{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "68ZAsMG9cq58",
        "outputId": "aca45133-b8af-4a86-bb45-8b9d4875e8ba"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (2.2.2)\n",
            "Requirement already satisfied: numpy>=1.22.4 in /usr/local/lib/python3.10/dist-packages (from pandas) (1.26.4)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 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) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.5.2)\n",
            "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.26.4)\n",
            "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.13.1)\n",
            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.5.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install pandas\n",
        "!pip install scikit-learn"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt  # Import matplotlib.pyplot and assign it the alias 'plt'\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "S_Xjfx66yNDs"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()  # This will prompt you to upload the file\n",
        "\n",
        "import pandas as pd\n",
        "# Load the uploaded CSV file (replace 'filename.csv' with your actual file name)\n",
        "df = pd.read_csv('Housing.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "id": "b6EiuUsjc1Kr",
        "outputId": "6486fc41-61c0-451d-f9a7-24c3e746d5cc"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-6b6fe03b-9edd-4eb8-9bfa-47957551fd4e\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-6b6fe03b-9edd-4eb8-9bfa-47957551fd4e\">\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 Housing.csv to Housing.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Load the dataset\n",
        "df = pd.read_csv(\"Housing.csv\")\n",
        "\n",
        "# Display the first few rows of the dataset\n",
        "df.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "BFMeb4-VdL-j",
        "outputId": "e0ae67b0-b42f-4a41-890d-d2c93ce5ac75"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      price  area  bedrooms  bathrooms  stories mainroad guestroom basement  \\\n",
              "0  13300000  7420         4          2        3      yes        no       no   \n",
              "1  12250000  8960         4          4        4      yes        no       no   \n",
              "2  12250000  9960         3          2        2      yes        no      yes   \n",
              "3  12215000  7500         4          2        2      yes        no      yes   \n",
              "4  11410000  7420         4          1        2      yes       yes      yes   \n",
              "\n",
              "  hotwaterheating airconditioning  parking prefarea furnishingstatus  \n",
              "0              no             yes        2      yes        furnished  \n",
              "1              no             yes        3       no        furnished  \n",
              "2              no              no        2      yes   semi-furnished  \n",
              "3              no             yes        3      yes        furnished  \n",
              "4              no             yes        2       no        furnished  "
            ],
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              "\n",
              "  <div id=\"df-abfbff33-a744-4658-9eb7-ba0f0967f4ce\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-abfbff33-a744-4658-9eb7-ba0f0967f4ce')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\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-abfbff33-a744-4658-9eb7-ba0f0967f4ce 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-abfbff33-a744-4658-9eb7-ba0f0967f4ce');\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-a08c6b38-20ba-490f-b42e-659ef13c0ff4\">\n",
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              "            title=\"Suggest charts\"\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <g>\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",
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              "    }\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-a08c6b38-20ba-490f-b42e-659ef13c0ff4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 545,\n  \"fields\": [\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1870439,\n        \"min\": 1750000,\n        \"max\": 13300000,\n        \"num_unique_values\": 219,\n        \"samples\": [\n          3773000,\n          5285000,\n          1820000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2170,\n        \"min\": 1650,\n        \"max\": 16200,\n        \"num_unique_values\": 284,\n        \"samples\": [\n          6000,\n          2684,\n          5360\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4,\n          3,\n          1\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\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"stories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mainroad\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"guestroom\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"basement\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hotwaterheating\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"airconditioning\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parking\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prefarea\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"furnishingstatus\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"furnished\",\n          \"semi-furnished\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        },
        "id": "I4MaqRQrdbxZ",
        "outputId": "ba379612-63a5-4e95-86ba-f9bba235559d"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "price                int64\n",
              "area                 int64\n",
              "bedrooms             int64\n",
              "bathrooms            int64\n",
              "stories              int64\n",
              "mainroad            object\n",
              "guestroom           object\n",
              "basement            object\n",
              "hotwaterheating     object\n",
              "airconditioning     object\n",
              "parking              int64\n",
              "prefarea            object\n",
              "furnishingstatus    object\n",
              "dtype: object"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
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              "    }\n",
              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "  </thead>\n",
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              "    <tr>\n",
              "      <th>price</th>\n",
              "      <td>int64</td>\n",
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              "      <td>int64</td>\n",
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              "    <tr>\n",
              "      <th>bedrooms</th>\n",
              "      <td>int64</td>\n",
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              "    <tr>\n",
              "      <th>bathrooms</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>stories</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mainroad</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>guestroom</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>basement</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hotwaterheating</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "BsiajYbodVe1",
        "outputId": "1039fd6d-e2b4-4f27-990c-f99004aedea5"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              price          area    bedrooms   bathrooms     stories  \\\n",
              "count  5.450000e+02    545.000000  545.000000  545.000000  545.000000   \n",
              "mean   4.766729e+06   5150.541284    2.965138    1.286239    1.805505   \n",
              "std    1.870440e+06   2170.141023    0.738064    0.502470    0.867492   \n",
              "min    1.750000e+06   1650.000000    1.000000    1.000000    1.000000   \n",
              "25%    3.430000e+06   3600.000000    2.000000    1.000000    1.000000   \n",
              "50%    4.340000e+06   4600.000000    3.000000    1.000000    2.000000   \n",
              "75%    5.740000e+06   6360.000000    3.000000    2.000000    2.000000   \n",
              "max    1.330000e+07  16200.000000    6.000000    4.000000    4.000000   \n",
              "\n",
              "          parking  \n",
              "count  545.000000  \n",
              "mean     0.693578  \n",
              "std      0.861586  \n",
              "min      0.000000  \n",
              "25%      0.000000  \n",
              "50%      0.000000  \n",
              "75%      1.000000  \n",
              "max      3.000000  "
            ],
            "text/html": [
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5.450000e+02</td>\n",
              "      <td>545.000000</td>\n",
              "      <td>545.000000</td>\n",
              "      <td>545.000000</td>\n",
              "      <td>545.000000</td>\n",
              "      <td>545.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.766729e+06</td>\n",
              "      <td>5150.541284</td>\n",
              "      <td>2.965138</td>\n",
              "      <td>1.286239</td>\n",
              "      <td>1.805505</td>\n",
              "      <td>0.693578</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.870440e+06</td>\n",
              "      <td>2170.141023</td>\n",
              "      <td>0.738064</td>\n",
              "      <td>0.502470</td>\n",
              "      <td>0.867492</td>\n",
              "      <td>0.861586</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.750000e+06</td>\n",
              "      <td>1650.000000</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.430000e+06</td>\n",
              "      <td>3600.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.340000e+06</td>\n",
              "      <td>4600.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5.740000e+06</td>\n",
              "      <td>6360.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.330000e+07</td>\n",
              "      <td>16200.000000</td>\n",
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              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "      --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-860eae4f-9a15-4222-8289-2fae0742ec47 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4050766.5892287116,\n        \"min\": 545.0,\n        \"max\": 13300000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          4766729.247706422,\n          4340000.0,\n          545.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4906.2698868605785,\n        \"min\": 545.0,\n        \"max\": 16200.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5150.54128440367,\n          4600.0,\n          545.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 191.74878444768592,\n        \"min\": 0.738063860568575,\n        \"max\": 545.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          545.0,\n          2.9651376146788992,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bathrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 192.14476345868238,\n        \"min\": 0.502469616053218,\n        \"max\": 545.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          545.0,\n          1.2862385321100918,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"stories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 192.04914398408363,\n        \"min\": 0.8674924629255298,\n        \"max\": 545.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          545.0,\n          1.8055045871559634,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parking\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 192.40854964721382,\n        \"min\": 0.0,\n        \"max\": 545.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          545.0,\n          0.6935779816513762,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        },
        "id": "iV3KFSBldhkp",
        "outputId": "25125882-80fa-4eb7-fdc7-dd6705993812"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "price               0\n",
              "area                0\n",
              "bedrooms            0\n",
              "bathrooms           0\n",
              "stories             0\n",
              "mainroad            0\n",
              "guestroom           0\n",
              "basement            0\n",
              "hotwaterheating     0\n",
              "airconditioning     0\n",
              "parking             0\n",
              "prefarea            0\n",
              "furnishingstatus    0\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>price</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>area</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bedrooms</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bathrooms</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>stories</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mainroad</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>guestroom</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>basement</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hotwaterheating</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>airconditioning</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>parking</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>prefarea</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>furnishingstatus</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "missing_percentage = df.isnull().mean() * 100\n",
        "threshold = 30\n",
        "# Filter columns with more than the threshold percentage of missing values\n",
        "columns_to_drop = missing_percentage[missing_percentage > threshold].index\n",
        "# Drop the columns\n",
        "df = df.drop(columns=columns_to_drop)\n",
        "# Display the resulting dataframe after removing columns with too many missing valu\n",
        "print(f\"Columns dropped: {list(columns_to_drop)}\")\n",
        "print(f\"Remaining columns: {df.columns}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oulDoQesdl4T",
        "outputId": "a526f13e-2e79-4b0c-fc13-95f704618ba8"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Columns dropped: []\n",
            "Remaining columns: Index(['price', 'area', 'bedrooms', 'bathrooms', 'stories', 'mainroad',\n",
            "       'guestroom', 'basement', 'hotwaterheating', 'airconditioning',\n",
            "       'parking', 'prefarea', 'furnishingstatus'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "label_encoder = LabelEncoder()\n",
        "for column in df.select_dtypes(include=['object']).columns:\n",
        "    # Indent the following line to be part of the for loop\n",
        "    df[column] = label_encoder.fit_transform(df[column])\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "KsTrqZj1dqMh",
        "outputId": "ad6eddd0-382b-487f-fdf2-aa217b6f3fb8"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      price  area  bedrooms  bathrooms  stories  mainroad  guestroom  \\\n",
              "0  13300000  7420         4          2        3         1          0   \n",
              "1  12250000  8960         4          4        4         1          0   \n",
              "2  12250000  9960         3          2        2         1          0   \n",
              "3  12215000  7500         4          2        2         1          0   \n",
              "4  11410000  7420         4          1        2         1          1   \n",
              "\n",
              "   basement  hotwaterheating  airconditioning  parking  prefarea  \\\n",
              "0         0                0                1        2         1   \n",
              "1         0                0                1        3         0   \n",
              "2         1                0                0        2         1   \n",
              "3         1                0                1        3         1   \n",
              "4         1                0                1        2         0   \n",
              "\n",
              "   furnishingstatus  \n",
              "0                 0  \n",
              "1                 0  \n",
              "2                 1  \n",
              "3                 0  \n",
              "4                 0  "
            ],
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              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-21761392-61c5-476a-812a-2c7d692890d3')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .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",
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              "      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",
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              "    }\n",
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              "      margin-bottom: 4px;\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
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              "      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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-21761392-61c5-476a-812a-2c7d692890d3 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-21761392-61c5-476a-812a-2c7d692890d3');\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-e234b439-f2fb-40da-a8b8-a57025ffc9d2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e234b439-f2fb-40da-a8b8-a57025ffc9d2')\"\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",
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              "    <g>\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-e234b439-f2fb-40da-a8b8-a57025ffc9d2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 545,\n  \"fields\": [\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1870439,\n        \"min\": 1750000,\n        \"max\": 13300000,\n        \"num_unique_values\": 219,\n        \"samples\": [\n          3773000,\n          5285000,\n          1820000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2170,\n        \"min\": 1650,\n        \"max\": 16200,\n        \"num_unique_values\": 284,\n        \"samples\": [\n          6000,\n          2684,\n          5360\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4,\n          3,\n          1\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\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"stories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mainroad\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"guestroom\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"basement\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hotwaterheating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"airconditioning\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parking\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prefarea\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"furnishingstatus\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fill missing values in columns with mode\n",
        "for column in df.select_dtypes(include=['float64']).columns:\n",
        "    # Indented line to be part of the for loop\n",
        "    mean_value = df[column].mean() # Get the mode (most frequent value)\n",
        "    df[column] = df[column].fillna(mean_value)\n",
        ""
      ],
      "metadata": {
        "id": "EIGBjVvLdrEb"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        },
        "id": "lEXMlB93dvA0",
        "outputId": "b05ba8f3-08b1-4e94-d2a5-98d6b2a72060"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "price               0\n",
              "area                0\n",
              "bedrooms            0\n",
              "bathrooms           0\n",
              "stories             0\n",
              "mainroad            0\n",
              "guestroom           0\n",
              "basement            0\n",
              "hotwaterheating     0\n",
              "airconditioning     0\n",
              "parking             0\n",
              "prefarea            0\n",
              "furnishingstatus    0\n",
              "dtype: int64"
            ],
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              "</div><br><label><b>dtype:</b> int64</label>"
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    {
      "cell_type": "code",
      "source": [
        "df"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "Mye4xDFAxgWh",
        "outputId": "abb98e07-26e4-4f7f-9381-7e2a9218a735"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "        price  area  bedrooms  bathrooms  stories  mainroad  guestroom  \\\n",
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              "[545 rows x 13 columns]"
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>544</th>\n",
              "      <td>1750000</td>\n",
              "      <td>3850</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>545 rows × 13 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-5f8d8952-16a8-4e3a-98f8-05d0c229c863')\"\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",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-5f8d8952-16a8-4e3a-98f8-05d0c229c863 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-5f8d8952-16a8-4e3a-98f8-05d0c229c863');\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-bdf7d80c-3d12-4778-a0fa-ecf1bdc0216c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bdf7d80c-3d12-4778-a0fa-ecf1bdc0216c')\"\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",
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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-bdf7d80c-3d12-4778-a0fa-ecf1bdc0216c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_828865b1-db29-49d5-b832-12898a363391\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_828865b1-db29-49d5-b832-12898a363391 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 545,\n  \"fields\": [\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1870439,\n        \"min\": 1750000,\n        \"max\": 13300000,\n        \"num_unique_values\": 219,\n        \"samples\": [\n          3773000,\n          5285000,\n          1820000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2170,\n        \"min\": 1650,\n        \"max\": 16200,\n        \"num_unique_values\": 284,\n        \"samples\": [\n          6000,\n          2684,\n          5360\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4,\n          3,\n          1\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\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"stories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mainroad\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"guestroom\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"basement\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hotwaterheating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"airconditioning\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parking\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prefarea\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"furnishingstatus\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def replace_outliers_with_nan(column):\n",
        "    Q1 = column.quantile(0.25)\n",
        "    Q3 = column.quantile(0.75)\n",
        "    IQR = Q3 - Q1\n",
        "    lower_bound = Q1 - 1.5 * IQR\n",
        "    upper_bound = Q3 + 1.5 * IQR\n",
        "    # Replace outliers with NaN\n",
        "    return column\n",
        "null_counts = df.isnull().sum() # Replace 'data' with 'df'\n",
        "print(\"Sum of null values in each column:\")\n",
        "print(null_counts)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N5eyJ82HxlEJ",
        "outputId": "e185784c-e1bb-4e7c-a454-11578aa672c8"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sum of null values in each column:\n",
            "price               0\n",
            "area                0\n",
            "bedrooms            0\n",
            "bathrooms           0\n",
            "stories             0\n",
            "mainroad            0\n",
            "guestroom           0\n",
            "basement            0\n",
            "hotwaterheating     0\n",
            "airconditioning     0\n",
            "parking             0\n",
            "prefarea            0\n",
            "furnishingstatus    0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "sns.histplot(df['price'], kde=True, color='blue')\n",
        "plt.title('Distribution of House Prices')\n",
        "plt.xlabel('Price')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "AfifDBKPxteJ",
        "outputId": "85bfdb17-940c-4df3-aa0b-d893025c9d95"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "sns.scatterplot(x='area', y='price', data=df)\n",
        "plt.title('Price vs Area')\n",
        "plt.xlabel('Area (sq ft)')\n",
        "plt.ylabel('Price')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "ip_yAlopyLRW",
        "outputId": "f3ab7e26-17e6-4a15-98ac-91d79420e558"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "sns.boxplot(x='bedrooms', y='price', data=df)\n",
        "plt.title('Price Distribution by Number of Bedrooms')\n",
        "plt.xlabel('Number of Bedrooms')\n",
        "plt.ylabel('Price')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "iA7BjVFEyWST",
        "outputId": "ff97bbcc-d966-4061-bb67-e6bad001f954"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(x='mainroad', y='price', data=df, estimator=lambda x: sum(x) / len(x))\n",
        "plt.title('Average House Price by Mainroad Access')\n",
        "plt.xlabel('Mainroad Access (1 = Yes, 0 = No)')\n",
        "plt.ylabel('Average Price')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "ShB_BF7PyiPN",
        "outputId": "de3ca2ec-dd6a-4568-8b16-c97c3356838a"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}