From 1e6b6e6ceda71bb9847bb58edc458178a7c21f41 Mon Sep 17 00:00:00 2001
From: Vrizz <156404102+ctrlv27@users.noreply.github.com>
Date: Wed, 7 Aug 2024 14:50:44 +0530
Subject: [PATCH] Delete practice.ipynb

---
 practice.ipynb | 1664 ------------------------------------------------
 1 file changed, 1664 deletions(-)
 delete mode 100644 practice.ipynb

diff --git a/practice.ipynb b/practice.ipynb
deleted file mode 100644
index ca0b747..0000000
--- a/practice.ipynb
+++ /dev/null
@@ -1,1664 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": [],
-      "authorship_tag": "ABX9TyPfqMnDc7BAvcI0UR60Fi0h",
-      "include_colab_link": true
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
-      },
-      "source": [
-        "<a href=\"https://colab.research.google.com/github/ctrlv27/java.scripting/blob/main/practice.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 226
-        },
-        "id": "NqS5tBSdxgRS",
-        "outputId": "83279482-9637-4da2-8179-c84b37b69e47"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "         price  area  bedrooms  bathrooms  stories mainroad guestroom  \\\n",
-              "540  1820000.0  3000         2        1.0        1      yes        no   \n",
-              "541  1767150.0  2400         3        1.0        1       no        no   \n",
-              "542  1750000.0  3620         2        1.0        1      yes        no   \n",
-              "543  1750000.0  2910         3        1.0        1       no        no   \n",
-              "544  1750000.0  3850         3        1.0        2      yes        no   \n",
-              "\n",
-              "    basement hotwaterheating airconditioning  parking prefarea  \\\n",
-              "540      yes              no              no        2       no   \n",
-              "541       no              no              no        0       no   \n",
-              "542       no              no              no        0       no   \n",
-              "543       no              no              no        0       no   \n",
-              "544       no              no              no        0       no   \n",
-              "\n",
-              "    furnishingstatus  \n",
-              "540      unfurnished  \n",
-              "541   semi-furnished  \n",
-              "542      unfurnished  \n",
-              "543        furnished  \n",
-              "544      unfurnished  "
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-d17f9f20-61c2-4e5e-94a1-90889c6b5bf5\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>price</th>\n",
-              "      <th>area</th>\n",
-              "      <th>bedrooms</th>\n",
-              "      <th>bathrooms</th>\n",
-              "      <th>stories</th>\n",
-              "      <th>mainroad</th>\n",
-              "      <th>guestroom</th>\n",
-              "      <th>basement</th>\n",
-              "      <th>hotwaterheating</th>\n",
-              "      <th>airconditioning</th>\n",
-              "      <th>parking</th>\n",
-              "      <th>prefarea</th>\n",
-              "      <th>furnishingstatus</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>540</th>\n",
-              "      <td>1820000.0</td>\n",
-              "      <td>3000</td>\n",
-              "      <td>2</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>yes</td>\n",
-              "      <td>no</td>\n",
-              "      <td>yes</td>\n",
-              "      <td>no</td>\n",
-              "      <td>no</td>\n",
-              "      <td>2</td>\n",
-              "      <td>no</td>\n",
-              "      <td>unfurnished</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>541</th>\n",
-              "      <td>1767150.0</td>\n",
-              "      <td>2400</td>\n",
-              "      <td>3</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>no</td>\n",
-              "      <td>no</td>\n",
-              "      <td>no</td>\n",
-              "      <td>no</td>\n",
-              "      <td>no</td>\n",
-              "      <td>0</td>\n",
-              "      <td>no</td>\n",
-              "      <td>semi-furnished</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>542</th>\n",
-              "      <td>1750000.0</td>\n",
-              "      <td>3620</td>\n",
-              "      <td>2</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>yes</td>\n",
-              "      <td>no</td>\n",
-              "      <td>no</td>\n",
-              "      <td>no</td>\n",
-              "      <td>no</td>\n",
-              "      <td>0</td>\n",
-              "      <td>no</td>\n",
-              "      <td>unfurnished</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>543</th>\n",
-              "      <td>1750000.0</td>\n",
-              "      <td>2910</td>\n",
-              "      <td>3</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>no</td>\n",
-              "      <td>no</td>\n",
-              "      <td>no</td>\n",
-              "      <td>no</td>\n",
-              "      <td>no</td>\n",
-              "      <td>0</td>\n",
-              "      <td>no</td>\n",
-              "      <td>furnished</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>544</th>\n",
-              "      <td>1750000.0</td>\n",
-              "      <td>3850</td>\n",
-              "      <td>3</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>2</td>\n",
-              "      <td>yes</td>\n",
-              "      <td>no</td>\n",
-              "      <td>no</td>\n",
-              "      <td>no</td>\n",
-              "      <td>no</td>\n",
-              "      <td>0</td>\n",
-              "      <td>no</td>\n",
-              "      <td>unfurnished</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d17f9f20-61c2-4e5e-94a1-90889c6b5bf5')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-d17f9f20-61c2-4e5e-94a1-90889c6b5bf5 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-d17f9f20-61c2-4e5e-94a1-90889c6b5bf5');\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-3647eb6e-1df9-4a93-876a-bda73ec84e37\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3647eb6e-1df9-4a93-876a-bda73ec84e37')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-3647eb6e-1df9-4a93-876a-bda73ec84e37 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\": 5,\n  \"fields\": [\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30311.29327494952,\n        \"min\": 1750000.0,\n        \"max\": 1820000.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1820000.0,\n          1767150.0,\n          1750000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 581,\n        \"min\": 2400,\n        \"max\": 3850,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2400,\n          3850,\n          3620\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2,\n        \"max\": 3,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bathrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\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\": 2,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2\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        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"guestroom\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\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          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hotwaterheating\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"airconditioning\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"no\"\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\": 2,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prefarea\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"furnishingstatus\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"unfurnished\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
-          },
-          "metadata": {},
-          "execution_count": 3
-        }
-      ],
-      "source": [
-        "import pandas as pd\n",
-        "import seaborn as sns\n",
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "#Load the dataset\n",
-        "data = 'Housing.csv'\n",
-        "df = pd.read_csv(data)\n",
-        "df.head(5)\n",
-        "df.tail(5)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df.describe()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 300
-        },
-        "id": "b5c0atj-0onX",
-        "outputId": "f46f425a-06c6-4a6c-c984-51ec47085709"
-      },
-      "execution_count": 4,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "              price          area    bedrooms   bathrooms     stories  \\\n",
-              "count  5.440000e+02    545.000000  545.000000  543.000000  545.000000   \n",
-              "mean   4.760855e+06   5150.541284    2.965138    1.285451    1.805505   \n",
-              "std    1.867122e+06   2170.141023    0.738064    0.502310    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",
-              "  <div id=\"df-b53bf18b-74f9-4ff3-9313-06f941beba71\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>price</th>\n",
-              "      <th>area</th>\n",
-              "      <th>bedrooms</th>\n",
-              "      <th>bathrooms</th>\n",
-              "      <th>stories</th>\n",
-              "      <th>parking</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>count</th>\n",
-              "      <td>5.440000e+02</td>\n",
-              "      <td>545.000000</td>\n",
-              "      <td>545.000000</td>\n",
-              "      <td>543.000000</td>\n",
-              "      <td>545.000000</td>\n",
-              "      <td>545.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>mean</th>\n",
-              "      <td>4.760855e+06</td>\n",
-              "      <td>5150.541284</td>\n",
-              "      <td>2.965138</td>\n",
-              "      <td>1.285451</td>\n",
-              "      <td>1.805505</td>\n",
-              "      <td>0.693578</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>std</th>\n",
-              "      <td>1.867122e+06</td>\n",
-              "      <td>2170.141023</td>\n",
-              "      <td>0.738064</td>\n",
-              "      <td>0.502310</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",
-              "      <td>1.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "    </tr>\n",
-              "    <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",
-              "    </tr>\n",
-              "    <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",
-              "      <td>6.000000</td>\n",
-              "      <td>4.000000</td>\n",
-              "      <td>4.000000</td>\n",
-              "      <td>3.000000</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b53bf18b-74f9-4ff3-9313-06f941beba71')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-b53bf18b-74f9-4ff3-9313-06f941beba71 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-b53bf18b-74f9-4ff3-9313-06f941beba71');\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-07ab310d-9d06-40bd-973f-233a8b8627e9\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-07ab310d-9d06-40bd-973f-233a8b8627e9')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-07ab310d-9d06-40bd-973f-233a8b8627e9 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\": 4050987.2502240124,\n        \"min\": 544.0,\n        \"max\": 13300000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          4760854.669117647,\n          4340000.0,\n          544.0\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\": 0.7380638605685743,\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\": 191.43771613213667,\n        \"min\": 0.5023102796865925,\n        \"max\": 543.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          543.0,\n          1.285451197053407,\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\": 0.8674924629255264,\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\": 192.40854964721385,\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": 4
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df.shape"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "0o-UJKN10sFI",
-        "outputId": "02aeea22-3ace-41b5-b009-bd2fdf7bc3f5"
-      },
-      "execution_count": 6,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "(545, 13)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 6
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df.info()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "sQRsz4tP0tez",
-        "outputId": "0f6c5765-9d34-4985-9fb3-44132df64831"
-      },
-      "execution_count": 7,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "<class 'pandas.core.frame.DataFrame'>\n",
-            "RangeIndex: 545 entries, 0 to 544\n",
-            "Data columns (total 13 columns):\n",
-            " #   Column            Non-Null Count  Dtype  \n",
-            "---  ------            --------------  -----  \n",
-            " 0   price             544 non-null    float64\n",
-            " 1   area              545 non-null    int64  \n",
-            " 2   bedrooms          545 non-null    int64  \n",
-            " 3   bathrooms         543 non-null    float64\n",
-            " 4   stories           545 non-null    int64  \n",
-            " 5   mainroad          545 non-null    object \n",
-            " 6   guestroom         544 non-null    object \n",
-            " 7   basement          544 non-null    object \n",
-            " 8   hotwaterheating   545 non-null    object \n",
-            " 9   airconditioning   544 non-null    object \n",
-            " 10  parking           545 non-null    int64  \n",
-            " 11  prefarea          545 non-null    object \n",
-            " 12  furnishingstatus  544 non-null    object \n",
-            "dtypes: float64(2), int64(4), object(7)\n",
-            "memory usage: 55.5+ KB\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df['basement'].value_counts()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "C6H2NAxm1OtP",
-        "outputId": "d95704fe-c0d4-4ceb-c7ed-13b76ed3e766"
-      },
-      "execution_count": 8,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "basement\n",
-              "no     354\n",
-              "yes    190\n",
-              "Name: count, dtype: int64"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 8
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "sns.histplot(df['basement'], kde=True)\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 449
-        },
-        "id": "kIkFbI791WIJ",
-        "outputId": "ee5da8d2-549e-4bdd-d0c4-cabad00b6d96"
-      },
-      "execution_count": 9,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": "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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "sns.boxplot(x='price',data=df)\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 449
-        },
-        "id": "rt4xv9Ws1nYp",
-        "outputId": "d2156ed1-60eb-49aa-c37a-ff61f7065978"
-      },
-      "execution_count": 10,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": "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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Select only the numeric columns\n",
-        "numeric_df=df.select_dtypes(include=['number'])\n",
-        "\n",
-        "#Perform the comparison on numeric columns\n",
-        "(numeric_df<0).sum()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "yYLHDjuf2PAS",
-        "outputId": "c25e9389-3b0e-4aa6-f8ae-15b577f7baec"
-      },
-      "execution_count": 14,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "price        0\n",
-              "area         0\n",
-              "bedrooms     0\n",
-              "bathrooms    0\n",
-              "stories      0\n",
-              "parking      0\n",
-              "dtype: int64"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 14
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df.isnull().sum()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "-WJVBOOr2tsq",
-        "outputId": "e7e9baa4-2880-4004-9013-b99e01c260d0"
-      },
-      "execution_count": 15,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "price               1\n",
-              "area                0\n",
-              "bedrooms            0\n",
-              "bathrooms           2\n",
-              "stories             0\n",
-              "mainroad            0\n",
-              "guestroom           1\n",
-              "basement            1\n",
-              "hotwaterheating     0\n",
-              "airconditioning     1\n",
-              "parking             0\n",
-              "prefarea            0\n",
-              "furnishingstatus    1\n",
-              "dtype: int64"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 15
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df.isna().sum()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "cdSFVfxC3aMt",
-        "outputId": "598d99df-b8ea-4a8c-d593-2d0640a47d7a"
-      },
-      "execution_count": 16,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "price               1\n",
-              "area                0\n",
-              "bedrooms            0\n",
-              "bathrooms           2\n",
-              "stories             0\n",
-              "mainroad            0\n",
-              "guestroom           1\n",
-              "basement            1\n",
-              "hotwaterheating     0\n",
-              "airconditioning     1\n",
-              "parking             0\n",
-              "prefarea            0\n",
-              "furnishingstatus    1\n",
-              "dtype: int64"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 16
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import seaborn as sns\n",
-        "import matplotlib.pyplot as plt\n",
-        "plt.figure(figsize=(10,6))\n",
-        "sns.heatmap(df.isnull(),cbar=False,cmap='viridis')\n",
-        "plt.title('Missing values in Housing Prices Dataset')\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 524
-        },
-        "id": "dbxCGHJi3e8j",
-        "outputId": "f3af5f1f-2e19-4367-a040-eb7839cd0692"
-      },
-      "execution_count": 18,
-      "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": [
-        "for col in ['mainroad','guestroom','basement','airconditioning','prefarea','furnishingstatus']:\n",
-        "  #Removed empty strings\n",
-        "  df[col]=df[col].astype('category').cat.codes\n",
-        "  df.head(5)\n",
-        "  df.info()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "cUKB_QI-48yJ",
-        "outputId": "4dae7b9e-fd76-4ca7-c40f-4d8b964cc3b1"
-      },
-      "execution_count": 21,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "<class 'pandas.core.frame.DataFrame'>\n",
-            "RangeIndex: 545 entries, 0 to 544\n",
-            "Data columns (total 13 columns):\n",
-            " #   Column            Non-Null Count  Dtype  \n",
-            "---  ------            --------------  -----  \n",
-            " 0   price             544 non-null    float64\n",
-            " 1   area              545 non-null    int64  \n",
-            " 2   bedrooms          545 non-null    int64  \n",
-            " 3   bathrooms         543 non-null    float64\n",
-            " 4   stories           545 non-null    int64  \n",
-            " 5   mainroad          545 non-null    int8   \n",
-            " 6   guestroom         545 non-null    int8   \n",
-            " 7   basement          545 non-null    int8   \n",
-            " 8   hotwaterheating   545 non-null    object \n",
-            " 9   airconditioning   545 non-null    int8   \n",
-            " 10  parking           545 non-null    int64  \n",
-            " 11  prefarea          545 non-null    object \n",
-            " 12  furnishingstatus  544 non-null    object \n",
-            "dtypes: float64(2), int64(4), int8(4), object(3)\n",
-            "memory usage: 40.6+ KB\n",
-            "<class 'pandas.core.frame.DataFrame'>\n",
-            "RangeIndex: 545 entries, 0 to 544\n",
-            "Data columns (total 13 columns):\n",
-            " #   Column            Non-Null Count  Dtype  \n",
-            "---  ------            --------------  -----  \n",
-            " 0   price             544 non-null    float64\n",
-            " 1   area              545 non-null    int64  \n",
-            " 2   bedrooms          545 non-null    int64  \n",
-            " 3   bathrooms         543 non-null    float64\n",
-            " 4   stories           545 non-null    int64  \n",
-            " 5   mainroad          545 non-null    int8   \n",
-            " 6   guestroom         545 non-null    int8   \n",
-            " 7   basement          545 non-null    int8   \n",
-            " 8   hotwaterheating   545 non-null    object \n",
-            " 9   airconditioning   545 non-null    int8   \n",
-            " 10  parking           545 non-null    int64  \n",
-            " 11  prefarea          545 non-null    object \n",
-            " 12  furnishingstatus  544 non-null    object \n",
-            "dtypes: float64(2), int64(4), int8(4), object(3)\n",
-            "memory usage: 40.6+ KB\n",
-            "<class 'pandas.core.frame.DataFrame'>\n",
-            "RangeIndex: 545 entries, 0 to 544\n",
-            "Data columns (total 13 columns):\n",
-            " #   Column            Non-Null Count  Dtype  \n",
-            "---  ------            --------------  -----  \n",
-            " 0   price             544 non-null    float64\n",
-            " 1   area              545 non-null    int64  \n",
-            " 2   bedrooms          545 non-null    int64  \n",
-            " 3   bathrooms         543 non-null    float64\n",
-            " 4   stories           545 non-null    int64  \n",
-            " 5   mainroad          545 non-null    int8   \n",
-            " 6   guestroom         545 non-null    int8   \n",
-            " 7   basement          545 non-null    int8   \n",
-            " 8   hotwaterheating   545 non-null    object \n",
-            " 9   airconditioning   545 non-null    int8   \n",
-            " 10  parking           545 non-null    int64  \n",
-            " 11  prefarea          545 non-null    object \n",
-            " 12  furnishingstatus  544 non-null    object \n",
-            "dtypes: float64(2), int64(4), int8(4), object(3)\n",
-            "memory usage: 40.6+ KB\n",
-            "<class 'pandas.core.frame.DataFrame'>\n",
-            "RangeIndex: 545 entries, 0 to 544\n",
-            "Data columns (total 13 columns):\n",
-            " #   Column            Non-Null Count  Dtype  \n",
-            "---  ------            --------------  -----  \n",
-            " 0   price             544 non-null    float64\n",
-            " 1   area              545 non-null    int64  \n",
-            " 2   bedrooms          545 non-null    int64  \n",
-            " 3   bathrooms         543 non-null    float64\n",
-            " 4   stories           545 non-null    int64  \n",
-            " 5   mainroad          545 non-null    int8   \n",
-            " 6   guestroom         545 non-null    int8   \n",
-            " 7   basement          545 non-null    int8   \n",
-            " 8   hotwaterheating   545 non-null    object \n",
-            " 9   airconditioning   545 non-null    int8   \n",
-            " 10  parking           545 non-null    int64  \n",
-            " 11  prefarea          545 non-null    object \n",
-            " 12  furnishingstatus  544 non-null    object \n",
-            "dtypes: float64(2), int64(4), int8(4), object(3)\n",
-            "memory usage: 40.6+ KB\n",
-            "<class 'pandas.core.frame.DataFrame'>\n",
-            "RangeIndex: 545 entries, 0 to 544\n",
-            "Data columns (total 13 columns):\n",
-            " #   Column            Non-Null Count  Dtype  \n",
-            "---  ------            --------------  -----  \n",
-            " 0   price             544 non-null    float64\n",
-            " 1   area              545 non-null    int64  \n",
-            " 2   bedrooms          545 non-null    int64  \n",
-            " 3   bathrooms         543 non-null    float64\n",
-            " 4   stories           545 non-null    int64  \n",
-            " 5   mainroad          545 non-null    int8   \n",
-            " 6   guestroom         545 non-null    int8   \n",
-            " 7   basement          545 non-null    int8   \n",
-            " 8   hotwaterheating   545 non-null    object \n",
-            " 9   airconditioning   545 non-null    int8   \n",
-            " 10  parking           545 non-null    int64  \n",
-            " 11  prefarea          545 non-null    int8   \n",
-            " 12  furnishingstatus  544 non-null    object \n",
-            "dtypes: float64(2), int64(4), int8(5), object(2)\n",
-            "memory usage: 36.8+ KB\n",
-            "<class 'pandas.core.frame.DataFrame'>\n",
-            "RangeIndex: 545 entries, 0 to 544\n",
-            "Data columns (total 13 columns):\n",
-            " #   Column            Non-Null Count  Dtype  \n",
-            "---  ------            --------------  -----  \n",
-            " 0   price             544 non-null    float64\n",
-            " 1   area              545 non-null    int64  \n",
-            " 2   bedrooms          545 non-null    int64  \n",
-            " 3   bathrooms         543 non-null    float64\n",
-            " 4   stories           545 non-null    int64  \n",
-            " 5   mainroad          545 non-null    int8   \n",
-            " 6   guestroom         545 non-null    int8   \n",
-            " 7   basement          545 non-null    int8   \n",
-            " 8   hotwaterheating   545 non-null    object \n",
-            " 9   airconditioning   545 non-null    int8   \n",
-            " 10  parking           545 non-null    int64  \n",
-            " 11  prefarea          545 non-null    int8   \n",
-            " 12  furnishingstatus  545 non-null    int8   \n",
-            "dtypes: float64(2), int64(4), int8(6), object(1)\n",
-            "memory usage: 33.1+ KB\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "single_value_columns = df.nunique() == 1\n",
-        "\n",
-        "#Check if any column has a single unique value\n",
-        "if single_value_columns.any():\n",
-        "  print(\"Yes, the dataset contains columns with unique single value\")\n",
-        "  print(\"Colums with a unique single value:\",df.columns[single_value_columns].tolist())\n",
-        "\n",
-        "else:\n",
-        "  print(\"No, the dataset does not contain any columns with a unique single value\")"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "1i0MLxF-5vD6",
-        "outputId": "0a949617-a651-45e5-b59c-a011390bbc4f"
-      },
-      "execution_count": 22,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "No, the dataset does not contain any columns with a unique single value\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df = df.drop(columns=['stories'])\n",
-        "df.head(5)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 226
-        },
-        "id": "NIM1X2fz7gEU",
-        "outputId": "73cbf7a2-6fc9-44f9-c6fd-446ed005057d"
-      },
-      "execution_count": 23,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "        price  area  bedrooms  bathrooms  mainroad  guestroom  basement  \\\n",
-              "0  13300000.0  7420         4        2.0         1          1         1   \n",
-              "1  12250000.0  8960         4        4.0         1          1         1   \n",
-              "2  12250000.0  9960         3        2.0         1          1         2   \n",
-              "3  12215000.0  7500         4        2.0         1          1         2   \n",
-              "4  11410000.0  7420         4        1.0         1          2         2   \n",
-              "\n",
-              "  hotwaterheating  airconditioning  parking  prefarea  furnishingstatus  \n",
-              "0              no                2        2         1                 0  \n",
-              "1              no                2        3         0                 0  \n",
-              "2              no                1        2         1                 1  \n",
-              "3              no                2        3         1                 0  \n",
-              "4              no                2        2         0                 0  "
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-ea8a5203-6247-4be5-9b01-12a882f4b057\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>price</th>\n",
-              "      <th>area</th>\n",
-              "      <th>bedrooms</th>\n",
-              "      <th>bathrooms</th>\n",
-              "      <th>mainroad</th>\n",
-              "      <th>guestroom</th>\n",
-              "      <th>basement</th>\n",
-              "      <th>hotwaterheating</th>\n",
-              "      <th>airconditioning</th>\n",
-              "      <th>parking</th>\n",
-              "      <th>prefarea</th>\n",
-              "      <th>furnishingstatus</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>13300000.0</td>\n",
-              "      <td>7420</td>\n",
-              "      <td>4</td>\n",
-              "      <td>2.0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>1</td>\n",
-              "      <td>1</td>\n",
-              "      <td>no</td>\n",
-              "      <td>2</td>\n",
-              "      <td>2</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>12250000.0</td>\n",
-              "      <td>8960</td>\n",
-              "      <td>4</td>\n",
-              "      <td>4.0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>1</td>\n",
-              "      <td>1</td>\n",
-              "      <td>no</td>\n",
-              "      <td>2</td>\n",
-              "      <td>3</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>12250000.0</td>\n",
-              "      <td>9960</td>\n",
-              "      <td>3</td>\n",
-              "      <td>2.0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>1</td>\n",
-              "      <td>2</td>\n",
-              "      <td>no</td>\n",
-              "      <td>1</td>\n",
-              "      <td>2</td>\n",
-              "      <td>1</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>12215000.0</td>\n",
-              "      <td>7500</td>\n",
-              "      <td>4</td>\n",
-              "      <td>2.0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>1</td>\n",
-              "      <td>2</td>\n",
-              "      <td>no</td>\n",
-              "      <td>2</td>\n",
-              "      <td>3</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>11410000.0</td>\n",
-              "      <td>7420</td>\n",
-              "      <td>4</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>2</td>\n",
-              "      <td>2</td>\n",
-              "      <td>no</td>\n",
-              "      <td>2</td>\n",
-              "      <td>2</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ea8a5203-6247-4be5-9b01-12a882f4b057')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-ea8a5203-6247-4be5-9b01-12a882f4b057 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-ea8a5203-6247-4be5-9b01-12a882f4b057');\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-28cb4517-a767-42db-92a3-2071077a81a9\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-28cb4517-a767-42db-92a3-2071077a81a9')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-28cb4517-a767-42db-92a3-2071077a81a9 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\": 1867121.9543386477,\n        \"min\": 1750000.0,\n        \"max\": 13300000.0,\n        \"num_unique_values\": 218,\n        \"samples\": [\n          5040000.0,\n          1820000.0,\n          4130000.0\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\": 0.5023102796865925,\n        \"min\": 1.0,\n        \"max\": 4.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4.0,\n          3.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mainroad\",\n      \"properties\": {\n        \"dtype\": \"int8\",\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\": \"int8\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"basement\",\n      \"properties\": {\n        \"dtype\": \"int8\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2\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\": \"int8\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\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\": \"int8\",\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\": \"int8\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1,\n          -1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
-          },
-          "metadata": {},
-          "execution_count": 23
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "year=[2000,2001,2002,2003,2004,2005]\n",
-        "itemA=[400,80,120,300,150,90]\n",
-        "itemB=[120,100,80,120,260,90]\n",
-        "plt.subplot(221)\n",
-        "\n",
-        "plt.fill_between(year, min(itemA), itemA, alpha=0.5)\n",
-        "plt.subplot(222)\n",
-        "plt.fill_between(year, min (itemA), itemA, alpha=0.5)\n",
-        "plt.fill_between (year, min (itemB), itemB, alpha=0.5)\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 228
-        },
-        "id": "1hSF4toF8Am7",
-        "outputId": "3d3a8cf9-c2c6-4420-cd85-532d8188f69f"
-      },
-      "execution_count": 32,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 2 Axes>"
-            ],
-            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAigAAADTCAYAAACrx+h2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA0K0lEQVR4nO3de3Cc1X038K/usmytZMmWZGEJTKAYxwYawhsrSZO2cewE05ZgppBS4j9I8+LKDJeUpH6HwsB0Xqe0M0zpEMhMW8w7xaUlDUlxudQ1YDCWbUm2bMkX+SbtrrTa1WXv9+dy3j+EFss37a6e6+73M6MZtPvsPudZsz/9znnO75wSIYQAERERkYWUmt0AIiIioosxQSEiIiLLYYJCRERElsMEhYiIiCyHCQoRERFZDhMUIiIishwmKERERGQ55WY3IB+qqsLj8aC2thYlJSVmN4eoKAkhEIlE0NraitJSe/R1GDuIzJVL3LBlguLxeNDW1mZ2M4gIgNvtxvLly81uRlYYO4isIZu4YcsEpba2FsD0BTocDpNbQ1ScwuEw2traMt9HO2DsIDJXLnHDlgnKzNCsw+FgkCEymZ1ulTB2EFlDNnHDHjeOiYiIqKjMK0H52c9+hpKSEjz22GOZx5LJJDo7O9HY2IhFixZh06ZN8Pl8s17ncrmwceNG1NTUoKmpCU8++SRkWZ5PU4jIJhg3iCgbeSco3d3d+MUvfoFbbrll1uOPP/443n77bbz55pvYu3cvPB4P7rnnnszziqJg48aNSKfT2L9/P1577TXs2LEDTz/9dP5XQUS2wLhBRFkTeYhEIuLGG28Uu3fvFt/85jfFo48+KoQQIhgMioqKCvHmm29mjj158qQAILq6uoQQQrzzzjuitLRUeL3ezDEvv/yycDgcIpVKZXX+UCgkAIhQKJRP84lIA7l+D82OG/m0mYi0lct3MK8RlM7OTmzcuBHr1q2b9Xhvby8kSZr1+MqVK9He3o6uri4AQFdXF9asWYPm5ubMMRs2bEA4HMbx48cve75UKoVwODzrh4jsxei4ATB2ENlZzlU8b7zxBg4fPozu7u5LnvN6vaisrER9ff2sx5ubm+H1ejPHXBhkZp6fee5ytm/fjmeffTbXpgIA/LE0GhZW5vVaItKGGXEDyD92xFIyykpLUF1RlvNriUgbOY2guN1uPProo3j99ddRXV2tV5susW3bNoRCocyP2+3O+rUuf1zHlhHRXMyKG0D+sUMVAiOBhM6tI6KrySlB6e3txfj4OL70pS+hvLwc5eXl2Lt3L1588UWUl5ejubkZ6XQawWBw1ut8Ph9aWloAAC0tLZfMzp/5feaYi1VVVWXWLch1/QJvKIGUrORwlUSkJbPiBjC/2OHyx7I+loi0l1OC8q1vfQv9/f3o6+vL/Hz5y1/GAw88kPnviooK7NmzJ/OawcFBuFwudHR0AAA6OjrQ39+P8fHxzDG7d++Gw+HAqlWrNLqsz6kC7AkRmciOcQMAXFMcfSUyU05zUGpra7F69epZjy1cuBCNjY2Zxx966CE88cQTaGhogMPhwCOPPIKOjg6sXbsWALB+/XqsWrUKDz74IJ5//nl4vV489dRT6OzsRFVVlUaXNZvLH8cXli7S5b2J6OrsGjcCcQnhpARHdYUu709EV6f5UvcvvPACSktLsWnTJqRSKWzYsAE///nPM8+XlZVh165d2LJlCzo6OrBw4UJs3rwZzz33nNZNyWBPiMjarBg3gOnYsfqaOl3PQUSXVyKEEGY3IlfhcBh1dXUIhUJz3lN+p38Mg94Ifvg7K1DLnhCRZnL5HlpFtm2OJCX84ydDuKmlFneuWWZgC4kKWy5xo2j24mE1DxHlyu2Pw4Z9OKKCUDwJCm/zEFGO4mkFE5GU2c0gKkrFk6CwJ0REeeDoK5E5iiZBiacVTETZEyKi3Dg5+kpkiqJJUIDp+8lERLnwBBOQFdXsZhAVnaJKUDhUS0S5klUBTzBpdjOIik5RJSijAfaEiCh3Ti57T2S4okpQJEVgLMSeEBHlhqOvRMYrqgQFYKAhotxNRFJIpLnpKJGRii5B4Yx8IsqVEOzcEBmt6BKU8UgSSYk9ISLKDRMUImMVXYIiBMuNiSh3TFCIjFV0CQrA2zxElLtwQkIglja7GURFoygTFPaEiCgfjB1ExinKBCWUkBCKS2Y3g4hshgkKkXGKMkEBuPASEeXOHYhDVbnpKJERijZBYU+IiHKVklT4IlzskcgIRZuguP0JCMGeEBHlxsVJ9kSGKNoEJSkp8IVTZjeDiGzGydFXIkMUbYIC8DYPEeXOG0oiLXPTUSK9FXWC4pziRFkiyo2iCowE2Lkh0ltRJyhjoSQkhT0hIsoNR1+J9FfUCYqiCowGEmY3g4hshttlEOmvqBMUgBPeiCh3k9E0oinZ7GYQFbSiT1A4VEtE+WC5MZG+ij5BmYqmEGNPiIhyxM4Nkb6KPkERgoGGiHLHeShE+ir6BAVggkJEuYumZExGudgjkV6YoIA9ISLKDzs3RPphggIgkpQxxZ4QEeWIE2WJ9MME5TPsCRFRrkaDCSgqNx0l0gMTlM8wQSGiXKVlFWMhLvZIpAcmKJ8ZCSSgsidERDnibR4ifTBB+UxaVjEWTprdDCKyGY6+EumDCcoFuLsxEeXKF04hKSlmN4Oo4DBBuQDLjYkoV6oQGAkwdhBpjQnKBbyhFFIye0JElBve5iHSXk4Jyssvv4xbbrkFDocDDocDHR0dePfddzPPJ5NJdHZ2orGxEYsWLcKmTZvg8/lmvYfL5cLGjRtRU1ODpqYmPPnkk5Bla+yFowoBt58z8om0Vuixw8mJskSayylBWb58OX72s5+ht7cXPT09+P3f/3380R/9EY4fPw4AePzxx/H222/jzTffxN69e+HxeHDPPfdkXq8oCjZu3Ih0Oo39+/fjtddew44dO/D0009re1XzwNs8RNor9NgRjEsIJSSzm0FUUEqEEPOqrW1oaMDf/u3f4t5778XSpUuxc+dO3HvvvQCAU6dO4eabb0ZXVxfWrl2Ld999F3fddRc8Hg+am5sBAK+88gp++tOfYmJiApWVlVmdMxwOo66uDqFQCA6H46rHvtM/hkFvJPvrWViJzV+9LuvjiYpVLt/Dy7Fy7IgkJfzjJ0M5Xc+3VzVj9TV1Ob2GqNjkEjfynoOiKAreeOMNxGIxdHR0oLe3F5IkYd26dZljVq5cifb2dnR1dQEAurq6sGbNmkyAAYANGzYgHA5nelKXk0qlEA6HZ/3oxR9LI5xkT8iOWElhD4UaO3ibx56SkoJ59tNJJzknKP39/Vi0aBGqqqrw8MMP46233sKqVavg9XpRWVmJ+vr6Wcc3NzfD6/UCALxe76wAM/P8zHNXsn37dtTV1WV+2tracm12Trjwkv2EkxJ+fWSUgcbCCj12uANx/v9nQ4eG/DiVwyg7GSfnBOWmm25CX18fDh48iC1btmDz5s04ceKEHm3L2LZtG0KhUObH7Xbrej7OQ7GfXmcAY6Ekjnv06yHT/BR67EikFUxEuOmonSQlBf2jIXx6dhKSoprdHLpIea4vqKysxA033AAAuP3229Hd3Y2///u/x3333Yd0Oo1gMDirJ+Tz+dDS0gIAaGlpwaFDh2a938xM/ZljLqeqqgpVVVW5NjVvLv90T6ikpMSwc1L+4mkZx0dDAID95yZxY/MiVJWXmdwqulgxxA6nP44mR7Vh56P5OeIKIi2rSMsqeoYD6PhCo9lNogvMex0UVVWRSqVw++23o6KiAnv27Mk8Nzg4CJfLhY6ODgBAR0cH+vv7MT4+njlm9+7dcDgcWLVq1Xybopl4WsFElD0huzjiCkJSpofWYykF3UMBk1tE2SjE2MHbw/aRllX0uYOZ33udfs4/tJicRlC2bduG7373u2hvb0ckEsHOnTvx0Ucf4f3330ddXR0eeughPPHEE2hoaIDD4cAjjzyCjo4OrF27FgCwfv16rFq1Cg8++CCef/55eL1ePPXUU+js7DS0l5MNtz+Oplr2hKwuKSk4OhKc9dgRVwBrrqlDXU2FOY2iSxRL7PAEE5AVFeVlXAPT6vpHg7Mm1kuKwKdnJvHdNctMbBVdKKcEZXx8HD/4wQ8wNjaGuro63HLLLXj//ffx7W9/GwDwwgsvoLS0FJs2bUIqlcKGDRvw85//PPP6srIy7Nq1C1u2bEFHRwcWLlyIzZs347nnntP2qjTgnIrj9msbzG4GzeHYSAgpafa9Y1kV+OTsBO66pdWkVtHFiiV2yKrAaDCBaxsXmt0UugpZUXHYGbzk8VPeCG5tq0dr/QLjG0WXmPc6KGbQcx2UGRVlJXj4m19gT8jCJEXFP+8bQjx9+fLie29fjraGGoNbVTzmuw6KGfRcB2XGl69bjN+5cWm+TSQDHBsJYs/J8cs+11JXjfvvaOMcRJ0Ysg5KoZMUAU8waXYz6CoGRkNXTE4AYO/pCZZ9kuG4Hoq1qapAz/CV56l5Q0mcHGPZsRUwQbkKbgBmXYoq0Ou8+mTYiUiKZcdkuMloCvG0NfYIokud8kbm3JZg/7lJpGWWHZuNCcpVMEGxrpNjYUSSc/8R2H9ukjtUk6GEADcdtSghBHqc/jmPiyTlrI4jfTFBuYrxSBKJq9xCIHMIIdAznF3wiKUUHBpioCFjOadiZjeBLuPcRBRT0XRWxx52Blh2bDImKFchxPTy1WQtp31RBOLZB44jriCC8eyCEpEWOPpqTd1XmXtyMUkR2HdmUsfW0FyYoMyBCy9ZT3eWoyczFFXgEwYaMlAkKSMQY1JsJc6pGLyh3AofBr0RjAZ5u84sTFDm4GRPyFLOT0Tz2u/k7HiUeyyRoRg7rCXfW717B1kNaBYmKHMIJyTeHrCQXEdPLsSyYzISb/NYhyeYwEggv5EQXziJE2OsBjQDE5QsMNBYg9sfn9faNBORFAZGGWjIGCOBOFSVCbEVzKdjAwD7z06x7NgETFCywIWXrGG+QQZg2TEZJyWp8Ia52KPZxiNJnJ+YX1VVNCVrEn8oN0xQsuAOxHlrwGS+cFKTRDGeVnDwPAMNGYOjr+a72qqxuTjsDMy5wBtpiwlKFlKSCl8494mZpB0t1zLpc7PsmIzBBMVcgVgap33aLFsvqyaWHSvFmRgxQckSF14yz1Q0hXMTUc3eT1EFPmbZMRnAG0py7oKJepwBaDn4fdoXwYgZa2Od+W8g4DT+vCZjgpIl9oTM0z2sbZABgHPjUa5xQ7pTVGHOHzRCJCnhpA7VN4ZXAwbdgLcfGN5n3DktgglKlsbYEzJFKC5h0KvPzqJ7z0ywyoJ0x86NOXqcASg6fL/HwwZuQqqqwJn3p5c1D7qAwLAx57UIJihZUlTBFQVN0OP0Q9WptzIZSWHAE9LlvYlmMEExXjwt4/ioft9tw6oBR7qB6MTnvxfZKAoTlBww0BgrlpJxQueeyv5zU0hKLDsm/UxF04hw0zlDHXEFISn6jY7GUgq6h7SpDrqiZAgY/mT2Y0E34B/S97wWwgQlBy5OlDVUrzMAWedbMIm0goPc7Zh0xs6NcVKygqMjQd3Pc8QVQCiHTUtzdvZ/Ll+9c3HSUsCYoORgMppGLCWb3YyikJQU9Os4RHuho+4gN3YjXXEfKOMcdYeQkvSfLyirAp+cnZj7wHxMngUmTl/+udAoMHVOn/NaDBOUHLEnZIwjrqBhk5Kny451CjREYNwwiqSoOOLS+dbLBc74otpXaSkScHb31Y8pkrkoTFByxGXv9ZeWVfS5g4ae8/xEjGvdkG5iKSWvXbgpNwOjIcTTxs4p+0jr3Y6dnwKJ4NWPCXuKYhSFCUqOOFSrv/7RoCkTVz8+zbJj0g9HUfSlqAK9TuNGT2ZMRDQsO45NAe5D2R1bBHNRmKDkKJqSMRVlT0gvsqLisDNoyrkno2nD5r1Q8WHnRl8nx8KIJM2ZI6hZ2fGZ9wE1y/cJj03PVSlgTFDy4GSg0c2JsTCiJk5E7jrPsmPSx0ggrsvCYQQIIdBj4m7DsZQy//3CvAO5L2df4KMoTFDywJ6QPlRVaLbzaL4SaQUHzk+Z2gYqTJIi4OFij7o4Mx5FQM+S3yz0uYL5lx1LSeDcntxfF/FeudqnADBBycNIIMGekA4GfRFLbGd+bCQEP8uOSQfs3OhDy93O8yXPpxrw/EdAOs//N4Y/geablVkEE5Q8pGUVYyH2hLRk9hDthRRV4BOWHZMOeHtYe0OTMctUSJ0dj+aehIY9wFhf/ieNjgOThTmKwgQlT5yRr61zE1FMRq0zanF+IobhSZYdk7Z84STnOGns0JC1bsnmtNuxqgKn35v/CEiBjqIwQckTh2q11W3y3JPL+Zi7HZPGhID2C3sVMbc/Dk8waXYzZpmIpDAwmmXZsecwEPHN/6TRCWBicP7vYzFMUPLkDaXYE9KIcyoGb8haQQaY3uTtGMuOSWNc7FE73Ra5LXyxrMqOUxFgaK92Jy3AURQmKHlShcBIgPNQtGCFCW5X0sXdjkljvD2sDV84adlkL55WcPD8HHHt7B5A1vC2dmwSGD+p3ftZABOUeXD5OUdhvjzBhKUTvaSkoItlx6ShYFzSdxfcImHljg0A9LmDCMavkID4z+uTTDg/LahRFCYo8+CyaPZuJ1Ydor3QMXeIqweTpjiKMj9T0RTOTUTNbsZVTW9COnmZJ2TgzBybAeYrNgmMn9DnvU3ABGUeAnEJ4SR7QvmaiKQwZINKGVVwt2PSFhOU+ekeDthioODcePTSjqyrC4jr2DEb/nS6OqgAMEGZJ46i5K972G+LIAMAw5NxWyRTZA/uQFzbHXCLSCghYdAbMbsZWdt7YTVg3A+4Duh7wvhUwYyiMEGZJ/aE8hOIpXHaZ58gA3C3Y9JOIq1g3CKLi9lNr9MP1UbJ3WQkhQHPZ9WAZ/4bUA3Ya8xZGKMoTFDmye1nTygfPU57DNFeyB9L4+hI0OxmUIFg5yZ3sZSM49muMWIhXeemkPIMAP4hY04Y9wPjx405l45ySlC2b9+OO+64A7W1tWhqasLdd9+NwcHZi8Mkk0l0dnaisbERixYtwqZNm+DzzV6IxuVyYePGjaipqUFTUxOefPJJyLJ5O9jORzytWGaZZbuIJCWcHLNfkAGAA+f9SKRZdpwrxo5LWbVE1soOuwKQbTiKmUomMNL9trEnLYC5KDklKHv37kVnZycOHDiA3bt3Q5IkrF+/HrHY5/fmH3/8cbz99tt48803sXfvXng8Htxzzz2Z5xVFwcaNG5FOp7F//3689tpr2LFjB55++mntrspg7AnlptcZsO1mi0mJux3ng7HjUmPBBCTF3n9AjJSUFBwbsefCictDPZicmkLCyDWVEgHA12/c+XRQIuZxf2JiYgJNTU3Yu3cvvvGNbyAUCmHp0qXYuXMn7r33XgDAqVOncPPNN6Orqwtr167Fu+++i7vuugsejwfNzc0AgFdeeQU//elPMTExgcrKykvOk0qlkEp9PkoRDofR1taGUCgEh8Nx1Ta+0z+m+4SqaxtrcM+Xlut6jkKRSCv4p33nISn2TFAAoLSkBH+6th2Ni6rMboqpwuEw6urqsvoeXszqsSOSlPCPn+g/HH/Pl67BtY0LdT9PIThwfgpd5+zXOViYnsRq71sogcDimgqsbMntuzIvC+qB//W/gVLrzObIJW7Mq9Wh0HQ229DQAADo7e2FJElYt25d5piVK1eivb0dXV1dAICuri6sWbMmE2AAYMOGDQiHwzh+/PL3zLZv3466urrMT1tb23yarbnRQAIye0JZOeIK2Do5AabLjveeZtnxfDB2TONtnuykZRV97qDZzcidEFjh34cSTMe8QFxCMGHgpqiJIOA9Ztz5NJZ3gqKqKh577DF87Wtfw+rVqwEAXq8XlZWVqK+vn3Vsc3MzvF5v5pgLA8zM8zPPXc62bdsQCoUyP263O99m60JWheU2rLKilKygr0AmmTqn4jhv8YWirIqx43O8PZyd/tGgLed+NUdPYlF6fNZjzimDCyuc+wHVfp8dAJTn+8LOzk4MDAxg3759WrbnsqqqqlBVZe3hdJc/jvbGGrObYWlH3SGkpMIZafrkzCSubVyIstISs5tiK4wdn5uMphBPy6ipzDsUFzxZUXHYGTS7GTkrVxJoC3Vf8ng8rcAXSaHFUW1MQ5Kh6VGU1t825nwaymsEZevWrdi1axc+/PBDLF/++dyLlpYWpNNpBIPBWcf7fD60tLRkjrl4Zv7M7zPH2JGT+/JclaSoOOIKmN0MTbHsOHeMHbMJwVGUuZwYCyOasl+l1rXBAyhXL1/hORKIQzaywsamoyg5JShCCGzduhVvvfUWPvjgA6xYsWLW87fffjsqKiqwZ8+ezGODg4NwuVzo6OgAAHR0dKC/vx/j458Pe+3evRsOhwOrVq2az7WYaiKSsuUQpFEGRkOIF+Dnc+D8FP/ds8DYcWVcjfrKVFWgZ9h+HRtH0oOlsTNXfF5ShLGbpCbDwFifcefTSE7jip2dndi5cyd+85vfoLa2NnPft66uDgsWLEBdXR0eeughPPHEE2hoaIDD4cAjjzyCjo4OrF27FgCwfv16rFq1Cg8++CCef/55eL1ePPXUU+js7LT0UOxcZnpCN7XUmt0Uy1FUgV6n/YJMNlKSiq7zk/j9lc1zH1zEGDuujCMoVzboiyCUsNd+ZyVCwYrA3LcvfeEkmh3VWFBRZkCrADi7gJZbgTL73E7MaQTl5ZdfRigUwu/+7u9i2bJlmZ9/+7d/yxzzwgsv4K677sKmTZvwjW98Ay0tLfjVr36Veb6srAy7du1CWVkZOjo68Kd/+qf4wQ9+gOeee067qzIJA83lnRwLI5K03xBttvpHwpjkbsdXxdhxZZGkDH/MwMoOmxBCoMcGu51frDV8DAuk4JzHqQJwThk4NSAVAcaOGnc+DcxrHRSz5FJHbcQ6KDMcCyrw0NdXzH1gERFC4LX9wwjE7dULylV7Qw023V5ca+HMZx0Us2TbZqPWQZnxeyubcFtbvWHns4Oz41G8fdRjdjNyUiVHcOvYmygV2XfIbm6pRX3NpWv46KJqEfCVLaaOohi2DgrNFk5ICLAnNMuZ8WjBJyfA9OjZOZYdU54M7UnbRLcNR0+uC3yaU3ICAE4j93NLRW01F4UJisZ4m2e2Q0P2CzL5+uT0hG2X8CdzjQQS3Cn7Aq6pOLwhe60ttTg+jMUJV86vmyk7NoyrC1Ds0WlkgqIxJiifG5qMFdVGioG4hD53YU4GJn2lZRXesL3+IOvpkM1GT0pVCdcF9uf9erffwLLjVBTwHDHmXPPEBEVj7kCcPaHPdBfR6MmMg0N+xNOFOyGY9MNl76eNhRJw26yjtzx8GFVK/rd4ZVXAbWTZseuALUZRmKBoLCWp8EXYExoJxDEaNPALZxEpSbXlhmZkPrv9UdaL3W4LL0j7sSw8//1ufKGkcbsdp2PA6GFjzjUPTFB0wIWX7DnBTSsDo+GiurVF2hgLJZGSi3vRv4lICkOTNpowLASuD3y+GeC83goGT5Z2HwBkaxd1MEHRgbPIe0K+cBLDk8X7GXC3Y8qHKgxeXdSCuof9sNPCF0tjp1GbuvxGlfkIxCUE4gYlDek44LH2KAoTFB14Q0mk5cLZFC9Xdhui1YPbH8fZcZYdU26KeZJ9MJ7GGZ99vjNlShLXBg9q/r4uI8uOXdYeRWGCogNFFUU5/wIApqIprgfymU/OsOyYclPM81C6hwNQbTR8cm3wIMpV7ecbxtOKcRVdUgIY7THmXHlggqKTYl14qXs4YKshWj0F41LB7eBM+pqKphFJWr+6QmuRpISTY2Gzm5G1RSkvmmKndXv/kUDCuLJj90FAtuacOSYoOinGnlAoIRm2rYBdsOyYclWMt3l6nQH7jDYKFdf79wEaTIy9ElkVcPsNGoWXksCINUdRmKDoZDKaRjRVXH+Yep1+Ww3RGiEtq9h/lmXHlL1i69wk0goGRkNmNyNryyIDqJH0n2fnCyeN69yMHJpOVCyGCYqOiqncOJaScXzUPkO0RhrwhDDOtXEoS4ZOkrSAI64AJMUe11spR7E81GvIuQQMrAiVkpaci8IERUfFNFR72BWAbJchWoMJAewdZNkxZSeWUjAZtW5lhZZSsoK+kaDZzcjadcEulAnj5ggFjSw7dltvFIUJio6KZag2KSk4NmKfIVozjAQSODvO+TmUHZe/OCbZHxsJISXZY0mG+oQLDfEhw8/rnIobc+tcTk3f6rEQJig6iqZkTEatOTtaS33uYFGv+5Ktj09PQlb4OdHcimH0VVJUHHbao8qtVJWxIvCpKedOSAp8RpUdj3RPlx5bBBMUnRV6oEnLKvrcQbObYQuhhIT3j/uQNGq/DbKt0UCi4JPZ454w4ml7fBeuCR9BlWzeCKg7kIA/ZkBnV05P3+qxCCYoOiv0ibL9oyEkbBJkrOC0L4J/OeAsmtt/lB9JERgLWWs+gJYUVaDHJvt1VUtBtEaOmtoGRRUY9EVxdiKq//oooz3Ty+BbABMUnY0GE/ap78+RbKMhWiuJJGX8x+ERfHyaK83SlRXy6OspbxiRpD2WYbg+sA8lwhqjWRORFPpHQgjruZifnLbMXBQmKDpLyyrGQta5p6elE2PholvrRStCTC9OtfOQqyjmKVHuCjVBEUKgZ9geHZslsTNwJD1mN2OWpKzihCcMl1/HybMj1hhFYYJigEK8zaOq9gkyVjYZSeFfD7rQ6wwU1doXNDdfOFmQ85XOjEfhj1m/jLpMTeHa4AGzm3FZAtOj8wOekD6LuSkS4Db/2pmgGKAQe0KDvghCieLbM0QPsirw8ekJ/MfhUX2HbslWhCjMpQq6bTL3pD3YjQrF2qPfsZSC/tEQxkIJCK2X3h89DKTNLXdngmIAXzhVUD2h6SFaewQZO3H74/iXA06c8nJFXppWaJ2bockYxsPWv6W5MDWO5uhJs5uRFVUAw1NxnBqLIK1l5ZciAS5zR1GYoBhAFQIjgcIJNOcmYkWz0qXRUpKKd/u9eKd/rKCSWsqPs8BuD3cP2aBjIwSuD+i7GaAeggkJR91BTGlZjuw5DKSi2r1fjpigGKSQekIcPdHfoJflyDS9dk4oXhi3/UYCcYwGrX3LBACaoyewMD1pdjPyIqsCp31RnB2PaFOOrMimzkVhgmKQQpko65qKF/T6DFYyU4780eB4wS/aRVdWKJ0bO8w9qVDiaA91m92MeZuIpnFsJKTNPEHPESBlziJ1TFAMEohLBTGp9JANgkwhEQI44griXw+5uCNykXIWwL484+Ekhietn2hdGziAMrUwbl+nZBUnx8Jw+mPzK0dWZNPmojBBMZDdh+vHQgnbX4NdTUbTeOOQGz3DfpYjFxm3P2H7f3M7dGzqkiNYEj9rdjM0JQB4gkkMjM6zHNnTZ8ooChMUA9l9wtshO0xwK2CKKvDJmUn8snekIEbjKDtJSYHPBpUvV+KPpXF23LyJltkoETJW+M3ZDNAIsfR0ObIn33JkVQacXdo3bA5MUAzkDsRt2xOaiKQwNGn/oeZCMBJI4F8OOHHCw3LkYmHneSjdw35YPey1ho+iWg6Z3QxdqWK6k3xyLIKUnEeF4NhRIGlszGGCYqBEWsFExH49oVhKxr6zE5YPMsUkLat4/7gX/3WM5ci6UNJYHB9GuWKNeT92TVCGJmM4NWbeLsDZqE+4cE24z+xmGCaUkHBsJJT7FhuqDLiMHUUpN/RsBKc/jiZHtdnNyEownkbPcAAnx8KQuamdJZ32ReAJJrD+i824tnGh2c0pHHIKN03+NwAgUVGPSGUzIlUtiFQ1I1lRb3hzxoIJSIqKijLr9ylVVeDMeBQ9Tr91F2UTKhrj53FN+ChqpCmzW2M4+bN/o0A8jRVLFqK8NMv/r8aOAu1rgeo6fRv4GSYoBnNNxXHHdQ1mN+OqfOEkuof9ODse5aiJDURTMt46Mopb2+rx9RuW2OKPmJ0skIJYIAXRFBsEAEhlCzIJS7iqBfHKRoiSMl3bIKsCo4EErlti3SRUUqY3set1Biw7R6pUlbE0NojWyDFUydYe2THCZDSNSFLGF5YuQt2CirlfoCqAcz9w03f1bxyYoBjOE0xAVlSUW/CPiHMqhu7hACt1bEgIoM8VhNsfx3e+2GKbUTo7qlASaEgMoyExDABQS8oRrVyaSViiVc1QSis1P6/LH7dkgpKUFBx1B9HnDiKetubtxjIliZboCbREjqNCtf5icUaaKUduqatGe0MNSktKrv4Cbz/Q3gEsqNe9bUxQDCarAqPBhGWG420xHEtZm4qm8Ua3G2uvb8Qd1y1GyVzBhuatVMhwpMbgSI3hGgBACeIVizMJS6SqBenyRfM+j9NiHYdIUsJhVxADoyGkZWsuJFgpR7Es0o+m6CmUCWuO6liBADAWSiKUkHBD0yIsrLxKajAzirLyTt3bxQTFBC5/3PQERVZUnBibHo4NFshS2jRNUQU+PTuJ4ckYNnyxBXU1WQzdkoYEaiQ/aiQ/mqMnAACpskWZOSyRqmbEKxqBHJPHqWgKsZSMhVXmhm1/LI2eYT9OeSNQLDo3bYEUQGv4KJbEz6JEWDN5sqJ4WsHAaAhti2uwrL4aJbjC/6PefuDaDmDBYl3bk/N9ho8//hh/8Ad/gNbWVpSUlODXv/71rOeFEHj66aexbNkyLFiwAOvWrcOZM2dmHeP3+/HAAw/A4XCgvr4eDz30EKJRa9fJa8nMGflJScGhIT/++dMh7Dk5zuSkgI0GE/iXg04MjJpfPlnscaNKiWJJ/CxWBD7FLd5f4Y7R17By/B1cEzoMR3IUperci2gJMb1UgVk8wQT+86gH/69rGMc9YUsmJ4tSXtw08T5uHfsllsZOMznJgyqmR+tOeMJXLkcW6vQois5yTlBisRhuvfVWvPTSS5d9/vnnn8eLL76IV155BQcPHsTChQuxYcMGJJOfl+s98MADOH78OHbv3o1du3bh448/xo9+9KP8r8JmJiKp+a3ql4doSsbHpyfwT/uG8OnZScRS1rxXTNpKyyp2n/Dh7aMeJEycH8C4MVuZmkZ9cgRtoR6sGv8v3DG6A6u9b+HaQBca4kOoUC6fiJix2OPQZAz/3uPGv3W7cc6KE+eFQH3CiS/6/hOrff+JxQkn7LYTsRWFkzKOjYQwcaVyZO8AENd38c4SMY+Vw0pKSvDWW2/h7rvvBjDdC2ptbcWPf/xj/MVf/AUAIBQKobm5GTt27MD999+PkydPYtWqVeju7saXv/xlAMB7772HO++8EyMjI2htbZ3zvOFwGHV1dQiFQnA4HFc99p3+MQx6rTdb+841y3BTS63u57HDcCwZY2FVGb69qgUrNJpomcv38EJmxY1c2hwJ+THwy/+b9TXpIVnu+Oy20PRclmRFPWqry/HD37le93OrqsCgL4IeZwCTVl27SahYEj+L1vBR1EgBs1tT0BoXVuL6pZcpR25ZDdz8Bzm9Vy5xQ9ObmUNDQ/B6vVi3bl3msbq6OnzlK19BV1cX7r//fnR1daG+vj4TZABg3bp1KC0txcGDB/G9733vkvdNpVJIpT7/koTD9l9B0+WP65qgjIUS6B4O4PyEBXs8ZIpYSsGvj4zi1rY6/M6NSy1TjqxX3ADsHTuq5TCq5TCWxk4DAOTSakSqmhEevA2OlhVA7TKgVNvyZklRMTAawmFXEGELlwo3xU5hWeQYqmR73OKzu6nYZ+XITQtRv+CCCjXfCeDarwE1+iydoWmC4vV6AQDNzc2zHm9ubs485/V60dTUNLsR5eVoaGjIHHOx7du349lnn9WyqaZzTumzbPzQZAw9w36MBFhKR5d31B2C25/Ad1a3oNkC5ch6xQ2gsGJHuZrE4oQTsZMTcHgWAKXlQG0LULccqGsD6q4BKhbk9d6JtII+dxBHR4Km3gq8mnIliZbocbREjqNctcYKv8Ukrag4NRZBc101rp0pRxYqMLwPWPWHupzTFlU827ZtwxNPPJH5PRwOo62tzcQWzV8kKSMQS2Pxwvmvl2CL4ViyFH9senfkr1zfgP91XQNKSwuzHLkQY0coIWFZ3YLppcdDI9M/ODBdFVTT+HmyUrd8ziqLcFJCrzOAE56wpUuFWyNH0RQdRKkwdu4ezSYAeENJhBMSbli6aLqibPzk9CjKwkbNz6dpgtLS0gIA8Pl8WLZsWeZxn8+H2267LXPM+Pj4rNfJsgy/3595/cWqqqpQVVWlZVMtwemPzytBkRQVxz9budGqw7FkXaoQ6Do3BefUdDlyfY32i4tlQ6+4ARRm7AgnZQghLl3jRgggNjn94zky/VjVos9HWBzXAIuagdJSTEZT6BkOYNAbgWrRe8A16Sm0Ro6iMXYOJZz0ainxtIIBTwjLF9egtb4aJc59wKo/0vw8miYoK1asQEtLC/bs2ZMJLOFwGAcPHsSWLVsAAB0dHQgGg+jt7cXtt98OAPjggw+gqiq+8pWvaNkcy3P547itrT7n1yWl6eHYPrd1h2PJPjzBJF4/6MI3blyKNcuN2WPjQowbuVFUgUhKhqM6i/VtUlFg/NT0D4BQGjiXqsP59GKEq5qBymag1Frr5NQmx3BNuA/1SbfZTaGrUMX037BgPI0vyAOovvZrwMIlmp4j5wQlGo3i7Nmzmd+HhobQ19eHhoYGtLe347HHHsNf//Vf48Ybb8SKFSvwV3/1V2htbc3M2L/55pvxne98B3/2Z3+GV155BZIkYevWrbj//vuznolfKEYCcaiqyHp4PZyUcNgZwHELD8eSPaVlFf9z0ofzk1Fs+GILqiu0nXzJuKGtUELKLkEBICAQiEvwBBOIJGUAU7gGwDUABEoQr2z8bAG5FoQrWyCVm7CIpBBYnHCiNXIUtSmf8eenvE2XIwex7MhutH39+5q+d84JSk9PD37v934v8/vM/d3Nmzdjx44d+MlPfoJYLIYf/ehHCAaD+PrXv4733nsP1dWfT8Z7/fXXsXXrVnzrW99CaWkpNm3ahBdffFGDy7GXlKTCG06itf7qE9tmhmNP+1gqTPo6PxHDaDCBLyyd/9LsF2Lc0FYoIaFtjkU8VSEwGU1hLJS84h45JRBYmJ7EwvQkWiLHAQCp8s9Wva2cLm9OVCzOedXbbJUIBUtiZ9EaOYoFUlCXc5D+FFVg1D0ErWd3zWsdFLMUwjooMzq+0Ii1119+ctFoMIGeYT+GJmMsFSbD/OFtrVklKPmug2ImO62DcjUlAL583eJL16XA9B8LXyQJbyiJlAYjrXJpFaKVTRdshrgUomR+swNKVQnN0ZNYFulHpaJPRSMZq6SmAWu//3/mPM60dVAody5/fFaCIoTA+ckYeocDGA2yVJiILiUwPbTecMHEZklR4Q0l4Q0nIWs40lquplCfdGfmhIiSUsQqlmQ2QoxUtUAuy65cvVxJYFlkAM3REyhXWXFIV8cExWTeUBJpWUVZaQkGvRH0Ov2YjKbNbhYRWVwoLqGhphJJScFYKImJSBKKASOtJULFovQ4FqXHgcgxAECioh6RyubMhojJivpZr6mSw2gNH8PS2GmWClPWCj5Baa1fAKsv8eANJeEOxBFJSlhaW4WltYVVFkn2UmvybrlWUFFRierla8xuxlWlKsrhq6mEJ5gAHAIVDsCsepxqANNTYvwA/FBKFiBR04pkTQuqEuNYFD6HkvoyoP5mk1pIeiuv1n5l9IKPRLe11QN5lPIarb2xxuwmENFnqmsW4bc3bDa7GVlpnvsQk90A4KtmN4JsyBqbcRARERFdgAkKERERWQ4TFCIiIrIcJihERERkOUxQiIiIyHJsWcUzs/htOBw2uSVExWvm+2enxagZO4jMlUvcsGWCEolML13f1qb1yv9ElKtIJIK6OuN3Qc4HYweRNWQTN2y5F4+qqvB4PKitrUXJVTaxCofDaGtrg9vtts1eIVbEz1EbhfY5CiEQiUTQ2tqK0svsCWNFjB3G4ueojUL6HHOJG7YcQSktLcXy5cuzPt7hcNj+H9UK+Dlqo5A+R7uMnMxg7DAHP0dtFMrnmG3csEe3h4iIiIoKExQiIiKynIJOUKqqqvDMM8+gqoqb780HP0dt8HO0D/5baYOfozaK9XO05SRZIiIiKmwFPYJCRERE9sQEhYiIiCyHCQoRERFZDhMUIiIishwmKERERGQ5lk9Qtm/fjjvuuAO1tbVoamrC3XffjcHBwVnHJJNJdHZ2orGxEYsWLcKmTZvg8/lmHeNyubBx40bU1NSgqakJTz75JGRZnnXMRx99hC996UuoqqrCDTfcgB07duh9eYYx6nP81a9+hW9/+9tYunQpHA4HOjo68P777xtyjUYw8v/HGZ9++inKy8tx22236XVZBYdxQxuMG9pg3MiTsLgNGzaIV199VQwMDIi+vj5x5513ivb2dhGNRjPHPPzww6KtrU3s2bNH9PT0iLVr14qvfvWrmedlWRarV68W69atE0eOHBHvvPOOWLJkidi2bVvmmPPnz4uamhrxxBNPiBMnToh/+Id/EGVlZeK9994z9Hr1YtTn+Oijj4q/+Zu/EYcOHRKnT58W27ZtExUVFeLw4cOGXq9ejPocZwQCAXH99deL9evXi1tvvdWISywIjBvaYNzQBuNGfiyfoFxsfHxcABB79+4VQggRDAZFRUWFePPNNzPHnDx5UgAQXV1dQggh3nnnHVFaWiq8Xm/mmJdfflk4HA6RSqWEEEL85Cc/EV/84hdnneu+++4TGzZs0PuSTKHX53g5q1atEs8++6xOV2IuvT/H++67Tzz11FPimWeesXWgMRvjhjYYN7TBuJEdy9/iuVgoFAIANDQ0AAB6e3shSRLWrVuXOWblypVob29HV1cXAKCrqwtr1qxBc3Nz5pgNGzYgHA7j+PHjmWMufI+ZY2beo9Do9TleTFVVRCKRzHkKjZ6f46uvvorz58/jmWeeMeJSChrjhjYYN7TBuJEdW+1mrKoqHnvsMXzta1/D6tWrAQBerxeVlZWor6+fdWxzczO8Xm/mmAv/UWeen3nuaseEw2EkEgksWLBAj0syhZ6f48X+7u/+DtFoFH/8x3+s8VWYT8/P8cyZM/jLv/xLfPLJJygvt9XX1HIYN7TBuKENxo3s2eoKOjs7MTAwgH379pndFFsz6nPcuXMnnn32WfzmN79BU1OTrucyg16fo6Io+JM/+RM8++yz+K3f+i1N37sYMW5og3FDG4wb2bPNLZ6tW7di165d+PDDD7F8+fLM4y0tLUin0wgGg7OO9/l8aGlpyRxz8Wzomd/nOsbhcBRUL0jvz3HGG2+8gR/+8If493//90uGwAuBnp9jJBJBT08Ptm7divLycpSXl+O5557D0aNHUV5ejg8++EDfiysgjBvaYNzQBuNGjsyeBDMXVVVFZ2enaG1tFadPn77k+ZnJRb/85S8zj506deqyk4t8Pl/mmF/84hfC4XCIZDIphJie7LZ69epZ7/3973+/YCa7GfU5CiHEzp07RXV1tfj1r3+t4xWZw4jPUVEU0d/fP+tny5Yt4qabbhL9/f2zZv7T5TFuaINxQxuMG/mxfIKyZcsWUVdXJz766CMxNjaW+YnH45ljHn74YdHe3i4++OAD0dPTIzo6OkRHR0fm+ZnyrPXr14u+vj7x3nvviaVLl162XPDJJ58UJ0+eFC+99FJBlQsa9Tm+/vrrory8XLz00kuzzhMMBg29Xr0Y9TlezO6z8Y3GuKENxg1tMG7kx/IJCoDL/rz66quZYxKJhPjzP/9zsXjxYlFTUyO+973vibGxsVnvMzw8LL773e+KBQsWiCVLlogf//jHQpKkWcd8+OGH4rbbbhOVlZXi+uuvn3UOuzPqc/zmN7952fNs3rzZoCvVl5H/P17I7oHGaIwb2mDc0AbjRn5KhBBC25tGRERERPNjm0myREREVDyYoBAREZHlMEEhIiIiy2GCQkRERJbDBIWIiIgshwkKERERWQ4TFCIiIrIcJihERERkOUxQiIiIyHKYoBAREZHlMEEhIiIiy/n/zgrkFsIwf6MAAAAASUVORK5CYII=\n"
-          },
-          "metadata": {}
-        }
-      ]
-    }
-  ]
-}
\ No newline at end of file