{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Import bibliotek"
      ],
      "metadata": {
        "id": "fbReA72OlQ_Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import sklearn\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from google.colab import files\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "lIs7iUiKlVvA"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Pobranie danych"
      ],
      "metadata": {
        "id": "PFLEmQ76IauU"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "plw8exY_D-2b",
        "outputId": "6cd21e52-fbfc-432e-e7f3-019e2ad2416c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.5.16)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.16.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from kaggle) (2024.2.2)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.31.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle) (4.66.2)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle) (8.0.4)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.0.7)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle) (6.1.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle) (0.5.1)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.6)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (1.5.3)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2023.4)\n",
            "Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pandas) (1.25.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n"
          ]
        }
      ],
      "source": [
        "#Zainstalujmy potrzebne biblioteki\n",
        "!pip install --user kaggle #API Kaggle, do pobrania zbioru\n",
        "!pip install --user pandas"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "files.upload()\n",
        "! mkdir ~/.kaggle\n",
        "! cp kaggle.json ~/.kaggle/\n",
        "! chmod 600 ~/.kaggle/kaggle.json"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "id": "vKwe6YuNFV0K",
        "outputId": "23d34751-9086-4508-bf1b-162d8b770e28"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-fe6c8433-f772-471c-b5fe-47a4b4599e3c\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-fe6c8433-f772-471c-b5fe-47a4b4599e3c\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving kaggle.json to kaggle (4).json\n",
            "mkdir: cannot create directory ‘/root/.kaggle’: File exists\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download -d muhammadbinimran/housing-price-prediction-data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V0tjpXGnHprW",
        "outputId": "8ab72502-fd6f-4e12-966e-4bd135225b92"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "housing-price-prediction-data.zip: Skipping, found more recently modified local copy (use --force to force download)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip -o housing-price-prediction-data.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KFdbSDSGH5hK",
        "outputId": "fe5639b9-9ff8-4c0c-c9f3-d0fd86f09c39"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  housing-price-prediction-data.zip\n",
            "  inflating: housing_price_dataset.csv  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Wczytanie zbioru"
      ],
      "metadata": {
        "id": "tH7ufJQWI2bT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --user pandas"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D_XnqsLfI1ki",
        "outputId": "c9983630-5453-42cf-e5a4-e32b47c5b8ee"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (1.5.3)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2023.4)\n",
            "Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pandas) (1.25.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "housing_price_dataset = pd.read_csv('housing_price_dataset.csv')\n",
        "housing_price_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "TKu6XCn2I5KF",
        "outputId": "006ac90f-d56e-4bc9-8495-af9450376102"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       SquareFeet  Bedrooms  Bathrooms Neighborhood  YearBuilt          Price\n",
              "0            2126         4          1        Rural       1969  215355.283618\n",
              "1            2459         3          2        Rural       1980  195014.221626\n",
              "2            1860         2          1       Suburb       1970  306891.012076\n",
              "3            2294         2          1        Urban       1996  206786.787153\n",
              "4            2130         5          2       Suburb       2001  272436.239065\n",
              "...           ...       ...        ...          ...        ...            ...\n",
              "49995        1282         5          3        Rural       1975  100080.865895\n",
              "49996        2854         2          2       Suburb       1988  374507.656727\n",
              "49997        2979         5          3       Suburb       1962  384110.555590\n",
              "49998        2596         5          2        Rural       1984  380512.685957\n",
              "49999        1572         5          3        Rural       2011  221618.583218\n",
              "\n",
              "[50000 rows x 6 columns]"
            ],
            "text/html": [
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              "  <div id=\"df-edad94fe-b9cc-4eb7-8eb9-112894937818\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SquareFeet</th>\n",
              "      <th>Bedrooms</th>\n",
              "      <th>Bathrooms</th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2126</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>Rural</td>\n",
              "      <td>1969</td>\n",
              "      <td>215355.283618</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2459</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>Rural</td>\n",
              "      <td>1980</td>\n",
              "      <td>195014.221626</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1860</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Suburb</td>\n",
              "      <td>1970</td>\n",
              "      <td>306891.012076</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2294</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Urban</td>\n",
              "      <td>1996</td>\n",
              "      <td>206786.787153</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2130</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>Suburb</td>\n",
              "      <td>2001</td>\n",
              "      <td>272436.239065</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49995</th>\n",
              "      <td>1282</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>Rural</td>\n",
              "      <td>1975</td>\n",
              "      <td>100080.865895</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49996</th>\n",
              "      <td>2854</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>Suburb</td>\n",
              "      <td>1988</td>\n",
              "      <td>374507.656727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49997</th>\n",
              "      <td>2979</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>Suburb</td>\n",
              "      <td>1962</td>\n",
              "      <td>384110.555590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49998</th>\n",
              "      <td>2596</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>Rural</td>\n",
              "      <td>1984</td>\n",
              "      <td>380512.685957</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49999</th>\n",
              "      <td>1572</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>Rural</td>\n",
              "      <td>2011</td>\n",
              "      <td>221618.583218</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>50000 rows × 6 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-edad94fe-b9cc-4eb7-8eb9-112894937818')\"\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 -960 960 960\">\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "        const element = document.querySelector('#df-edad94fe-b9cc-4eb7-8eb9-112894937818');\n",
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              "\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",
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              "  .colab-df-quickchart:hover {\n",
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              "    border-color: transparent;\n",
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              "    animation:\n",
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              "  @keyframes spin {\n",
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              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "housing_price_dataset",
              "summary": "{\n  \"name\": \"housing_price_dataset\",\n  \"rows\": 50000,\n  \"fields\": [\n    {\n      \"column\": \"SquareFeet\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 575,\n        \"min\": 1000,\n        \"max\": 2999,\n        \"num_unique_values\": 2000,\n        \"samples\": [\n          2578,\n          2250,\n          1585\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2,\n        \"max\": 5,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          5,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bathrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Neighborhood\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Rural\",\n          \"Suburb\",\n          \"Urban\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YearBuilt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20,\n        \"min\": 1950,\n        \"max\": 2021,\n        \"num_unique_values\": 72,\n        \"samples\": [\n          2001,\n          1967,\n          1962\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 76141.84296604691,\n        \"min\": -36588.16539749279,\n        \"max\": 492195.2599720151,\n        \"num_unique_values\": 50000,\n        \"samples\": [\n          170835.03571295898,\n          126913.4699981214,\n          246611.88309182983\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Podział zbioru"
      ],
      "metadata": {
        "id": "2PIqECUhIvcd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hp_train_test, hp_dev = sklearn.model_selection.train_test_split(housing_price_dataset, test_size=0.1)\n",
        "hp_train, hp_test = sklearn.model_selection.train_test_split(hp_train_test, test_size=1000)"
      ],
      "metadata": {
        "id": "Rb5GTCQGIUzE"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Normalizacja danych"
      ],
      "metadata": {
        "id": "v9X6AQHYjLA2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "housing_price_dataset[\"Neighborhood\"].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iU0adUbpjdSS",
        "outputId": "d8bb2852-9017-40b6-bf1b-1619c869c8de"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Rural', 'Suburb', 'Urban'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hp_train = pd.get_dummies(hp_train, columns=['Neighborhood'])\n",
        "hp_dev = pd.get_dummies(hp_dev, columns=['Neighborhood'])\n",
        "hp_test = pd.get_dummies(hp_test, columns=['Neighborhood'])"
      ],
      "metadata": {
        "id": "oLibzeZ5kivR"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hp_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "1Pjm-8iKsMH-",
        "outputId": "6bdf19b2-ac29-4f7e-a479-5217df193eba"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       SquareFeet  Bedrooms  Bathrooms  YearBuilt          Price  \\\n",
              "7616         2027         3          3       2013  237960.032012   \n",
              "47787        1292         5          1       2021   86121.435887   \n",
              "35285        1964         2          3       1970  208054.904277   \n",
              "8718         2581         4          2       1990  230475.439055   \n",
              "36680        2020         5          2       2011  278860.337033   \n",
              "...           ...       ...        ...        ...            ...   \n",
              "22830        1245         5          1       1975  167679.728402   \n",
              "43699        2065         4          2       2021  257521.317661   \n",
              "21160        1967         3          1       1951  262332.423882   \n",
              "30915        2867         2          3       1990  311233.596471   \n",
              "19117        1631         3          1       1967  200594.974438   \n",
              "\n",
              "       Neighborhood_Rural  Neighborhood_Suburb  Neighborhood_Urban  \n",
              "7616                    0                    0                   1  \n",
              "47787                   0                    1                   0  \n",
              "35285                   0                    0                   1  \n",
              "8718                    1                    0                   0  \n",
              "36680                   0                    0                   1  \n",
              "...                   ...                  ...                 ...  \n",
              "22830                   1                    0                   0  \n",
              "43699                   0                    1                   0  \n",
              "21160                   0                    1                   0  \n",
              "30915                   0                    0                   1  \n",
              "19117                   1                    0                   0  \n",
              "\n",
              "[44000 rows x 8 columns]"
            ],
            "text/html": [
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              "      <th></th>\n",
              "      <th>SquareFeet</th>\n",
              "      <th>Bedrooms</th>\n",
              "      <th>Bathrooms</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>Price</th>\n",
              "      <th>Neighborhood_Rural</th>\n",
              "      <th>Neighborhood_Suburb</th>\n",
              "      <th>Neighborhood_Urban</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7616</th>\n",
              "      <td>2027</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2013</td>\n",
              "      <td>237960.032012</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47787</th>\n",
              "      <td>1292</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2021</td>\n",
              "      <td>86121.435887</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35285</th>\n",
              "      <td>1964</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1970</td>\n",
              "      <td>208054.904277</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8718</th>\n",
              "      <td>2581</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1990</td>\n",
              "      <td>230475.439055</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36680</th>\n",
              "      <td>2020</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2011</td>\n",
              "      <td>278860.337033</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22830</th>\n",
              "      <td>1245</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1975</td>\n",
              "      <td>167679.728402</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43699</th>\n",
              "      <td>2065</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2021</td>\n",
              "      <td>257521.317661</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21160</th>\n",
              "      <td>1967</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1951</td>\n",
              "      <td>262332.423882</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30915</th>\n",
              "      <td>2867</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1990</td>\n",
              "      <td>311233.596471</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19117</th>\n",
              "      <td>1631</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1967</td>\n",
              "      <td>200594.974438</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>44000 rows × 8 columns</p>\n",
              "</div>\n",
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              "      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-601e59b6-c587-4e32-955e-084019ad4fa2 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-601e59b6-c587-4e32-955e-084019ad4fa2');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
              "<div id=\"df-e5dfecf4-d4d2-4c5d-8938-cf2f49863177\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e5dfecf4-d4d2-4c5d-8938-cf2f49863177')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e5dfecf4-d4d2-4c5d-8938-cf2f49863177 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "hp_train",
              "summary": "{\n  \"name\": \"hp_train\",\n  \"rows\": 44000,\n  \"fields\": [\n    {\n      \"column\": \"SquareFeet\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 575,\n        \"min\": 1000,\n        \"max\": 2999,\n        \"num_unique_values\": 2000,\n        \"samples\": [\n          2015,\n          2776,\n          1529\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2,\n        \"max\": 5,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          5,\n          4,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bathrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YearBuilt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20,\n        \"min\": 1950,\n        \"max\": 2021,\n        \"num_unique_values\": 72,\n        \"samples\": [\n          2011,\n          1950,\n          1966\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 76107.65251634463,\n        \"min\": -36588.16539749279,\n        \"max\": 492195.2599720151,\n        \"num_unique_values\": 44000,\n        \"samples\": [\n          127869.24389754632,\n          331602.267141956,\n          149546.59653504143\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Neighborhood_Rural\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Neighborhood_Suburb\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Neighborhood_Urban\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hp_dev"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "ab4RCTHUt9Vt",
        "outputId": "6ccc34ad-8a8c-4677-c521-c6d821776e11"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       SquareFeet  Bedrooms  Bathrooms  YearBuilt          Price  \\\n",
              "46301        2845         4          3       1954  354875.353057   \n",
              "10023        2362         4          3       2010  292371.871755   \n",
              "37044        1058         3          2       2007  155277.040755   \n",
              "17462        2891         5          1       2005  239120.147027   \n",
              "13804        2244         5          2       1966  254005.280471   \n",
              "...           ...       ...        ...        ...            ...   \n",
              "35925        1684         4          1       1950  212224.505489   \n",
              "21799        1021         5          3       1995  139005.940982   \n",
              "4318         2741         4          2       1962  339074.548520   \n",
              "31492        2053         3          3       2014  239382.414641   \n",
              "26727        2963         3          1       2004  321585.613385   \n",
              "\n",
              "       Neighborhood_Rural  Neighborhood_Suburb  Neighborhood_Urban  \n",
              "46301                   0                    0                   1  \n",
              "10023                   1                    0                   0  \n",
              "37044                   0                    1                   0  \n",
              "17462                   1                    0                   0  \n",
              "13804                   1                    0                   0  \n",
              "...                   ...                  ...                 ...  \n",
              "35925                   1                    0                   0  \n",
              "21799                   1                    0                   0  \n",
              "4318                    1                    0                   0  \n",
              "31492                   0                    0                   1  \n",
              "26727                   0                    1                   0  \n",
              "\n",
              "[5000 rows x 8 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SquareFeet</th>\n",
              "      <th>Bedrooms</th>\n",
              "      <th>Bathrooms</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>Price</th>\n",
              "      <th>Neighborhood_Rural</th>\n",
              "      <th>Neighborhood_Suburb</th>\n",
              "      <th>Neighborhood_Urban</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>46301</th>\n",
              "      <td>2845</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>1954</td>\n",
              "      <td>354875.353057</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>10023</th>\n",
              "      <td>2362</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2010</td>\n",
              "      <td>292371.871755</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37044</th>\n",
              "      <td>1058</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2007</td>\n",
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              "      <td>0</td>\n",
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              "      <th>17462</th>\n",
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              "    <tr>\n",
              "      <th>13804</th>\n",
              "      <td>2244</td>\n",
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              "      <td>2</td>\n",
              "      <td>1966</td>\n",
              "      <td>254005.280471</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35925</th>\n",
              "      <td>1684</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1950</td>\n",
              "      <td>212224.505489</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21799</th>\n",
              "      <td>1021</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>1995</td>\n",
              "      <td>139005.940982</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4318</th>\n",
              "      <td>2741</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1962</td>\n",
              "      <td>339074.548520</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31492</th>\n",
              "      <td>2053</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2014</td>\n",
              "      <td>239382.414641</td>\n",
              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
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              "      <td>2963</td>\n",
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              "      <td>2004</td>\n",
              "      <td>321585.613385</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5000 rows × 8 columns</p>\n",
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              "\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-93811741-2640-42ae-b1db-3d88d736a520 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "hp_dev",
              "summary": "{\n  \"name\": \"hp_dev\",\n  \"rows\": 5000,\n  \"fields\": [\n    {\n      \"column\": \"SquareFeet\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 576,\n        \"min\": 1000,\n        \"max\": 2999,\n        \"num_unique_values\": 1829,\n        \"samples\": [\n          2667,\n          2963,\n          2213\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2,\n        \"max\": 5,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          2,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bathrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YearBuilt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20,\n        \"min\": 1950,\n        \"max\": 2021,\n        \"num_unique_values\": 72,\n        \"samples\": [\n          1966,\n          1986,\n          2021\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 76778.00565792067,\n        \"min\": -18159.685676249966,\n        \"max\": 467492.8278233021,\n        \"num_unique_values\": 5000,\n        \"samples\": [\n          186133.49424564492,\n          217865.6155495013,\n          194238.86404489263\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Neighborhood_Rural\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Neighborhood_Suburb\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Neighborhood_Urban\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hp_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "zjOYohYCt-md",
        "outputId": "723811f9-e6b4-4878-f949-0cfdced5ca3d"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       SquareFeet  Bedrooms  Bathrooms  YearBuilt          Price  \\\n",
              "49356        1174         5          3       1996  143866.306649   \n",
              "18656        1776         3          1       1964  125553.381347   \n",
              "27368        2524         2          2       2010  327261.077660   \n",
              "27243        1633         2          1       1953  241231.423110   \n",
              "24653        2811         4          2       1982  315724.479288   \n",
              "...           ...       ...        ...        ...            ...   \n",
              "20015        2106         2          2       2014  216406.701646   \n",
              "40921        1704         3          3       1986  153770.810572   \n",
              "30027        1150         5          3       1973  138938.157678   \n",
              "16008        2822         2          2       1982  296193.916437   \n",
              "23919        1348         2          2       1983  133497.577808   \n",
              "\n",
              "       Neighborhood_Rural  Neighborhood_Suburb  Neighborhood_Urban  \n",
              "49356                   0                    1                   0  \n",
              "18656                   0                    0                   1  \n",
              "27368                   1                    0                   0  \n",
              "27243                   1                    0                   0  \n",
              "24653                   1                    0                   0  \n",
              "...                   ...                  ...                 ...  \n",
              "20015                   0                    0                   1  \n",
              "40921                   1                    0                   0  \n",
              "30027                   0                    0                   1  \n",
              "16008                   0                    1                   0  \n",
              "23919                   1                    0                   0  \n",
              "\n",
              "[1000 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ac9089e6-45f0-4578-97d1-ff8843413135\" 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>SquareFeet</th>\n",
              "      <th>Bedrooms</th>\n",
              "      <th>Bathrooms</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>Price</th>\n",
              "      <th>Neighborhood_Rural</th>\n",
              "      <th>Neighborhood_Suburb</th>\n",
              "      <th>Neighborhood_Urban</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>49356</th>\n",
              "      <td>1174</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>1996</td>\n",
              "      <td>143866.306649</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18656</th>\n",
              "      <td>1776</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1964</td>\n",
              "      <td>125553.381347</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27368</th>\n",
              "      <td>2524</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2010</td>\n",
              "      <td>327261.077660</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27243</th>\n",
              "      <td>1633</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1953</td>\n",
              "      <td>241231.423110</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24653</th>\n",
              "      <td>2811</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1982</td>\n",
              "      <td>315724.479288</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20015</th>\n",
              "      <td>2106</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2014</td>\n",
              "      <td>216406.701646</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40921</th>\n",
              "      <td>1704</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1986</td>\n",
              "      <td>153770.810572</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30027</th>\n",
              "      <td>1150</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>1973</td>\n",
              "      <td>138938.157678</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16008</th>\n",
              "      <td>2822</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1982</td>\n",
              "      <td>296193.916437</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23919</th>\n",
              "      <td>1348</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1983</td>\n",
              "      <td>133497.577808</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 8 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ac9089e6-45f0-4578-97d1-ff8843413135')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-ac9089e6-45f0-4578-97d1-ff8843413135 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-ac9089e6-45f0-4578-97d1-ff8843413135');\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",
              "\n",
              "\n",
              "<div id=\"df-cf201a7d-0bc3-4ad7-982e-4424ba424285\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cf201a7d-0bc3-4ad7-982e-4424ba424285')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-cf201a7d-0bc3-4ad7-982e-4424ba424285 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "hp_test",
              "summary": "{\n  \"name\": \"hp_test\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"SquareFeet\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 581,\n        \"min\": 1000,\n        \"max\": 2999,\n        \"num_unique_values\": 792,\n        \"samples\": [\n          2084,\n          2990,\n          1245\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2,\n        \"max\": 5,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          4,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bathrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YearBuilt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20,\n        \"min\": 1950,\n        \"max\": 2021,\n        \"num_unique_values\": 72,\n        \"samples\": [\n          1982,\n          2016,\n          1960\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 74475.15532686812,\n        \"min\": -7550.50457435759,\n        \"max\": 437047.71344105,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          230653.38480715267,\n          204995.43595068945,\n          231582.08580545988\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Neighborhood_Rural\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Neighborhood_Suburb\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Neighborhood_Urban\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Statystyki"
      ],
      "metadata": {
        "id": "NOERGp9pYt2R"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Wielkość podzbiorów"
      ],
      "metadata": {
        "id": "8qLEM0Ahis-X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "housing_price_dataset.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "Cp-IN7cc2Dgr",
        "outputId": "d75f9cad-e097-4858-cd49-db618dcd42a3"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         SquareFeet      Bedrooms     Bathrooms     YearBuilt          Price\n",
              "count  50000.000000  50000.000000  50000.000000  50000.000000   50000.000000\n",
              "mean    2006.374680      3.498700      1.995420   1985.404420  224827.325151\n",
              "std      575.513241      1.116326      0.815851     20.719377   76141.842966\n",
              "min     1000.000000      2.000000      1.000000   1950.000000  -36588.165397\n",
              "25%     1513.000000      3.000000      1.000000   1967.000000  169955.860225\n",
              "50%     2007.000000      3.000000      2.000000   1985.000000  225052.141166\n",
              "75%     2506.000000      4.000000      3.000000   2003.000000  279373.630052\n",
              "max     2999.000000      5.000000      3.000000   2021.000000  492195.259972"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7aae1c56-9f56-4494-a4ac-b562730ae374\" class=\"colab-df-container\">\n",
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              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
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              "        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>SquareFeet</th>\n",
              "      <th>Bedrooms</th>\n",
              "      <th>Bathrooms</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>50000.000000</td>\n",
              "      <td>50000.000000</td>\n",
              "      <td>50000.000000</td>\n",
              "      <td>50000.000000</td>\n",
              "      <td>50000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2006.374680</td>\n",
              "      <td>3.498700</td>\n",
              "      <td>1.995420</td>\n",
              "      <td>1985.404420</td>\n",
              "      <td>224827.325151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>575.513241</td>\n",
              "      <td>1.116326</td>\n",
              "      <td>0.815851</td>\n",
              "      <td>20.719377</td>\n",
              "      <td>76141.842966</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1000.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1950.000000</td>\n",
              "      <td>-36588.165397</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1513.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1967.000000</td>\n",
              "      <td>169955.860225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2007.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1985.000000</td>\n",
              "      <td>225052.141166</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2506.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2003.000000</td>\n",
              "      <td>279373.630052</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2999.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2021.000000</td>\n",
              "      <td>492195.259972</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-7aae1c56-9f56-4494-a4ac-b562730ae374 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-7aae1c56-9f56-4494-a4ac-b562730ae374');\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",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d68153f8-bf7b-47f9-8c6e-765901ebe973')\"\n",
              "            title=\"Suggest charts\"\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",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d68153f8-bf7b-47f9-8c6e-765901ebe973 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"housing_price_dataset\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"SquareFeet\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17058.702043862784,\n        \"min\": 575.513241276615,\n        \"max\": 50000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2006.37468,\n          2007.0,\n          50000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17676.577845369047,\n        \"min\": 1.1163257739856558,\n        \"max\": 50000.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          50000.0,\n          3.4987,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bathrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17677.02248379316,\n        \"min\": 0.8158506823228849,\n        \"max\": 50000.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          50000.0,\n          1.99542,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YearBuilt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17088.851960342447,\n        \"min\": 20.71937668741524,\n        \"max\": 50000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1985.40442,\n          1985.0,\n          50000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 162949.740052687,\n        \"min\": -36588.16539749279,\n        \"max\": 492195.2599720151,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          224827.32515099045,\n          225052.14116600397,\n          50000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hp_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Icm98vi1X6Pe",
        "outputId": "207de571-34f3-4044-d970-9680ee895643"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(44000, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hp_dev.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LlqC13x0Ymm6",
        "outputId": "890f1281-0073-48ea-93d5-f86b03bf4564"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5000, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hp_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8iwbOv4AYpk4",
        "outputId": "54f3ca4c-033d-47e9-f285-2a1d2c07538b"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Statystyki kolumn"
      ],
      "metadata": {
        "id": "Y2HnsCXxiypY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hp_train.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "wAUskqnzi8Cl",
        "outputId": "9f558980-671c-4916-9877-604fa2537e5c"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         SquareFeet      Bedrooms     Bathrooms     YearBuilt          Price  \\\n",
              "count  44000.000000  44000.000000  44000.000000  44000.000000   44000.000000   \n",
              "mean    2006.261182      3.499636      1.997864   1985.416750  224928.983383   \n",
              "std      575.306280      1.117315      0.815760     20.700559   76107.652516   \n",
              "min     1000.000000      2.000000      1.000000   1950.000000  -36588.165397   \n",
              "25%     1513.000000      3.000000      1.000000   1967.000000  170088.571867   \n",
              "50%     2007.000000      3.000000      2.000000   1985.000000  225246.904135   \n",
              "75%     2505.000000      5.000000      3.000000   2003.000000  279365.119289   \n",
              "max     2999.000000      5.000000      3.000000   2021.000000  492195.259972   \n",
              "\n",
              "       Neighborhood_Rural  Neighborhood_Suburb  Neighborhood_Urban  \n",
              "count        44000.000000         44000.000000        44000.000000  \n",
              "mean             0.332841             0.333636            0.333523  \n",
              "std              0.471235             0.471517            0.471477  \n",
              "min              0.000000             0.000000            0.000000  \n",
              "25%              0.000000             0.000000            0.000000  \n",
              "50%              0.000000             0.000000            0.000000  \n",
              "75%              1.000000             1.000000            1.000000  \n",
              "max              1.000000             1.000000            1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2223dbc3-5627-46ea-be81-2587126788d7\" 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>SquareFeet</th>\n",
              "      <th>Bedrooms</th>\n",
              "      <th>Bathrooms</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>Price</th>\n",
              "      <th>Neighborhood_Rural</th>\n",
              "      <th>Neighborhood_Suburb</th>\n",
              "      <th>Neighborhood_Urban</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>44000.000000</td>\n",
              "      <td>44000.000000</td>\n",
              "      <td>44000.000000</td>\n",
              "      <td>44000.000000</td>\n",
              "      <td>44000.000000</td>\n",
              "      <td>44000.000000</td>\n",
              "      <td>44000.000000</td>\n",
              "      <td>44000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2006.261182</td>\n",
              "      <td>3.499636</td>\n",
              "      <td>1.997864</td>\n",
              "      <td>1985.416750</td>\n",
              "      <td>224928.983383</td>\n",
              "      <td>0.332841</td>\n",
              "      <td>0.333636</td>\n",
              "      <td>0.333523</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>575.306280</td>\n",
              "      <td>1.117315</td>\n",
              "      <td>0.815760</td>\n",
              "      <td>20.700559</td>\n",
              "      <td>76107.652516</td>\n",
              "      <td>0.471235</td>\n",
              "      <td>0.471517</td>\n",
              "      <td>0.471477</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1000.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1950.000000</td>\n",
              "      <td>-36588.165397</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1513.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1967.000000</td>\n",
              "      <td>170088.571867</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2007.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1985.000000</td>\n",
              "      <td>225246.904135</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2505.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2003.000000</td>\n",
              "      <td>279365.119289</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2999.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2021.000000</td>\n",
              "      <td>492195.259972</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2223dbc3-5627-46ea-be81-2587126788d7')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2223dbc3-5627-46ea-be81-2587126788d7 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-2223dbc3-5627-46ea-be81-2587126788d7');\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",
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              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-134a70fe-f3be-4778-aa13-c049d26c4190\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-134a70fe-f3be-4778-aa13-c049d26c4190')\"\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-134a70fe-f3be-4778-aa13-c049d26c4190 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"hp_train\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"SquareFeet\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14939.968071261836,\n        \"min\": 575.3062795316038,\n        \"max\": 44000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2006.2611818181817,\n          2007.0,\n          44000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15555.206914611437,\n        \"min\": 1.1173145826824615,\n        \"max\": 44000.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          44000.0,\n          3.4996363636363634,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bathrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15555.70202423901,\n        \"min\": 0.8157604462168441,\n        \"max\": 44000.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          44000.0,\n          1.9978636363636364,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YearBuilt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14969.494474985573,\n        \"min\": 20.70055860487858,\n        \"max\": 44000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1985.41675,\n          1985.0,\n          44000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 163684.07731051612,\n        \"min\": -36588.16539749279,\n        \"max\": 492195.2599720151,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          224928.9833827127,\n          225246.9041353957,\n          44000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Neighborhood_Rural\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15556.207564412634,\n        \"min\": 0.0,\n        \"max\": 44000.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.3328409090909091,\n          1.0,\n          0.47123548806324117\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Neighborhood_Suburb\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15556.207510022008,\n        \"min\": 0.0,\n        \"max\": 44000.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.3336363636363636,\n          1.0,\n          0.4715169068117858\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Neighborhood_Urban\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15556.207517787427,\n        \"min\": 0.0,\n        \"max\": 44000.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.33352272727272725,\n          1.0,\n          0.47147679658615904\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hp_dev.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "BmM4_vWsjBK3",
        "outputId": "b0a1906f-9eac-46a5-84b6-0cdbea344d69"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        SquareFeet     Bedrooms    Bathrooms    YearBuilt          Price  \\\n",
              "count  5000.000000  5000.000000  5000.000000  5000.000000    5000.000000   \n",
              "mean   2008.190800     3.487200     1.972600  1985.485400  224290.794530   \n",
              "std     576.206366     1.104753     0.816077    20.960049   76778.005658   \n",
              "min    1000.000000     2.000000     1.000000  1950.000000  -18159.685676   \n",
              "25%    1510.750000     3.000000     1.000000  1967.000000  169103.151768   \n",
              "50%    2007.000000     3.000000     2.000000  1985.000000  223614.924625   \n",
              "75%    2503.000000     4.000000     3.000000  2004.000000  279651.548644   \n",
              "max    2999.000000     5.000000     3.000000  2021.000000  467492.827823   \n",
              "\n",
              "       Neighborhood_Rural  Neighborhood_Suburb  Neighborhood_Urban  \n",
              "count         5000.000000          5000.000000         5000.000000  \n",
              "mean             0.337800             0.341600            0.320600  \n",
              "std              0.473007             0.474294            0.466754  \n",
              "min              0.000000             0.000000            0.000000  \n",
              "25%              0.000000             0.000000            0.000000  \n",
              "50%              0.000000             0.000000            0.000000  \n",
              "75%              1.000000             1.000000            1.000000  \n",
              "max              1.000000             1.000000            1.000000  "
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              "      <th>count</th>\n",
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              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2008.190800</td>\n",
              "      <td>3.487200</td>\n",
              "      <td>1.972600</td>\n",
              "      <td>1985.485400</td>\n",
              "      <td>224290.794530</td>\n",
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              "      <td>76778.005658</td>\n",
              "      <td>0.473007</td>\n",
              "      <td>0.474294</td>\n",
              "      <td>0.466754</td>\n",
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              "      <th>min</th>\n",
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              "      <td>1.000000</td>\n",
              "      <td>1950.000000</td>\n",
              "      <td>-18159.685676</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
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              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1967.000000</td>\n",
              "      <td>169103.151768</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2007.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1985.000000</td>\n",
              "      <td>223614.924625</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2503.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2004.000000</td>\n",
              "      <td>279651.548644</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2999.000000</td>\n",
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              "      <td>3.000000</td>\n",
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              "      <td>467492.827823</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"hp_dev\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"SquareFeet\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1373.0060319958575,\n        \"min\": 576.2063661142855,\n        \"max\": 5000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2008.1908,\n          2007.0,\n          5000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1766.6767900146253,\n        \"min\": 1.1047534820271943,\n        \"max\": 5000.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          5000.0,\n          3.4872,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bathrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1767.1212371098363,\n        \"min\": 0.8160774696603855,\n        \"max\": 5000.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          5000.0,\n          1.9726,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YearBuilt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1352.889275943266,\n        \"min\": 20.9600489400744,\n        \"max\": 5000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1985.4854,\n          1985.0,\n          5000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 159056.8559402586,\n        \"min\": -18159.685676249966,\n        \"max\": 467492.8278233021,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          224290.7945297919,\n          223614.92462488014,\n          5000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Neighborhood_Rural\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1767.6250346212444,\n        \"min\": 0.0,\n        \"max\": 5000.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.3378,\n          1.0,\n          0.4730073014039385\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Neighborhood_Suburb\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1767.6247777120989,\n        \"min\": 0.0,\n        \"max\": 5000.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.3416,\n          1.0,\n          0.474293612529388\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Neighborhood_Urban\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1767.626219259249,\n        \"min\": 0.0,\n        \"max\": 5000.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.3206,\n          1.0,\n          0.4667539092952179\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hp_test.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "T7edA8gVjBfU",
        "outputId": "99be05f6-e25f-45ae-9e4f-7f293d1ac14c"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        SquareFeet     Bedrooms    Bathrooms    YearBuilt          Price  \\\n",
              "count  1000.000000  1000.000000  1000.000000  1000.000000    1000.000000   \n",
              "mean   2002.288000     3.515000     2.002000  1984.457000  223037.016061   \n",
              "std     581.670136     1.130953     0.817719    20.330949   74475.155327   \n",
              "min    1000.000000     2.000000     1.000000  1950.000000   -7550.504574   \n",
              "25%    1507.250000     2.000000     1.000000  1967.000000  168905.529102   \n",
              "50%    2021.500000     4.000000     2.000000  1983.000000  220416.485632   \n",
              "75%    2524.000000     5.000000     3.000000  2002.000000  279628.697596   \n",
              "max    2999.000000     5.000000     3.000000  2021.000000  437047.713441   \n",
              "\n",
              "       Neighborhood_Rural  Neighborhood_Suburb  Neighborhood_Urban  \n",
              "count         1000.000000          1000.000000         1000.000000  \n",
              "mean             0.342000             0.333000            0.325000  \n",
              "std              0.474617             0.471522            0.468609  \n",
              "min              0.000000             0.000000            0.000000  \n",
              "25%              0.000000             0.000000            0.000000  \n",
              "50%              0.000000             0.000000            0.000000  \n",
              "75%              1.000000             1.000000            1.000000  \n",
              "max              1.000000             1.000000            1.000000  "
            ],
            "text/html": [
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SquareFeet</th>\n",
              "      <th>Bedrooms</th>\n",
              "      <th>Bathrooms</th>\n",
              "      <th>YearBuilt</th>\n",
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              "      <th>Neighborhood_Urban</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2002.288000</td>\n",
              "      <td>3.515000</td>\n",
              "      <td>2.002000</td>\n",
              "      <td>1984.457000</td>\n",
              "      <td>223037.016061</td>\n",
              "      <td>0.342000</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>581.670136</td>\n",
              "      <td>1.130953</td>\n",
              "      <td>0.817719</td>\n",
              "      <td>20.330949</td>\n",
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              "      <td>0.474617</td>\n",
              "      <td>0.471522</td>\n",
              "      <td>0.468609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1000.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1950.000000</td>\n",
              "      <td>-7550.504574</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1507.250000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1967.000000</td>\n",
              "      <td>168905.529102</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2021.500000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1983.000000</td>\n",
              "      <td>220416.485632</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2524.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2002.000000</td>\n",
              "      <td>279628.697596</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2999.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2021.000000</td>\n",
              "      <td>437047.713441</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-83a49760-99ab-4703-b53f-d2e8d4fc84c3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-64da87a7-4bfb-4db0-8cba-1e1d0ad9438a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"hp_test\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"SquareFeet\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 830.5567967260185,\n        \"min\": 581.6701360764563,\n        \"max\": 2999.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1000.0,\n          2002.288,\n          2524.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 352.4125101562337,\n        \"min\": 1.1309527196368794,\n        \"max\": 1000.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1000.0,\n          3.515,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bathrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 352.9069492337987,\n        \"min\": 0.8177191844787945,\n        \"max\": 1000.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1000.0,\n          2.002,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YearBuilt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 731.3239730266098,\n        \"min\": 20.330949276866008,\n        \"max\": 2021.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1984.457,\n          1983.0,\n          1000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 150108.67450064773,\n        \"min\": -7550.50457435759,\n        \"max\": 437047.71344105,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          223037.01606120248,\n          220416.4856317892,\n          1000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Neighborhood_Rural\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 353.41137428502344,\n        \"min\": 0.0,\n        \"max\": 1000.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.342,\n          1.0,\n          0.4746169626775482\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Neighborhood_Suburb\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 353.4119852977801,\n        \"min\": 0.0,\n        \"max\": 1000.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.333,\n          1.0,\n          0.4715223571935199\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Neighborhood_Urban\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 353.4125366409159,\n        \"min\": 0.0,\n        \"max\": 1000.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.325,\n          1.0,\n          0.46860921309188386\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def print_sum(df_name, df):\n",
        "  columns = ['Neighborhood_Rural', 'Neighborhood_Suburb', 'Neighborhood_Urban']\n",
        "  print(df_name)\n",
        "  for col in columns:\n",
        "    print(col, df[col].sum())\n",
        "  print()\n",
        "\n",
        "print_sum(\"hp_train\", hp_train)\n",
        "print_sum(\"hp_dev\", hp_dev)\n",
        "print_sum(\"hp_test\", hp_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7RBghGHvwEUe",
        "outputId": "e472b811-18fe-4530-b28f-37a9a9f4ed70"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "hp_train\n",
            "Neighborhood_Rural 14645\n",
            "Neighborhood_Suburb 14680\n",
            "Neighborhood_Urban 14675\n",
            "\n",
            "hp_dev\n",
            "Neighborhood_Rural 1689\n",
            "Neighborhood_Suburb 1708\n",
            "Neighborhood_Urban 1603\n",
            "\n",
            "hp_test\n",
            "Neighborhood_Rural 342\n",
            "Neighborhood_Suburb 333\n",
            "Neighborhood_Urban 325\n",
            "\n"
          ]
        }
      ]
    }
  ]
}