Logistic_Regression_Binary.ipynb

{
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    {
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        "<a href=\"https://colab.research.google.com/github/kingsmen732/etc/blob/main/Logistic_Regression_Binary.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cfafe2b6-2875-461d-9b76-bc6c8aad04d4",
      "metadata": {
        "id": "cfafe2b6-2875-461d-9b76-bc6c8aad04d4"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "50e67223-3e3c-4967-8e60-4fb6bb22f145",
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        "outputId": "20c4e827-0a58-4b7c-b114-588a24f68aae"
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      "outputs": [
        {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>6</td>\n",
              "      <td>148</td>\n",
              "      <td>72</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>33.6</td>\n",
              "      <td>0.627</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>85</td>\n",
              "      <td>66</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>26.6</td>\n",
              "      <td>0.351</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>183</td>\n",
              "      <td>64</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>23.3</td>\n",
              "      <td>0.672</td>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
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              "      <th>3</th>\n",
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              "      <td>89</td>\n",
              "      <td>66</td>\n",
              "      <td>23</td>\n",
              "      <td>94</td>\n",
              "      <td>28.1</td>\n",
              "      <td>0.167</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
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              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>137</td>\n",
              "      <td>40</td>\n",
              "      <td>35</td>\n",
              "      <td>168</td>\n",
              "      <td>43.1</td>\n",
              "      <td>2.288</td>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
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              "    <tr>\n",
              "      <th>763</th>\n",
              "      <td>10</td>\n",
              "      <td>101</td>\n",
              "      <td>76</td>\n",
              "      <td>48</td>\n",
              "      <td>180</td>\n",
              "      <td>32.9</td>\n",
              "      <td>0.171</td>\n",
              "      <td>63</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>764</th>\n",
              "      <td>2</td>\n",
              "      <td>122</td>\n",
              "      <td>70</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>36.8</td>\n",
              "      <td>0.340</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>765</th>\n",
              "      <td>5</td>\n",
              "      <td>121</td>\n",
              "      <td>72</td>\n",
              "      <td>23</td>\n",
              "      <td>112</td>\n",
              "      <td>26.2</td>\n",
              "      <td>0.245</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>766</th>\n",
              "      <td>1</td>\n",
              "      <td>126</td>\n",
              "      <td>60</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.1</td>\n",
              "      <td>0.349</td>\n",
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              "      <td>93</td>\n",
              "      <td>70</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>30.4</td>\n",
              "      <td>0.315</td>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
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              "<p>768 rows × 9 columns</p>\n",
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            ],
            "text/plain": [
              "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "0              6      148             72             35        0  33.6   \n",
              "1              1       85             66             29        0  26.6   \n",
              "2              8      183             64              0        0  23.3   \n",
              "3              1       89             66             23       94  28.1   \n",
              "4              0      137             40             35      168  43.1   \n",
              "..           ...      ...            ...            ...      ...   ...   \n",
              "763           10      101             76             48      180  32.9   \n",
              "764            2      122             70             27        0  36.8   \n",
              "765            5      121             72             23      112  26.2   \n",
              "766            1      126             60              0        0  30.1   \n",
              "767            1       93             70             31        0  30.4   \n",
              "\n",
              "     DiabetesPedigreeFunction  Age  Outcome  \n",
              "0                       0.627   50        1  \n",
              "1                       0.351   31        0  \n",
              "2                       0.672   32        1  \n",
              "3                       0.167   21        0  \n",
              "4                       2.288   33        1  \n",
              "..                        ...  ...      ...  \n",
              "763                     0.171   63        0  \n",
              "764                     0.340   27        0  \n",
              "765                     0.245   30        0  \n",
              "766                     0.349   47        1  \n",
              "767                     0.315   23        0  \n",
              "\n",
              "[768 rows x 9 columns]"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df=pd.read_csv('Desktop/diabetes.csv')\n",
        "df\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "218527e0-ff9e-4f25-a22d-9c8ded68884b",
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      "outputs": [
        {
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              "      <th>SkinThickness</th>\n",
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              "      <td>89</td>\n",
              "      <td>66</td>\n",
              "      <td>23</td>\n",
              "      <td>94</td>\n",
              "      <td>28.1</td>\n",
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              "      <td>21</td>\n",
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              "      <td>137</td>\n",
              "      <td>40</td>\n",
              "      <td>35</td>\n",
              "      <td>168</td>\n",
              "      <td>43.1</td>\n",
              "      <td>2.288</td>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
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              "      <td>116</td>\n",
              "      <td>74</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>25.6</td>\n",
              "      <td>0.201</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>3</td>\n",
              "      <td>78</td>\n",
              "      <td>50</td>\n",
              "      <td>32</td>\n",
              "      <td>88</td>\n",
              "      <td>31.0</td>\n",
              "      <td>0.248</td>\n",
              "      <td>26</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>10</td>\n",
              "      <td>115</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>35.3</td>\n",
              "      <td>0.134</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2</td>\n",
              "      <td>197</td>\n",
              "      <td>70</td>\n",
              "      <td>45</td>\n",
              "      <td>543</td>\n",
              "      <td>30.5</td>\n",
              "      <td>0.158</td>\n",
              "      <td>53</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>8</td>\n",
              "      <td>125</td>\n",
              "      <td>96</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.232</td>\n",
              "      <td>54</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>4</td>\n",
              "      <td>110</td>\n",
              "      <td>92</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>37.6</td>\n",
              "      <td>0.191</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>10</td>\n",
              "      <td>168</td>\n",
              "      <td>74</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>0.537</td>\n",
              "      <td>34</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>10</td>\n",
              "      <td>139</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>27.1</td>\n",
              "      <td>1.441</td>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1</td>\n",
              "      <td>189</td>\n",
              "      <td>60</td>\n",
              "      <td>23</td>\n",
              "      <td>846</td>\n",
              "      <td>30.1</td>\n",
              "      <td>0.398</td>\n",
              "      <td>59</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>5</td>\n",
              "      <td>166</td>\n",
              "      <td>72</td>\n",
              "      <td>19</td>\n",
              "      <td>175</td>\n",
              "      <td>25.8</td>\n",
              "      <td>0.587</td>\n",
              "      <td>51</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>7</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0.484</td>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0</td>\n",
              "      <td>118</td>\n",
              "      <td>84</td>\n",
              "      <td>47</td>\n",
              "      <td>230</td>\n",
              "      <td>45.8</td>\n",
              "      <td>0.551</td>\n",
              "      <td>31</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>7</td>\n",
              "      <td>107</td>\n",
              "      <td>74</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>29.6</td>\n",
              "      <td>0.254</td>\n",
              "      <td>31</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>1</td>\n",
              "      <td>103</td>\n",
              "      <td>30</td>\n",
              "      <td>38</td>\n",
              "      <td>83</td>\n",
              "      <td>43.3</td>\n",
              "      <td>0.183</td>\n",
              "      <td>33</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1</td>\n",
              "      <td>115</td>\n",
              "      <td>70</td>\n",
              "      <td>30</td>\n",
              "      <td>96</td>\n",
              "      <td>34.6</td>\n",
              "      <td>0.529</td>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "0             6      148             72             35        0  33.6   \n",
              "1             1       85             66             29        0  26.6   \n",
              "2             8      183             64              0        0  23.3   \n",
              "3             1       89             66             23       94  28.1   \n",
              "4             0      137             40             35      168  43.1   \n",
              "5             5      116             74              0        0  25.6   \n",
              "6             3       78             50             32       88  31.0   \n",
              "7            10      115              0              0        0  35.3   \n",
              "8             2      197             70             45      543  30.5   \n",
              "9             8      125             96              0        0   0.0   \n",
              "10            4      110             92              0        0  37.6   \n",
              "11           10      168             74              0        0  38.0   \n",
              "12           10      139             80              0        0  27.1   \n",
              "13            1      189             60             23      846  30.1   \n",
              "14            5      166             72             19      175  25.8   \n",
              "15            7      100              0              0        0  30.0   \n",
              "16            0      118             84             47      230  45.8   \n",
              "17            7      107             74              0        0  29.6   \n",
              "18            1      103             30             38       83  43.3   \n",
              "19            1      115             70             30       96  34.6   \n",
              "\n",
              "    DiabetesPedigreeFunction  Age  Outcome  \n",
              "0                      0.627   50        1  \n",
              "1                      0.351   31        0  \n",
              "2                      0.672   32        1  \n",
              "3                      0.167   21        0  \n",
              "4                      2.288   33        1  \n",
              "5                      0.201   30        0  \n",
              "6                      0.248   26        1  \n",
              "7                      0.134   29        0  \n",
              "8                      0.158   53        1  \n",
              "9                      0.232   54        1  \n",
              "10                     0.191   30        0  \n",
              "11                     0.537   34        1  \n",
              "12                     1.441   57        0  \n",
              "13                     0.398   59        1  \n",
              "14                     0.587   51        1  \n",
              "15                     0.484   32        1  \n",
              "16                     0.551   31        1  \n",
              "17                     0.254   31        1  \n",
              "18                     0.183   33        0  \n",
              "19                     0.529   32        1  "
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head(20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e5119b84-d7e3-4b7f-81c5-2501e88893d5",
      "metadata": {
        "id": "e5119b84-d7e3-4b7f-81c5-2501e88893d5",
        "outputId": "cabfe394-cd78-46dd-cf9b-9058cfdaf5a2"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Pregnancies</th>\n",
              "      <td>6.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>8.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>5.000</td>\n",
              "      <td>3.000</td>\n",
              "      <td>10.000</td>\n",
              "      <td>2.000</td>\n",
              "      <td>8.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Glucose</th>\n",
              "      <td>148.000</td>\n",
              "      <td>85.000</td>\n",
              "      <td>183.000</td>\n",
              "      <td>89.000</td>\n",
              "      <td>137.000</td>\n",
              "      <td>116.000</td>\n",
              "      <td>78.000</td>\n",
              "      <td>115.000</td>\n",
              "      <td>197.000</td>\n",
              "      <td>125.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BloodPressure</th>\n",
              "      <td>72.000</td>\n",
              "      <td>66.000</td>\n",
              "      <td>64.000</td>\n",
              "      <td>66.000</td>\n",
              "      <td>40.000</td>\n",
              "      <td>74.000</td>\n",
              "      <td>50.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>70.000</td>\n",
              "      <td>96.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SkinThickness</th>\n",
              "      <td>35.000</td>\n",
              "      <td>29.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>23.000</td>\n",
              "      <td>35.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>32.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>45.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Insulin</th>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>94.000</td>\n",
              "      <td>168.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>88.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>543.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BMI</th>\n",
              "      <td>33.600</td>\n",
              "      <td>26.600</td>\n",
              "      <td>23.300</td>\n",
              "      <td>28.100</td>\n",
              "      <td>43.100</td>\n",
              "      <td>25.600</td>\n",
              "      <td>31.000</td>\n",
              "      <td>35.300</td>\n",
              "      <td>30.500</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <td>0.627</td>\n",
              "      <td>0.351</td>\n",
              "      <td>0.672</td>\n",
              "      <td>0.167</td>\n",
              "      <td>2.288</td>\n",
              "      <td>0.201</td>\n",
              "      <td>0.248</td>\n",
              "      <td>0.134</td>\n",
              "      <td>0.158</td>\n",
              "      <td>0.232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>50.000</td>\n",
              "      <td>31.000</td>\n",
              "      <td>32.000</td>\n",
              "      <td>21.000</td>\n",
              "      <td>33.000</td>\n",
              "      <td>30.000</td>\n",
              "      <td>26.000</td>\n",
              "      <td>29.000</td>\n",
              "      <td>53.000</td>\n",
              "      <td>54.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Outcome</th>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                0       1        2       3        4        5  \\\n",
              "Pregnancies                 6.000   1.000    8.000   1.000    0.000    5.000   \n",
              "Glucose                   148.000  85.000  183.000  89.000  137.000  116.000   \n",
              "BloodPressure              72.000  66.000   64.000  66.000   40.000   74.000   \n",
              "SkinThickness              35.000  29.000    0.000  23.000   35.000    0.000   \n",
              "Insulin                     0.000   0.000    0.000  94.000  168.000    0.000   \n",
              "BMI                        33.600  26.600   23.300  28.100   43.100   25.600   \n",
              "DiabetesPedigreeFunction    0.627   0.351    0.672   0.167    2.288    0.201   \n",
              "Age                        50.000  31.000   32.000  21.000   33.000   30.000   \n",
              "Outcome                     1.000   0.000    1.000   0.000    1.000    0.000   \n",
              "\n",
              "                               6        7        8        9  \n",
              "Pregnancies                3.000   10.000    2.000    8.000  \n",
              "Glucose                   78.000  115.000  197.000  125.000  \n",
              "BloodPressure             50.000    0.000   70.000   96.000  \n",
              "SkinThickness             32.000    0.000   45.000    0.000  \n",
              "Insulin                   88.000    0.000  543.000    0.000  \n",
              "BMI                       31.000   35.300   30.500    0.000  \n",
              "DiabetesPedigreeFunction   0.248    0.134    0.158    0.232  \n",
              "Age                       26.000   29.000   53.000   54.000  \n",
              "Outcome                    1.000    0.000    1.000    1.000  "
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head(10).transpose()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "43876979-433c-464e-96a5-d1aaf7de9f0e",
      "metadata": {
        "id": "43876979-433c-464e-96a5-d1aaf7de9f0e",
        "outputId": "59beda6b-4285-48b7-a381-ab3982aa1237"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Pregnancies                   int64\n",
              "Glucose                       int64\n",
              "BloodPressure                 int64\n",
              "SkinThickness                 int64\n",
              "Insulin                       int64\n",
              "BMI                         float64\n",
              "DiabetesPedigreeFunction    float64\n",
              "Age                           int64\n",
              "Outcome                       int64\n",
              "dtype: object"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "163e1d84-d21e-40ac-b5c4-b369a987d676",
      "metadata": {
        "id": "163e1d84-d21e-40ac-b5c4-b369a987d676",
        "outputId": "6b82f0bb-aeed-419b-cfc5-3473afb1b2bf"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<bound method NDFrame.describe of      Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "0              6      148             72             35        0  33.6   \n",
              "1              1       85             66             29        0  26.6   \n",
              "2              8      183             64              0        0  23.3   \n",
              "3              1       89             66             23       94  28.1   \n",
              "4              0      137             40             35      168  43.1   \n",
              "..           ...      ...            ...            ...      ...   ...   \n",
              "763           10      101             76             48      180  32.9   \n",
              "764            2      122             70             27        0  36.8   \n",
              "765            5      121             72             23      112  26.2   \n",
              "766            1      126             60              0        0  30.1   \n",
              "767            1       93             70             31        0  30.4   \n",
              "\n",
              "     DiabetesPedigreeFunction  Age  Outcome  \n",
              "0                       0.627   50        1  \n",
              "1                       0.351   31        0  \n",
              "2                       0.672   32        1  \n",
              "3                       0.167   21        0  \n",
              "4                       2.288   33        1  \n",
              "..                        ...  ...      ...  \n",
              "763                     0.171   63        0  \n",
              "764                     0.340   27        0  \n",
              "765                     0.245   30        0  \n",
              "766                     0.349   47        1  \n",
              "767                     0.315   23        0  \n",
              "\n",
              "[768 rows x 9 columns]>"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.describe"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e2951b4f-e253-417b-9855-af8c7a4c53de",
      "metadata": {
        "id": "e2951b4f-e253-417b-9855-af8c7a4c53de",
        "outputId": "7cc48c64-2399-4e49-ed89-2347e7283f87"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6</td>\n",
              "      <td>148</td>\n",
              "      <td>72</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>33.6</td>\n",
              "      <td>0.627</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>85</td>\n",
              "      <td>66</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>26.6</td>\n",
              "      <td>0.351</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>183</td>\n",
              "      <td>64</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>23.3</td>\n",
              "      <td>0.672</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>89</td>\n",
              "      <td>66</td>\n",
              "      <td>23</td>\n",
              "      <td>94</td>\n",
              "      <td>28.1</td>\n",
              "      <td>0.167</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>137</td>\n",
              "      <td>40</td>\n",
              "      <td>35</td>\n",
              "      <td>168</td>\n",
              "      <td>43.1</td>\n",
              "      <td>2.288</td>\n",
              "      <td>33</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>763</th>\n",
              "      <td>10</td>\n",
              "      <td>101</td>\n",
              "      <td>76</td>\n",
              "      <td>48</td>\n",
              "      <td>180</td>\n",
              "      <td>32.9</td>\n",
              "      <td>0.171</td>\n",
              "      <td>63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>764</th>\n",
              "      <td>2</td>\n",
              "      <td>122</td>\n",
              "      <td>70</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>36.8</td>\n",
              "      <td>0.340</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>765</th>\n",
              "      <td>5</td>\n",
              "      <td>121</td>\n",
              "      <td>72</td>\n",
              "      <td>23</td>\n",
              "      <td>112</td>\n",
              "      <td>26.2</td>\n",
              "      <td>0.245</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>766</th>\n",
              "      <td>1</td>\n",
              "      <td>126</td>\n",
              "      <td>60</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.1</td>\n",
              "      <td>0.349</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>767</th>\n",
              "      <td>1</td>\n",
              "      <td>93</td>\n",
              "      <td>70</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>30.4</td>\n",
              "      <td>0.315</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>768 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "0              6      148             72             35        0  33.6   \n",
              "1              1       85             66             29        0  26.6   \n",
              "2              8      183             64              0        0  23.3   \n",
              "3              1       89             66             23       94  28.1   \n",
              "4              0      137             40             35      168  43.1   \n",
              "..           ...      ...            ...            ...      ...   ...   \n",
              "763           10      101             76             48      180  32.9   \n",
              "764            2      122             70             27        0  36.8   \n",
              "765            5      121             72             23      112  26.2   \n",
              "766            1      126             60              0        0  30.1   \n",
              "767            1       93             70             31        0  30.4   \n",
              "\n",
              "     DiabetesPedigreeFunction  Age  \n",
              "0                       0.627   50  \n",
              "1                       0.351   31  \n",
              "2                       0.672   32  \n",
              "3                       0.167   21  \n",
              "4                       2.288   33  \n",
              "..                        ...  ...  \n",
              "763                     0.171   63  \n",
              "764                     0.340   27  \n",
              "765                     0.245   30  \n",
              "766                     0.349   47  \n",
              "767                     0.315   23  \n",
              "\n",
              "[768 rows x 8 columns]"
            ]
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X=df.drop(\"Outcome\",axis=1)\n",
        "X"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "afab26bc-84a2-4014-ba27-49276fe6fae9",
      "metadata": {
        "id": "afab26bc-84a2-4014-ba27-49276fe6fae9",
        "outputId": "8ca36d9c-0675-4481-9c5f-20b9c4ceeffb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0      1\n",
              "1      0\n",
              "2      1\n",
              "3      0\n",
              "4      1\n",
              "      ..\n",
              "763    0\n",
              "764    0\n",
              "765    0\n",
              "766    1\n",
              "767    0\n",
              "Name: Outcome, Length: 768, dtype: int64"
            ]
          },
          "execution_count": 51,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Y=df[\"Outcome\"]\n",
        "Y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ab17ee73-00c5-4ccc-ba18-488d7051c2db",
      "metadata": {
        "id": "ab17ee73-00c5-4ccc-ba18-488d7051c2db"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test=train_test_split(X,Y,test_size=0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d99eb3d1-519b-41d7-8583-c086d111f4e0",
      "metadata": {
        "id": "d99eb3d1-519b-41d7-8583-c086d111f4e0"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "a = LogisticRegression()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5a1f2f99-9644-4e33-9141-4c963746a548",
      "metadata": {
        "id": "5a1f2f99-9644-4e33-9141-4c963746a548",
        "outputId": "938846bf-36c6-468e-e669-0fa65c736d1c"
      },
      "outputs": [
        {
          "ename": "NameError",
          "evalue": "name 'a' is not defined",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "Cell \u001b[1;32mIn[8], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m a\u001b[38;5;241m.\u001b[39mfit(x_train,y_train)\n",
            "\u001b[1;31mNameError\u001b[0m: name 'a' is not defined"
          ]
        }
      ],
      "source": [
        "a.fit(x_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "95e57736-fad9-4eae-ae3f-93537d6cc4a6",
      "metadata": {
        "id": "95e57736-fad9-4eae-ae3f-93537d6cc4a6",
        "outputId": "2faf0999-de00-4deb-ef38-847ba6a60ea3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
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            ]
          },
          "execution_count": 55,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pred=a.predict(x_train)\n",
        "pred\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "855930c7-5dab-4e8d-9023-788aa02802dc",
      "metadata": {
        "id": "855930c7-5dab-4e8d-9023-788aa02802dc",
        "outputId": "05bdd2e0-df3b-4cb5-b49a-ffcbde36aea0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "614"
            ]
          },
          "execution_count": 56,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(x_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "129a1dd0-2c78-46b2-b7d8-f54a874c9790",
      "metadata": {
        "id": "129a1dd0-2c78-46b2-b7d8-f54a874c9790",
        "outputId": "087c537c-6103-4bd4-8087-517afb630037"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "154"
            ]
          },
          "execution_count": 57,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e25835c2-7b8d-44fc-9bab-347e96d3c98d",
      "metadata": {
        "id": "e25835c2-7b8d-44fc-9bab-347e96d3c98d",
        "outputId": "d04dde63-e1d4-4074-dc9f-bf112fd2c11b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
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          },
          "execution_count": 58,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pred1=a.predict(x_test)\n",
        "pred1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "eb4a889c-e1d8-4239-8a93-f426a0fceca3",
      "metadata": {
        "id": "eb4a889c-e1d8-4239-8a93-f426a0fceca3",
        "outputId": "edf409dd-0586-44ff-9892-dc2ddfca0768"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.762214983713355"
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "a.score(x_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9d62e0cf-e024-40f2-9f53-e8808453557e",
      "metadata": {
        "id": "9d62e0cf-e024-40f2-9f53-e8808453557e",
        "outputId": "421541d3-91f0-4cfa-f016-6b43cbf42124"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8246753246753247"
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "m=a.score(x_test,y_test)\n",
        "m"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9144e99a-0ffc-49ce-a417-38700847b794",
      "metadata": {
        "id": "9144e99a-0ffc-49ce-a417-38700847b794",
        "outputId": "4ecfef42-e70a-4b33-97b0-80f0f95871bb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8246753246753247"
            ]
          },
          "execution_count": 74,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "m=a.score(x_test,y_test)\n",
        "m"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ce292381-b307-49a0-8c53-3ae92544b5b4",
      "metadata": {
        "id": "ce292381-b307-49a0-8c53-3ae92544b5b4",
        "outputId": "9affe310-4a12-460a-c4b0-ba328eaaf638"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.8246753246753247\n",
            "[[348  50]\n",
            " [ 96 120]]\n"
          ]
        }
      ],
      "source": [
        "print(m)\n",
        "from sklearn import metrics\n",
        "print(metrics.confusion_matrix(y_train,pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "55b19b00-100a-4d3e-bbed-739ed456e24d",
      "metadata": {
        "id": "55b19b00-100a-4d3e-bbed-739ed456e24d",
        "outputId": "bfd9dc70-7a55-4715-f89e-269a0cbc2e32"
      },
      "outputs": [
        {
          "data": {
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>714</th>\n",
              "      <td>3</td>\n",
              "      <td>102</td>\n",
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              "      <td>0</td>\n",
              "      <td>29.5</td>\n",
              "      <td>0.121</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
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              "      <th>546</th>\n",
              "      <td>5</td>\n",
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              "      <td>8</td>\n",
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              "      <td>23</td>\n",
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              "      <td>32.0</td>\n",
              "      <td>0.600</td>\n",
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              "      <td>35</td>\n",
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              "      <td>64</td>\n",
              "      <td>13</td>\n",
              "      <td>387</td>\n",
              "      <td>31.2</td>\n",
              "      <td>0.295</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>556</th>\n",
              "      <td>1</td>\n",
              "      <td>97</td>\n",
              "      <td>70</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>38.1</td>\n",
              "      <td>0.218</td>\n",
              "      <td>30</td>\n",
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              "      <td>230</td>\n",
              "      <td>45.8</td>\n",
              "      <td>0.551</td>\n",
              "      <td>31</td>\n",
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              "      <td>0.427</td>\n",
              "      <td>23</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>154 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "714            3      102             74              0        0  29.5   \n",
              "546            5      187             76             27      207  43.6   \n",
              "737            8       65             72             23        0  32.0   \n",
              "126            3      120             70             30      135  42.9   \n",
              "302            5       77             82             41       42  35.8   \n",
              "..           ...      ...            ...            ...      ...   ...   \n",
              "620            2      112             86             42      160  38.4   \n",
              "710            3      158             64             13      387  31.2   \n",
              "556            1       97             70             40        0  38.1   \n",
              "16             0      118             84             47      230  45.8   \n",
              "247            0      165             90             33      680  52.3   \n",
              "\n",
              "     DiabetesPedigreeFunction  Age  \n",
              "714                     0.121   32  \n",
              "546                     1.034   53  \n",
              "737                     0.600   42  \n",
              "126                     0.452   30  \n",
              "302                     0.156   35  \n",
              "..                        ...  ...  \n",
              "620                     0.246   28  \n",
              "710                     0.295   24  \n",
              "556                     0.218   30  \n",
              "16                      0.551   31  \n",
              "247                     0.427   23  \n",
              "\n",
              "[154 rows x 8 columns]"
            ]
          },
          "execution_count": 75,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d2b9b692-0bf8-46c2-a6ad-22b909682a56",
      "metadata": {
        "id": "d2b9b692-0bf8-46c2-a6ad-22b909682a56",
        "outputId": "01d77d90-5bd5-4aa7-f7be-cdebf6807b51"
      },
      "outputs": [
        {
          "data": {
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Actual Outcome</th>\n",
              "      <th>Predicted Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>714</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>546</th>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>737</th>\n",
              "      <td>0</td>\n",
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              "      <th>126</th>\n",
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              "      <th>302</th>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>620</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>710</th>\n",
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              "    <tr>\n",
              "      <th>556</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1</td>\n",
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              "      <th>247</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>154 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Actual Outcome  Predicted Outcome\n",
              "714               0                  0\n",
              "546               1                  1\n",
              "737               0                  0\n",
              "126               0                  0\n",
              "302               0                  0\n",
              "..              ...                ...\n",
              "620               0                  0\n",
              "710               0                  0\n",
              "556               0                  0\n",
              "16                1                  0\n",
              "247               0                  1\n",
              "\n",
              "[154 rows x 2 columns]"
            ]
          },
          "execution_count": 80,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "output=pd.DataFrame( {'Actual Outcome':y_test,'Predicted Outcome' : pred1})\n",
        "output"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "741aadf5-acd5-401b-a5f2-257dca2989e9",
      "metadata": {
        "id": "741aadf5-acd5-401b-a5f2-257dca2989e9",
        "outputId": "aa512c68-8409-4b9e-d599-d1965bd61120"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8246753246753247"
            ]
          },
          "execution_count": 81,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "a.score(x_test,y_test)"
      ]
    },
    {
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