Learning/3_logistic_regression.ipynb

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  "metadata": {
    "colab": {
      "name": "3_logistic_regression.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNeSQMLV6K0FMMKlD/wVP+b",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/34-anish/Machine-Learning/blob/master/3_logistic_regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "FW3x5SasCJEH"
      },
      "outputs": [],
      "source": [
        "from sklearn.datasets import load_breast_cancer\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = load_breast_cancer()\n",
        "X = pd.DataFrame(df['data'],columns = df['feature_names'])"
      ],
      "metadata": {
        "id": "v65mMaM5CTe-"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = pd.DataFrame(df['target'],columns =['Target'])"
      ],
      "metadata": {
        "id": "APg589MxDIZj"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Checking the data is balanced or not\n",
        "y.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-E9f5w6LDVq0",
        "outputId": "3e195a92-9319-4a02-c45b-ead467ddefc0"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Target\n",
              "1         357\n",
              "0         212\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Train Test Split\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 = .3,random_state = 42)"
      ],
      "metadata": {
        "id": "TXI6CDyUDnkp"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "jk5MZ1hCDyVW",
        "outputId": "0b375ba6-ea3b-4f5e-dce4-853281d5d986"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
              "149       13.740         17.91           88.12      585.0          0.07944   \n",
              "124       13.370         16.39           86.10      553.5          0.07115   \n",
              "421       14.690         13.98           98.22      656.1          0.10310   \n",
              "195       12.910         16.33           82.53      516.4          0.07941   \n",
              "545       13.620         23.23           87.19      573.2          0.09246   \n",
              "..           ...           ...             ...        ...              ...   \n",
              "71         8.888         14.64           58.79      244.0          0.09783   \n",
              "106       11.640         18.33           75.17      412.5          0.11420   \n",
              "270       14.290         16.82           90.30      632.6          0.06429   \n",
              "435       13.980         19.62           91.12      599.5          0.10600   \n",
              "102       12.180         20.52           77.22      458.7          0.08013   \n",
              "\n",
              "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
              "149           0.06376         0.02881              0.01329         0.1473   \n",
              "124           0.07325         0.08092              0.02800         0.1422   \n",
              "421           0.18360         0.14500              0.06300         0.2086   \n",
              "195           0.05366         0.03873              0.02377         0.1829   \n",
              "545           0.06747         0.02974              0.02443         0.1664   \n",
              "..                ...             ...                  ...            ...   \n",
              "71            0.15310         0.08606              0.02872         0.1902   \n",
              "106           0.10170         0.07070              0.03485         0.1801   \n",
              "270           0.02675         0.00725              0.00625         0.1508   \n",
              "435           0.11330         0.11260              0.06463         0.1669   \n",
              "102           0.04038         0.02383              0.01770         0.1739   \n",
              "\n",
              "     mean fractal dimension  ...  worst radius  worst texture  \\\n",
              "149                 0.05580  ...        15.340          22.46   \n",
              "124                 0.05823  ...        14.260          22.75   \n",
              "421                 0.07406  ...        16.460          18.34   \n",
              "195                 0.05667  ...        13.880          22.00   \n",
              "545                 0.05801  ...        15.350          29.09   \n",
              "..                      ...  ...           ...            ...   \n",
              "71                  0.08980  ...         9.733          15.67   \n",
              "106                 0.06520  ...        13.140          29.26   \n",
              "270                 0.05376  ...        14.910          20.65   \n",
              "435                 0.06544  ...        17.040          30.80   \n",
              "102                 0.05677  ...        13.340          32.84   \n",
              "\n",
              "     worst perimeter  worst area  worst smoothness  worst compactness  \\\n",
              "149            97.19       725.9           0.09711            0.18240   \n",
              "124            91.99       632.1           0.10250            0.25310   \n",
              "421           114.10       809.2           0.13120            0.36350   \n",
              "195            90.81       600.6           0.10970            0.15060   \n",
              "545            97.58       729.8           0.12160            0.15170   \n",
              "..               ...         ...               ...                ...   \n",
              "71             62.56       284.4           0.12070            0.24360   \n",
              "106            85.51       521.7           0.16880            0.26600   \n",
              "270            94.44       684.6           0.08567            0.05036   \n",
              "435           113.90       869.3           0.16130            0.35680   \n",
              "102            84.58       547.8           0.11230            0.08862   \n",
              "\n",
              "     worst concavity  worst concave points  worst symmetry  \\\n",
              "149          0.15640               0.06019          0.2350   \n",
              "124          0.33080               0.08978          0.2048   \n",
              "421          0.32190               0.11080          0.2827   \n",
              "195          0.17640               0.08235          0.3024   \n",
              "545          0.10490               0.07174          0.2642   \n",
              "..               ...                   ...             ...   \n",
              "71           0.14340               0.04786          0.2254   \n",
              "106          0.28730               0.12180          0.2806   \n",
              "270          0.03866               0.03333          0.2458   \n",
              "435          0.40690               0.18270          0.3179   \n",
              "102          0.11450               0.07431          0.2694   \n",
              "\n",
              "     worst fractal dimension  \n",
              "149                  0.07014  \n",
              "124                  0.07628  \n",
              "421                  0.09208  \n",
              "195                  0.06949  \n",
              "545                  0.06953  \n",
              "..                       ...  \n",
              "71                   0.10840  \n",
              "106                  0.09097  \n",
              "270                  0.06120  \n",
              "435                  0.10550  \n",
              "102                  0.06878  \n",
              "\n",
              "[398 rows x 30 columns]"
            ],
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              "\n",
              "  <div id=\"df-dba23a1a-52fb-4835-a7b7-11a399a9646d\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>...</th>\n",
              "      <th>worst radius</th>\n",
              "      <th>worst texture</th>\n",
              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>worst concave points</th>\n",
              "      <th>worst symmetry</th>\n",
              "      <th>worst fractal dimension</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>13.740</td>\n",
              "      <td>17.91</td>\n",
              "      <td>88.12</td>\n",
              "      <td>585.0</td>\n",
              "      <td>0.07944</td>\n",
              "      <td>0.06376</td>\n",
              "      <td>0.02881</td>\n",
              "      <td>0.01329</td>\n",
              "      <td>0.1473</td>\n",
              "      <td>0.05580</td>\n",
              "      <td>...</td>\n",
              "      <td>15.340</td>\n",
              "      <td>22.46</td>\n",
              "      <td>97.19</td>\n",
              "      <td>725.9</td>\n",
              "      <td>0.09711</td>\n",
              "      <td>0.18240</td>\n",
              "      <td>0.15640</td>\n",
              "      <td>0.06019</td>\n",
              "      <td>0.2350</td>\n",
              "      <td>0.07014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>13.370</td>\n",
              "      <td>16.39</td>\n",
              "      <td>86.10</td>\n",
              "      <td>553.5</td>\n",
              "      <td>0.07115</td>\n",
              "      <td>0.07325</td>\n",
              "      <td>0.08092</td>\n",
              "      <td>0.02800</td>\n",
              "      <td>0.1422</td>\n",
              "      <td>0.05823</td>\n",
              "      <td>...</td>\n",
              "      <td>14.260</td>\n",
              "      <td>22.75</td>\n",
              "      <td>91.99</td>\n",
              "      <td>632.1</td>\n",
              "      <td>0.10250</td>\n",
              "      <td>0.25310</td>\n",
              "      <td>0.33080</td>\n",
              "      <td>0.08978</td>\n",
              "      <td>0.2048</td>\n",
              "      <td>0.07628</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>421</th>\n",
              "      <td>14.690</td>\n",
              "      <td>13.98</td>\n",
              "      <td>98.22</td>\n",
              "      <td>656.1</td>\n",
              "      <td>0.10310</td>\n",
              "      <td>0.18360</td>\n",
              "      <td>0.14500</td>\n",
              "      <td>0.06300</td>\n",
              "      <td>0.2086</td>\n",
              "      <td>0.07406</td>\n",
              "      <td>...</td>\n",
              "      <td>16.460</td>\n",
              "      <td>18.34</td>\n",
              "      <td>114.10</td>\n",
              "      <td>809.2</td>\n",
              "      <td>0.13120</td>\n",
              "      <td>0.36350</td>\n",
              "      <td>0.32190</td>\n",
              "      <td>0.11080</td>\n",
              "      <td>0.2827</td>\n",
              "      <td>0.09208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>12.910</td>\n",
              "      <td>16.33</td>\n",
              "      <td>82.53</td>\n",
              "      <td>516.4</td>\n",
              "      <td>0.07941</td>\n",
              "      <td>0.05366</td>\n",
              "      <td>0.03873</td>\n",
              "      <td>0.02377</td>\n",
              "      <td>0.1829</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>...</td>\n",
              "      <td>13.880</td>\n",
              "      <td>22.00</td>\n",
              "      <td>90.81</td>\n",
              "      <td>600.6</td>\n",
              "      <td>0.10970</td>\n",
              "      <td>0.15060</td>\n",
              "      <td>0.17640</td>\n",
              "      <td>0.08235</td>\n",
              "      <td>0.3024</td>\n",
              "      <td>0.06949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>545</th>\n",
              "      <td>13.620</td>\n",
              "      <td>23.23</td>\n",
              "      <td>87.19</td>\n",
              "      <td>573.2</td>\n",
              "      <td>0.09246</td>\n",
              "      <td>0.06747</td>\n",
              "      <td>0.02974</td>\n",
              "      <td>0.02443</td>\n",
              "      <td>0.1664</td>\n",
              "      <td>0.05801</td>\n",
              "      <td>...</td>\n",
              "      <td>15.350</td>\n",
              "      <td>29.09</td>\n",
              "      <td>97.58</td>\n",
              "      <td>729.8</td>\n",
              "      <td>0.12160</td>\n",
              "      <td>0.15170</td>\n",
              "      <td>0.10490</td>\n",
              "      <td>0.07174</td>\n",
              "      <td>0.2642</td>\n",
              "      <td>0.06953</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",
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              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>8.888</td>\n",
              "      <td>14.64</td>\n",
              "      <td>58.79</td>\n",
              "      <td>244.0</td>\n",
              "      <td>0.09783</td>\n",
              "      <td>0.15310</td>\n",
              "      <td>0.08606</td>\n",
              "      <td>0.02872</td>\n",
              "      <td>0.1902</td>\n",
              "      <td>0.08980</td>\n",
              "      <td>...</td>\n",
              "      <td>9.733</td>\n",
              "      <td>15.67</td>\n",
              "      <td>62.56</td>\n",
              "      <td>284.4</td>\n",
              "      <td>0.12070</td>\n",
              "      <td>0.24360</td>\n",
              "      <td>0.14340</td>\n",
              "      <td>0.04786</td>\n",
              "      <td>0.2254</td>\n",
              "      <td>0.10840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
              "      <td>11.640</td>\n",
              "      <td>18.33</td>\n",
              "      <td>75.17</td>\n",
              "      <td>412.5</td>\n",
              "      <td>0.11420</td>\n",
              "      <td>0.10170</td>\n",
              "      <td>0.07070</td>\n",
              "      <td>0.03485</td>\n",
              "      <td>0.1801</td>\n",
              "      <td>0.06520</td>\n",
              "      <td>...</td>\n",
              "      <td>13.140</td>\n",
              "      <td>29.26</td>\n",
              "      <td>85.51</td>\n",
              "      <td>521.7</td>\n",
              "      <td>0.16880</td>\n",
              "      <td>0.26600</td>\n",
              "      <td>0.28730</td>\n",
              "      <td>0.12180</td>\n",
              "      <td>0.2806</td>\n",
              "      <td>0.09097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>270</th>\n",
              "      <td>14.290</td>\n",
              "      <td>16.82</td>\n",
              "      <td>90.30</td>\n",
              "      <td>632.6</td>\n",
              "      <td>0.06429</td>\n",
              "      <td>0.02675</td>\n",
              "      <td>0.00725</td>\n",
              "      <td>0.00625</td>\n",
              "      <td>0.1508</td>\n",
              "      <td>0.05376</td>\n",
              "      <td>...</td>\n",
              "      <td>14.910</td>\n",
              "      <td>20.65</td>\n",
              "      <td>94.44</td>\n",
              "      <td>684.6</td>\n",
              "      <td>0.08567</td>\n",
              "      <td>0.05036</td>\n",
              "      <td>0.03866</td>\n",
              "      <td>0.03333</td>\n",
              "      <td>0.2458</td>\n",
              "      <td>0.06120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>435</th>\n",
              "      <td>13.980</td>\n",
              "      <td>19.62</td>\n",
              "      <td>91.12</td>\n",
              "      <td>599.5</td>\n",
              "      <td>0.10600</td>\n",
              "      <td>0.11330</td>\n",
              "      <td>0.11260</td>\n",
              "      <td>0.06463</td>\n",
              "      <td>0.1669</td>\n",
              "      <td>0.06544</td>\n",
              "      <td>...</td>\n",
              "      <td>17.040</td>\n",
              "      <td>30.80</td>\n",
              "      <td>113.90</td>\n",
              "      <td>869.3</td>\n",
              "      <td>0.16130</td>\n",
              "      <td>0.35680</td>\n",
              "      <td>0.40690</td>\n",
              "      <td>0.18270</td>\n",
              "      <td>0.3179</td>\n",
              "      <td>0.10550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>12.180</td>\n",
              "      <td>20.52</td>\n",
              "      <td>77.22</td>\n",
              "      <td>458.7</td>\n",
              "      <td>0.08013</td>\n",
              "      <td>0.04038</td>\n",
              "      <td>0.02383</td>\n",
              "      <td>0.01770</td>\n",
              "      <td>0.1739</td>\n",
              "      <td>0.05677</td>\n",
              "      <td>...</td>\n",
              "      <td>13.340</td>\n",
              "      <td>32.84</td>\n",
              "      <td>84.58</td>\n",
              "      <td>547.8</td>\n",
              "      <td>0.11230</td>\n",
              "      <td>0.08862</td>\n",
              "      <td>0.11450</td>\n",
              "      <td>0.07431</td>\n",
              "      <td>0.2694</td>\n",
              "      <td>0.06878</td>\n",
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              "      \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",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-dba23a1a-52fb-4835-a7b7-11a399a9646d 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-dba23a1a-52fb-4835-a7b7-11a399a9646d');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import GridSearchCV"
      ],
      "metadata": {
        "id": "YYhCe85xEgid"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "params =[{'C':[1,5,10]},{'max_iter':[100,150]}]\n",
        "model1 = LogisticRegression(C=100,max_iter=100)\n",
        "model = GridSearchCV(model1,param_grid = params, scoring='f1',cv =5 )"
      ],
      "metadata": {
        "id": "YNxWuzB0EM0_"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "to4en-f2FYCG",
        "outputId": "695f02f2-4c78-434d-9ffe-cd875eb9845a"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=LogisticRegression(C=100),\n",
              "             param_grid=[{'C': [1, 5, 10]}, {'max_iter': [100, 150]}],\n",
              "             scoring='f1')"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vaVqV3hwFaYT",
        "outputId": "5b1e388e-e8cb-4c06-9d61-442d35ed885d"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'C': 5}"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.best_score_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "livOu-qnFh0o",
        "outputId": "b50b0daa-c630-4c6f-daab-297639227606"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9637405674708198"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)"
      ],
      "metadata": {
        "id": "R5qnKbxpFl0A"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix , classification_report"
      ],
      "metadata": {
        "id": "lr3e5HsIFoC1"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wEWWhDduF8FQ",
        "outputId": "82ee9a54-fdb2-4eaa-d262-31bc3a423f85"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.94      0.96        63\n",
            "           1       0.96      0.99      0.98       108\n",
            "\n",
            "    accuracy                           0.97       171\n",
            "   macro avg       0.97      0.96      0.97       171\n",
            "weighted avg       0.97      0.97      0.97       171\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5wPFsRiwFu4V",
        "outputId": "5fa1cd81-3f5b-4540-f6ed-f8bac891b2a8"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 59,   4],\n",
              "       [  1, 107]])"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "bicWUXWxFt9j"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}