self_Exercise_Random_Forests.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "self_Exercise: Random Forests",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPJL+7/1JLx0+VYb5RuDYgh",
      "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/hiro106/colab_notebooks/blob/main/self_Exercise_Random_Forests.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Recap"
      ],
      "metadata": {
        "id": "tNzdZnETK2Al"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "OrJrXyCFsV7t"
      },
      "outputs": [],
      "source": [
        "# Refer to...\n",
        "# https://www.kaggle.com/code/scratchpad/notebook5d0021c7ba/edit"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Data fields:\n",
        "# https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques/data"
      ],
      "metadata": {
        "id": "n-fLOjRB-OS-"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/drive')"
      ],
      "metadata": {
        "id": "zsp7-2XHtT1I",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c1861be4-e1b7-429e-cc8e-3b3337334ac1"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /drive\n",
            "Drive already mounted at /drive; to attempt to forcibly remount, call drive.mount(\"/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "gi9ObxECtT4A"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pd"
      ],
      "metadata": {
        "id": "uF4S3Aijt3vH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "409f24eb-0a3e-49f5-ba52-b2069c015f35"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<module 'pandas' from '/usr/local/lib/python3.7/dist-packages/pandas/__init__.py'>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_absolute_error"
      ],
      "metadata": {
        "id": "LVa9LnaFt3xt"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(mean_absolute_error)"
      ],
      "metadata": {
        "id": "KPrwfYkpuekv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7519e5c3-1bb5-4404-c5e0-ae153ffda5df"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "function"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_absolute_error"
      ],
      "metadata": {
        "id": "fXsLi-Mlt30a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "75da4c49-801b-47e2-a747-8aa1101d2375"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function sklearn.metrics._regression.mean_absolute_error>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "ZJOUgNY0t329"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_test_split"
      ],
      "metadata": {
        "id": "ApTx9lYtt35v",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3e0742dc-6461-4632-fe61-99c2e5cc1fa1"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function sklearn.model_selection._split.train_test_split>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(train_test_split)"
      ],
      "metadata": {
        "id": "DLThnbPXubFS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "63831dfe-c86a-4294-b75a-409ae3bdca50"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "function"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeRegressor"
      ],
      "metadata": {
        "id": "nyHn0AWft38F"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "DecisionTreeRegressor"
      ],
      "metadata": {
        "id": "D-7yIyiNt3-j",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4634c1cd-3ac3-4cba-b060-cf16ae6e5897"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sklearn.tree._classes.DecisionTreeRegressor"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(DecisionTreeRegressor)"
      ],
      "metadata": {
        "id": "CCh5R7cIuXBL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "352339dc-edf4-488d-afde-79d507b2b859"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "abc.ABCMeta"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "W6xphm1JuaNu"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "iowa_file_path = '/drive/My Drive/Colab Notebooks/iowa_train.csv'"
      ],
      "metadata": {
        "id": "Pl2_Z5LxuaQR"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "home_data = pd.read_csv(iowa_file_path)"
      ],
      "metadata": {
        "id": "Q83dyxdguaTb"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "home_data.info()"
      ],
      "metadata": {
        "id": "p0ECGPrquaVb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "45e2ec0b-fbee-49e0-f150-14efdacbddd9"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1460 entries, 0 to 1459\n",
            "Data columns (total 81 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Id             1460 non-null   int64  \n",
            " 1   MSSubClass     1460 non-null   int64  \n",
            " 2   MSZoning       1460 non-null   object \n",
            " 3   LotFrontage    1201 non-null   float64\n",
            " 4   LotArea        1460 non-null   int64  \n",
            " 5   Street         1460 non-null   object \n",
            " 6   Alley          91 non-null     object \n",
            " 7   LotShape       1460 non-null   object \n",
            " 8   LandContour    1460 non-null   object \n",
            " 9   Utilities      1460 non-null   object \n",
            " 10  LotConfig      1460 non-null   object \n",
            " 11  LandSlope      1460 non-null   object \n",
            " 12  Neighborhood   1460 non-null   object \n",
            " 13  Condition1     1460 non-null   object \n",
            " 14  Condition2     1460 non-null   object \n",
            " 15  BldgType       1460 non-null   object \n",
            " 16  HouseStyle     1460 non-null   object \n",
            " 17  OverallQual    1460 non-null   int64  \n",
            " 18  OverallCond    1460 non-null   int64  \n",
            " 19  YearBuilt      1460 non-null   int64  \n",
            " 20  YearRemodAdd   1460 non-null   int64  \n",
            " 21  RoofStyle      1460 non-null   object \n",
            " 22  RoofMatl       1460 non-null   object \n",
            " 23  Exterior1st    1460 non-null   object \n",
            " 24  Exterior2nd    1460 non-null   object \n",
            " 25  MasVnrType     1452 non-null   object \n",
            " 26  MasVnrArea     1452 non-null   float64\n",
            " 27  ExterQual      1460 non-null   object \n",
            " 28  ExterCond      1460 non-null   object \n",
            " 29  Foundation     1460 non-null   object \n",
            " 30  BsmtQual       1423 non-null   object \n",
            " 31  BsmtCond       1423 non-null   object \n",
            " 32  BsmtExposure   1422 non-null   object \n",
            " 33  BsmtFinType1   1423 non-null   object \n",
            " 34  BsmtFinSF1     1460 non-null   int64  \n",
            " 35  BsmtFinType2   1422 non-null   object \n",
            " 36  BsmtFinSF2     1460 non-null   int64  \n",
            " 37  BsmtUnfSF      1460 non-null   int64  \n",
            " 38  TotalBsmtSF    1460 non-null   int64  \n",
            " 39  Heating        1460 non-null   object \n",
            " 40  HeatingQC      1460 non-null   object \n",
            " 41  CentralAir     1460 non-null   object \n",
            " 42  Electrical     1459 non-null   object \n",
            " 43  1stFlrSF       1460 non-null   int64  \n",
            " 44  2ndFlrSF       1460 non-null   int64  \n",
            " 45  LowQualFinSF   1460 non-null   int64  \n",
            " 46  GrLivArea      1460 non-null   int64  \n",
            " 47  BsmtFullBath   1460 non-null   int64  \n",
            " 48  BsmtHalfBath   1460 non-null   int64  \n",
            " 49  FullBath       1460 non-null   int64  \n",
            " 50  HalfBath       1460 non-null   int64  \n",
            " 51  BedroomAbvGr   1460 non-null   int64  \n",
            " 52  KitchenAbvGr   1460 non-null   int64  \n",
            " 53  KitchenQual    1460 non-null   object \n",
            " 54  TotRmsAbvGrd   1460 non-null   int64  \n",
            " 55  Functional     1460 non-null   object \n",
            " 56  Fireplaces     1460 non-null   int64  \n",
            " 57  FireplaceQu    770 non-null    object \n",
            " 58  GarageType     1379 non-null   object \n",
            " 59  GarageYrBlt    1379 non-null   float64\n",
            " 60  GarageFinish   1379 non-null   object \n",
            " 61  GarageCars     1460 non-null   int64  \n",
            " 62  GarageArea     1460 non-null   int64  \n",
            " 63  GarageQual     1379 non-null   object \n",
            " 64  GarageCond     1379 non-null   object \n",
            " 65  PavedDrive     1460 non-null   object \n",
            " 66  WoodDeckSF     1460 non-null   int64  \n",
            " 67  OpenPorchSF    1460 non-null   int64  \n",
            " 68  EnclosedPorch  1460 non-null   int64  \n",
            " 69  3SsnPorch      1460 non-null   int64  \n",
            " 70  ScreenPorch    1460 non-null   int64  \n",
            " 71  PoolArea       1460 non-null   int64  \n",
            " 72  PoolQC         7 non-null      object \n",
            " 73  Fence          281 non-null    object \n",
            " 74  MiscFeature    54 non-null     object \n",
            " 75  MiscVal        1460 non-null   int64  \n",
            " 76  MoSold         1460 non-null   int64  \n",
            " 77  YrSold         1460 non-null   int64  \n",
            " 78  SaleType       1460 non-null   object \n",
            " 79  SaleCondition  1460 non-null   object \n",
            " 80  SalePrice      1460 non-null   int64  \n",
            "dtypes: float64(3), int64(35), object(43)\n",
            "memory usage: 924.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "home_data.head()"
      ],
      "metadata": {
        "id": "xFhvL8kYuaYA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "3e37c2f7-b0fb-481d-9ff8-7030483b7ccb"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
              "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
              "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
              "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
              "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
              "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
              "\n",
              "  LandContour Utilities  ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold  \\\n",
              "0         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
              "1         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      5   \n",
              "2         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      9   \n",
              "3         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
              "4         Lvl    AllPub  ...        0    NaN   NaN         NaN       0     12   \n",
              "\n",
              "  YrSold  SaleType  SaleCondition  SalePrice  \n",
              "0   2008        WD         Normal     208500  \n",
              "1   2007        WD         Normal     181500  \n",
              "2   2008        WD         Normal     223500  \n",
              "3   2006        WD        Abnorml     140000  \n",
              "4   2008        WD         Normal     250000  \n",
              "\n",
              "[5 rows x 81 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9af9da99-c1c8-4047-a1c4-ca85eb3f4b23\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\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>...</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>...</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>...</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>...</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>...</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>...</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>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9af9da99-c1c8-4047-a1c4-ca85eb3f4b23')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9af9da99-c1c8-4047-a1c4-ca85eb3f4b23 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9af9da99-c1c8-4047-a1c4-ca85eb3f4b23');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "home_data.tail()"
      ],
      "metadata": {
        "id": "Jcz387hGuXED",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "7dea5ce7-4135-4a36-f6c0-172edafc3ce5"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
              "1455  1456          60       RL         62.0     7917   Pave   NaN      Reg   \n",
              "1456  1457          20       RL         85.0    13175   Pave   NaN      Reg   \n",
              "1457  1458          70       RL         66.0     9042   Pave   NaN      Reg   \n",
              "1458  1459          20       RL         68.0     9717   Pave   NaN      Reg   \n",
              "1459  1460          20       RL         75.0     9937   Pave   NaN      Reg   \n",
              "\n",
              "     LandContour Utilities  ... PoolArea PoolQC  Fence MiscFeature MiscVal  \\\n",
              "1455         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
              "1456         Lvl    AllPub  ...        0    NaN  MnPrv         NaN       0   \n",
              "1457         Lvl    AllPub  ...        0    NaN  GdPrv        Shed    2500   \n",
              "1458         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
              "1459         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
              "\n",
              "     MoSold YrSold  SaleType  SaleCondition  SalePrice  \n",
              "1455      8   2007        WD         Normal     175000  \n",
              "1456      2   2010        WD         Normal     210000  \n",
              "1457      5   2010        WD         Normal     266500  \n",
              "1458      4   2010        WD         Normal     142125  \n",
              "1459      6   2008        WD         Normal     147500  \n",
              "\n",
              "[5 rows x 81 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8803fad3-edf3-4b53-88c9-d32dc5f92d3c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\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>...</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>1455</th>\n",
              "      <td>1456</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>62.0</td>\n",
              "      <td>7917</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>2007</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>175000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1456</th>\n",
              "      <td>1457</td>\n",
              "      <td>20</td>\n",
              "      <td>RL</td>\n",
              "      <td>85.0</td>\n",
              "      <td>13175</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MnPrv</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2010</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>210000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1457</th>\n",
              "      <td>1458</td>\n",
              "      <td>70</td>\n",
              "      <td>RL</td>\n",
              "      <td>66.0</td>\n",
              "      <td>9042</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>GdPrv</td>\n",
              "      <td>Shed</td>\n",
              "      <td>2500</td>\n",
              "      <td>5</td>\n",
              "      <td>2010</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>266500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1458</th>\n",
              "      <td>1459</td>\n",
              "      <td>20</td>\n",
              "      <td>RL</td>\n",
              "      <td>68.0</td>\n",
              "      <td>9717</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>2010</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>142125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1459</th>\n",
              "      <td>1460</td>\n",
              "      <td>20</td>\n",
              "      <td>RL</td>\n",
              "      <td>75.0</td>\n",
              "      <td>9937</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</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>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>147500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 81 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8803fad3-edf3-4b53-88c9-d32dc5f92d3c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8803fad3-edf3-4b53-88c9-d32dc5f92d3c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8803fad3-edf3-4b53-88c9-d32dc5f92d3c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = home_data.SalePrice"
      ],
      "metadata": {
        "id": "6O421kP1uXG2"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "id": "6s3LEjx2uXJE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "15a93922-8120-45af-c616-75f9d77db34d"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       208500\n",
              "1       181500\n",
              "2       223500\n",
              "3       140000\n",
              "4       250000\n",
              "         ...  \n",
              "1455    175000\n",
              "1456    210000\n",
              "1457    266500\n",
              "1458    142125\n",
              "1459    147500\n",
              "Name: SalePrice, Length: 1460, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(y)"
      ],
      "metadata": {
        "id": "GQffQtdeuXLc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "47b5ea35-fd46-4d30-945f-f447c39ce276"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features = ['LotArea', 'YearBuilt', '1stFlrSF', '2ndFlrSF',\n",
        "            'FullBath', 'BedroomAbvGr', 'TotRmsAbvGrd']"
      ],
      "metadata": {
        "id": "XvDdvSvZ8qLb"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = home_data[features]"
      ],
      "metadata": {
        "id": "kKA21CNF8qOH"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=1)"
      ],
      "metadata": {
        "id": "cum_bCWt8qQ5"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_X.shape"
      ],
      "metadata": {
        "id": "WpOJqBhz8qTe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8ed5d727-4143-42ab-8c68-40f1350fd27d"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1095, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "val_X.shape"
      ],
      "metadata": {
        "id": "bOCv5Fd5uXOY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f55ac584-d267-43b9-ae0c-b2bc102f5f75"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(365, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(train_y)"
      ],
      "metadata": {
        "id": "iYqbC1kwuXRG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "76f7c2cf-3acc-4b3d-e42b-ca9ca1b34622"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1095"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(val_y)"
      ],
      "metadata": {
        "id": "5V54wwxCuXT9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c1c91144-a152-4786-c18b-9ea7e32dd0d2"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "365"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "iowa_model = DecisionTreeRegressor(random_state=1)"
      ],
      "metadata": {
        "id": "aLRASxb__ewP"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "iowa_model.fit(train_X, train_y)"
      ],
      "metadata": {
        "id": "5tEjWoed_ey5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b80a9382-9003-4a93-d2bb-a453fcab59e3"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeRegressor(random_state=1)"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "val_predictions = iowa_model.predict(val_X)"
      ],
      "metadata": {
        "id": "BucaWJ7z_e1u"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "val_predictions"
      ],
      "metadata": {
        "id": "fW3F4H5I_e3_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(val_predictions)"
      ],
      "metadata": {
        "id": "LLEuG6rP_e6m",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "da03800f-8201-46b5-8bc7-cc30ae5b6274"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(val_predictions)"
      ],
      "metadata": {
        "id": "IChNLDWx_e-b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d8d3da12-f7fd-4a53-a567-17031c85af47"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "365"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "val_mae = mean_absolute_error(val_predictions, val_y)"
      ],
      "metadata": {
        "id": "nIuoaJ__KKHC"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Validation MAE for best value of max_leaf_nodes: {:,.0f}\".format(val_mae))"
      ],
      "metadata": {
        "id": "WICKb6uGKKJz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "75076f6f-1b9d-4225-b460-38e30860dcc3"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation MAE for best value of max_leaf_nodes: 29,653\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "vzaNLKqsKKMI"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "_L7OmFEtLCtO"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "bDz3g2xrLCv5"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "eD2pbIMBLCy2"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "wfyTlqwrLC1v"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "-I9nX7G2LC4q"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "5xqf93yC_fCp"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "b_wAUg2G_fGr"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "4wCb_2YDuXW4"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "P41TAYykt4Ba"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "bO1YnFpvtTyk"
      },
      "execution_count": 42,
      "outputs": []
    }
  ]
}