肝病患者分類.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## 1. 前置動作"
      ],
      "metadata": {
        "id": "fTXNktty9opx"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PC_1WcFCRsPh",
        "outputId": "9bbc1a2f-2926-4730-aca8-8f2848bdfb80"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/gdown/cli.py:131: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  category=FutureWarning,\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1Y2gYY8XUWgcIA_GbytBuXoRkLlAWxnAF\n",
            "To: /content/project1_indian_liver_patient.zip\n",
            "100% 8.37k/8.37k [00:00<00:00, 7.55MB/s]\n",
            "Archive:  project1_indian_liver_patient.zip\n",
            "  inflating: project1_test.csv       \n",
            "  inflating: project1_train.csv      \n"
          ]
        }
      ],
      "source": [
        "# Download from Google Drive\n",
        "!gdown --id 1Y2gYY8XUWgcIA_GbytBuXoRkLlAWxnAFc\n",
        "\n",
        "!unzip project1_indian_liver_patient.zip\n",
        "# if seeing the message: \"replace project1_test.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename:\"\n",
        "# you may enter \"A\""
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import warnings\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
        "from sklearn.utils import resample\n",
        "\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "VMYFaaJDRyyI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('project1_train.csv')\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KEKD-eoiR1Vg",
        "outputId": "00091e73-8e4b-4206-eb06-38c26c00edbc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 467 entries, 0 to 466\n",
            "Data columns (total 11 columns):\n",
            " #   Column                      Non-Null Count  Dtype  \n",
            "---  ------                      --------------  -----  \n",
            " 0   Age                         467 non-null    int64  \n",
            " 1   Gender                      467 non-null    object \n",
            " 2   Total_Bilirubin             467 non-null    float64\n",
            " 3   Direct_Bilirubin            467 non-null    float64\n",
            " 4   Alkaline_Phosphotase        467 non-null    int64  \n",
            " 5   Alamine_Aminotransferase    467 non-null    int64  \n",
            " 6   Aspartate_Aminotransferase  467 non-null    int64  \n",
            " 7   Total_Protiens              467 non-null    float64\n",
            " 8   Albumin                     467 non-null    float64\n",
            " 9   Albumin_and_Globulin_Ratio  463 non-null    float64\n",
            " 10  Label                       467 non-null    int64  \n",
            "dtypes: float64(5), int64(5), object(1)\n",
            "memory usage: 40.3+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. 圖表分析"
      ],
      "metadata": {
        "id": "9553IKvmrRzw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 畫出熱力圖\n",
        "plt.subplots(figsize=(12, 10))\n",
        "plt.title('Pearson Correlation of Features')\n",
        "sns.heatmap(df.corr(),linewidths=0.25, vmax=1.0, square=True,annot=True)\n",
        "plt.show()\n",
        "\n",
        "new_df = df.drop(['Total_Bilirubin', 'Alamine_Aminotransferase', 'Total_Protiens', 'Albumin_and_Globulin_Ratio', 'Gender', 'Label'], axis = 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 732
        },
        "id": "BxWHqeOFpXhq",
        "outputId": "22921e3d-142e-4a52-9bb0-9b657566b1c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v4VVY3nk1Zlh",
        "outputId": "be101315-1b7c-4ac8-d828-77fab402ca21"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Age', 'Direct_Bilirubin', 'Alkaline_Phosphotase',\n",
              "       'Aspartate_Aminotransferase', 'Albumin'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 查看各資料的偏度和峰度\n",
        "plt.figsize = (10, 30)\n",
        "\n",
        "skewness = round(new_df['Age'].skew(), 2)\n",
        "kurtosis = round(new_df['Age'].kurt(), 2)\n",
        "\n",
        "print('Age 偏度(Skewness):',skewness, '峰度(Kurtosis):',kurtosis)\n",
        "\n",
        "sns.histplot(new_df['Age'], kde = True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "wobbe4Q4sHWs",
        "outputId": "f45326c5-852e-4fd7-acb8-07bcbc299680"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Age 偏度(Skewness): -0.11 峰度(Kurtosis): -0.57\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figsize = (10, 30)\n",
        "\n",
        "skewness = round(new_df['Direct_Bilirubin'].skew(), 2)\n",
        "kurtosis = round(new_df['Direct_Bilirubin'].kurt(), 2)\n",
        "\n",
        "print('Direct_Bilirubin 偏度(Skewness):',skewness, '峰度(Kurtosis):',kurtosis)\n",
        "\n",
        "sns.histplot(new_df['Direct_Bilirubin'], kde = True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "iON-mppbyaH9",
        "outputId": "457a8923-7a4f-432e-9ccb-4ca3fde0c519"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Direct_Bilirubin 偏度(Skewness): 3.17 峰度(Kurtosis): 11.06\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figsize = (10, 30)\n",
        "\n",
        "skewness = round(new_df['Alkaline_Phosphotase'].skew(), 2)\n",
        "kurtosis = round(new_df['Alkaline_Phosphotase'].kurt(), 2)\n",
        "\n",
        "print('Alkaline_Phosphotase 偏度(Skewness):',skewness, '峰度(Kurtosis):',kurtosis)\n",
        "\n",
        "sns.histplot(new_df['Alkaline_Phosphotase'], kde = True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "txSOQVg8ygT4",
        "outputId": "d1dab1ce-760b-4fe3-aa5d-6d571535e3bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Alkaline_Phosphotase 偏度(Skewness): 3.8 峰度(Kurtosis): 18.36\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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oOXiM7W0dLJxRnrZ11tXV8bWHlvLQymbaD+ynOwH//IuVyUQhIpIvlBT66D2fsGhm+pICwJRZc6iZv4hFl1xMQdToLqtO6/pFRNJBSaGPVU1tFMYizJ8yLiPrj0aMGRWjaemM4a67m0Ukvygp9LGquY2Lp42jMF13rfWjpnIMxxIRmg6q11QRyS860ZziWHecdTvaed+VNcNeR7ynm/r6+hPK6uvricdLk+PV44PnPa9p6eH2YW9JRCT9lBRSrNvRTnfcWXQGJ5lbmhvZ0NbG+s6yZFn9cyuZOHsB54fjY4pilMXivLSn+wwjFhFJLyWFFL2d1S08w5PMldNqqJm/KDm+s7HhpHkmFnfzWluUfYc7GV+iy1JFJD/onEKKVU1tVI8fTWUWDtITi3pw4OmGzPX0KiIyVEoKIXdndXPbGdcSBmtcLMG4IvWaKiL5RUkh1LTvKHsPd1E7syIr2zODSycW8MxrrXT26CokEckPSgqhVRm6ae10Fk8q4HBnDy9s2pe1bYqInI6SQmhVcxulRTFmTyjJ2jbnV8YoKYrx23V6drOI5AclhdDqpjYunVlOJGJZ22ZB1Lhm7gSWrm+hJ54YeAERkQxTUgAOHuumoeXQGd2fMFS9N7lVxw6w/0gX3//1s3R25qy3cBERQEkBgDXNB3DP7vmEluZGfvbCBjZt200E5zvPNqrXVBHJOSUFgpPMEYOLp2emE7xTqZxWw+yLFlFdWcLBoip1kCciOaekAKxubmPOpLGUFhfkZPvnTyjhWCJCY7suTRWR3BrxSSGecF5qPsCimWUDz5whNZVjMJyVu9QXkojk1ohPCg27D3G4syer5xP6Ki6IUlkYZ8WubjUhiUhOjfiksHLrfoCs3cl8KlOKu2k5kmDtjvacxiEiI9uITwrLt+xjyrhippWPymkck4u7iUXgFy/tzGkcIjKyjeik4O6s2LKfJbPGY5a9m9b6UxiBSyYU8Kv6ncQTakISkdwY0Ulhc+sR9h7uYklNbpuOer1hagGthzp5YfPeXIciIiPUiE4Ky7cEHdFdlidJ4dKJBYwtjvHwqu25DkVERqgRnRRWbNlPVWkRNZVjch0KAIVR46ZLpvL4ut20d+jyVBHJvqwnBTObbmZPmdmrZvaKmX00LK8ws6VmtjF8z+g1ou7O8sb9LKmpyPn5hFTvrp1OZ0+CR1/WCWcRyb5c1BR6gE+6+zzgcuDDZjYPuBtY5u6zgWXheMY07z/K7oPH8uZ8Qq8Lp45l7qRSfrJyW65DEZERKOtJwd13ufvqcPgQsB6YCtwE3B/Odj9wcybjWL4luD9hyazxmdzMkJkZt102g7U72lmz7UCuwxGRESan5xTMrBq4FFgOTHT3XeGk3cDEUyxzl5nVmVlda+vwH3q/vHE/FWMKs/pQncH604VTGVMY5YE/bs11KCIywuQsKZhZCfAw8DF3P5g6zYO+Hvq9WN/d73H3WnevraqqGvb2l2/Zx+Lq8rw6n9CrtLiAP1s0jV+/vIt9h/WMBRHJnpwkBTMrIEgID7r7I2Fxi5lNDqdPBvZkavs7DnSwva2DJTX51XSU6j2vn0lXPMGDy5tzHYqIjCC5uPrIgO8B6939qymTHgXuCIfvAH6ZqRhWhPcnLJmVXyeZU50/oZQ3z6ni/he20tGlLrVFJDtyUVO4AvhL4BozWxO+bgS+CFxvZhuB68LxjLh+3iTuu3MxcyeNzdQm0uJDV5/PviNd/HSVrkQSkeyIZXuD7v4ccKqG/GuzEUNJUYyr50zIxqbOyOLqchbNLOc/n2nk1sUzKIyN6HsNRSQLdJTJY2bGR645nx0HOnioTrUFEck8JYU8d/UFVSyuLuebyzbq3IKIZJySQp4zMz791rnsOdTJ91/YkutwROQcp6RwFrispoLr503kW09uYld7R67DEZFzWNZPNOerzs5O6urqTiirr68nHi/NUUQn+vt3zOParz7DFx5bz7f+28JchyMi5yglhVBdXR1fe2gpU2bNSZbVP7eSibMXcH4O4+o1vWI0H3rTeXxj2UZuubSFa1/Xby8gIiJnRM1HKabMmkPN/EXJV+WUGbkO6QR//ebzmDuplLsfWcuBo125DkdEzkGqKeSJeE839fX1J5XX1tZSVFQEQFEsylf+/GJu/vfnufvhtXz7vy/My76bROTspaSQJ1qaG9nQ1sb6zrJk2c7GBj4OXHHFFcmyC6eO4zM3zOULv1nPd/7QyF1vPC8H0YrIuUpJIY9UTquhZv6i5Pipag9/edkiXtrWxhcf38CsyhKum6fzCyKSHkoKeex0tYcvv2sJO9o6+PAPV/ODDyxhcXX+du4nImcPnWjOc721h95X79VRY4pi3PvexUwtH8Ud967g+U17cxypiJwLlBTOMr1NSs8//zwbXq7jk5dEmVZWzJ3fX8lP1T+SiJwhNR+dZfo2Ke1sbOCTt1zLA5vL+fTP6lnd3Mbn3j6PkiJ9tCIydDpynIX6npAuKYxw/52X8ZXfv8Z/PruZZ1/by9+/cx5vmTcRM+v3bm048XJXERFQUjhnxKIR7n7bXK573QQ++8ha/sd/rWLhjDL+6qpZlLRv5hs/eeKEu7X7u9xVRGREJoV87+foTNRWV/CLD17Gv/78j/xq80E+9OBqxkV7mFw1n4pZ8xg3qiDXIYpIHhuRSSHf+zk6U2teWk398me5vGYOuwtj1O86woZ4FRte2MqE0iLOqyoh2hUlnvBchyoieWZEJgU43s9Rr52NDTmMJv16/75ZgP/qh/RwmNLzF7Nxz2H+2LgPGMOq37VzReNK3nBeJUtmVTB30liiEXWbITKSjdikMNIUWQ8LZ5azcGY5HV1x6ta8TMJhbVOEJ9bvAaA4CueVRZhbWciciigzRicoihmFhYUnrEsnqEXOXUoKI9CowiiRlg20t7WxZNHlHB1j7O+KsrF5FxuPTeCVfSWAgSco6j7E1LJiKgrjVBTE2d+0QSeoRc5hSgojWN9LWyO7XiFamODSq97KrvZjrKir43BhGc3Himk8Gpx/GF2ykH9+cjtLmp7kgooYU0oiXLZ48Qk1B10CK3L2UlKQkxQVRKmuHMOO7h1EC4+w+E1vo/VQJzvbO1i7fiPru0ezrj54LGjE48x+4SmunDeDi6aO46Jp49jZsEaXwIqcpZQUZEDRiDFpXDGTxhXT8fImIqPLmPv669jVfoxNW7eRcPjBi0109iQAKI5B8biFHKGM8jGFVIwppLQnQk/K1U6qTYjkJyWFs1x/3Wtn+p4LMygfXUj56EJG7T/GXyyewJLLX8+m1sPUb29nad0GXtp5lG1tHazffShcqoRnftPO5OefZPK4Ygq6D9PY1ExVRTmjogmKIs6BHZv5264E173pSiKRwd+J3d98Si4nUhKWwVJSOMv11712Lu65iEUjzJ00lrmTxjK1s5lRx/ZSM/91dPbEaTvazWuvvcaMqjIipRXsau+gsTVOa8EkWg6lXAI7agF3/e4g0aWPUz66gCJ6aG8/QOnoURRGnIKI09HWyvVbO7lk3hxKi2OUFhewecM6fvjb55g2s4aYwa4taqrqq797c9SkJ/3Ju6RgZjcA3wCiwHfd/Ys5Dinv9T1hnM17LgaqqRTFokwaG+VwwVHmdreyYNoomAb18a2sPVLK1Nct5PCxHjq642zZ1MD4gh5KKydxqMvZ3nqYjuJRdMXGcKArTmdPnEThNO5b2wFr15wYyKiLWRtcWUtszCI++Ju9jH3qcUYXGMUxo8Ccomhw5VVR1CiOQpQExTEYXRSjOGoURY0F8+dSMqqIgmiEWNRI9PSw/tV1RA2iZsR7uokajCouDMsgYrB4gJPtXV3BM7VzeXlv33tz+pOuWld/6+lvHwym1jfcGDIpm7XTbO+TvEoKZhYF/h24HtgOrDSzR9391dxGJqcy2JpK3/l655ldGGN0YfA13P7CBpra2pi/6HJKgK76pcyevYDL33gjAO7O5ldWc+OCacxdcCmHjvVw+FgPK9es5ZmN+yidOJ2ungRbXnuVjq44idgE2rqMuMPhw0fwaAGRwmLibsQ99Sa97uODK1YN8i/vOGEs+tgTFMaixKJGQTRCoqebox3HiBUUEMHpOnKQiEFJaSlRcyJA55F2Ln75KDOnTaGoIEJRLErxIN4TDt3xBN09CTrD9+640x1P0BWWdfUk6OyJh+8JtjZ3sL69mE2vthBPOPGEc/jgKHauPMIPtqwiYoYZ7Nu3l4am3YwuKcWAjsPtXPTyUaZMnkTEjGjEwneI9A6b4TjukHBwnO3bd1C3YStjxh1/+FPrzmYisUIqJkzGgSPtbSyoP8rESZNwDz7f3S0trGvcQcnYcURwIgZH2/Zy1cZjzD6vhqJYhMLeVzR64ngsQmE0QlEs2Fd9ywuilnymubuTcIgnnIQH+yPuTiKROgwJD6YHy5B8X7Wqju899jwTptcAxp5tjdx5OM7CRYswgiZWI9in9I6bnTTNAPqMJ/x4fAl3Vqys43u//gMTps/CgyVo2baFD3cmuPGaqwb5fR28vEoKwGXAJndvBDCzHwM3AWlPCn1/Te/d2Ux0VBtbXll1yrJ0zZPt5TK/7pPPX+zdvqWf5UrPeJ6WLQ00Fx+grDh4FIgBxXs3ENvdQvno4ODe0vQ8paNKmX/e8XXVr3+C6KhS5i+6HAj+sV9+4UlsdBnnX7SIuBu7dzRz8YzxzKieRY9DIuE0NjWzbsdBxlVNJgFs37QBKxzFxOnVwUEQONi2j5njS6ionEDcnXgCWvcdYvuRDooLxpFw40D8GEQLiTt0JyIkgM5ICSt3HGX5rqbgoJ44aTemRdSCWlEiYcQ6DxAxiJrT09VNPHGE3Ufi4YEIjnV2cjQyhmPdMdyhJzKOFTs6iO4OLihIEByoehNA76v3gNb77p4gEZvA3o7jn1O8dCZmxtGOGIaTiFXwx20dxHZto/dG+p6ebjoj4zjYESOOBXEVTuWhDcdgw/oz2g9GUKsLElcajFoAe48Pf/KpQ/DU0+lYcz/buvj4tgBGXcTjjZ3ceE36N2Xuadk9aWFm7wJucPcPhON/CSxx94+kzHMXcFc4OgdIPbpXcuKuyxf5GJdiGrx8jCsfY4L8jEsxnWymu1f1NyHfagoDcvd7gHv6m2Zmde5em+WQBpSPcSmmwcvHuPIxJsjPuBTT0OTb4zh3ANNTxqeFZSIikgX5lhRWArPNrMbMCoFbgUdzHJOIyIiRV81H7t5jZh8BfkdwSeq97v7KEFbRb7NSHsjHuBTT4OVjXPkYE+RnXIppCPLqRLOIiORWvjUfiYhIDikpiIhI0jmTFMzsBjNrMLNNZnZ3Frc73cyeMrNXzewVM/toWP4PZrbDzNaErxtTlvlsGGeDmb01Q3FtNbO14bbrwrIKM1tqZhvD9/Kw3Mzs38KY6s1sYYZimpOyP9aY2UEz+1i295WZ3Wtme8xsXUrZkPeNmd0Rzr/RzO7IUFxfNrMN4bZ/bmZlYXm1mXWk7LP/SFlmUfjZbwpjH/YzVk8R05A/r3T+f54ipodS4tlqZmvC8qzsp3B9pzoW5Py7NSTufta/CE5KbwZmAYXAy8C8LG17MrAwHC4FXgPmAf8AfKqf+eeF8RUBNWHc0QzEtRWo7FP2L8Dd4fDdwJfC4RuBxwlu+rwcWJ6lz2w3MDPb+wp4I7AQWDfcfQNUAI3he3k4XJ6BuN4CxMLhL6XEVZ06X5/1rAhjtTD2t6U5piF9Xun+/+wvpj7T/xX4+2zup3B9pzoW5Py7NZTXuVJTSHaP4e5dQG/3GBnn7rvcfXU4fAhYD0w9zSI3AT9290533wJsIog/G24C7g+H7wduTil/wAMvAmVmNjnDsVwLbHb3ptPMk5F95e7PAvv72dZQ9s1bgaXuvt/d24ClwA3pjsvdf+/uPeHoiwT37pxSGNtYd3/RgyPMAyl/S1piOo1TfV5p/f88XUzhr/13Az863TrSvZ/CuE51LMj5d2sozpWkMBXYljK+ndMfmDPCzKqBS4HlYdFHwmrhvb1VRrIXqwO/N7NVFnQNAjDR3XeFw7uBiVmOKdWtnPiPm8t9BUPfN7nYZ+8j+GXZq8bMXjKzZ8yst2e0qWEsmY5rKJ9XNvfVVUCLu29MKbVSctYAAAcVSURBVMv6fupzLDgbvltJ50pSyDkzKwEeBj7m7geBbwPnAZcAuwiqtNl0pbsvBN4GfNjM3pg6Mfx1lJPrkS24MfFPgJ+GRbneVyfI5b45FTP7HNADPBgW7QJmuPulwCeAH5rZ2CyFk1efVx+3ceKPjazvp36OBUn5+N3q61xJCjntHsPMCgi+BA+6+yMA7t7i7nF3TwDf4XizR1Zidfcd4fse4Ofh9lt6m4XC9z3ZjCnF24DV7t4SxpjTfRUa6r7JWmxm9l7gHcDt4UGFsIlmXzi8iqDN/oIwhtQmprTHNYzPKyv7ysxiwJ8CD6XEmtX91N+xgDz+bvXnXEkKOeseI2zD/B6w3t2/mlKe2iZ/C9B7pcSjwK1mVmRmNcBsghNe6YxpjJmV9g4TnKxcF26790qGO4BfpsT0nvBqiMuB9pTqbiac8Gsul/sqxVD3ze+At5hZedh88pawLK0seOjU3wF/4u5HU8qrLHj+CGY2i2DfNIaxHTSzy8Pv5ntS/pZ0xTTUzytb/5/XARvcPdkslM39dKpjAXn63TqlbJ3RzvSL4Ez+awS/BD6Xxe1eSVAdrAfWhK8bgf8C1obljwKTU5b5XBhnA2d4xcMpYppFcIXHy8ArvfsDGA8sAzYCTwAVYbkRPNxocxhzbQb31xhgHzAupSyr+4ogIe0ieLrOduD9w9k3BG38m8LXnRmKaxNB+3Lvd+s/wnn/LPxs1wCrgXemrKeW4EC9GfgWYc8FaYxpyJ9XOv8/+4spLL8P+GCfebOyn8L1nepYkPPv1lBe6uZCRESSzpXmIxERSQMlBRERSVJSEBGRJCUFERFJUlIQEZEkJQUREUlSUpC8YGY3m5mb2dxwvNrCrpHN7L1m9q0hrOsfzOxT4fD/MbPr0hjn1WbWbkE3zOvN7PPDiXEY203+TYOcv8zM/jpT8ci5S0lB8sVtwHPhe9q4+9+7+xPpXCfwB3e/hODmp/9uGXr+xBkqA5QUZMiUFCTnwg7EriS4W/bWAeZ9u5n90cwqzeyvzGylmb1sZg+b2eh+5r/PzN4VDm81s/9tZqsteLhKb61kTNjb54qwN81Bdevs7keAVcD5YdEUM/utBQ9G+ZeUGG4Lt7fOzL4UlkXD2NaF0z4elj9tZt8IayLrzCy1q/B54fRGM/vblPV/Ipx3nZl9LCz+InBeuJ4vm1mJmS1L+dtvSvnbHwv34Toz+4uwfJEFvYquMrPfWea7Upc8Ect1ACIE/cr/1t1fM7N9ZraIoCuME5jZLQQ9Xd7o7m1m9oi7fyec9k8ESeWbA2xrr7svDJtWPgV8gKBrhifd/X0WPNlshZk9ER70T8nMxhM8HOUfgcUEvYZeCnQCDWb2TSBO8HCcRUAbQXfmNxN0XTHV3S8M11WWsurR7n6JBT3b3gtcGJbPBd5M8ACXBjP7NrAAuBNYQtBtwnIze4bgYS4XhjWa3s7ibnH3g2ZWCbxoZo8S9NO/093fHs43zoJO3b4J3OTurWGi+AJB1wtyjlNNQfLBbQQPXiF8768J6RrgM8DbPXjwCMCFZvYHM1sL3A7MH8S2enuuXEXwVC4IOhy724JHOD4NFAMzTrOOq8zsJeD3wBfd/ZWwfJm7t7v7MeBVgqfKLQaedvdWDx6W8yDBk8MagVlm9k0LOr1L7WL5R5B8mMzYlITxmAe9fu4l6GlzIkEN6+fufsTdD4d/31WczID/a2b1BP3vTA2XXwtcb2ZfMrOr3L0dmEOQiJaG++R/MsDDfeTcoZqC5JSZVRAc8C8yMyd4dKMTdBSWqvdxjhcAdWHZfcDN7v6yBd1LXz2ITXaG73GOf/8N+DN3bxhk2H9w93ecZt1913+SsKZzMcFTtj5I8LSw3l/ifTsk6x0f9Pr7cTtQBSxy924z2woUh7WzhQQdt/2TmS0j6Gr9FXd//RDWL+cI1RQk194F/Je7z3T3anefDmzhxP7kAZoIerx8wMx6awSlwK6wueP2M4jhd8DfmAUPbjezS89gXX2tAN4UngOJEtSCngmbcCLu/jDBL/HUk9W97fpXEnSn3H6a9f8BuNnMRlvQTfotYdkhgv3TaxywJ0wIbyaoxWBmU4Cj7v4D4MthHA1AlZm9PpynIGWfyzlONQXJtdsI2txTPQx8tu+M7r7BzG4Hfmpm7wT+F8HjDlvD99K+ywzSPwJfB+rNLEKQlPqrCQyZu+8ys7uBpwhqJI+5+y/DWsL3w+3BiX/vsbB5qoAB2vHdfbWZ3cfx50x8191fAjCz5y24rPdxgn38q7CprQ7YEM5/EfBlM0sQdEX9IXfvCk/O/5uZjSM4TnydoAtqOcep62yRPGJmTwOfcve6geYVyQQ1H4mISJJqCiL9MLO3cnKz1hZ3vyUX8Yhki5KCiIgkqflIRESSlBRERCRJSUFERJKUFEREJOn/A+K1tIys9tedAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figsize = (10, 30)\n",
        "\n",
        "skewness = round(new_df['Alamine_Aminotransferase'].skew(), 2)\n",
        "kurtosis = round(new_df['Alamine_Aminotransferase'].kurt(), 2)\n",
        "\n",
        "print('Alamine_Aminotransferase 偏度(Skewness):',skewness, '峰度(Kurtosis):',kurtosis)\n",
        "\n",
        "sns.histplot(new_df['Alamine_Aminotransferase'], kde = True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "YV_NqywQOsFm",
        "outputId": "1f06afe4-e423-4c84-bbab-677e063aefb2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Alamine_Aminotransferase 偏度(Skewness): 6.96 峰度(Kurtosis): 58.87\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figsize = (10, 30)\n",
        "\n",
        "skewness = round(new_df['Aspartate_Aminotransferase'].skew(), 2)\n",
        "kurtosis = round(new_df['Aspartate_Aminotransferase'].kurt(), 2)\n",
        "\n",
        "print('Aspartate_Aminotransferase 偏度(Skewness):',skewness, '峰度(Kurtosis):',kurtosis)\n",
        "\n",
        "sns.histplot(new_df['Aspartate_Aminotransferase'], kde = True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "LqqCpf0ZylOs",
        "outputId": "f4e3357c-ec38-4244-f0f9-9b1d3b77f01d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Aspartate_Aminotransferase 偏度(Skewness): 10.74 峰度(Kurtosis): 148.68\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figsize = (10, 30)\n",
        "\n",
        "skewness = round(new_df['Albumin'].skew(), 2)\n",
        "kurtosis = round(new_df['Albumin'].kurt(), 2)\n",
        "\n",
        "print('Albumin 偏度(Skewness):',skewness, '峰度(Kurtosis):',kurtosis)\n",
        "\n",
        "sns.histplot(new_df['Albumin'], kde = True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "pITNl7uPynCO",
        "outputId": "56c5baf9-e435-44b0-afc6-4c6a49b6188e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Albumin 偏度(Skewness): -0.01 峰度(Kurtosis): -0.34\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. 前處理"
      ],
      "metadata": {
        "id": "dVYQZNtM8laK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#前處理 二號 嘗試調整偏度\n",
        "def pre_processing2(data) :\n",
        "\n",
        "  dropna_Data = data.dropna()\n",
        "  dropna_Data = dropna_Data.reset_index()\n",
        "  dropna_Data = dropna_Data.drop(['index'], axis = 1)\n",
        "\n",
        "  le = LabelEncoder()\n",
        "  gender_to_number = le.fit_transform(dropna_Data['Gender'])\n",
        "  ohe = OneHotEncoder(sparse = False)\n",
        "  gender_reshape = gender_to_number.reshape(len(dropna_Data), 1) \n",
        "  new_gender = ohe.fit_transform(gender_reshape)\n",
        "\n",
        "  mmsc = MinMaxScaler()\n",
        "  alkphos_array = dropna_Data['Alkaline_Phosphotase'].values\n",
        "  alkphos_array_reshape = alkphos_array.reshape(len(dropna_Data), 1)\n",
        "  db_array = dropna_Data['Direct_Bilirubin'].values\n",
        "  db_array_reshape = db_array.reshape(len(dropna_Data), 1)\n",
        "  sgot_array = dropna_Data['Aspartate_Aminotransferase'].values\n",
        "  sgot_array_reshape = sgot_array.reshape(len(dropna_Data), 1)\n",
        "\n",
        "  alkphos_standard = mmsc.fit_transform(alkphos_array_reshape)\n",
        "  db_standard = mmsc.fit_transform(db_array_reshape)\n",
        "  sgot_standard = mmsc.fit_transform(sgot_array_reshape)\n",
        "\n",
        "  new_data = pd.concat([dropna_Data.drop(['Gender', 'Alkaline_Phosphotase', 'Direct_Bilirubin', 'Aspartate_Aminotransferase'], axis = 1), \n",
        "                        pd.DataFrame(new_gender, columns = ['Male_gender', 'Female_gender']),\n",
        "                        pd.DataFrame(alkphos_standard, columns = ['Alkaline_Phosphotase']), \n",
        "                        pd.DataFrame(db_standard, columns = ['Direct_Bilirubin']),\n",
        "                        pd.DataFrame(sgot_standard, columns = ['Aspartate_Aminotransferase'])], axis = 1)\n",
        "  return new_data"
      ],
      "metadata": {
        "id": "DLh6VhhkrCdr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dropna_Data = df.dropna()\n",
        "dropna_Data = dropna_Data.reset_index()\n",
        "dropna_Data = dropna_Data.drop(['index'], axis = 1)\n",
        "\n",
        "negative = dropna_Data[dropna_Data.Label==0]\n",
        "positive = dropna_Data[dropna_Data.Label==1]\n",
        "negative_Sample = resample(negative, replace=True, n_samples=positive.shape[0])\n",
        "negative_Sample = negative_Sample.reset_index()\n",
        "negative_Sample = negative_Sample.drop(['index'], axis = 1)\n",
        "dropna_Data = pd.concat([positive, negative_Sample], axis = 0, ignore_index = True)\n",
        "\n",
        "print(negative_Sample.shape)\n",
        "print(positive.shape)\n",
        "\n",
        "dropna_Data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "id": "koDDaYos974_",
        "outputId": "c1f4c534-cbfa-49e8-9dfb-f1fadcced4f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(331, 11)\n",
            "(331, 11)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Age  Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
              "0     40  Female              0.9               0.3                   293   \n",
              "1     78    Male              1.0               0.3                   152   \n",
              "2     60    Male              2.0               0.8                   190   \n",
              "3     75    Male             10.6               5.0                   562   \n",
              "4     19  Female              0.7               0.2                   186   \n",
              "..   ...     ...              ...               ...                   ...   \n",
              "657   21    Male              1.0               0.3                   142   \n",
              "658   38  Female              0.7               0.1                   152   \n",
              "659   22    Male              0.8               0.2                   300   \n",
              "660   27    Male              1.0               0.3                   180   \n",
              "661   54  Female              1.4               0.7                   195   \n",
              "\n",
              "     Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
              "0                         232                         245             6.8   \n",
              "1                          28                          70             6.3   \n",
              "2                          45                          40             6.0   \n",
              "3                          37                          29             5.1   \n",
              "4                         166                         397             5.5   \n",
              "..                        ...                         ...             ...   \n",
              "657                        27                          21             6.4   \n",
              "658                        90                          21             7.1   \n",
              "659                        57                          40             7.9   \n",
              "660                        56                         111             6.8   \n",
              "661                        36                          16             7.9   \n",
              "\n",
              "     Albumin  Albumin_and_Globulin_Ratio  Label  \n",
              "0        3.1                        0.80      1  \n",
              "1        3.1                        0.90      1  \n",
              "2        2.8                        0.80      1  \n",
              "3        1.8                        0.50      1  \n",
              "4        3.0                        1.20      1  \n",
              "..       ...                         ...    ...  \n",
              "657      3.5                        1.20      0  \n",
              "658      4.2                        1.40      0  \n",
              "659      3.8                        0.90      0  \n",
              "660      3.9                        1.85      0  \n",
              "661      3.7                        0.90      0  \n",
              "\n",
              "[662 rows x 11 columns]"
            ],
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              "\n",
              "  <div id=\"df-0d3002b0-e300-402d-a193-13e201bcee2c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
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              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Total_Bilirubin</th>\n",
              "      <th>Direct_Bilirubin</th>\n",
              "      <th>Alkaline_Phosphotase</th>\n",
              "      <th>Alamine_Aminotransferase</th>\n",
              "      <th>Aspartate_Aminotransferase</th>\n",
              "      <th>Total_Protiens</th>\n",
              "      <th>Albumin</th>\n",
              "      <th>Albumin_and_Globulin_Ratio</th>\n",
              "      <th>Label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>40</td>\n",
              "      <td>Female</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.3</td>\n",
              "      <td>293</td>\n",
              "      <td>232</td>\n",
              "      <td>245</td>\n",
              "      <td>6.8</td>\n",
              "      <td>3.1</td>\n",
              "      <td>0.80</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>78</td>\n",
              "      <td>Male</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>152</td>\n",
              "      <td>28</td>\n",
              "      <td>70</td>\n",
              "      <td>6.3</td>\n",
              "      <td>3.1</td>\n",
              "      <td>0.90</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>60</td>\n",
              "      <td>Male</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>190</td>\n",
              "      <td>45</td>\n",
              "      <td>40</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2.8</td>\n",
              "      <td>0.80</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>75</td>\n",
              "      <td>Male</td>\n",
              "      <td>10.6</td>\n",
              "      <td>5.0</td>\n",
              "      <td>562</td>\n",
              "      <td>37</td>\n",
              "      <td>29</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>19</td>\n",
              "      <td>Female</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.2</td>\n",
              "      <td>186</td>\n",
              "      <td>166</td>\n",
              "      <td>397</td>\n",
              "      <td>5.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.20</td>\n",
              "      <td>1</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>657</th>\n",
              "      <td>21</td>\n",
              "      <td>Male</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>142</td>\n",
              "      <td>27</td>\n",
              "      <td>21</td>\n",
              "      <td>6.4</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.20</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>658</th>\n",
              "      <td>38</td>\n",
              "      <td>Female</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.1</td>\n",
              "      <td>152</td>\n",
              "      <td>90</td>\n",
              "      <td>21</td>\n",
              "      <td>7.1</td>\n",
              "      <td>4.2</td>\n",
              "      <td>1.40</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>659</th>\n",
              "      <td>22</td>\n",
              "      <td>Male</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.2</td>\n",
              "      <td>300</td>\n",
              "      <td>57</td>\n",
              "      <td>40</td>\n",
              "      <td>7.9</td>\n",
              "      <td>3.8</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>660</th>\n",
              "      <td>27</td>\n",
              "      <td>Male</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>180</td>\n",
              "      <td>56</td>\n",
              "      <td>111</td>\n",
              "      <td>6.8</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1.85</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>661</th>\n",
              "      <td>54</td>\n",
              "      <td>Female</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.7</td>\n",
              "      <td>195</td>\n",
              "      <td>36</td>\n",
              "      <td>16</td>\n",
              "      <td>7.9</td>\n",
              "      <td>3.7</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>662 rows × 11 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0d3002b0-e300-402d-a193-13e201bcee2c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      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",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "          document.querySelector('#df-0d3002b0-e300-402d-a193-13e201bcee2c 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-0d3002b0-e300-402d-a193-13e201bcee2c');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 102
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Error: Runtime no longer has a reference to this dataframe, please re-run this cell and try again.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#前處理(將字串轉為數字，以利機器進行訓練測試) 三號 重新採樣 舊方法\n",
        "def pre_processing3(data):\n",
        "\n",
        "  dropna_Data = data.dropna()\n",
        "  dropna_Data = dropna_Data.reset_index()\n",
        "  dropna_Data = dropna_Data.drop(['index'], axis = 1)\n",
        "\n",
        "  negative = dropna_Data[dropna_Data.Label==0]\n",
        "  positive = dropna_Data[dropna_Data.Label==1]\n",
        "  negative_Sample = resample(negative, replace=True, n_samples=positive.shape[0])\n",
        "  negative_Sample = negative_Sample.reset_index()\n",
        "  negative_Sample = negative_Sample.drop(['index'], axis = 1)\n",
        "  dropna_Data = pd.concat([positive, negative_Sample], axis = 0, ignore_index = True)\n",
        "\n",
        "  le = LabelEncoder()\n",
        "  gender_to_number = le.fit_transform(dropna_Data['Gender'])\n",
        "  ohe = OneHotEncoder(sparse = False)\n",
        "  gender_reshape = gender_to_number.reshape(len(dropna_Data), 1) \n",
        "  new_gender = ohe.fit_transform(gender_reshape)\n",
        "\n",
        "  mmsc = MinMaxScaler()\n",
        "  alkphos_array = dropna_Data['Alkaline_Phosphotase'].values\n",
        "  alkphos_array_reshape = alkphos_array.reshape(len(dropna_Data), 1)\n",
        "  sgpt_array = dropna_Data['Alamine_Aminotransferase'].values\n",
        "  sgpt_array_reshape = sgpt_array.reshape(len(dropna_Data), 1)\n",
        "  sgot_array = dropna_Data['Aspartate_Aminotransferase'].values\n",
        "  sgot_array_reshape = sgot_array.reshape(len(dropna_Data), 1)\n",
        "\n",
        "  alkphos_standard = mmsc.fit_transform(alkphos_array_reshape)\n",
        "  sgpt_standard = mmsc.fit_transform(sgpt_array_reshape)\n",
        "  sgot_standard = mmsc.fit_transform(sgot_array_reshape)\n",
        "\n",
        "  new_data = pd.concat([dropna_Data.drop(['Gender', 'Alkaline_Phosphotase', 'Alamine_Aminotransferase', 'Aspartate_Aminotransferase'], axis = 1), \n",
        "                        pd.DataFrame(new_gender, columns = ['Male_gender', 'Female_gender']),\n",
        "                        pd.DataFrame(alkphos_standard, columns = ['Alkaline_Phosphotase']), \n",
        "                        pd.DataFrame(sgpt_standard, columns = ['Alamine_Aminotransferase']),\n",
        "                        pd.DataFrame(sgot_standard, columns = ['Aspartate_Aminotransferase'])], axis = 1)\n",
        "  return new_data\n",
        "\n",
        "def pre_processing4(data):\n",
        "\n",
        "  dropna_Data = data.dropna()\n",
        "  dropna_Data = dropna_Data.reset_index()\n",
        "  dropna_Data = dropna_Data.drop(['index'], axis = 1) \n",
        "\n",
        "  le = LabelEncoder()\n",
        "  gender_to_number = le.fit_transform(dropna_Data['Gender'])\n",
        "  ohe = OneHotEncoder(sparse = False)\n",
        "  gender_reshape = gender_to_number.reshape(len(dropna_Data), 1) \n",
        "  new_gender = ohe.fit_transform(gender_reshape)\n",
        "\n",
        "  mmsc = MinMaxScaler()\n",
        "  alkphos_array = dropna_Data['Alkaline_Phosphotase'].values\n",
        "  alkphos_array_reshape = alkphos_array.reshape(len(dropna_Data), 1)\n",
        "  sgpt_array = dropna_Data['Alamine_Aminotransferase'].values\n",
        "  sgpt_array_reshape = sgpt_array.reshape(len(dropna_Data), 1)\n",
        "  sgot_array = dropna_Data['Aspartate_Aminotransferase'].values\n",
        "  sgot_array_reshape = sgot_array.reshape(len(dropna_Data), 1)\n",
        "\n",
        "  alkphos_standard = mmsc.fit_transform(alkphos_array_reshape)\n",
        "  sgpt_standard = mmsc.fit_transform(sgpt_array_reshape)\n",
        "  sgot_standard = mmsc.fit_transform(sgot_array_reshape)\n",
        "\n",
        "  new_data = pd.concat([dropna_Data.drop(['Gender', 'Alkaline_Phosphotase', 'Alamine_Aminotransferase', 'Aspartate_Aminotransferase'], axis = 1), \n",
        "                        pd.DataFrame(new_gender, columns = ['Male_gender', 'Female_gender']),\n",
        "                        pd.DataFrame(alkphos_standard, columns = ['Alkaline_Phosphotase']), \n",
        "                        pd.DataFrame(sgpt_standard, columns = ['Alamine_Aminotransferase']),\n",
        "                        pd.DataFrame(sgot_standard, columns = ['Aspartate_Aminotransferase'])], axis = 1)\n",
        "  return new_data"
      ],
      "metadata": {
        "id": "lxLmp-IB9qDp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#前處理(將字串轉為數字，以利機器進行訓練測試) 三號 重新採樣 新方法\n",
        "def pre_processing3(data):\n",
        "\n",
        "  dropna_Data = data.dropna()\n",
        "  dropna_Data = dropna_Data.reset_index()\n",
        "  dropna_Data = dropna_Data.drop(['index'], axis = 1)\n",
        "\n",
        "  negative = dropna_Data[dropna_Data.Label==0]\n",
        "  positive = dropna_Data[dropna_Data.Label==1]\n",
        "  negative_Sample = resample(negative, replace=True, n_samples=positive.shape[0])\n",
        "  negative_Sample = negative_Sample.reset_index()\n",
        "  negative_Sample = negative_Sample.drop(['index'], axis = 1)\n",
        "  dropna_Data = pd.concat([positive, negative_Sample], axis = 0, ignore_index = True)\n",
        "\n",
        "  le = LabelEncoder()\n",
        "  gender_to_number = le.fit_transform(dropna_Data['Gender'])\n",
        "  ohe = OneHotEncoder(sparse = False)\n",
        "  gender_reshape = gender_to_number.reshape(len(dropna_Data), 1) \n",
        "  new_gender = ohe.fit_transform(gender_reshape)\n",
        "\n",
        "  mmsc = MinMaxScaler()\n",
        "  alkphos_array = dropna_Data['Alkaline_Phosphotase'].values\n",
        "  alkphos_array_reshape = alkphos_array.reshape(len(dropna_Data), 1)\n",
        "  sgpt_array = dropna_Data['Alamine_Aminotransferase'].values\n",
        "  sgpt_array_reshape = sgpt_array.reshape(len(dropna_Data), 1)\n",
        "  sgot_array = dropna_Data['Aspartate_Aminotransferase'].values\n",
        "  sgot_array_reshape = sgot_array.reshape(len(dropna_Data), 1)\n",
        "\n",
        "  alkphos_standard = mmsc.fit_transform(alkphos_array_reshape)\n",
        "  sgpt_standard = mmsc.fit_transform(sgpt_array_reshape)\n",
        "  sgot_standard = mmsc.fit_transform(sgot_array_reshape)\n",
        "\n",
        "  new_data = pd.concat([dropna_Data.drop(['Gender', 'Alkaline_Phosphotase', 'Alamine_Aminotransferase', 'Aspartate_Aminotransferase'], axis = 1), \n",
        "                        pd.DataFrame(new_gender, columns = ['Male_gender', 'Female_gender']),\n",
        "                        pd.DataFrame(alkphos_standard, columns = ['Alkaline_Phosphotase']), \n",
        "                        pd.DataFrame(sgpt_standard, columns = ['Alamine_Aminotransferase']),\n",
        "                        pd.DataFrame(sgot_standard, columns = ['Aspartate_Aminotransferase'])], axis = 1)\n",
        "  return new_data\n",
        "\n",
        "def pre_processing4(data):\n",
        "\n",
        "  dropna_Data = data.dropna()\n",
        "  dropna_Data = dropna_Data.reset_index()\n",
        "  dropna_Data = dropna_Data.drop(['index'], axis = 1) \n",
        "\n",
        "  le = LabelEncoder()\n",
        "  gender_to_number = le.fit_transform(dropna_Data['Gender'])\n",
        "  ohe = OneHotEncoder(sparse = False)\n",
        "  gender_reshape = gender_to_number.reshape(len(dropna_Data), 1) \n",
        "  new_gender = ohe.fit_transform(gender_reshape)\n",
        "\n",
        "  mmsc = MinMaxScaler()\n",
        "  alkphos_array = dropna_Data['Alkaline_Phosphotase'].values\n",
        "  alkphos_array_reshape = alkphos_array.reshape(len(dropna_Data), 1)\n",
        "  sgpt_array = dropna_Data['Alamine_Aminotransferase'].values\n",
        "  sgpt_array_reshape = sgpt_array.reshape(len(dropna_Data), 1)\n",
        "  sgot_array = dropna_Data['Aspartate_Aminotransferase'].values\n",
        "  sgot_array_reshape = sgot_array.reshape(len(dropna_Data), 1)\n",
        "\n",
        "  alkphos_standard = mmsc.fit_transform(alkphos_array_reshape)\n",
        "  sgpt_standard = mmsc.fit_transform(sgpt_array_reshape)\n",
        "  sgot_standard = mmsc.fit_transform(sgot_array_reshape)\n",
        "\n",
        "  new_data = pd.concat([dropna_Data.drop(['Gender', 'Alkaline_Phosphotase', 'Alamine_Aminotransferase', 'Aspartate_Aminotransferase'], axis = 1), \n",
        "                        pd.DataFrame(new_gender, columns = ['Male_gender', 'Female_gender']),\n",
        "                        pd.DataFrame(alkphos_standard, columns = ['Alkaline_Phosphotase']), \n",
        "                        pd.DataFrame(sgpt_standard, columns = ['Alamine_Aminotransferase']),\n",
        "                        pd.DataFrame(sgot_standard, columns = ['Aspartate_Aminotransferase'])], axis = 1)\n",
        "  return new_data"
      ],
      "metadata": {
        "id": "jQkWRbDKkgOU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#前處理並且將其包裝成函式，這樣要處理資料直接帶入函式就好，不必再將整個前處理程式碼複製過去(主要採納的前處理)\n",
        "def pre_processing(data) :\n",
        "\n",
        "  #將缺失值丟去\n",
        "  dropna_Data = data.dropna()\n",
        "  dropna_Data = dropna_Data.reset_index()\n",
        "  dropna_Data = dropna_Data.drop(['index'], axis = 1)\n",
        "\n",
        "  #將 Gender 轉換乘數字，以利機器去做學習\n",
        "  le = LabelEncoder()\n",
        "  gender_to_number = le.fit_transform(dropna_Data['Gender'])\n",
        "  ohe = OneHotEncoder(sparse = False)\n",
        "  gender_reshape = gender_to_number.reshape(len(dropna_Data), 1) \n",
        "  new_gender = ohe.fit_transform(gender_reshape)\n",
        "\n",
        "  #把數字範圍很大的欄位做最小最大標準化處理\n",
        "  mmsc = MinMaxScaler()\n",
        "  alkphos_array = dropna_Data['Alkaline_Phosphotase'].values\n",
        "  alkphos_array_reshape = alkphos_array.reshape(len(dropna_Data), 1)\n",
        "  sgpt_array = dropna_Data['Alamine_Aminotransferase'].values\n",
        "  sgpt_array_reshape = sgpt_array.reshape(len(dropna_Data), 1)\n",
        "  sgot_array = dropna_Data['Aspartate_Aminotransferase'].values\n",
        "  sgot_array_reshape = sgot_array.reshape(len(dropna_Data), 1)\n",
        "\n",
        "  alkphos_standard = mmsc.fit_transform(alkphos_array_reshape)\n",
        "  sgpt_standard = mmsc.fit_transform(sgpt_array_reshape)\n",
        "  sgot_standard = mmsc.fit_transform(sgot_array_reshape)\n",
        "\n",
        "  #將以上處理過的資料進行彙整\n",
        "  new_data = pd.concat([dropna_Data.drop(['Gender', 'Alkaline_Phosphotase', 'Alamine_Aminotransferase', 'Aspartate_Aminotransferase'], axis = 1), \n",
        "                        pd.DataFrame(new_gender, columns = ['Male_gender', 'Female_gender']),\n",
        "                        pd.DataFrame(alkphos_standard, columns = ['Alkaline_Phosphotase']), \n",
        "                        pd.DataFrame(sgpt_standard, columns = ['Alamine_Aminotransferase']),\n",
        "                        pd.DataFrame(sgot_standard, columns = ['Aspartate_Aminotransferase'])], axis = 1)\n",
        "  return new_data"
      ],
      "metadata": {
        "id": "F8sYXQ0LR1qT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4. 模型訓練"
      ],
      "metadata": {
        "id": "2x_83btu87Tb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#套用前處理的函示\n",
        "new_df = pre_processing(df)\n",
        "\n",
        "#設定X，Y值\n",
        "X = new_df.drop(['Label', 'Total_Protiens', 'Albumin_and_Globulin_Ratio'], axis = 1)\n",
        "y = new_df['Label']\n",
        "\n",
        "#將資料進行分割，分成訓練集和驗證集\n",
        "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size = 0.3, random_state = 0)\n",
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "j7K71dakR8vF",
        "outputId": "cce47778-c12b-4365-a447-99feb055b789"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Age  Total_Bilirubin  Direct_Bilirubin  Albumin  Male_gender  \\\n",
              "0     40              0.9               0.3      3.1          1.0   \n",
              "1     78              1.0               0.3      3.1          0.0   \n",
              "2     60              2.0               0.8      2.8          0.0   \n",
              "3     75             10.6               5.0      1.8          0.0   \n",
              "4     19              0.7               0.2      3.0          1.0   \n",
              "..   ...              ...               ...      ...          ...   \n",
              "458   32              0.7               0.2      2.9          0.0   \n",
              "459   58              0.8               0.2      4.4          0.0   \n",
              "460   34              5.9               2.5      2.7          0.0   \n",
              "461   36              0.8               0.2      3.8          0.0   \n",
              "462   54              2.2               1.2      3.7          0.0   \n",
              "\n",
              "     Female_gender  Alkaline_Phosphotase  Alamine_Aminotransferase  \\\n",
              "0              0.0              0.107125                  0.111558   \n",
              "1              1.0              0.037838                  0.009045   \n",
              "2              1.0              0.056511                  0.017588   \n",
              "3              1.0              0.239312                  0.013568   \n",
              "4              0.0              0.054545                  0.078392   \n",
              "..             ...                   ...                       ...   \n",
              "458            1.0              0.098771                  0.046231   \n",
              "459            1.0              0.051597                  0.011055   \n",
              "460            1.0              0.105651                  0.017588   \n",
              "461            1.0              0.052580                  0.010553   \n",
              "462            1.0              0.058968                  0.022613   \n",
              "\n",
              "     Aspartate_Aminotransferase  \n",
              "0                      0.047774  \n",
              "1                      0.012198  \n",
              "2                      0.006099  \n",
              "3                      0.003863  \n",
              "4                      0.078675  \n",
              "..                          ...  \n",
              "458                    0.036593  \n",
              "459                    0.003049  \n",
              "460                    0.045334  \n",
              "461                    0.004879  \n",
              "462                    0.017280  \n",
              "\n",
              "[463 rows x 9 columns]"
            ],
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              "\n",
              "  <div id=\"df-cc277cfe-1a2d-44df-a007-1c25c2cfd162\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Total_Bilirubin</th>\n",
              "      <th>Direct_Bilirubin</th>\n",
              "      <th>Albumin</th>\n",
              "      <th>Male_gender</th>\n",
              "      <th>Female_gender</th>\n",
              "      <th>Alkaline_Phosphotase</th>\n",
              "      <th>Alamine_Aminotransferase</th>\n",
              "      <th>Aspartate_Aminotransferase</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>40</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.3</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.107125</td>\n",
              "      <td>0.111558</td>\n",
              "      <td>0.047774</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>78</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.037838</td>\n",
              "      <td>0.009045</td>\n",
              "      <td>0.012198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>60</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>2.8</td>\n",
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              "      <td>0.056511</td>\n",
              "      <td>0.017588</td>\n",
              "      <td>0.006099</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>75</td>\n",
              "      <td>10.6</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.239312</td>\n",
              "      <td>0.013568</td>\n",
              "      <td>0.003863</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>19</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.2</td>\n",
              "      <td>3.0</td>\n",
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              "    <tr>\n",
              "      <th>458</th>\n",
              "      <td>32</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.2</td>\n",
              "      <td>2.9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.098771</td>\n",
              "      <td>0.046231</td>\n",
              "      <td>0.036593</td>\n",
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              "    <tr>\n",
              "      <th>459</th>\n",
              "      <td>58</td>\n",
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              "      <td>0.051597</td>\n",
              "      <td>0.011055</td>\n",
              "      <td>0.003049</td>\n",
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              "      <th>460</th>\n",
              "      <td>34</td>\n",
              "      <td>5.9</td>\n",
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              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>461</th>\n",
              "      <td>36</td>\n",
              "      <td>0.8</td>\n",
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              "      <td>0.010553</td>\n",
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              "      <th>462</th>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#使用決策樹分類器\n",
        "tree = DecisionTreeClassifier()\n",
        "param_grid = {'max_depth' : [3, 4, 5], 'criterion' : ['gini', 'entropy'], 'min_samples_split' : [2, 3, 4, 5], 'min_samples_leaf' : [2, 3, 4, 5]}\n",
        "\n",
        "cv = GridSearchCV(tree, param_grid = param_grid, cv = 10)\n",
        "cv.fit(X_train, y_train)\n",
        "y_pred = cv.predict(X_val)\n",
        "\n",
        "print(classification_report(y_val, y_pred))\n",
        "print(cv.best_params_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ngaEcqgRSDbW",
        "outputId": "62046d1e-0cb0-4af6-bcd5-23050e1e2818"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.43      0.15      0.23        39\n",
            "           1       0.74      0.92      0.82       100\n",
            "\n",
            "    accuracy                           0.71       139\n",
            "   macro avg       0.58      0.54      0.52       139\n",
            "weighted avg       0.65      0.71      0.65       139\n",
            "\n",
            "{'criterion': 'entropy', 'max_depth': 5, 'min_samples_leaf': 5, 'min_samples_split': 5}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#使用隨機森林分類器\n",
        "forest = RandomForestClassifier()\n",
        "param_grid = {'n_estimators' : [10, 20, 30], 'max_depth' : [4, 5, 6]}\n",
        "\n",
        "cv = GridSearchCV(forest, param_grid = param_grid, cv = 10)\n",
        "cv.fit(X_train, y_train)\n",
        "y_pred = cv.predict(X_validation)\n",
        "\n",
        "print(classification_report(y_validation, y_pred))\n",
        "print(cv.best_params_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VOi8CEaISDy0",
        "outputId": "25fe79cb-bb3c-4379-dc62-548e4c45e4ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.54      0.36      0.43        39\n",
            "           1       0.78      0.88      0.83       100\n",
            "\n",
            "    accuracy                           0.73       139\n",
            "   macro avg       0.66      0.62      0.63       139\n",
            "weighted avg       0.71      0.73      0.72       139\n",
            "\n",
            "{'max_depth': 6, 'n_estimators': 20}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#使用邏輯回歸分類器\n",
        "logreg = LogisticRegression()\n",
        "logreg.fit(X_train, y_train)\n",
        "\n",
        "y_pred = logreg.predict(X_validation)\n",
        "\n",
        "print(classification_report(y_validation, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YxKC4zK1SfJ_",
        "outputId": "cdd344b5-a3fc-4fc7-cd77-f387556fa9f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.43      0.26      0.32        39\n",
            "           1       0.75      0.87      0.81       100\n",
            "\n",
            "    accuracy                           0.70       139\n",
            "   macro avg       0.59      0.56      0.56       139\n",
            "weighted avg       0.66      0.70      0.67       139\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5. 模型測試"
      ],
      "metadata": {
        "id": "V6viccBISkGf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "new_df = pre_processing(df)\n",
        "\n",
        "X = new_df.drop(['Label', 'Total_Protiens', 'Albumin_and_Globulin_Ratio'], axis = 1)\n",
        "y = new_df['Label']\n",
        "\n",
        "X_train, X_validation, y_train, y_validation = train_test_split(X, y, test_size = 0.3, random_state = 0)"
      ],
      "metadata": {
        "id": "PJKlEyVxShCM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "tree = DecisionTreeClassifier()\n",
        "param_grid = {'max_depth' : [3, 4, 5], 'criterion' : ['gini', 'entropy'],\n",
        "              'min_samples_split' : [2, 3, 4, 5],'min_samples_leaf' : [2, 3, 4, 5]}\n",
        "\n",
        "model = GridSearchCV(tree, param_grid = param_grid, cv = 10)\n",
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "ZpUbLySWv-9i",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2eaa2969-217f-41e4-8ae8-b5d830c38a90"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=10, estimator=DecisionTreeClassifier(),\n",
              "             param_grid={'criterion': ['gini', 'entropy'],\n",
              "                         'max_depth': [3, 4, 5],\n",
              "                         'min_samples_leaf': [2, 3, 4, 5],\n",
              "                         'min_samples_split': [2, 3, 4, 5]})"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "'''\n",
        "#使用隨機森林分類器\n",
        "forest = RandomForestClassifier()\n",
        "param_grid = {'n_estimators' : [100, 150, 200], 'max_depth' : [4, 5, 6]}\n",
        "\n",
        "model = GridSearchCV(forest, param_grid = param_grid, cv = 10)\n",
        "model.fit(X_train, y_train)\n",
        "'''"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i33DLOk5wCpx",
        "outputId": "5792efa6-bde9-4834-dfb2-58e87b7cd42a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=10, estimator=RandomForestClassifier(),\n",
              "             param_grid={'max_depth': [4, 5, 6],\n",
              "                         'n_estimators': [100, 150, 200]})"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test = pd.read_csv('project1_test.csv')\n",
        "\n",
        "x_test = pre_processing(x_test)\n",
        "\n",
        "x_test = x_test.drop(['Total_Protiens', 'Albumin_and_Globulin_Ratio'], axis = 1)\n",
        "\n",
        "\n",
        "df_submit = pd.DataFrame([], columns=['Id', 'Category'])\n",
        "df_submit['Id'] = [f'{i:03d}' for i in range(len(x_test))]\n",
        "df_submit['Category'] = model.predict(x_test)"
      ],
      "metadata": {
        "id": "vuXxB43XSnZs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "D9J-hue-v2NG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_submit.to_csv('submission.csv', index=None)"
      ],
      "metadata": {
        "id": "BFInXcblSqkk"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}