03_선형회귀.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "03.선형회귀.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOe2xC3Wom3zpaV9Br/V08l",
      "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/threegenie/studying_pytorch/blob/main/03_%EC%84%A0%ED%98%95%ED%9A%8C%EA%B7%80.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5D-0DwZmEnvz"
      },
      "source": [
        "> PyTorch로 시작하는 딥러닝 입문 - <nn.Module로 구현하는 선형 회귀, 클래스로 파이토치 모델 구현하기>를 참고하여 직접 linear regression model을 만들어보기(sklearn과 pytorch 비교)\n",
        "- Kaggle House Prices 데이터를 사용하여 집값예측\n",
        "(https://www.kaggle.com/c/house-prices-advanced-regression-techniques/overview)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6pdQGEkbY6CC"
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torch.autograd import Variable\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import os, sys\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VD4X0pYu-woX",
        "outputId": "27a14ea3-a917-41a2-899b-3cb945f9348f"
      },
      "source": [
        "# random seed 고정\n",
        "torch.manual_seed(1)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<torch._C.Generator at 0x7fafa157db90>"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OTLHXviPFBJ3"
      },
      "source": [
        "### Data import"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C7TSg8Kh-1A3"
      },
      "source": [
        "df = pd.read_csv('https://ds-lecture-data.s3.ap-northeast-2.amazonaws.com/house-prices/house_prices_train.csv')\n",
        "df_t = pd.read_csv('https://ds-lecture-data.s3.ap-northeast-2.amazonaws.com/house-prices/house_prices_test.csv')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "id": "kLW4V5RlAtfX",
        "outputId": "8351d884-193c-4d1e-c410-b508deffa698"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 4,
      "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>Id</th>\n",
              "      <th>MSSubClass</th>\n",
              "      <th>MSZoning</th>\n",
              "      <th>LotFrontage</th>\n",
              "      <th>LotArea</th>\n",
              "      <th>Street</th>\n",
              "      <th>Alley</th>\n",
              "      <th>LotShape</th>\n",
              "      <th>LandContour</th>\n",
              "      <th>Utilities</th>\n",
              "      <th>LotConfig</th>\n",
              "      <th>LandSlope</th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Condition1</th>\n",
              "      <th>Condition2</th>\n",
              "      <th>BldgType</th>\n",
              "      <th>HouseStyle</th>\n",
              "      <th>OverallQual</th>\n",
              "      <th>OverallCond</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>YearRemodAdd</th>\n",
              "      <th>RoofStyle</th>\n",
              "      <th>RoofMatl</th>\n",
              "      <th>Exterior1st</th>\n",
              "      <th>Exterior2nd</th>\n",
              "      <th>MasVnrType</th>\n",
              "      <th>MasVnrArea</th>\n",
              "      <th>ExterQual</th>\n",
              "      <th>ExterCond</th>\n",
              "      <th>Foundation</th>\n",
              "      <th>BsmtQual</th>\n",
              "      <th>BsmtCond</th>\n",
              "      <th>BsmtExposure</th>\n",
              "      <th>BsmtFinType1</th>\n",
              "      <th>BsmtFinSF1</th>\n",
              "      <th>BsmtFinType2</th>\n",
              "      <th>BsmtFinSF2</th>\n",
              "      <th>BsmtUnfSF</th>\n",
              "      <th>TotalBsmtSF</th>\n",
              "      <th>Heating</th>\n",
              "      <th>...</th>\n",
              "      <th>CentralAir</th>\n",
              "      <th>Electrical</th>\n",
              "      <th>1stFlrSF</th>\n",
              "      <th>2ndFlrSF</th>\n",
              "      <th>LowQualFinSF</th>\n",
              "      <th>GrLivArea</th>\n",
              "      <th>BsmtFullBath</th>\n",
              "      <th>BsmtHalfBath</th>\n",
              "      <th>FullBath</th>\n",
              "      <th>HalfBath</th>\n",
              "      <th>BedroomAbvGr</th>\n",
              "      <th>KitchenAbvGr</th>\n",
              "      <th>KitchenQual</th>\n",
              "      <th>TotRmsAbvGrd</th>\n",
              "      <th>Functional</th>\n",
              "      <th>Fireplaces</th>\n",
              "      <th>FireplaceQu</th>\n",
              "      <th>GarageType</th>\n",
              "      <th>GarageYrBlt</th>\n",
              "      <th>GarageFinish</th>\n",
              "      <th>GarageCars</th>\n",
              "      <th>GarageArea</th>\n",
              "      <th>GarageQual</th>\n",
              "      <th>GarageCond</th>\n",
              "      <th>PavedDrive</th>\n",
              "      <th>WoodDeckSF</th>\n",
              "      <th>OpenPorchSF</th>\n",
              "      <th>EnclosedPorch</th>\n",
              "      <th>3SsnPorch</th>\n",
              "      <th>ScreenPorch</th>\n",
              "      <th>PoolArea</th>\n",
              "      <th>PoolQC</th>\n",
              "      <th>Fence</th>\n",
              "      <th>MiscFeature</th>\n",
              "      <th>MiscVal</th>\n",
              "      <th>MoSold</th>\n",
              "      <th>YrSold</th>\n",
              "      <th>SaleType</th>\n",
              "      <th>SaleCondition</th>\n",
              "      <th>SalePrice</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>65.0</td>\n",
              "      <td>8450</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>CollgCr</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>2Story</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2003</td>\n",
              "      <td>2003</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>BrkFace</td>\n",
              "      <td>196.0</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>PConc</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>No</td>\n",
              "      <td>GLQ</td>\n",
              "      <td>706</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>856</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>856</td>\n",
              "      <td>854</td>\n",
              "      <td>0</td>\n",
              "      <td>1710</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>8</td>\n",
              "      <td>Typ</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>2003.0</td>\n",
              "      <td>RFn</td>\n",
              "      <td>2</td>\n",
              "      <td>548</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>61</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>208500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>RL</td>\n",
              "      <td>80.0</td>\n",
              "      <td>9600</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>FR2</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>Veenker</td>\n",
              "      <td>Feedr</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>1Story</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>1976</td>\n",
              "      <td>1976</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>MetalSd</td>\n",
              "      <td>MetalSd</td>\n",
              "      <td>None</td>\n",
              "      <td>0.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>CBlock</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>Gd</td>\n",
              "      <td>ALQ</td>\n",
              "      <td>978</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0</td>\n",
              "      <td>284</td>\n",
              "      <td>1262</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>1262</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1262</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>TA</td>\n",
              "      <td>6</td>\n",
              "      <td>Typ</td>\n",
              "      <td>1</td>\n",
              "      <td>TA</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>1976.0</td>\n",
              "      <td>RFn</td>\n",
              "      <td>2</td>\n",
              "      <td>460</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>298</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2007</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>181500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>68.0</td>\n",
              "      <td>11250</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>CollgCr</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>2Story</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2001</td>\n",
              "      <td>2002</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>BrkFace</td>\n",
              "      <td>162.0</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>PConc</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>Mn</td>\n",
              "      <td>GLQ</td>\n",
              "      <td>486</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0</td>\n",
              "      <td>434</td>\n",
              "      <td>920</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>920</td>\n",
              "      <td>866</td>\n",
              "      <td>0</td>\n",
              "      <td>1786</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>6</td>\n",
              "      <td>Typ</td>\n",
              "      <td>1</td>\n",
              "      <td>TA</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>2001.0</td>\n",
              "      <td>RFn</td>\n",
              "      <td>2</td>\n",
              "      <td>608</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>223500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>70</td>\n",
              "      <td>RL</td>\n",
              "      <td>60.0</td>\n",
              "      <td>9550</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Corner</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>Crawfor</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>2Story</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>1915</td>\n",
              "      <td>1970</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>Wd Sdng</td>\n",
              "      <td>Wd Shng</td>\n",
              "      <td>None</td>\n",
              "      <td>0.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>BrkTil</td>\n",
              "      <td>TA</td>\n",
              "      <td>Gd</td>\n",
              "      <td>No</td>\n",
              "      <td>ALQ</td>\n",
              "      <td>216</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0</td>\n",
              "      <td>540</td>\n",
              "      <td>756</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>961</td>\n",
              "      <td>756</td>\n",
              "      <td>0</td>\n",
              "      <td>1717</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>7</td>\n",
              "      <td>Typ</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>Detchd</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>3</td>\n",
              "      <td>642</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>272</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2006</td>\n",
              "      <td>WD</td>\n",
              "      <td>Abnorml</td>\n",
              "      <td>140000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>84.0</td>\n",
              "      <td>14260</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>FR2</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>NoRidge</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>2Story</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>2000</td>\n",
              "      <td>2000</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>BrkFace</td>\n",
              "      <td>350.0</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>PConc</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>Av</td>\n",
              "      <td>GLQ</td>\n",
              "      <td>655</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0</td>\n",
              "      <td>490</td>\n",
              "      <td>1145</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>1145</td>\n",
              "      <td>1053</td>\n",
              "      <td>0</td>\n",
              "      <td>2198</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>9</td>\n",
              "      <td>Typ</td>\n",
              "      <td>1</td>\n",
              "      <td>TA</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>RFn</td>\n",
              "      <td>3</td>\n",
              "      <td>836</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>192</td>\n",
              "      <td>84</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>250000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 81 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   Id  MSSubClass MSZoning  ...  SaleType  SaleCondition SalePrice\n",
              "0   1          60       RL  ...        WD         Normal    208500\n",
              "1   2          20       RL  ...        WD         Normal    181500\n",
              "2   3          60       RL  ...        WD         Normal    223500\n",
              "3   4          70       RL  ...        WD        Abnorml    140000\n",
              "4   5          60       RL  ...        WD         Normal    250000\n",
              "\n",
              "[5 rows x 81 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "yjctviIVAwLU",
        "outputId": "af0f7f59-1ef1-47c1-8216-306a75b2322b"
      },
      "source": [
        "df_t.head()"
      ],
      "execution_count": 5,
      "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>Id</th>\n",
              "      <th>MSSubClass</th>\n",
              "      <th>MSZoning</th>\n",
              "      <th>LotFrontage</th>\n",
              "      <th>LotArea</th>\n",
              "      <th>Street</th>\n",
              "      <th>Alley</th>\n",
              "      <th>LotShape</th>\n",
              "      <th>LandContour</th>\n",
              "      <th>Utilities</th>\n",
              "      <th>LotConfig</th>\n",
              "      <th>LandSlope</th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Condition1</th>\n",
              "      <th>Condition2</th>\n",
              "      <th>BldgType</th>\n",
              "      <th>HouseStyle</th>\n",
              "      <th>OverallQual</th>\n",
              "      <th>OverallCond</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>YearRemodAdd</th>\n",
              "      <th>RoofStyle</th>\n",
              "      <th>RoofMatl</th>\n",
              "      <th>Exterior1st</th>\n",
              "      <th>Exterior2nd</th>\n",
              "      <th>MasVnrType</th>\n",
              "      <th>MasVnrArea</th>\n",
              "      <th>ExterQual</th>\n",
              "      <th>ExterCond</th>\n",
              "      <th>Foundation</th>\n",
              "      <th>BsmtQual</th>\n",
              "      <th>BsmtCond</th>\n",
              "      <th>BsmtExposure</th>\n",
              "      <th>BsmtFinType1</th>\n",
              "      <th>BsmtFinSF1</th>\n",
              "      <th>BsmtFinType2</th>\n",
              "      <th>BsmtFinSF2</th>\n",
              "      <th>BsmtUnfSF</th>\n",
              "      <th>TotalBsmtSF</th>\n",
              "      <th>Heating</th>\n",
              "      <th>HeatingQC</th>\n",
              "      <th>CentralAir</th>\n",
              "      <th>Electrical</th>\n",
              "      <th>1stFlrSF</th>\n",
              "      <th>2ndFlrSF</th>\n",
              "      <th>LowQualFinSF</th>\n",
              "      <th>GrLivArea</th>\n",
              "      <th>BsmtFullBath</th>\n",
              "      <th>BsmtHalfBath</th>\n",
              "      <th>FullBath</th>\n",
              "      <th>HalfBath</th>\n",
              "      <th>BedroomAbvGr</th>\n",
              "      <th>KitchenAbvGr</th>\n",
              "      <th>KitchenQual</th>\n",
              "      <th>TotRmsAbvGrd</th>\n",
              "      <th>Functional</th>\n",
              "      <th>Fireplaces</th>\n",
              "      <th>FireplaceQu</th>\n",
              "      <th>GarageType</th>\n",
              "      <th>GarageYrBlt</th>\n",
              "      <th>GarageFinish</th>\n",
              "      <th>GarageCars</th>\n",
              "      <th>GarageArea</th>\n",
              "      <th>GarageQual</th>\n",
              "      <th>GarageCond</th>\n",
              "      <th>PavedDrive</th>\n",
              "      <th>WoodDeckSF</th>\n",
              "      <th>OpenPorchSF</th>\n",
              "      <th>EnclosedPorch</th>\n",
              "      <th>3SsnPorch</th>\n",
              "      <th>ScreenPorch</th>\n",
              "      <th>PoolArea</th>\n",
              "      <th>PoolQC</th>\n",
              "      <th>Fence</th>\n",
              "      <th>MiscFeature</th>\n",
              "      <th>MiscVal</th>\n",
              "      <th>MoSold</th>\n",
              "      <th>YrSold</th>\n",
              "      <th>SaleType</th>\n",
              "      <th>SaleCondition</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1461</td>\n",
              "      <td>20</td>\n",
              "      <td>RH</td>\n",
              "      <td>80.0</td>\n",
              "      <td>11622</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>NAmes</td>\n",
              "      <td>Feedr</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>1Story</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>1961</td>\n",
              "      <td>1961</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>None</td>\n",
              "      <td>0.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>CBlock</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>No</td>\n",
              "      <td>Rec</td>\n",
              "      <td>468.0</td>\n",
              "      <td>LwQ</td>\n",
              "      <td>144.0</td>\n",
              "      <td>270.0</td>\n",
              "      <td>882.0</td>\n",
              "      <td>GasA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>896</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>896</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>TA</td>\n",
              "      <td>5</td>\n",
              "      <td>Typ</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>1961.0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>1.0</td>\n",
              "      <td>730.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>140</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MnPrv</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2010</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1462</td>\n",
              "      <td>20</td>\n",
              "      <td>RL</td>\n",
              "      <td>81.0</td>\n",
              "      <td>14267</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Corner</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>NAmes</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>1Story</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>1958</td>\n",
              "      <td>1958</td>\n",
              "      <td>Hip</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>Wd Sdng</td>\n",
              "      <td>Wd Sdng</td>\n",
              "      <td>BrkFace</td>\n",
              "      <td>108.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>CBlock</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>No</td>\n",
              "      <td>ALQ</td>\n",
              "      <td>923.0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0.0</td>\n",
              "      <td>406.0</td>\n",
              "      <td>1329.0</td>\n",
              "      <td>GasA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>1329</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1329</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>6</td>\n",
              "      <td>Typ</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>1958.0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>1.0</td>\n",
              "      <td>312.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>393</td>\n",
              "      <td>36</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Gar2</td>\n",
              "      <td>12500</td>\n",
              "      <td>6</td>\n",
              "      <td>2010</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1463</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>74.0</td>\n",
              "      <td>13830</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>Gilbert</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>2Story</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>1997</td>\n",
              "      <td>1998</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>None</td>\n",
              "      <td>0.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>PConc</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>No</td>\n",
              "      <td>GLQ</td>\n",
              "      <td>791.0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0.0</td>\n",
              "      <td>137.0</td>\n",
              "      <td>928.0</td>\n",
              "      <td>GasA</td>\n",
              "      <td>Gd</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>928</td>\n",
              "      <td>701</td>\n",
              "      <td>0</td>\n",
              "      <td>1629</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>TA</td>\n",
              "      <td>6</td>\n",
              "      <td>Typ</td>\n",
              "      <td>1</td>\n",
              "      <td>TA</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>1997.0</td>\n",
              "      <td>Fin</td>\n",
              "      <td>2.0</td>\n",
              "      <td>482.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>212</td>\n",
              "      <td>34</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MnPrv</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2010</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1464</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>78.0</td>\n",
              "      <td>9978</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>Gilbert</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>2Story</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>1998</td>\n",
              "      <td>1998</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>BrkFace</td>\n",
              "      <td>20.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>PConc</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>No</td>\n",
              "      <td>GLQ</td>\n",
              "      <td>602.0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0.0</td>\n",
              "      <td>324.0</td>\n",
              "      <td>926.0</td>\n",
              "      <td>GasA</td>\n",
              "      <td>Ex</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>926</td>\n",
              "      <td>678</td>\n",
              "      <td>0</td>\n",
              "      <td>1604</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>7</td>\n",
              "      <td>Typ</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>Fin</td>\n",
              "      <td>2.0</td>\n",
              "      <td>470.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>360</td>\n",
              "      <td>36</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2010</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1465</td>\n",
              "      <td>120</td>\n",
              "      <td>RL</td>\n",
              "      <td>43.0</td>\n",
              "      <td>5005</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>HLS</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>StoneBr</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>TwnhsE</td>\n",
              "      <td>1Story</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>1992</td>\n",
              "      <td>1992</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>HdBoard</td>\n",
              "      <td>HdBoard</td>\n",
              "      <td>None</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>PConc</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>No</td>\n",
              "      <td>ALQ</td>\n",
              "      <td>263.0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1017.0</td>\n",
              "      <td>1280.0</td>\n",
              "      <td>GasA</td>\n",
              "      <td>Ex</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>1280</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1280</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>5</td>\n",
              "      <td>Typ</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>1992.0</td>\n",
              "      <td>RFn</td>\n",
              "      <td>2.0</td>\n",
              "      <td>506.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>82</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>144</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2010</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Id  MSSubClass MSZoning  ...  YrSold  SaleType SaleCondition\n",
              "0  1461          20       RH  ...    2010        WD        Normal\n",
              "1  1462          20       RL  ...    2010        WD        Normal\n",
              "2  1463          60       RL  ...    2010        WD        Normal\n",
              "3  1464          60       RL  ...    2010        WD        Normal\n",
              "4  1465         120       RL  ...    2010        WD        Normal\n",
              "\n",
              "[5 rows x 80 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ngEri3h-Az35"
      },
      "source": [
        "### Scikit-Learn으로 Simple Linear Regression 모델 만들기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ioj3kLMRAywK"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aPa_NPvnBIkJ"
      },
      "source": [
        "# linear model create\n",
        "model = LinearRegression()"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "APCaOeUNBMo3"
      },
      "source": [
        "# 예측에 사용할 특성 분리\n",
        "feature = ['GrLivArea'] #지상생활면적\n",
        "target = ['SalePrice'] #주택판매가격(달러)\n",
        "X_train = df[feature]\n",
        "y_train = df[target]"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MeZSSFcRBk1C",
        "outputId": "bff1e2a9-52e3-4f11-97cf-3adfb66320d4"
      },
      "source": [
        "# model fit\n",
        "model.fit(X_train, y_train)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8V410K4HBpPh"
      },
      "source": [
        "# 테스트 데이터를 통해 예측\n",
        "X_test = [[x] for x in df_t['GrLivArea']]\n",
        "y_pred = model.predict(X_test)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6GysimN0BzX9",
        "outputId": "6f1b5377-6165-47be-fb7c-e01e59f2ba6c"
      },
      "source": [
        "#전체 예측값\n",
        "y_pred"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[114557.82748987],\n",
              "       [160945.27292207],\n",
              "       [193084.38061182],\n",
              "       ...,\n",
              "       [149696.58523066],\n",
              "       [122485.47405334],\n",
              "       [232829.74378814]])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "YlaRelgDB3Sa",
        "outputId": "d5f75d03-0dd1-4a65-8fbc-abf694653b1a"
      },
      "source": [
        "## train 데이터에 대한 그래프\n",
        "plt.scatter(X_train, y_train, color='black', linewidth=1)\n",
        "\n",
        "## test 데이터에 대한 예측을 파란색 점으로 나타내기\n",
        "plt.scatter(X_test, y_pred, color='blue', linewidth=1)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7faf8cfc97d0>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uurmMGWXMG6N"
      },
      "source": [
        "### Pytorch로 Simple Linear Regression 모델 만들기\n",
        "- https://github.com/Benuraj/Linear-Regression 참고"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qsFrt21xQBcp"
      },
      "source": [
        "# 예측에 사용할 특성 분리\n",
        "space = df['GrLivArea']\n",
        "price = df['SalePrice']"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w-TRpGIVQBcp"
      },
      "source": [
        "# pandas -> numpy - float\n",
        "x_train = np.array(space).reshape(-1, 1).astype('float32')\n",
        "y_train = np.array(price).reshape(-1,1).astype('float32')"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9pw-hEjiQBcp"
      },
      "source": [
        "# linear regression model built\n",
        "class LinearRegressionModel(nn.Module):\n",
        "    def __init__(self, input_dim, output_dim):\n",
        "        super(LinearRegressionModel, self).__init__()\n",
        "        self.linear = nn.Linear(input_dim, output_dim)\n",
        "    \n",
        "    def forward(self, x):\n",
        "        out = self.linear(x)\n",
        "        return out"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PkIe1hmzQBcq",
        "outputId": "19761d4e-88ae-40e9-f6d1-8206aa3043f8"
      },
      "source": [
        "# Linear regression model, Loss and optimizer\n",
        "model = LinearRegressionModel(1, 1)\n",
        "criterion = nn.MSELoss()\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr = 0.00000001)  \n",
        "\n",
        "# Train the model\n",
        "num_epochs = 600\n",
        "for epoch in range(num_epochs):\n",
        "    # Convert numpy arrays to torch tensors\n",
        "    inputs = Variable(torch.from_numpy(x_train))\n",
        "    labels = Variable(torch.from_numpy(y_train))\n",
        "\n",
        "    # Clearing the gradients w.r.t. parameters\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "    # Forward pass\n",
        "    # model = model.double() # torch complains that tensor type mismatch if not included\n",
        "    outputs = model.forward(inputs)\n",
        "    loss = criterion(outputs, labels)\n",
        "    \n",
        "    # Backward and optimize\n",
        "    loss.backward() # Backpropagation\n",
        "    optimizer.step() # Update of parameters\n",
        "    \n",
        "    if (epoch+1) % 5 == 0:\n",
        "        print ('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))\n"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [5/600], Loss: 26474874880.0000\n",
            "Epoch [10/600], Loss: 16914568192.0000\n",
            "Epoch [15/600], Loss: 11277314048.0000\n",
            "Epoch [20/600], Loss: 7953293312.0000\n",
            "Epoch [25/600], Loss: 5993281536.0000\n",
            "Epoch [30/600], Loss: 4837555200.0000\n",
            "Epoch [35/600], Loss: 4156079360.0000\n",
            "Epoch [40/600], Loss: 3754247424.0000\n",
            "Epoch [45/600], Loss: 3517305344.0000\n",
            "Epoch [50/600], Loss: 3377591552.0000\n",
            "Epoch [55/600], Loss: 3295209984.0000\n",
            "Epoch [60/600], Loss: 3246632704.0000\n",
            "Epoch [65/600], Loss: 3217989376.0000\n",
            "Epoch [70/600], Loss: 3201099264.0000\n",
            "Epoch [75/600], Loss: 3191140352.0000\n",
            "Epoch [80/600], Loss: 3185268224.0000\n",
            "Epoch [85/600], Loss: 3181805568.0000\n",
            "Epoch [90/600], Loss: 3179763712.0000\n",
            "Epoch [95/600], Loss: 3178560000.0000\n",
            "Epoch [100/600], Loss: 3177849856.0000\n",
            "Epoch [105/600], Loss: 3177431296.0000\n",
            "Epoch [110/600], Loss: 3177184512.0000\n",
            "Epoch [115/600], Loss: 3177038848.0000\n",
            "Epoch [120/600], Loss: 3176952832.0000\n",
            "Epoch [125/600], Loss: 3176902400.0000\n",
            "Epoch [130/600], Loss: 3176872448.0000\n",
            "Epoch [135/600], Loss: 3176854784.0000\n",
            "Epoch [140/600], Loss: 3176844800.0000\n",
            "Epoch [145/600], Loss: 3176838656.0000\n",
            "Epoch [150/600], Loss: 3176834816.0000\n",
            "Epoch [155/600], Loss: 3176832768.0000\n",
            "Epoch [160/600], Loss: 3176831232.0000\n",
            "Epoch [165/600], Loss: 3176830464.0000\n",
            "Epoch [170/600], Loss: 3176830208.0000\n",
            "Epoch [175/600], Loss: 3176830208.0000\n",
            "Epoch [180/600], Loss: 3176829696.0000\n",
            "Epoch [185/600], Loss: 3176829696.0000\n",
            "Epoch [190/600], Loss: 3176829696.0000\n",
            "Epoch [195/600], Loss: 3176829696.0000\n",
            "Epoch [200/600], Loss: 3176829696.0000\n",
            "Epoch [205/600], Loss: 3176829696.0000\n",
            "Epoch [210/600], Loss: 3176829696.0000\n",
            "Epoch [215/600], Loss: 3176829696.0000\n",
            "Epoch [220/600], Loss: 3176829696.0000\n",
            "Epoch [225/600], Loss: 3176829440.0000\n",
            "Epoch [230/600], Loss: 3176829440.0000\n",
            "Epoch [235/600], Loss: 3176829696.0000\n",
            "Epoch [240/600], Loss: 3176829696.0000\n",
            "Epoch [245/600], Loss: 3176829440.0000\n",
            "Epoch [250/600], Loss: 3176829696.0000\n",
            "Epoch [255/600], Loss: 3176829696.0000\n",
            "Epoch [260/600], Loss: 3176829696.0000\n",
            "Epoch [265/600], Loss: 3176829696.0000\n",
            "Epoch [270/600], Loss: 3176829696.0000\n",
            "Epoch [275/600], Loss: 3176829696.0000\n",
            "Epoch [280/600], Loss: 3176829696.0000\n",
            "Epoch [285/600], Loss: 3176829696.0000\n",
            "Epoch [290/600], Loss: 3176829696.0000\n",
            "Epoch [295/600], Loss: 3176829696.0000\n",
            "Epoch [300/600], Loss: 3176829696.0000\n",
            "Epoch [305/600], Loss: 3176829696.0000\n",
            "Epoch [310/600], Loss: 3176829696.0000\n",
            "Epoch [315/600], Loss: 3176829696.0000\n",
            "Epoch [320/600], Loss: 3176829696.0000\n",
            "Epoch [325/600], Loss: 3176829696.0000\n",
            "Epoch [330/600], Loss: 3176829696.0000\n",
            "Epoch [335/600], Loss: 3176829696.0000\n",
            "Epoch [340/600], Loss: 3176829696.0000\n",
            "Epoch [345/600], Loss: 3176829696.0000\n",
            "Epoch [350/600], Loss: 3176829696.0000\n",
            "Epoch [355/600], Loss: 3176829696.0000\n",
            "Epoch [360/600], Loss: 3176829696.0000\n",
            "Epoch [365/600], Loss: 3176829696.0000\n",
            "Epoch [370/600], Loss: 3176829696.0000\n",
            "Epoch [375/600], Loss: 3176829696.0000\n",
            "Epoch [380/600], Loss: 3176829696.0000\n",
            "Epoch [385/600], Loss: 3176829696.0000\n",
            "Epoch [390/600], Loss: 3176829696.0000\n",
            "Epoch [395/600], Loss: 3176829696.0000\n",
            "Epoch [400/600], Loss: 3176829696.0000\n",
            "Epoch [405/600], Loss: 3176829696.0000\n",
            "Epoch [410/600], Loss: 3176829696.0000\n",
            "Epoch [415/600], Loss: 3176829696.0000\n",
            "Epoch [420/600], Loss: 3176829696.0000\n",
            "Epoch [425/600], Loss: 3176829696.0000\n",
            "Epoch [430/600], Loss: 3176829696.0000\n",
            "Epoch [435/600], Loss: 3176829696.0000\n",
            "Epoch [440/600], Loss: 3176829696.0000\n",
            "Epoch [445/600], Loss: 3176829696.0000\n",
            "Epoch [450/600], Loss: 3176829696.0000\n",
            "Epoch [455/600], Loss: 3176829696.0000\n",
            "Epoch [460/600], Loss: 3176829696.0000\n",
            "Epoch [465/600], Loss: 3176829696.0000\n",
            "Epoch [470/600], Loss: 3176829696.0000\n",
            "Epoch [475/600], Loss: 3176829696.0000\n",
            "Epoch [480/600], Loss: 3176829696.0000\n",
            "Epoch [485/600], Loss: 3176829696.0000\n",
            "Epoch [490/600], Loss: 3176829696.0000\n",
            "Epoch [495/600], Loss: 3176829696.0000\n",
            "Epoch [500/600], Loss: 3176829696.0000\n",
            "Epoch [505/600], Loss: 3176829696.0000\n",
            "Epoch [510/600], Loss: 3176829696.0000\n",
            "Epoch [515/600], Loss: 3176829696.0000\n",
            "Epoch [520/600], Loss: 3176829696.0000\n",
            "Epoch [525/600], Loss: 3176829696.0000\n",
            "Epoch [530/600], Loss: 3176829696.0000\n",
            "Epoch [535/600], Loss: 3176829696.0000\n",
            "Epoch [540/600], Loss: 3176829696.0000\n",
            "Epoch [545/600], Loss: 3176829696.0000\n",
            "Epoch [550/600], Loss: 3176829696.0000\n",
            "Epoch [555/600], Loss: 3176829696.0000\n",
            "Epoch [560/600], Loss: 3176829696.0000\n",
            "Epoch [565/600], Loss: 3176829696.0000\n",
            "Epoch [570/600], Loss: 3176829696.0000\n",
            "Epoch [575/600], Loss: 3176829696.0000\n",
            "Epoch [580/600], Loss: 3176829696.0000\n",
            "Epoch [585/600], Loss: 3176829696.0000\n",
            "Epoch [590/600], Loss: 3176829696.0000\n",
            "Epoch [595/600], Loss: 3176829696.0000\n",
            "Epoch [600/600], Loss: 3176829696.0000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "PWSMGLtwQBcq",
        "outputId": "e4c93f9d-ca3f-49ea-f190-65ae62f1b215"
      },
      "source": [
        "# Plot the graph\n",
        "predicted = model.forward(torch.from_numpy(x_train)).detach().numpy()\n",
        "plt.plot(x_train, y_train, 'ro', label='Original data')\n",
        "plt.plot(x_train, predicted, label='Prediction')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ncJbRXO3B-XB"
      },
      "source": [
        "### Pytorch로 Simple Linear Regression 모델 만들기 \n",
        "(교재 예시대로 했지만 실패...)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E9P9WTQcCGC4"
      },
      "source": [
        "# # 예측에 사용할 특성 분리\n",
        "# feature = ['GrLivArea'] #지상생활면적\n",
        "# target = ['SalePrice'] #주택판매가격(달러)\n",
        "# X_train = df[feature]\n",
        "# y_train = df[target]"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MbpE7VQ9Cs9J"
      },
      "source": [
        "# # convert pandas dataframe to numpy array\n",
        "# trainX = X_train.values\n",
        "# trainY = y_train.values"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FTeqDRQbDQqx"
      },
      "source": [
        "# # numpy array to Torch tensor\n",
        "# X_train_ts = torch.from_numpy(trainX)\n",
        "# y_train_ts = torch.from_numpy(trainY)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "24Me_qYQFxP-"
      },
      "source": [
        "# # long tensor to float tensor\n",
        "# X_train_ts = X_train_ts.float()\n",
        "# y_train_ts = y_train_ts.float()"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WLr27FzsEcnL"
      },
      "source": [
        "# # linear model create\n",
        "# model = nn.Linear(1,1) #단순 선형 회귀이므로 imput_dim = 1, output_dim = 1"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "voa_NFQ5FHDK"
      },
      "source": [
        "# # optimizer 설정\n",
        "# optimizer = torch.optim.SGD(model.parameters(), lr=1e-4) "
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xw6hs0ljGRia"
      },
      "source": [
        "# # loss 설정\n",
        "# criterion = torch.nn.MSELoss(reduction='sum')"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CuNufU3UI_GI"
      },
      "source": [
        "# num_epochs = 600\n",
        "# for epoch in range(num_epochs):\n",
        "#     # Convert numpy arrays to torch tensors\n",
        "#     inputs = Variable(torch.from_numpy(x_train))\n",
        "#     labels = Variable(torch.from_numpy(y_train))\n",
        "\n",
        "#     # Clearing the gradients w.r.t. parameters\n",
        "#     optimizer.zero_grad()\n",
        "\n",
        "#     # Forward pass\n",
        "#     # model = model.double() # torch complains that tensor type mismatch if not included\n",
        "#     outputs = model.forward(inputs)\n",
        "#     loss = criterion(outputs, labels)\n",
        "    \n",
        "#     # Backward and optimize\n",
        "#     loss.backward() # Backpropagation\n",
        "#     optimizer.step() # Update of parameters\n",
        "    \n",
        "#     if (epoch+1) % 5 == 0:\n",
        "#         print ('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))"
      ],
      "execution_count": 25,
      "outputs": []
    }
  ]
}