material/library/flawed_iris_preprocessing.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "flawed_iris_preprocessing.ipynb",
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JrWp7ASRKgf5"
      },
      "source": [
        "# 이상한 IRIS 데이터 전처리"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AIqzwCZWt5tK"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nE5q2m2EuH0K"
      },
      "source": [
        "# 데이터 로딩"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z2QjI1slJcC4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f4de21a6-e41d-4558-dafc-c31228644b95"
      },
      "source": [
        "!wget https://raw.githubusercontent.com/dhrim/hongik_2021/master/material/library/flawed_iris.csv"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-05-20 01:50:07--  https://raw.githubusercontent.com/dhrim/hongik_2021/master/material/library/flawed_iris.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.109.133, 185.199.108.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 2782 (2.7K) [text/plain]\n",
            "Saving to: ‘flawed_iris.csv’\n",
            "\n",
            "flawed_iris.csv     100%[===================>]   2.72K  --.-KB/s    in 0s      \n",
            "\n",
            "2021-05-20 01:50:07 (55.9 MB/s) - ‘flawed_iris.csv’ saved [2782/2782]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CSfwmveYAfrX"
      },
      "source": [
        "df = pd.read_csv(\"flawed_iris.csv\")"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Km592YpuEJ0",
        "outputId": "bb3f5536-e985-4515-91af-f9c2a5140c06"
      },
      "source": [
        "print(df)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     septal_length septal_width  petal_length  petal_width   color  class\n",
            "0              6.4          2.8           5.6          2.2   light    2.0\n",
            "1              5.0          2.3           3.3          1.0  medium    1.0\n",
            "2              4.9          2.5           4.5          1.7  medium    2.0\n",
            "3              4.9          3.1           1.5          0.1    dark    0.0\n",
            "4              5.7          3.8           1.7          0.3    dark    0.0\n",
            "..             ...          ...           ...          ...     ...    ...\n",
            "115            5.5          2.6           4.4          1.2   light    1.0\n",
            "116            5.7            3           NaN          1.2   light    1.0\n",
            "117            4.4          2.9           1.4          0.2   light    0.0\n",
            "118            4.8            3           1.4          0.1    dark    0.0\n",
            "119            5.5          2.4           3.7         -1.0    dark    1.0\n",
            "\n",
            "[120 rows x 6 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9fZkPr01uE3W",
        "outputId": "ae245d50-ffcd-4cfa-dbe2-028e1052fc66"
      },
      "source": [
        "print(df.info())"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 120 entries, 0 to 119\n",
            "Data columns (total 6 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   septal_length  117 non-null    float64\n",
            " 1   septal_width   118 non-null    object \n",
            " 2   petal_length   117 non-null    float64\n",
            " 3   petal_width    118 non-null    float64\n",
            " 4   color          117 non-null    object \n",
            " 5   class          119 non-null    float64\n",
            "dtypes: float64(4), object(2)\n",
            "memory usage: 5.8+ KB\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UYbidMT5u1pv"
      },
      "source": [
        "# 이상한 실수값 \"2..3\" 처리"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "id": "ypLW1lMbvSct",
        "outputId": "f51564ae-71dd-4c20-b657-ff264fd77ce8"
      },
      "source": [
        "df[\"septal_width\"] = df[\"septal_width\"].astype(float)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-6-29c758fa88e1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"septal_width\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"septal_width\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m   5546\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5547\u001b[0m             \u001b[0;31m# else, only a single dtype is given\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5548\u001b[0;31m             \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5549\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5550\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    602\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"raise\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    603\u001b[0m     ) -> \"BlockManager\":\n\u001b[0;32m--> 604\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    605\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    606\u001b[0m     def convert(\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, align_keys, **kwargs)\u001b[0m\n\u001b[1;32m    407\u001b[0m                 \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    408\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 409\u001b[0;31m                 \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    410\u001b[0m             \u001b[0mresult_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_extend_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    411\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    593\u001b[0m             \u001b[0mvals1d\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    594\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 595\u001b[0;31m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_nansafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvals1d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    596\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    597\u001b[0m                 \u001b[0;31m# e.g. astype_nansafe can fail on object-dtype of strings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mastype_nansafe\u001b[0;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[1;32m    995\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    996\u001b[0m         \u001b[0;31m# Explicit copy, or required since NumPy can't view from / to object.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 997\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    998\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    999\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: could not convert string to float: '2..3'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oLE7zk-Jvte2",
        "outputId": "22ff01b3-979b-4abe-a1e7-8f8e2399fada"
      },
      "source": [
        "print(df[df.septal_width==\"2..3\"])"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "    septal_length septal_width  petal_length  petal_width color  class\n",
            "79            6.4         2..3           1.9          2.0   NaN    NaN\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EsZZWPt8v9lV",
        "outputId": "28378164-86b8-4180-96bd-d614ed4881c0"
      },
      "source": [
        "print(df.loc[df.septal_width==\"2..3\",\"septal_width\"])"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "79    2..3\n",
            "Name: septal_width, dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n1BCouaOwrWe"
      },
      "source": [
        "df.loc[df.septal_width==\"2..3\",\"septal_width\"] = \"2.3\""
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qog8LY5Qwv5c"
      },
      "source": [
        "df[\"septal_width\"] = df[\"septal_width\"].astype(float)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QpAUOneUwxBh",
        "outputId": "6e7af3dc-5cfa-408f-e8e1-f050ff1d7003"
      },
      "source": [
        "print(df.info())"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 120 entries, 0 to 119\n",
            "Data columns (total 6 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   septal_length  117 non-null    float64\n",
            " 1   septal_width   118 non-null    float64\n",
            " 2   petal_length   117 non-null    float64\n",
            " 3   petal_width    118 non-null    float64\n",
            " 4   color          117 non-null    object \n",
            " 5   class          119 non-null    float64\n",
            "dtypes: float64(5), object(1)\n",
            "memory usage: 5.8+ KB\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GD0YG-R85FDT"
      },
      "source": [
        "# 코드성 컬럼 color 인코딩"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_rV_YJq36vnO",
        "outputId": "2a8ea30f-e34b-4d48-c68c-1b77f19e5e88"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 120 entries, 0 to 119\n",
            "Data columns (total 6 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   septal_length  117 non-null    float64\n",
            " 1   septal_width   118 non-null    float64\n",
            " 2   petal_length   117 non-null    float64\n",
            " 3   petal_width    118 non-null    float64\n",
            " 4   color          117 non-null    object \n",
            " 5   class          119 non-null    float64\n",
            "dtypes: float64(5), object(1)\n",
            "memory usage: 5.8+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2y_do57I7NIP",
        "outputId": "5425d8b8-af9f-4d83-8488-14a79e492d4b"
      },
      "source": [
        "df['color'].value_counts()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dark      46\n",
              "medium    38\n",
              "light     33\n",
              "Name: color, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eeV_Jl7_6drw"
      },
      "source": [
        "df[\"color\"] = df[\"color\"].astype('category')"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I4jv2IVK6zxR",
        "outputId": "21549ec1-7f0b-4a80-d92a-086b8011fe50"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 120 entries, 0 to 119\n",
            "Data columns (total 6 columns):\n",
            " #   Column         Non-Null Count  Dtype   \n",
            "---  ------         --------------  -----   \n",
            " 0   septal_length  117 non-null    float64 \n",
            " 1   septal_width   118 non-null    float64 \n",
            " 2   petal_length   117 non-null    float64 \n",
            " 3   petal_width    118 non-null    float64 \n",
            " 4   color          117 non-null    category\n",
            " 5   class          119 non-null    float64 \n",
            "dtypes: category(1), float64(5)\n",
            "memory usage: 5.0 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b9nDCXJt79_2"
      },
      "source": [
        "df['color_code'] = df.color.cat.codes"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "VT9rDOrR8CTg",
        "outputId": "94606394-e796-4622-b6fd-f00d3ff0052d"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>septal_length</th>\n",
              "      <th>septal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>color</th>\n",
              "      <th>class</th>\n",
              "      <th>color_code</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6.4</td>\n",
              "      <td>2.8</td>\n",
              "      <td>5.6</td>\n",
              "      <td>2.2</td>\n",
              "      <td>light</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.0</td>\n",
              "      <td>2.3</td>\n",
              "      <td>3.3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>medium</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.9</td>\n",
              "      <td>2.5</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.7</td>\n",
              "      <td>medium</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.1</td>\n",
              "      <td>dark</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.7</td>\n",
              "      <td>3.8</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.3</td>\n",
              "      <td>dark</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   septal_length  septal_width  petal_length  ...   color class  color_code\n",
              "0            6.4           2.8           5.6  ...   light   2.0           1\n",
              "1            5.0           2.3           3.3  ...  medium   1.0           2\n",
              "2            4.9           2.5           4.5  ...  medium   2.0           2\n",
              "3            4.9           3.1           1.5  ...    dark   0.0           0\n",
              "4            5.7           3.8           1.7  ...    dark   0.0           0\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PmU1r9--xKA8"
      },
      "source": [
        "# histogram 보기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iNbafZpPyggp"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "S9gnicCQxkw8",
        "outputId": "887dc3dd-055c-4a81-8a48-1999dcd7eaa3"
      },
      "source": [
        "sns.histplot(df.septal_length)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f165010c810>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "xpYCGPlvxMSY",
        "outputId": "1a53a234-b026-4b6d-d6a1-d51c809e2c1e"
      },
      "source": [
        "sns.histplot(df.septal_width)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f164fd97e10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "y4iJLGOXxprZ",
        "outputId": "8c7b9e54-1170-48eb-8f86-dc48f1275f0f"
      },
      "source": [
        "sns.histplot(df.petal_length)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f164f763d50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "ZsCuOvtdxq2J",
        "outputId": "46a8855f-e520-427d-e0e1-4f7fd82a4e0d"
      },
      "source": [
        "sns.histplot(df.petal_width)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f164f6ef490>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BGbqlBaTyj6L"
      },
      "source": [
        "# 이상치 처리"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iOMfJ8z2ylfD"
      },
      "source": [
        "## septal_length"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "f9K8ZWt3yn-K",
        "outputId": "3e178587-65d2-462b-9ad0-2337aefbd0f5"
      },
      "source": [
        "sns.histplot(df.septal_length)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f164f5d6750>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ihkn9YSlytYz",
        "outputId": "faf7f3f2-59f7-4aa0-b697-efe564f52f4f"
      },
      "source": [
        "print(df.loc[df.septal_length<2.5, \"septal_length\"])"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "41   -5.8\n",
            "Name: septal_length, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iTXaCQHry3D2",
        "outputId": "2cc01728-8f4a-4b58-9128-cb31b246a5cd"
      },
      "source": [
        "print(df.loc[df.septal_length>10.0, \"septal_length\"])"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "90    14.5\n",
            "Name: septal_length, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n79uiiNry68M"
      },
      "source": [
        "전혀 동떨어진 -5.8, 14.5의 값을 이 둘을 제외한 것들의 최소값 최대값으로 바꾸자."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dg_jFZDyzDP1",
        "outputId": "2c6f97e1-3def-4087-a7a3-7ea70c540ae3"
      },
      "source": [
        "filtered = df.loc[ (df.septal_length>=2.5) & (df.septal_length<=10.0), \"septal_length\"]\n",
        "print(filtered.count())\n",
        "min = filtered.min()\n",
        "max = filtered.max()\n",
        "\n",
        "print(\"min = \",min)\n",
        "print(\"max = \",max)\n",
        "\n"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "115\n",
            "min =  4.4\n",
            "max =  7.9\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-j19iyqE1cmm"
      },
      "source": [
        "df.loc[ df.septal_length<2.5, \"septal_length\"] = min\n",
        "df.loc[ df.septal_length>10.0, \"septal_length\"] = max"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "yKO6sk2kz59f",
        "outputId": "89acfb3e-f1ba-420d-ff4d-1b0a6076c490"
      },
      "source": [
        "sns.histplot(df.septal_length)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f164f513ed0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GF6iqL1TVV7uXbwW+f7wR769rfKpqB4PjkkcvWmSqhtRYLW9V3VNVX+meXwXsk+TgsQcd/MW5raqu615fxuADdtg0bdtV807Rtt3lacD1VXXnEvOmadvusmzeKdq2Pwx8tqoWqurrwOXAsYuW+ca27Q4TPRr4wkpv2kQBVNWvV9WhVbWRwa7eB6rqfu256DjkMxmcLJ6IJPslOWDXc+BHgZsWLXYl8NPdtyqezGCXcPuYowKj5U3ybbuORyY5msG/vRX/cfahqj4PbE3y+G7SScDHFy02Ndt2lLzTsm2HnMHyh1OmZtsOWTbvFG3bzwFPTvLILs9JPPAz6krgzO75qQw+51a80re1bwHdT5LfBear6krgF5M8E9gJfBF4wQSjrQWu6P7drQH+qqquTvJzAFX1ZuAq4OnAp4H/AX5mQllhtLynAj+fZCfwv8Dpq/3j7NHLgLd3u/6fAX5mirctrJ53arZt9wfAjwBnD02b2m07Qt6p2LZVdV2SyxgcktoJfBTYvOgz7HzgL5N8msFn2Omrva9DQUhSo5o4BCRJeiALQJIaZQFIUqMsAElqlAUgSY2yACSpURaAmtYN97v4isqllntBkj9eYf65SV6xZ9NBknOSPHLo9Vf29DrULgtArTuBB15SP03OYenhn6Xd1vSVwJpt3VWc72QwnszewKsYXGH6BmB/4C7gBVW1Pck/AP8O/BCDf/c/C+wAfg64txtY62XAgcBvAfsyuOT/ecuMabNSru8E/gSYY3C164ur6hNJLgTuATYB3wb8WlVdlmQv4I+BpzIYzfHrwAUMBv06BLg2yV1VdWL3/q8GnsHgytRTHmw+aRf3ADTLTgbuqKonVtUTgKsZjNh4alV9P4MP0VcPLf/IqjoS+AXggqq6FXgz8MaqOrKq/hH4MPDkbuTNdzAYQvzB2gy8rMvwCuBPh+atA45n8AH+mm7aTwAbgSOA5wPHAFTVmxiM+X7irg9/YD/gX7rRKT8EvPgh5JMA9wA0224Efj/Ja4H3Al8CngBc041LtDeDsdN3uRigqj6U5FFJDlziPQ8FLukGB9wX+OyDCZRkfwaHlC4duhfHI4YW+euqug/4eJK13bTjgUu76Z9Pcu0Kq/gag/9WgC0MxrGRHhILQDOrqv4jg3vKPh34PeADwM1Vdcxyv7LKaxjsQbyhqq5McgJw7oOMtRfwX92exlK+OvR8xbs1LePrQ4OR3Yv/D2s3eAhIMyvJIcD/VNXbgNcxuLfsXLqbpifZZ9EdnE7rph/PYBjiu4EvAwcMLfNovjk+/Zk8SFV1D/DZJM/p1pUkT1zl1/6Jwd3o9ur2Ck4Ymrc4n7TH+NeDZtn3Aq9Lch+DE6c/z2Co3DdlcC/dNQxuBXpzt/z/JfkosA+Dk8AwuBPcZUlOYXAS+FwGh2++xGCP4vCHkOt5wHlJfqtb1zsYnIBezrv45jj/WxkM+Xt3N28zcHWSO4bOA0h7hMNBqwndt4BeUVXzk86ylCT7V9VXkjwW+FfguO5mMFJv3AOQpsN7u5PS+wKv8sNf4+AegPQgJPlN4DmLJl9aVa9eanlpmlkAktQovwUkSY2yACSpURaAJDXKApCkRv0/RPKEjbQSpoYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4tNYGuV20BXS"
      },
      "source": [
        "## septal_width"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "Q3e_5QCY0BXV",
        "outputId": "7c3803dd-74c4-456e-9da2-12b2f1f4a6be"
      },
      "source": [
        "sns.histplot(df.septal_width)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f164f4ca050>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U52gF_Ub0BXW",
        "outputId": "2cbb34dd-6a30-4f17-fec4-5f5a0e9a6005"
      },
      "source": [
        "print(df.loc[df.septal_width<0.0, \"septal_width\"])"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "55   -3.0\n",
            "Name: septal_width, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GeT278Xh0BXX",
        "outputId": "532886b3-63a5-4b27-a23c-a466eaf88dd8"
      },
      "source": [
        "print(df.loc[df.septal_width>10.0, \"septal_width\"])"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "59    16.0\n",
            "74    15.0\n",
            "Name: septal_width, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FLE9z1_q0BXX"
      },
      "source": [
        "전혀 동떨어진 -3.0, 16.0, 15.0의 값을 이 둘을 제외한 것들의 최소값 최대값으로 바꾸자."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1mubU-UV0BXX",
        "outputId": "8e627a89-ec8b-4b0e-a376-5ec661459be0"
      },
      "source": [
        "filtered = df.loc[ (df.septal_width>=0.0) & (df.septal_width<=10.0), \"septal_width\"]\n",
        "print(filtered.count())\n",
        "min = filtered.min()\n",
        "max = filtered.max()\n",
        "\n",
        "print(\"min = \",min)\n",
        "print(\"max = \",max)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "115\n",
            "min =  2.2\n",
            "max =  4.4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ckXal1r11fmK"
      },
      "source": [
        "df.loc[ df.septal_width<2.5, \"septal_width\"] = min\n",
        "df.loc[ df.septal_width>10.0, \"septal_width\"] = max"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "xM5Vc1bE0BXY",
        "outputId": "fc2cab2e-58a1-463f-e912-4d036f96bdff"
      },
      "source": [
        "sns.histplot(df.septal_width)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f164f31b950>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CL16GHTJ0eQL"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SpoW0egz0e0_"
      },
      "source": [
        "## petal_length"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "hcr04ye20e0_",
        "outputId": "d94132fd-e32c-4c6c-b4b1-e4a0f0b98714"
      },
      "source": [
        "sns.histplot(df.petal_length)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f164f2a0310>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "Qa6IQF4R0zQN",
        "outputId": "78ec7cd9-aacb-442d-82d8-09b3032571e4"
      },
      "source": [
        "sns.histplot(df.petal_length, bins=20)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f164f2c2350>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gn2yRU6m0e1A",
        "outputId": "3f1534af-b8d3-4dd9-eb77-9a51f12f912c"
      },
      "source": [
        "print(df.loc[df.petal_length<0.0, \"petal_length\"])"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "73    -6.1\n",
            "89    -1.3\n",
            "105   -1.4\n",
            "Name: petal_length, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bRN5v11Y0e1B"
      },
      "source": [
        "전혀 동떨어진 0보다 작은 값들을 제외한 것들의 최소값 최대값으로 바꾸자."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ax9hqcDI0e1B",
        "outputId": "9cbafbe3-0f54-4e77-dc61-81f5ddf7bf4b"
      },
      "source": [
        "filtered = df.loc[ (df.petal_length>0.0), \"petal_length\"]\n",
        "print(filtered.count())\n",
        "min = filtered.min()\n",
        "max = filtered.max()\n",
        "\n",
        "print(\"min = \",min)\n",
        "print(\"max = \",max)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "114\n",
            "min =  1.0\n",
            "max =  6.9\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PxxBMdb01OKO"
      },
      "source": [
        "df.loc[ df.petal_length<0.0, \"petal_length\"] = min"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "ktDVI22A0e1C",
        "outputId": "5156b123-cfc7-434a-a8b8-c5f7d0e28d98"
      },
      "source": [
        "sns.histplot(df.petal_length)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f164f1c5750>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "87g1hD1H0e1C"
      },
      "source": [
        "## petal_width"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "8jqoiRb60e1C",
        "outputId": "9e08d56a-6b5b-4abb-b29e-62dd86d57d91"
      },
      "source": [
        "sns.histplot(df.petal_width)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f164f14a3d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "-xAuTMam1-Gd",
        "outputId": "c21518d3-4047-4e32-c353-16d7826e74cb"
      },
      "source": [
        "sns.histplot(df.petal_width, bins=100)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f164f018990>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PdePlpPb0e1C",
        "outputId": "ab023a3b-5589-48d4-f9ed-33466817ec49"
      },
      "source": [
        "print(df.loc[df.petal_width>5.0, \"petal_width\"])"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "70     22.2\n",
            "81     15.4\n",
            "102    23.3\n",
            "Name: petal_width, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r6rsFDgN0e1D"
      },
      "source": [
        "전혀 동떨어진 5.0보다 큰 값들을 제외한 것들의 최대값으로 처리하자."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ob56v8nw0e1D",
        "outputId": "94999832-f7dd-4d8c-8f2e-a564216fe322"
      },
      "source": [
        "filtered = df.loc[ (df.petal_width<5.0), \"petal_width\"]\n",
        "print(filtered.count())\n",
        "min = filtered.min()\n",
        "max = filtered.max()\n",
        "\n",
        "print(\"min = \",min)\n",
        "print(\"max = \",max)\n"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "115\n",
            "min =  -1.0\n",
            "max =  2.5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pfPwqhNG1yVd"
      },
      "source": [
        "df.loc[ df.petal_width>5.0, \"petal_width\"] = max"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "dj2tWU110e1D",
        "outputId": "cede9f48-ff87-445e-d1fe-1693a0803c8c"
      },
      "source": [
        "sns.histplot(df.petal_width, bins=20)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f164f669250>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O5FltBjT3EWc",
        "outputId": "d8c70afe-d3f9-4104-b18c-b5d9bd361757"
      },
      "source": [
        "print(df.loc[df.petal_width<0.0, \"petal_width\"])"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "119   -1.0\n",
            "Name: petal_width, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5hkxduut3I9l"
      },
      "source": [
        "0.0 보다 작은 것은 이를 제외한 것들의 최소값으로 처리하자."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uvBU6UX-3NSG",
        "outputId": "90dc4b16-f08d-4896-e238-fefa55b2e6d7"
      },
      "source": [
        "filtered = df.loc[ (df.petal_width>=0.0), \"petal_width\"]\n",
        "print(filtered.count())\n",
        "min = filtered.min()\n",
        "max = filtered.max()\n",
        "\n",
        "print(\"min = \",min)\n",
        "print(\"max = \",max)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "117\n",
            "min =  0.1\n",
            "max =  2.5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lBiOU10i3a49"
      },
      "source": [
        "df.loc[ df.petal_width<0.0, \"petal_width\"] = min"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "Ip4E0Csm3efJ",
        "outputId": "75d67edf-fde0-4675-a9e4-2a816d18c5a8"
      },
      "source": [
        "sns.histplot(df.petal_width, bins=20)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f164ee8a250>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "szkaBSG2uScb"
      },
      "source": [
        "# 결측치 처리"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "57t4JgmxuMH9",
        "outputId": "d973e556-ee01-448f-c9eb-31f26ba802cc"
      },
      "source": [
        "print(df.isnull().any())"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "septal_length     True\n",
            "septal_width      True\n",
            "petal_length      True\n",
            "petal_width       True\n",
            "color             True\n",
            "class             True\n",
            "color_code       False\n",
            "dtype: bool\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JT_kBjFcuXK-",
        "outputId": "3e123591-35de-4247-b10e-c617c11eb333"
      },
      "source": [
        "print(df.isnull().sum())"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "septal_length    3\n",
            "septal_width     2\n",
            "petal_length     3\n",
            "petal_width      2\n",
            "color            3\n",
            "class            1\n",
            "color_code       0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TUTxXPsE3pSI"
      },
      "source": [
        "## class"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "akwXAfJe3rAx"
      },
      "source": [
        "class 값이 없는 것은 버리자"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1AzMoKGE40KX",
        "outputId": "6bfd4e76-4e78-456f-e790-7d84805f9766"
      },
      "source": [
        "print(df['class'].isnull().any())"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 78
        },
        "id": "X1mw50-suszw",
        "outputId": "2e9aa148-afd9-491a-9c04-97b6b1d4a812"
      },
      "source": [
        "df.loc[df['class'].isnull()]"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>septal_length</th>\n",
              "      <th>septal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>color</th>\n",
              "      <th>class</th>\n",
              "      <th>color_code</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>6.4</td>\n",
              "      <td>2.2</td>\n",
              "      <td>1.9</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    septal_length  septal_width  petal_length  ...  color class  color_code\n",
              "79            6.4           2.2           1.9  ...    NaN   NaN          -1\n",
              "\n",
              "[1 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5r14RdjD37FY"
      },
      "source": [
        "working_df = df.loc[~df['class'].isnull()]"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d2uLupli4LlH",
        "outputId": "a6cccf76-a0a6-42fd-a2a9-d44aac56d144"
      },
      "source": [
        "working_df.info()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 119 entries, 0 to 119\n",
            "Data columns (total 7 columns):\n",
            " #   Column         Non-Null Count  Dtype   \n",
            "---  ------         --------------  -----   \n",
            " 0   septal_length  116 non-null    float64 \n",
            " 1   septal_width   117 non-null    float64 \n",
            " 2   petal_length   116 non-null    float64 \n",
            " 3   petal_width    117 non-null    float64 \n",
            " 4   color          117 non-null    category\n",
            " 5   class          119 non-null    float64 \n",
            " 6   color_code     119 non-null    int8    \n",
            "dtypes: category(1), float64(5), int8(1)\n",
            "memory usage: 5.9 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bGqrz9U64NKX",
        "outputId": "7b1605b4-7a11-48ca-d270-4ad394ebe0c3"
      },
      "source": [
        "print(working_df['class'].isnull().any())"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "False\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y0FTwXKB4Q5d",
        "outputId": "eae02938-0eea-4a1a-a504-727c9eee7d00"
      },
      "source": [
        "print(working_df.isnull().sum())"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "septal_length    3\n",
            "septal_width     2\n",
            "petal_length     3\n",
            "petal_width      2\n",
            "color            2\n",
            "class            0\n",
            "color_code       0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qAOXENQ08p0U"
      },
      "source": [
        ""
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W2Xb3pDL9r6m"
      },
      "source": [
        "## color"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        },
        "id": "xQgy8Wah8z0M",
        "outputId": "8000570f-46ef-4e32-e81c-7e9e503dc88a"
      },
      "source": [
        "df.loc[df['color'].isnull()]"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>septal_length</th>\n",
              "      <th>septal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>color</th>\n",
              "      <th>class</th>\n",
              "      <th>color_code</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>6.1</td>\n",
              "      <td>2.8</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>6.4</td>\n",
              "      <td>2.2</td>\n",
              "      <td>1.9</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    septal_length  septal_width  petal_length  ...  color class  color_code\n",
              "52            6.1           2.8           4.0  ...    NaN   1.0          -1\n",
              "79            6.4           2.2           1.9  ...    NaN   NaN          -1\n",
              "92            5.1           3.5           1.4  ...    NaN   0.0          -1\n",
              "\n",
              "[3 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "__H58Fur9wml"
      },
      "source": [
        "-1로 코딩되었다. 내비려 두자"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_smO1b8191Tb"
      },
      "source": [
        "## 나머지 실수 컬럼들"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BW_vlQT295CV"
      },
      "source": [
        "평균값으로 처리하자"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7fizYF8O8zxW"
      },
      "source": [
        "working_df = working_df.fillna(working_df.mean())"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lmm6EWZ3-I7u",
        "outputId": "603869f1-d043-4e75-cd42-14f05abc82ab"
      },
      "source": [
        "working_df.info()"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 119 entries, 0 to 119\n",
            "Data columns (total 7 columns):\n",
            " #   Column         Non-Null Count  Dtype   \n",
            "---  ------         --------------  -----   \n",
            " 0   septal_length  119 non-null    float64 \n",
            " 1   septal_width   119 non-null    float64 \n",
            " 2   petal_length   119 non-null    float64 \n",
            " 3   petal_width    119 non-null    float64 \n",
            " 4   color          117 non-null    category\n",
            " 5   class          119 non-null    float64 \n",
            " 6   color_code     119 non-null    int8    \n",
            "dtypes: category(1), float64(5), int8(1)\n",
            "memory usage: 5.9 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mq0dz5vW-xfp"
      },
      "source": [
        "## 뒷정리"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1zffsAjx-LXw",
        "outputId": "ed4e5b37-0662-46f1-85d6-0dd8b1f085fd"
      },
      "source": [
        "working_df.isnull().any()"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "septal_length    False\n",
              "septal_width     False\n",
              "petal_length     False\n",
              "petal_width      False\n",
              "color             True\n",
              "class            False\n",
              "color_code       False\n",
              "dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J9olbxYC-PW-"
      },
      "source": [
        "working_df.drop('color', axis='columns', inplace=True)"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1eMs4guy-n-V",
        "outputId": "7f18518c-69cf-4960-e3f3-d2edbc63ec91"
      },
      "source": [
        "working_df.info()"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 119 entries, 0 to 119\n",
            "Data columns (total 6 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   septal_length  119 non-null    float64\n",
            " 1   septal_width   119 non-null    float64\n",
            " 2   petal_length   119 non-null    float64\n",
            " 3   petal_width    119 non-null    float64\n",
            " 4   class          119 non-null    float64\n",
            " 5   color_code     119 non-null    int8   \n",
            "dtypes: float64(5), int8(1)\n",
            "memory usage: 5.7 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Pfdp_l7-pah",
        "outputId": "e78a2a89-8997-4f00-c4b0-966857e6f10a"
      },
      "source": [
        "working_df.isnull().any().any()"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L_RbNUQy-4kj"
      },
      "source": [
        "# Normalization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1zt9JqV2-sRT"
      },
      "source": [
        "normalized_df = ( working_df - working_df.min() ) / ( working_df.max() - working_df.min())"
      ],
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 543
        },
        "id": "JfRMPA_XGKGZ",
        "outputId": "33dc5caa-d8b2-404c-9a15-382eef4d0c39"
      },
      "source": [
        "sns.histplot(working_df.septal_length)\n",
        "plt.show()\n",
        "sns.histplot(normalized_df.septal_length)\n",
        "plt.show()"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "06tvWxCnGEm5",
        "outputId": "5e10a29b-567f-4d39-8f1a-23c133938520"
      },
      "source": [
        "sns.histplot(working_df.septal_width)\n",
        "plt.show()\n",
        "sns.histplot(normalized_df.septal_width)\n",
        "plt.show()"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 543
        },
        "id": "QvU1oyx3GVtJ",
        "outputId": "7ebe6d63-617c-45a3-ffeb-d8ee258bc000"
      },
      "source": [
        "sns.histplot(working_df.petal_length)\n",
        "plt.show()\n",
        "sns.histplot(normalized_df.petal_length)\n",
        "plt.show()"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 543
        },
        "id": "kSu1ya8LGac3",
        "outputId": "09fc6548-084b-4066-ee67-27120014b8d0"
      },
      "source": [
        "sns.histplot(working_df.petal_width)\n",
        "plt.show()\n",
        "sns.histplot(normalized_df.petal_width)\n",
        "plt.show()"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "HmjmNvsSGjUD",
        "outputId": "aca8fae7-946d-459d-aaef-edfbabadfd5f"
      },
      "source": [
        "normalized_df"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>septal_length</th>\n",
              "      <th>septal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>class</th>\n",
              "      <th>color_code</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.571429</td>\n",
              "      <td>0.272727</td>\n",
              "      <td>0.779661</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.171429</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.389831</td>\n",
              "      <td>0.375000</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.136364</td>\n",
              "      <td>0.593220</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.409091</td>\n",
              "      <td>0.084746</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.371429</td>\n",
              "      <td>0.727273</td>\n",
              "      <td>0.118644</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.333333</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115</th>\n",
              "      <td>0.314286</td>\n",
              "      <td>0.181818</td>\n",
              "      <td>0.576271</td>\n",
              "      <td>0.458333</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>0.371429</td>\n",
              "      <td>0.363636</td>\n",
              "      <td>0.447253</td>\n",
              "      <td>0.458333</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.318182</td>\n",
              "      <td>0.067797</td>\n",
              "      <td>0.041667</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118</th>\n",
              "      <td>0.114286</td>\n",
              "      <td>0.363636</td>\n",
              "      <td>0.067797</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119</th>\n",
              "      <td>0.314286</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.457627</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>119 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     septal_length  septal_width  petal_length  petal_width  class  color_code\n",
              "0         0.571429      0.272727      0.779661     0.875000    1.0    0.666667\n",
              "1         0.171429      0.000000      0.389831     0.375000    0.5    1.000000\n",
              "2         0.142857      0.136364      0.593220     0.666667    1.0    1.000000\n",
              "3         0.142857      0.409091      0.084746     0.000000    0.0    0.333333\n",
              "4         0.371429      0.727273      0.118644     0.083333    0.0    0.333333\n",
              "..             ...           ...           ...          ...    ...         ...\n",
              "115       0.314286      0.181818      0.576271     0.458333    0.5    0.666667\n",
              "116       0.371429      0.363636      0.447253     0.458333    0.5    0.666667\n",
              "117       0.000000      0.318182      0.067797     0.041667    0.0    0.666667\n",
              "118       0.114286      0.363636      0.067797     0.000000    0.0    0.333333\n",
              "119       0.314286      0.000000      0.457627     0.000000    0.5    0.333333\n",
              "\n",
              "[119 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RI17sHS3GiE1"
      },
      "source": [
        "target_column_names = [\"septal_length\", \"septal_width\", \"petal_length\", \"petal_width\"]\n",
        "working_df[target_column_names] = normalized_df[target_column_names]"
      ],
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "CpLIMtM8Ib50",
        "outputId": "62fe60b8-c371-4c33-9e8a-eb2232af08ea"
      },
      "source": [
        "working_df"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>septal_length</th>\n",
              "      <th>septal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>class</th>\n",
              "      <th>color_code</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.571429</td>\n",
              "      <td>0.272727</td>\n",
              "      <td>0.779661</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.171429</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.389831</td>\n",
              "      <td>0.375000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.136364</td>\n",
              "      <td>0.593220</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.409091</td>\n",
              "      <td>0.084746</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.371429</td>\n",
              "      <td>0.727273</td>\n",
              "      <td>0.118644</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115</th>\n",
              "      <td>0.314286</td>\n",
              "      <td>0.181818</td>\n",
              "      <td>0.576271</td>\n",
              "      <td>0.458333</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>0.371429</td>\n",
              "      <td>0.363636</td>\n",
              "      <td>0.447253</td>\n",
              "      <td>0.458333</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.318182</td>\n",
              "      <td>0.067797</td>\n",
              "      <td>0.041667</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118</th>\n",
              "      <td>0.114286</td>\n",
              "      <td>0.363636</td>\n",
              "      <td>0.067797</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119</th>\n",
              "      <td>0.314286</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.457627</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>119 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     septal_length  septal_width  petal_length  petal_width  class  color_code\n",
              "0         0.571429      0.272727      0.779661     0.875000    2.0           1\n",
              "1         0.171429      0.000000      0.389831     0.375000    1.0           2\n",
              "2         0.142857      0.136364      0.593220     0.666667    2.0           2\n",
              "3         0.142857      0.409091      0.084746     0.000000    0.0           0\n",
              "4         0.371429      0.727273      0.118644     0.083333    0.0           0\n",
              "..             ...           ...           ...          ...    ...         ...\n",
              "115       0.314286      0.181818      0.576271     0.458333    1.0           1\n",
              "116       0.371429      0.363636      0.447253     0.458333    1.0           1\n",
              "117       0.000000      0.318182      0.067797     0.041667    0.0           1\n",
              "118       0.114286      0.363636      0.067797     0.000000    0.0           0\n",
              "119       0.314286      0.000000      0.457627     0.000000    1.0           0\n",
              "\n",
              "[119 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8l5QbyVbImIG"
      },
      "source": [
        ""
      ],
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "76vozIS_t9Eo"
      },
      "source": [
        "# 머신러닝 예측"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h-u-Bt9LgO5Q"
      },
      "source": [
        "## 데이터 준비"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v1fWvZf2ffJK",
        "outputId": "390c05d2-5083-4108-ed64-5b4706791c55",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "working_df.columns"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['septal_length', 'septal_width', 'petal_length', 'petal_width', 'class',\n",
              "       'color_code'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lmaXzVRYe85X"
      },
      "source": [
        "data_x = working_df.loc[:, ['septal_length', 'septal_width', 'petal_length', 'petal_width', 'color_code']]\n",
        "data_y = working_df.loc[:, 'class' ]"
      ],
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TR4rQ0rufyFa",
        "outputId": "77d170eb-3046-49a1-ada3-ac3d94ebb970",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "source": [
        "data_x.head()"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>septal_length</th>\n",
              "      <th>septal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>color_code</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.571429</td>\n",
              "      <td>0.272727</td>\n",
              "      <td>0.779661</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.171429</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.389831</td>\n",
              "      <td>0.375000</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.136364</td>\n",
              "      <td>0.593220</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.409091</td>\n",
              "      <td>0.084746</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.371429</td>\n",
              "      <td>0.727273</td>\n",
              "      <td>0.118644</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   septal_length  septal_width  petal_length  petal_width  color_code\n",
              "0       0.571429      0.272727      0.779661     0.875000           1\n",
              "1       0.171429      0.000000      0.389831     0.375000           2\n",
              "2       0.142857      0.136364      0.593220     0.666667           2\n",
              "3       0.142857      0.409091      0.084746     0.000000           0\n",
              "4       0.371429      0.727273      0.118644     0.083333           0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b2roYdjLf0Jh",
        "outputId": "ae5c2147-b968-421c-a2c8-a375bfa383b0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "data_y.head()"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    2.0\n",
              "1    1.0\n",
              "2    2.0\n",
              "3    0.0\n",
              "4    0.0\n",
              "Name: class, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8EVgAcmugByi"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train_x, test_x, train_y, test_y = train_test_split(data_x, data_y, test_size=0.33, random_state=321)"
      ],
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N6lMZwjqgT-a",
        "outputId": "c69421f3-9a2f-4057-db54-d36c7349a375",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        }
      },
      "source": [
        "train_x"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>septal_length</th>\n",
              "      <th>septal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>color_code</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>0.171429</td>\n",
              "      <td>0.545455</td>\n",
              "      <td>0.101695</td>\n",
              "      <td>0.125000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.363636</td>\n",
              "      <td>0.593220</td>\n",
              "      <td>0.583333</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>0.457143</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.677966</td>\n",
              "      <td>0.583333</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>0.314286</td>\n",
              "      <td>0.407148</td>\n",
              "      <td>0.474576</td>\n",
              "      <td>0.416667</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.227273</td>\n",
              "      <td>0.694915</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>0.314286</td>\n",
              "      <td>0.590909</td>\n",
              "      <td>0.050847</td>\n",
              "      <td>0.041667</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.227273</td>\n",
              "      <td>0.694915</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.136364</td>\n",
              "      <td>0.338983</td>\n",
              "      <td>0.416667</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.181818</td>\n",
              "      <td>0.508475</td>\n",
              "      <td>0.458333</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.318182</td>\n",
              "      <td>0.067797</td>\n",
              "      <td>0.041667</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>79 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     septal_length  septal_width  petal_length  petal_width  color_code\n",
              "104       0.171429      0.545455      0.101695     0.125000           1\n",
              "28        0.285714      0.363636      0.593220     0.583333           0\n",
              "45        0.457143      0.000000      0.677966     0.583333           0\n",
              "76        0.314286      0.407148      0.474576     0.416667           0\n",
              "57        0.400000      0.227273      0.694915     0.750000           2\n",
              "..             ...           ...           ...          ...         ...\n",
              "72        0.314286      0.590909      0.050847     0.041667           1\n",
              "41        0.000000      0.227273      0.694915     0.750000           2\n",
              "31        0.200000      0.136364      0.338983     0.416667           0\n",
              "26        0.400000      0.181818      0.508475     0.458333           1\n",
              "117       0.000000      0.318182      0.067797     0.041667           1\n",
              "\n",
              "[79 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UWoGTF37gioh",
        "outputId": "6dee00fe-5a87-4cb6-bf12-93a0e6f4ee59",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "train_y"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "104    0.0\n",
              "28     1.0\n",
              "45     2.0\n",
              "76     1.0\n",
              "57     2.0\n",
              "      ... \n",
              "72     0.0\n",
              "41     2.0\n",
              "31     1.0\n",
              "26     1.0\n",
              "117    0.0\n",
              "Name: class, Length: 79, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "prB-n5ucgkls",
        "outputId": "9949b5c1-a4fc-45c4-885e-db4e50f1f6f1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "test_y"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "44     0.0\n",
              "105    0.0\n",
              "92     0.0\n",
              "68     2.0\n",
              "6      0.0\n",
              "59     1.0\n",
              "8      1.0\n",
              "114    0.0\n",
              "77     1.0\n",
              "22     0.0\n",
              "80     0.0\n",
              "32     1.0\n",
              "103    1.0\n",
              "35     2.0\n",
              "53     0.0\n",
              "36     2.0\n",
              "58     2.0\n",
              "66     0.0\n",
              "91     1.0\n",
              "70     0.0\n",
              "99     0.0\n",
              "100    0.0\n",
              "51     1.0\n",
              "88     1.0\n",
              "15     1.0\n",
              "94     2.0\n",
              "20     1.0\n",
              "74     2.0\n",
              "65     2.0\n",
              "42     2.0\n",
              "23     2.0\n",
              "85     0.0\n",
              "81     1.0\n",
              "113    2.0\n",
              "67     2.0\n",
              "46     0.0\n",
              "73     2.0\n",
              "56     2.0\n",
              "25     0.0\n",
              "60     1.0\n",
              "Name: class, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Woogd5ogijT",
        "outputId": "f1bc4db2-3107-4581-8dc3-64dc09e15ca8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "test_x"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>septal_length</th>\n",
              "      <th>septal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>color_code</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>0.085714</td>\n",
              "      <td>0.454545</td>\n",
              "      <td>0.101695</td>\n",
              "      <td>0.041667</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>0.228571</td>\n",
              "      <td>0.545455</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.041667</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.590909</td>\n",
              "      <td>0.067797</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.363636</td>\n",
              "      <td>0.762712</td>\n",
              "      <td>0.708333</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.545455</td>\n",
              "      <td>0.084746</td>\n",
              "      <td>0.125000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>0.171429</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.423729</td>\n",
              "      <td>0.375000</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.657143</td>\n",
              "      <td>0.409091</td>\n",
              "      <td>0.576271</td>\n",
              "      <td>0.541667</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>0.171429</td>\n",
              "      <td>0.590909</td>\n",
              "      <td>0.101695</td>\n",
              "      <td>0.208333</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>0.371429</td>\n",
              "      <td>0.318182</td>\n",
              "      <td>0.542373</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.772727</td>\n",
              "      <td>0.050847</td>\n",
              "      <td>0.125000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.681818</td>\n",
              "      <td>0.084746</td>\n",
              "      <td>0.041667</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>0.571429</td>\n",
              "      <td>0.454545</td>\n",
              "      <td>0.593220</td>\n",
              "      <td>0.583333</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.227273</td>\n",
              "      <td>0.525424</td>\n",
              "      <td>0.375000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>0.714286</td>\n",
              "      <td>0.454545</td>\n",
              "      <td>0.796610</td>\n",
              "      <td>0.916667</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>0.171429</td>\n",
              "      <td>0.454545</td>\n",
              "      <td>0.033898</td>\n",
              "      <td>0.041667</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.454545</td>\n",
              "      <td>0.694915</td>\n",
              "      <td>0.791667</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>0.514286</td>\n",
              "      <td>0.545455</td>\n",
              "      <td>0.745763</td>\n",
              "      <td>0.916667</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.363636</td>\n",
              "      <td>0.050847</td>\n",
              "      <td>0.041667</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>0.485714</td>\n",
              "      <td>0.272727</td>\n",
              "      <td>0.447253</td>\n",
              "      <td>0.458333</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>0.085714</td>\n",
              "      <td>0.454545</td>\n",
              "      <td>0.050847</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.727273</td>\n",
              "      <td>0.101695</td>\n",
              "      <td>0.041667</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.363636</td>\n",
              "      <td>0.067797</td>\n",
              "      <td>0.041667</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>0.628571</td>\n",
              "      <td>0.363636</td>\n",
              "      <td>0.576271</td>\n",
              "      <td>0.541667</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>0.457143</td>\n",
              "      <td>0.227273</td>\n",
              "      <td>0.694915</td>\n",
              "      <td>0.625000</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.542857</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.627119</td>\n",
              "      <td>0.625000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.363636</td>\n",
              "      <td>0.813559</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0.657143</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.796610</td>\n",
              "      <td>0.791667</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>0.485714</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.661017</td>\n",
              "      <td>0.708333</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>0.828571</td>\n",
              "      <td>0.318182</td>\n",
              "      <td>0.898305</td>\n",
              "      <td>0.708333</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>0.657143</td>\n",
              "      <td>0.363636</td>\n",
              "      <td>0.711864</td>\n",
              "      <td>0.916667</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>0.485714</td>\n",
              "      <td>0.181818</td>\n",
              "      <td>0.779661</td>\n",
              "      <td>0.541667</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.409091</td>\n",
              "      <td>0.084746</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>0.485714</td>\n",
              "      <td>0.318182</td>\n",
              "      <td>0.627119</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>0.542857</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.847458</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.636364</td>\n",
              "      <td>0.864407</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>0.114286</td>\n",
              "      <td>0.545455</td>\n",
              "      <td>0.101695</td>\n",
              "      <td>0.041667</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>0.942857</td>\n",
              "      <td>0.363636</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.916667</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>0.857143</td>\n",
              "      <td>0.272727</td>\n",
              "      <td>0.864407</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>0.228571</td>\n",
              "      <td>0.590909</td>\n",
              "      <td>0.084746</td>\n",
              "      <td>0.041667</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>0.342857</td>\n",
              "      <td>0.136364</td>\n",
              "      <td>0.491525</td>\n",
              "      <td>0.416667</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     septal_length  septal_width  petal_length  petal_width  color_code\n",
              "44        0.085714      0.454545      0.101695     0.041667           0\n",
              "105       0.228571      0.545455      0.000000     0.041667           0\n",
              "92        0.200000      0.590909      0.067797     0.083333          -1\n",
              "68        0.600000      0.363636      0.762712     0.708333           1\n",
              "6         0.285714      0.545455      0.084746     0.125000           0\n",
              "59        0.171429      1.000000      0.423729     0.375000           2\n",
              "8         0.657143      0.409091      0.576271     0.541667           2\n",
              "114       0.171429      0.590909      0.101695     0.208333           0\n",
              "77        0.371429      0.318182      0.542373     0.500000           0\n",
              "22        0.285714      0.772727      0.050847     0.125000           0\n",
              "80        0.285714      0.681818      0.084746     0.041667           0\n",
              "32        0.571429      0.454545      0.593220     0.583333           0\n",
              "103       0.400000      0.227273      0.525424     0.375000           1\n",
              "35        0.714286      0.454545      0.796610     0.916667           2\n",
              "53        0.171429      0.454545      0.033898     0.041667           2\n",
              "36        0.600000      0.454545      0.694915     0.791667           0\n",
              "58        0.514286      0.545455      0.745763     0.916667           1\n",
              "66        0.000000      0.363636      0.050847     0.041667           1\n",
              "91        0.485714      0.272727      0.447253     0.458333           0\n",
              "70        0.085714      0.454545      0.050847     1.000000           2\n",
              "99        0.200000      0.727273      0.101695     0.041667           2\n",
              "100       0.142857      0.363636      0.067797     0.041667           1\n",
              "51        0.628571      0.363636      0.576271     0.541667           2\n",
              "88        0.457143      0.227273      0.694915     0.625000           2\n",
              "15        0.542857      0.500000      0.627119     0.625000           0\n",
              "94        0.600000      0.363636      0.813559     0.875000           0\n",
              "20        0.657143      0.500000      0.796610     0.791667           2\n",
              "74        0.485714      1.000000      0.661017     0.708333           1\n",
              "65        0.828571      0.318182      0.898305     0.708333           1\n",
              "42        0.657143      0.363636      0.711864     0.916667           0\n",
              "23        0.485714      0.181818      0.779661     0.541667           1\n",
              "85        0.142857      0.409091      0.084746     0.000000           0\n",
              "81        0.485714      0.318182      0.627119     1.000000           2\n",
              "113       0.542857      0.500000      0.847458     1.000000           0\n",
              "67        0.800000      0.636364      0.864407     1.000000           0\n",
              "46        0.114286      0.545455      0.101695     0.041667           1\n",
              "73        0.942857      0.363636      0.000000     0.916667           2\n",
              "56        0.857143      0.272727      0.864407     0.750000           2\n",
              "25        0.228571      0.590909      0.084746     0.041667           1\n",
              "60        0.342857      0.136364      0.491525     0.416667           0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OutOvWsXdeJB"
      },
      "source": [
        "## 학습과 예측 실행"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "atpUiVK1dhMA"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression #Logistic(Regression)Classifier\n",
        "from sklearn.tree import DecisionTreeClassifier #Decision Tree\n",
        "from sklearn.svm import SVC #Support Vector Machine\n",
        "from sklearn.naive_bayes import GaussianNB #Naive Bayesian\n",
        "from sklearn.neighbors import KNeighborsClassifier #K Nearest Neighbor\n",
        "from sklearn.ensemble import RandomForestClassifier #Random Forest\n",
        "from sklearn.ensemble import GradientBoostingClassifier #Gradient Boosing\n",
        "from sklearn.neural_network import MLPClassifier #Neural Network\n",
        "\n"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WB-KX9E8dtVz"
      },
      "source": [
        "#logistic (Regression) Classifier, Decision tree, support vector machine, naive bayesian, K Nearest Neighbor, Random Forest, Gradient Boosing, Neural Network\n",
        "models = []\n",
        "models.append((\"LR\", LogisticRegression()))\n",
        "models.append((\"DT\", DecisionTreeClassifier()))\n",
        "models.append((\"SVM\", SVC()))\n",
        "models.append((\"NB\", GaussianNB()))\n",
        "models.append((\"KNN\", KNeighborsClassifier()))\n",
        "models.append((\"RF\", RandomForestClassifier()))\n",
        "models.append((\"GB\", GradientBoostingClassifier()))\n",
        "models.append((\"ANN\", MLPClassifier()))"
      ],
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KV7efnJ1dzNT",
        "outputId": "bdf1a6dd-b457-4e74-8810-f10d59175eb0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "for name, model in models:\n",
        "    model.fit(train_x, train_y.values.ravel())\n",
        "    predicted_y = model.predict(test_x)\n",
        "    print(name, \"'s Accuracy is \", accuracy_score(test_y, predicted_y))"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "LR 's Accuracy is  0.85\n",
            "DT 's Accuracy is  0.9\n",
            "SVM 's Accuracy is  0.875\n",
            "NB 's Accuracy is  0.9\n",
            "KNN 's Accuracy is  0.85\n",
            "RF 's Accuracy is  0.9\n",
            "GB 's Accuracy is  0.9\n",
            "ANN 's Accuracy is  0.9\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  % self.max_iter, ConvergenceWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H_iyZvUTsp8a"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}