metadata": {
        "id": "H4KTVCtGUyFV"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#getting all the models\n",
        "model = models(X_train,Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4NTypqKmZzYi",
        "outputId": "9eaed7d0-eaf6-48b6-a726-6c971383047e"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]Logistic Regression Training Accueacy: 0.9906103286384976\n",
            "[1]Decision Tree Classifier Training Accueacy: 1.0\n",
            "[2] Randm Forest Classifier Training Accueacy: 0.9953051643192489\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# test model sccuracy on tests dataon confusion matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "for i in range(len(model) ):\n",
        "  print('Model', i)\n",
        "  cm = confusion_matrix(Y_test, model[i].predict(X_test))\n",
        "  \n",
        "  TP = cm[0][0]\n",
        "  TN = cm[1][1]\n",
        "  FN = cm[1][0]\n",
        "  FP = cm[0][1]\n",
        "\n",
        "  print(cm)\n",
        "  print('Teseting Accuracy = ', (TP + TN)/ (TP + TN + FN + FP))\n",
        "  print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lmF_cZruaDJq",
        "outputId": "79d01051-6809-4aeb-cf4d-83c621777cca"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model 0\n",
            "[[86  4]\n",
            " [ 3 50]]\n",
            "Teseting Accuracy =  0.951048951048951\n",
            "\n",
            "Model 1\n",
            "[[83  7]\n",
            " [ 2 51]]\n",
            "Teseting Accuracy =  0.9370629370629371\n",
            "\n",
            "Model 2\n",
            "[[87  3]\n",
            " [ 2 51]]\n",
            "Teseting Accuracy =  0.965034965034965\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#show another way to get metrics of the models\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "for i in range(len(model) ):\n",
        "  print('Model', i)\n",
        "  print( classification_report(Y_test, model[i].predict(X_test)))\n",
        "  print(accuracy_score(Y_test,model[i].predict(X_test)))\n",
        "  print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "psjvlMc4b4dl",
        "outputId": "4afb7eae-09f6-4300-fa91-cae5ebe97873"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model 0\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.96      0.96        90\n",
            "           1       0.93      0.94      0.93        53\n",
            "\n",
            "    accuracy                           0.95       143\n",
            "   macro avg       0.95      0.95      0.95       143\n",
            "weighted avg       0.95      0.95      0.95       143\n",
            "\n",
            "0.951048951048951\n",
            "\n",
            "Model 1\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.92      0.95        90\n",
            "           1       0.88      0.96      0.92        53\n",
            "\n",
            "    accuracy                           0.94       143\n",
            "   macro avg       0.93      0.94      0.93       143\n",
            "weighted avg       0.94      0.94      0.94       143\n",
            "\n",
            "0.9370629370629371\n",
            "\n",
            "Model 2\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.97      0.97        90\n",
            "           1       0.94      0.96      0.95        53\n",
            "\n",
            "    accuracy                           0.97       143\n",
            "   macro avg       0.96      0.96      0.96       143\n",
            "weighted avg       0.97      0.97      0.97       143\n",
            "\n",
            "0.965034965034965\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#print the prediction of random foest classifier model\n",
        "pred = model[2].predict(X_test)\n",
        "print(pred)\n",
        "print()\n",
        "print(Y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m-9vSUW0gCjr",
        "outputId": "fc07bc3e-0828-4e04-bc8d-8f384fdb9a51"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            " 1 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 1]\n",
            "\n",
            "[1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 0 0 1 0 0 1 0 1 0 1 0 1 0 1 0\n",
            " 1 0 1 1 0 1 0 0 1 0 0 0 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 1 0 1 1 1 0 0 1 0 1\n",
            " 1 0 0 0 0 0 1 1 1 0 1 0 0 0 1 1 0 1 0 1 0 0 1 0 0 0 0 0 0 0 1 0 1 0 1 1 0\n",
            " 1 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "aMPvIVYshYeE"
      },
      "execution_count": 101,
      "outputs": []
    }
  ]
}