{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "BUmEHpBBJtG1"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.datasets import load_breast_cancer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix,precision_score, recall_score,f1_score,roc_curve, roc_auc_score\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = load_breast_cancer()\n"
      ],
      "metadata": {
        "id": "n2wC1EiuJ3aT"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = pd.DataFrame(data.data, columns=data.feature_names)\n",
        "y = pd.Series(data.target)"
      ],
      "metadata": {
        "id": "LVsKpY3nJ3db"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X, y, test_size=0.25, random_state=42\n",
        ")"
      ],
      "metadata": {
        "id": "498a3iM2J3fb"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n"
      ],
      "metadata": {
        "id": "phEi-wQNJ3h-"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = LogisticRegression(max_iter=1000)\n",
        "model.fit(X_train, y_train)\n"
      ],
      "metadata": {
        "id": "HG6pa4-DJ3lB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "outputId": "20ee3b79-204f-4802-fe90-8257f89db8c6"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(max_iter=1000)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-2 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-2 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-2 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-2 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-2 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=1000)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)\n",
        "y_pred"
      ],
      "metadata": {
        "id": "YtRkVBKDJ3oD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c91bacdf-d775-4bac-b493-1e18506258ee"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1,\n",
              "       0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1,\n",
              "       0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1,\n",
              "       0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0,\n",
              "       1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "f\"accuracy: {round(accuracy,2)}\"\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "1Hpb6f-8szFM",
        "outputId": "5967111b-4b23-4346-e1be-bfe78b47d022"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'accuracy: 0.98'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(y_test, y_pred)\n",
        "cm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TEX1SGt_szIJ",
        "outputId": "7efa387c-1412-48f9-d7fc-d98bb8ad8c3b"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[53,  1],\n",
              "       [ 2, 87]])"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "precision = precision_score(y_test, y_pred)\n",
        "f\"Precision: {round(precision,2)}\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "EFXHunnIv8Vg",
        "outputId": "8c267c17-b110-4d9e-fc15-bcbb0f0d3b0f"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Precision: 0.99'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "recall = recall_score(y_test, y_pred)\n",
        "f\"Recall: {round(recall,2)}\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "AHGQQHn9v8Ye",
        "outputId": "281260de-a3c0-46ca-9bb0-2d784d8164b7"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Recall: 0.98'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f1 = f1_score(y_test, y_pred)\n",
        "f\"f1 score: {round(f1,2)}\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "eomPHDGf1wrR",
        "outputId": "7ccc2794-d257-4cef-9aeb-361c8a60cd54"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'f1 score: 0.98'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_prob = model.predict_proba(X_test)[:, 1]\n",
        "\n",
        "fpr, tpr, thresholds = roc_curve(y_test, y_prob)\n",
        "\n",
        "roc_auc = roc_auc_score(y_test, y_prob)\n",
        "\n",
        "plt.figure()\n",
        "plt.plot(fpr, tpr, label=f\"ROC Curve (AUC = {roc_auc:.2f})\")\n",
        "plt.plot([0, 1], [0, 1], linestyle=\"--\")\n",
        "plt.xlabel(\"False Positive Rate\")\n",
        "plt.ylabel(\"True Positive Rate\")\n",
        "plt.title(\"ROC Curve\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "WFh7Ej221wuC",
        "outputId": "43709e58-3824-4450-c124-c24367b03252"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_TutvwJe6Ohj"
      },
      "execution_count": 38,
      "outputs": []
    }
  ]
}