Tobigs_Week1_framework_RandomForest.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/koojahyeob/ToBigs20/blob/main/Tobigs_Week1_framework_RandomForest.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### matplotlib 한글 폰트 깨짐 방지\n",
        "### 이 셀 실행시키고 런타임->런타임 다시 시작 한 다음에\n",
        "### 이 셀은 건너뛰고 아래부터 실행 시키면 됨.\n",
        "\n",
        "!sudo apt-get install -y fonts-nanum\n",
        "!sudo fc-cache -fv\n",
        "!rm ~/.cache/matplotlib -rf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6oMohjBKWQ0O",
        "outputId": "5307f605-ef84-40b1-aaed-a38f79c2c87d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "fonts-nanum is already the newest version (20200506-1).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 8 not upgraded.\n",
            "/usr/share/fonts: caching, new cache contents: 0 fonts, 2 dirs\n",
            "/usr/share/fonts/opentype: caching, new cache contents: 0 fonts, 2 dirs\n",
            "/usr/share/fonts/opentype/font-awesome: caching, new cache contents: 1 fonts, 0 dirs\n",
            "/usr/share/fonts/opentype/mathjax: caching, new cache contents: 24 fonts, 0 dirs\n",
            "/usr/share/fonts/truetype: caching, new cache contents: 0 fonts, 5 dirs\n",
            "/usr/share/fonts/truetype/font-awesome: caching, new cache contents: 1 fonts, 0 dirs\n",
            "/usr/share/fonts/truetype/glyphicons: caching, new cache contents: 1 fonts, 0 dirs\n",
            "/usr/share/fonts/truetype/humor-sans: caching, new cache contents: 1 fonts, 0 dirs\n",
            "/usr/share/fonts/truetype/liberation: caching, new cache contents: 16 fonts, 0 dirs\n",
            "/usr/share/fonts/truetype/nanum: caching, new cache contents: 12 fonts, 0 dirs\n",
            "/usr/local/share/fonts: caching, new cache contents: 0 fonts, 0 dirs\n",
            "/root/.local/share/fonts: skipping, no such directory\n",
            "/root/.fonts: skipping, no such directory\n",
            "/usr/share/fonts/opentype: skipping, looped directory detected\n",
            "/usr/share/fonts/truetype: skipping, looped directory detected\n",
            "/usr/share/fonts/opentype/font-awesome: skipping, looped directory detected\n",
            "/usr/share/fonts/opentype/mathjax: skipping, looped directory detected\n",
            "/usr/share/fonts/truetype/font-awesome: skipping, looped directory detected\n",
            "/usr/share/fonts/truetype/glyphicons: skipping, looped directory detected\n",
            "/usr/share/fonts/truetype/humor-sans: skipping, looped directory detected\n",
            "/usr/share/fonts/truetype/liberation: skipping, looped directory detected\n",
            "/usr/share/fonts/truetype/nanum: skipping, looped directory detected\n",
            "/var/cache/fontconfig: cleaning cache directory\n",
            "/root/.cache/fontconfig: not cleaning non-existent cache directory\n",
            "/root/.fontconfig: not cleaning non-existent cache directory\n",
            "fc-cache: succeeded\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FacznXw1QzQq",
        "outputId": "bc6d8845-d1da-4469-ebe7-4ad57b860cb0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VNVIPO2MQsVv"
      },
      "outputs": [],
      "source": [
        "import pandas as pd #필요한 라이브러리 import\n",
        "import numpy as np #필요한 라이브러리 import\n",
        "import matplotlib.pyplot as plt #필요한 라이브러리 import\n",
        "from matplotlib import font_manager, rc # matplot 한글 폰트 깨짐을 방지하기 위한\n",
        "import matplotlib.pyplot as plt\n",
        "plt.rc('font', family='NanumBarunGothic') # 폰트 \"맑은 고딕\" 설정\n",
        "import warnings\n",
        "warnings.filterwarnings(action='ignore') # 경고 무시\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/drive/MyDrive/AI/bundang.csv\") # df에 csv 파일 저장\n",
        "df = df.drop(columns=['시군구','단지명','건축년도']) # 컬럼 삭제\n",
        "df.head() # 데이터 처음 5줄 출력"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        },
        "id": "uEH04h4vQwuc",
        "outputId": "2fa26396-378c-4318-a91c-457aac553ce9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-7-0ddeaf7359a2>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/content/drive/MyDrive/AI/bundang.csv\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# df에 csv 파일 저장\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'시군구'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'단지명'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'건축년도'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# 컬럼 삭제\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# 데이터 처음 5줄 출력\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    209\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    210\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\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    212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwrapper\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/python3.10/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    329\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfind_stack_level\u001b[0m\u001b[0;34m(\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    330\u001b[0m                 )\n\u001b[0;32m--> 331\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\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    332\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m         \u001b[0;31m# error: \"Callable[[VarArg(Any), KwArg(Any)], Any]\" has no\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m    948\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    949\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 950\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\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    951\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    952\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    603\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    604\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 605\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\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    606\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    607\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\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/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1441\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1442\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\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   1443\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1444\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\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/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1733\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1734\u001b[0m                     \u001b[0mmode\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m\"b\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1735\u001b[0;31m             self.handles = get_handle(\n\u001b[0m\u001b[1;32m   1736\u001b[0m                 \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1737\u001b[0m                 \u001b[0mmode\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/python3.10/dist-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    854\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    855\u001b[0m             \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 856\u001b[0;31m             handle = open(\n\u001b[0m\u001b[1;32m    857\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    858\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/content/drive/MyDrive/AI/bundang.csv'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum() # 결측치 확인"
      ],
      "metadata": {
        "id": "ti1k6F9-Qwwy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df['거래금액'],bins=100) # 거리 금액에 대한 히스토그램 파악 // 막대 100개 설정"
      ],
      "metadata": {
        "id": "gekeGukSQwzK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['거래금액_log'] = np.log(df['거래금액']+1) # 로그 변환\n",
        "df = df.drop(columns=['거래금액']) # 로그 변환한 컬럼이 생겼으니 기존 컬럼은 삭제\n",
        "plt.hist(df['거래금액_log'],bins=100) # 로그 변환한 거리 금액에 대한 히스토그램 파악 // 막대 100개 설정"
      ],
      "metadata": {
        "id": "feJ3CrxPQw15"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split # sklearn 라이브러리\n",
        "from sklearn.ensemble import RandomForestRegressor # 앙상블 모델 중 랜덤포레스트 import\n",
        "\n",
        "Y = df['거래금액_log'].values # Y 값 설정\n",
        "Y = Y.astype('float32') # Y 값 문자열 타입 설정\n",
        "X = df.drop(columns=['거래금액_log'])  # X 값에서 거래금액_log 값 제거\n",
        "X = X.astype('float32') # X 값 문자열 타입 설정\n",
        "\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size=0.2, random_state=42) # 학습 데이터 테스트 데이터 8:2로 나누기\n",
        "RF_model = RandomForestRegressor(n_estimators=2000, random_state=42)\n",
        "# n_estimators : boosting 반복횟수 설정\n",
        "# random_state : 호출할 때마다 동일한 학습/테스트용 데이터 세트를 생성하기 위해 주어지는 난수 값\n",
        "RF_model.fit(X_train,Y_train) # 트레이닝 데이터 학습시키기"
      ],
      "metadata": {
        "id": "eB8-PjXQQw4q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "outputId": "e2797abf-2791-48e9-9d54-e1111ba911bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-5-5e739dfe2fa4>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensemble\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mRandomForestRegressor\u001b[0m \u001b[0;31m# 앙상블 모델 중 랜덤포레스트 import\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mY\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'거래금액_log'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m \u001b[0;31m# Y 값 설정\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mY\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'float32'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# 문자열 타입 설정\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'거래금액_log'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# X 값에서 거래금액_log 값 제거\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Train_Prediction = (RF_model.predict(X_train)) # 트레이닝 데이터로 예측\n",
        "Test_Prediction = (RF_model.predict(X_test)) # 학습된 모델을 통해 예측\n",
        "\n",
        "RF_Train_MAE = round(np.mean(np.abs(Y_train-Train_Prediction)),3) # train data에 대한 평균절대오차 값 설정 (3자리까지 반올림)\n",
        "RF_Train_MAPE = round((np.mean(np.abs((Y_train-Train_Prediction)/(Y_train)))*100),3) # train data에 대한 평균절대 백분율 오차 값 설정 (3자리까지 반올림)\n",
        "\n",
        "RF_Test_MAE = round(np.mean(np.abs(Y_test-Test_Prediction)),3) # test data에 대한 평균절대오차 값 설정 (3자리까지 반올림)\n",
        "RF_Test_MAPE = round((np.mean(np.abs((Y_test-Test_Prediction)/(Y_test)))*100),3) # test data에 대한 평균절대 백분율 오차 값 설정 (3자리까지 반올림)\n",
        "\n",
        "# 위에서 구한 각 값에 대해서 print로 출력\n",
        "print(\"Mean Absolute Error for Train Set is : {:.3f}\".format(RF_Train_MAE))\n",
        "print(\"Mean Absolute Percent Error for Train Set is : {:.3f} %\".format(RF_Train_MAPE))\n",
        "print(\"Mean Absolute Error for Test Set is : {:.3f}\".format(RF_Test_MAE))\n",
        "print(\"Mean Absolute Percent Error for Test Set is : {:.3f} %\".format(RF_Test_MAPE))"
      ],
      "metadata": {
        "id": "hg0XB_PpQw7z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ncBg-8U9wgrQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Original_Predictions = (np.round(np.exp(Test_Prediction+1),-2)).astype(int) #로그 변환한 값 다시 exp 자연상수를 취함으로써 원래 예측값으로 되돌리는 설정 (2자리까지 반올림 // 타입 형태 int)\n",
        "print(Original_Predictions[:10]) #처음 10개 예측값 출력\n",
        "Original_Values = (np.round(np.exp(Y_test+1),-2)).astype(int) # 위와 같이 로그 변환감 원래 값으로 되돌림\n",
        "print(Original_Values[:10]) # 처음 10개 예측값 출력\n",
        "\n",
        "# 데이터와 컬럼 이름으로 dataframe 생성\n",
        "data = {'Original':  Original_Values,'Prediction': Original_Predictions}\n",
        "final = pd.DataFrame(data)\n",
        "final"
      ],
      "metadata": {
        "id": "rxmAwJRhQxAy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 중요도 낮은 특성부터 오름차순으로 정렬한 인덱스 생성\n",
        "sorted_idx = RF_model.feature_importances_.argsort() # argsort() : 중요도 낮은 순서대로 인덱스 정렬\n",
        "# 중요도 낮은 특성부터 막대그래프 그리기\n",
        "plt.barh(X.columns[sorted_idx], RF_model.feature_importances_[sorted_idx])\n",
        "plt.xlabel(\"Random Forest Feature Importance\") # x축 레이블 생성"
      ],
      "metadata": {
        "id": "0q6OKrUwQxDa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DlFOYZp9TGSj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tUOUjI5STGX8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# XGBoost"
      ],
      "metadata": {
        "id": "UnuZmzfLSApu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import xgboost # XGBoost 라이브러리 import\n",
        "\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size=0.2, random_state=41) # X, Y 8:2로 트레이닝 데이터 테스트 데이터 구분\n",
        "XGB_model = xgboost.XGBRegressor(n_estimators=2000, random_state=41) # boosting 반복횟수 2000으로 설정\n",
        "XGB_model.fit(X_train,Y_train)"
      ],
      "metadata": {
        "id": "FJ7-4rclQxGC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 트레이닝 데이터 학습시키기\n",
        "Train_Prediction = (XGB_model.predict(X_train))\n",
        "Test_Prediction = (XGB_model.predict(X_test))\n",
        "\n",
        "XGB_Train_MAE = round(np.mean(np.abs(Y_train-Train_Prediction)),3) # train data에 대한 평균절대오차 값 설정 (3자리까지 반올림)\n",
        "XGB_Train_MAPE = round((np.mean(np.abs((Y_train-Train_Prediction)/(Y_train)))*100),3) # train data에 대한 평균절대 백분율 오차 값 설정 (3자리까지 반올림)\n",
        "\n",
        "\n",
        "XGB_Test_MAE = round(np.mean(np.abs(Y_test-Test_Prediction)),3) # test data에 대한 평균절대오차 값 설정 (3자리까지 반올림)\n",
        "XGB_Test_MAPE = round((np.mean(np.abs((Y_test-Test_Prediction)/(Y_test)))*100),3) # test data에 대한 평균절대 백분율 오차 값 설정 (3자리까지 반올림)\n",
        "\n",
        "# 위에서 구한 각 값에 대해서 print로 출력\n",
        "print(\"Mean Absolute Error for Train Set is : {:.3f}\".format(XGB_Train_MAE))\n",
        "print(\"Mean Absolute Percent Error for Train Set is : {:.3f} %\".format(XGB_Train_MAPE))\n",
        "print(\"Mean Absolute Error for Test Set is : {:.3f}\".format(XGB_Test_MAE))\n",
        "print(\"Mean Absolute Percent Error for Test Set is : {:.3f} %\".format(XGB_Test_MAPE))"
      ],
      "metadata": {
        "id": "Ila5zFZgQxIq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xgboost.plot_importance(XGB_model)"
      ],
      "metadata": {
        "id": "LsMb89cbS2Ie"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ckpNyu3XR_7J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wRjmQymvR_-b"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}