house-rental-prediction-machine-learning.ipynb

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      "provenance": [],
      "collapsed_sections": [
        "sk87IEuv16Lx",
        "IFoMpxkV2Fiz"
      ]
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Nama : Irbah Labibah Nur Saidah"
      ],
      "metadata": {
        "id": "r7VpXuptPoEa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dataset ini diambil dari : https://www.kaggle.com/datasets/iamsouravbanerjee/house-rent-prediction-dataset"
      ],
      "metadata": {
        "id": "d39W6lcQmJjo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Collection"
      ],
      "metadata": {
        "id": "mJa6DLqPybsZ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 304,
      "metadata": {
        "id": "jmBsD_36HG1Q"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!gdown --id \"11vCKuk81QvTQcwibMX7Wo6k4q0HDzLOz\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U7TAX7JnqGNz",
        "outputId": "103dc2d8-0dd4-426b-9a7d-78ecdbccb439"
      },
      "execution_count": 305,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/gdown/cli.py:131: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  category=FutureWarning,\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=11vCKuk81QvTQcwibMX7Wo6k4q0HDzLOz\n",
            "To: /content/House_Rent_Dataset.csv\n",
            "100% 567k/567k [00:00<00:00, 104MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/House_Rent_Dataset.csv\")\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "mgQaz9H8qUMc",
        "outputId": "ae32508f-2280-433c-a016-6e1930f7c3a4"
      },
      "execution_count": 306,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Posted On  BHK   Rent  Size            Floor    Area Type  \\\n",
              "0  2022-05-18    2  10000  1100  Ground out of 2   Super Area   \n",
              "1  2022-05-13    2  20000   800       1 out of 3   Super Area   \n",
              "2  2022-05-16    2  17000  1000       1 out of 3   Super Area   \n",
              "3  2022-07-04    2  10000   800       1 out of 2   Super Area   \n",
              "4  2022-05-09    2   7500   850       1 out of 2  Carpet Area   \n",
              "\n",
              "              Area Locality     City Furnishing Status  Tenant Preferred  \\\n",
              "0                    Bandel  Kolkata       Unfurnished  Bachelors/Family   \n",
              "1  Phool Bagan, Kankurgachi  Kolkata    Semi-Furnished  Bachelors/Family   \n",
              "2   Salt Lake City Sector 2  Kolkata    Semi-Furnished  Bachelors/Family   \n",
              "3               Dumdum Park  Kolkata       Unfurnished  Bachelors/Family   \n",
              "4             South Dum Dum  Kolkata       Unfurnished         Bachelors   \n",
              "\n",
              "   Bathroom Point of Contact  \n",
              "0         2    Contact Owner  \n",
              "1         1    Contact Owner  \n",
              "2         1    Contact Owner  \n",
              "3         1    Contact Owner  \n",
              "4         1    Contact Owner  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-36c1522d-cf63-4d6d-b98c-f6fe3cc137f3\">\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Posted On</th>\n",
              "      <th>BHK</th>\n",
              "      <th>Rent</th>\n",
              "      <th>Size</th>\n",
              "      <th>Floor</th>\n",
              "      <th>Area Type</th>\n",
              "      <th>Area Locality</th>\n",
              "      <th>City</th>\n",
              "      <th>Furnishing Status</th>\n",
              "      <th>Tenant Preferred</th>\n",
              "      <th>Bathroom</th>\n",
              "      <th>Point of Contact</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2022-05-18</td>\n",
              "      <td>2</td>\n",
              "      <td>10000</td>\n",
              "      <td>1100</td>\n",
              "      <td>Ground out of 2</td>\n",
              "      <td>Super Area</td>\n",
              "      <td>Bandel</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Unfurnished</td>\n",
              "      <td>Bachelors/Family</td>\n",
              "      <td>2</td>\n",
              "      <td>Contact Owner</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2022-05-13</td>\n",
              "      <td>2</td>\n",
              "      <td>20000</td>\n",
              "      <td>800</td>\n",
              "      <td>1 out of 3</td>\n",
              "      <td>Super Area</td>\n",
              "      <td>Phool Bagan, Kankurgachi</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Semi-Furnished</td>\n",
              "      <td>Bachelors/Family</td>\n",
              "      <td>1</td>\n",
              "      <td>Contact Owner</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2022-05-16</td>\n",
              "      <td>2</td>\n",
              "      <td>17000</td>\n",
              "      <td>1000</td>\n",
              "      <td>1 out of 3</td>\n",
              "      <td>Super Area</td>\n",
              "      <td>Salt Lake City Sector 2</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Semi-Furnished</td>\n",
              "      <td>Bachelors/Family</td>\n",
              "      <td>1</td>\n",
              "      <td>Contact Owner</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2022-07-04</td>\n",
              "      <td>2</td>\n",
              "      <td>10000</td>\n",
              "      <td>800</td>\n",
              "      <td>1 out of 2</td>\n",
              "      <td>Super Area</td>\n",
              "      <td>Dumdum Park</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Unfurnished</td>\n",
              "      <td>Bachelors/Family</td>\n",
              "      <td>1</td>\n",
              "      <td>Contact Owner</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2022-05-09</td>\n",
              "      <td>2</td>\n",
              "      <td>7500</td>\n",
              "      <td>850</td>\n",
              "      <td>1 out of 2</td>\n",
              "      <td>Carpet Area</td>\n",
              "      <td>South Dum Dum</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Unfurnished</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>1</td>\n",
              "      <td>Contact Owner</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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          },
          "metadata": {},
          "execution_count": 306
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Understanding & Removing Outlier"
      ],
      "metadata": {
        "id": "Rp-ZcVIgypZP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vx579kTCFik2",
        "outputId": "2f4813f0-3c96-460c-e589-bdb5edd9e932"
      },
      "execution_count": 307,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4746, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 307
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7d2NdGt4qkg9",
        "outputId": "ac8d9547-2f6f-4657-a9e3-4369917488aa"
      },
      "execution_count": 308,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4746 entries, 0 to 4745\n",
            "Data columns (total 12 columns):\n",
            " #   Column             Non-Null Count  Dtype \n",
            "---  ------             --------------  ----- \n",
            " 0   Posted On          4746 non-null   object\n",
            " 1   BHK                4746 non-null   int64 \n",
            " 2   Rent               4746 non-null   int64 \n",
            " 3   Size               4746 non-null   int64 \n",
            " 4   Floor              4746 non-null   object\n",
            " 5   Area Type          4746 non-null   object\n",
            " 6   Area Locality      4746 non-null   object\n",
            " 7   City               4746 non-null   object\n",
            " 8   Furnishing Status  4746 non-null   object\n",
            " 9   Tenant Preferred   4746 non-null   object\n",
            " 10  Bathroom           4746 non-null   int64 \n",
            " 11  Point of Contact   4746 non-null   object\n",
            "dtypes: int64(4), object(8)\n",
            "memory usage: 445.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fitur Point of Contract dan Posted On tidak mempengaruhi harga sewa model sehingga akan didrop\n",
        "df = df.drop(['Posted On', 'Point of Contact'], axis = 'columns')"
      ],
      "metadata": {
        "id": "UYg9hF3JLTbw"
      },
      "execution_count": 309,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Univariate Analysis"
      ],
      "metadata": {
        "id": "8QdPc_EEy4nh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('Area Type')['Area Type'].agg('count')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZPOC9oZFG-7u",
        "outputId": "29097c30-57ff-4afc-f587-8bff478d107b"
      },
      "execution_count": 310,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Area Type\n",
              "Built Area        2\n",
              "Carpet Area    2298\n",
              "Super Area     2446\n",
              "Name: Area Type, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 310
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fitur Area Type hanya terdapat 2 sample Built Area sehingga 2 sample tersebut akan dihapus\n",
        "df.drop(df.index[df['Area Type'] == 'Built Area'], inplace = True)"
      ],
      "metadata": {
        "id": "I_hp5YluNPJQ"
      },
      "execution_count": 311,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('Area Type')['Area Type'].agg('count')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sX8Cc1CuNdqL",
        "outputId": "8a80288d-4b08-482f-99d7-8c3907145d32"
      },
      "execution_count": 312,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Area Type\n",
              "Carpet Area    2298\n",
              "Super Area     2446\n",
              "Name: Area Type, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 312
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('City')['City'].agg('count')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bzITYYrnImLM",
        "outputId": "0a61442c-b363-4ffe-f984-16628b7647e4"
      },
      "execution_count": 313,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "City\n",
              "Bangalore    886\n",
              "Chennai      890\n",
              "Delhi        605\n",
              "Hyderabad    867\n",
              "Kolkata      524\n",
              "Mumbai       972\n",
              "Name: City, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 313
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('Furnishing Status')['Furnishing Status'].agg('count')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FHiXjDNBI4G8",
        "outputId": "26ab7ff7-3043-41c9-b367-e94f881e0f66"
      },
      "execution_count": 314,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Furnishing Status\n",
              "Furnished          679\n",
              "Semi-Furnished    2251\n",
              "Unfurnished       1814\n",
              "Name: Furnishing Status, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 314
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('Tenant Preferred')['Tenant Preferred'].agg('count')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qs7S6SNiJSNy",
        "outputId": "43a4ae1d-e246-49b0-aa6b-8c4435145030"
      },
      "execution_count": 315,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Tenant Preferred\n",
              "Bachelors            830\n",
              "Bachelors/Family    3442\n",
              "Family               472\n",
              "Name: Tenant Preferred, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 315
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('Floor')['Floor'].agg('count')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "igtzzxBlJIZY",
        "outputId": "53b587e7-4a37-4734-df3d-579acdf62e11"
      },
      "execution_count": 316,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Floor\n",
              "1                             2\n",
              "1 out of 1                  134\n",
              "1 out of 10                   4\n",
              "1 out of 11                   1\n",
              "1 out of 12                   2\n",
              "                           ... \n",
              "Upper Basement out of 4       3\n",
              "Upper Basement out of 40      1\n",
              "Upper Basement out of 5       1\n",
              "Upper Basement out of 7       2\n",
              "Upper Basement out of 9       2\n",
              "Name: Floor, Length: 480, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 316
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('Area Locality')['Area Locality'].agg('count')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oMmJ1tWWId8e",
        "outputId": "a3ae25f5-f293-4b8f-f8c3-ae62c11f6457"
      },
      "execution_count": 317,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Area Locality\n",
              " Beeramguda, Ramachandra Puram, NH 9     1\n",
              " in Boduppal, NH 2 2                     1\n",
              " in Erragadda, NH 9                      1\n",
              " in Miyapur, NH 9                        1\n",
              "117 Residency, Chembur East              1\n",
              "                                        ..\n",
              "vanamali chs ghatla, Ghatla              1\n",
              "venkatapuram                             1\n",
              "venkatesa perumal nagar                  1\n",
              "villvam towers tnhb colony               1\n",
              "whitefield                              12\n",
              "Name: Area Locality, Length: 2234, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 317
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fitur Floor dan Area Locality memiliki banyak sekali nilai unique sehingga akan di drop\n",
        "df = df.drop(['Floor', 'Area Locality'], axis = 'columns')"
      ],
      "metadata": {
        "id": "gN5-XsNXMBBE"
      },
      "execution_count": 318,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "QfJipWtDMOXZ",
        "outputId": "61976518-f0e3-4d22-889a-56afe7fa191f"
      },
      "execution_count": 319,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   BHK   Rent  Size    Area Type     City Furnishing Status  Tenant Preferred  \\\n",
              "0    2  10000  1100   Super Area  Kolkata       Unfurnished  Bachelors/Family   \n",
              "1    2  20000   800   Super Area  Kolkata    Semi-Furnished  Bachelors/Family   \n",
              "2    2  17000  1000   Super Area  Kolkata    Semi-Furnished  Bachelors/Family   \n",
              "3    2  10000   800   Super Area  Kolkata       Unfurnished  Bachelors/Family   \n",
              "4    2   7500   850  Carpet Area  Kolkata       Unfurnished         Bachelors   \n",
              "\n",
              "   Bathroom  \n",
              "0         2  \n",
              "1         1  \n",
              "2         1  \n",
              "3         1  \n",
              "4         1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-04127d1d-c570-49a8-a8ff-97649251b520\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>BHK</th>\n",
              "      <th>Rent</th>\n",
              "      <th>Size</th>\n",
              "      <th>Area Type</th>\n",
              "      <th>City</th>\n",
              "      <th>Furnishing Status</th>\n",
              "      <th>Tenant Preferred</th>\n",
              "      <th>Bathroom</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>10000</td>\n",
              "      <td>1100</td>\n",
              "      <td>Super Area</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Unfurnished</td>\n",
              "      <td>Bachelors/Family</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>20000</td>\n",
              "      <td>800</td>\n",
              "      <td>Super Area</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Semi-Furnished</td>\n",
              "      <td>Bachelors/Family</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>17000</td>\n",
              "      <td>1000</td>\n",
              "      <td>Super Area</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Semi-Furnished</td>\n",
              "      <td>Bachelors/Family</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>10000</td>\n",
              "      <td>800</td>\n",
              "      <td>Super Area</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Unfurnished</td>\n",
              "      <td>Bachelors/Family</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>7500</td>\n",
              "      <td>850</td>\n",
              "      <td>Carpet Area</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Unfurnished</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-04127d1d-c570-49a8-a8ff-97649251b520')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-04127d1d-c570-49a8-a8ff-97649251b520 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-04127d1d-c570-49a8-a8ff-97649251b520');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 319
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.hist(bins=50, figsize=(10,10))\n",
        "plt.ticklabel_format(useOffset=False, style='plain')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "id": "PU3N-07GB1T8",
        "outputId": "62c1c9ed-72cf-49b2-e002-be3da33f0f23"
      },
      "execution_count": 320,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Rent.describe().apply(lambda x: format(x, 'f'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A4E1OwrsLS_8",
        "outputId": "03f4ce17-be51-4f76-9da2-5a2683c419fd"
      },
      "execution_count": 321,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count       4744.000000\n",
              "mean       35003.777403\n",
              "std        78121.204493\n",
              "min         1200.000000\n",
              "25%        10000.000000\n",
              "50%        16000.000000\n",
              "75%        33000.000000\n",
              "max      3500000.000000\n",
              "Name: Rent, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 321
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Multivariate Analysis"
      ],
      "metadata": {
        "id": "YULDdMQazW8L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Menambahkan fitur baru price per sqft\n",
        "df['Price_per_sqft'] = df['Rent']*1000/df['Size']"
      ],
      "metadata": {
        "id": "HYX1ItlZg9qo"
      },
      "execution_count": 322,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Lirh6S03heaU",
        "outputId": "c52bec96-f5e4-480f-b415-7a9056df28aa"
      },
      "execution_count": 323,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   BHK   Rent  Size    Area Type     City Furnishing Status  Tenant Preferred  \\\n",
              "0    2  10000  1100   Super Area  Kolkata       Unfurnished  Bachelors/Family   \n",
              "1    2  20000   800   Super Area  Kolkata    Semi-Furnished  Bachelors/Family   \n",
              "2    2  17000  1000   Super Area  Kolkata    Semi-Furnished  Bachelors/Family   \n",
              "3    2  10000   800   Super Area  Kolkata       Unfurnished  Bachelors/Family   \n",
              "4    2   7500   850  Carpet Area  Kolkata       Unfurnished         Bachelors   \n",
              "\n",
              "   Bathroom  Price_per_sqft  \n",
              "0         2     9090.909091  \n",
              "1         1    25000.000000  \n",
              "2         1    17000.000000  \n",
              "3         1    12500.000000  \n",
              "4         1     8823.529412  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7aa3f850-32df-4295-a2a5-41c306ffff77\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>BHK</th>\n",
              "      <th>Rent</th>\n",
              "      <th>Size</th>\n",
              "      <th>Area Type</th>\n",
              "      <th>City</th>\n",
              "      <th>Furnishing Status</th>\n",
              "      <th>Tenant Preferred</th>\n",
              "      <th>Bathroom</th>\n",
              "      <th>Price_per_sqft</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
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              "      <td>25000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>17000</td>\n",
              "      <td>1000</td>\n",
              "      <td>Super Area</td>\n",
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              "      <td>17000.000000</td>\n",
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              "      <th>3</th>\n",
              "      <td>2</td>\n",
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              "      <td>12500.000000</td>\n",
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              "      <th>4</th>\n",
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    {
      "cell_type": "code",
      "source": [
        "# Mendeteksi size per BHK outlier\n",
        "# 100 sqft untuk 1 BHK itu tidak biasa sehingga anggap saja batasan tresholdnya 300 sqft/bhk\n",
        "\n",
        "df[(df.Size/df.BHK) < 300].head()"
      ],
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        "id": "DNhLyqOxW2Mx",
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      "execution_count": 324,
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              "    BHK  Rent  Size    Area Type     City Furnishing Status  Tenant Preferred  \\\n",
              "7     1  5000   250   Super Area  Kolkata       Unfurnished         Bachelors   \n",
              "12    1  6500   250  Carpet Area  Kolkata         Furnished         Bachelors   \n",
              "21    2  9000   400  Carpet Area  Kolkata       Unfurnished         Bachelors   \n",
              "32    2  6000   550   Super Area  Kolkata    Semi-Furnished  Bachelors/Family   \n",
              "33    2  5000   500  Carpet Area  Kolkata       Unfurnished  Bachelors/Family   \n",
              "\n",
              "    Bathroom  Price_per_sqft  \n",
              "7          1    20000.000000  \n",
              "12         1    26000.000000  \n",
              "21         2    22500.000000  \n",
              "32         1    10909.090909  \n",
              "33         2    10000.000000  "
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              "                                                     [key], {});\n",
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    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kMYcIFBNYT9W",
        "outputId": "cace0cfe-8a20-43d3-f9e5-aeb0ef929621"
      },
      "execution_count": 325,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4744, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 325
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Menghapus size per BHK outlier\n",
        "df1 = df[~(df.Size/df.BHK < 300)]\n",
        "df1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "d3ZaFTWbYiBO",
        "outputId": "8823729d-3b56-4eca-d348-68ff46e59441"
      },
      "execution_count": 326,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   BHK   Rent  Size    Area Type     City Furnishing Status  Tenant Preferred  \\\n",
              "0    2  10000  1100   Super Area  Kolkata       Unfurnished  Bachelors/Family   \n",
              "1    2  20000   800   Super Area  Kolkata    Semi-Furnished  Bachelors/Family   \n",
              "2    2  17000  1000   Super Area  Kolkata    Semi-Furnished  Bachelors/Family   \n",
              "3    2  10000   800   Super Area  Kolkata       Unfurnished  Bachelors/Family   \n",
              "4    2   7500   850  Carpet Area  Kolkata       Unfurnished         Bachelors   \n",
              "\n",
              "   Bathroom  Price_per_sqft  \n",
              "0         2     9090.909091  \n",
              "1         1    25000.000000  \n",
              "2         1    17000.000000  \n",
              "3         1    12500.000000  \n",
              "4         1     8823.529412  "
            ],
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              "\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9eee2cce-7312-4146-9124-4f77e4f547a2 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-9eee2cce-7312-4146-9124-4f77e4f547a2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 326
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-t06QFxuApy8",
        "outputId": "50217f97-18b2-4495-8822-a5d0df4073d2"
      },
      "execution_count": 327,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4196, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 327
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Mendeteksi price per sqft outlier\n",
        "df1.Price_per_sqft.describe().apply(lambda x: format(x, 'f'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DsZIhoQAiKxh",
        "outputId": "a59d07dd-f44a-4125-a24e-235a678ba221"
      },
      "execution_count": 328,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count       4196.000000\n",
              "mean       32827.385605\n",
              "std        41300.048982\n",
              "min          571.428571\n",
              "25%        13000.000000\n",
              "50%        18511.595708\n",
              "75%        34896.788991\n",
              "max      1400000.000000\n",
              "Name: Price_per_sqft, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 328
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Harga 571 per sqft sangat rendah dan harga 1400000 per sqft sangat tinggi"
      ],
      "metadata": {
        "id": "dn5tYRtQjt0-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Menghapus price per sqft outlier dengan mean dan one standard deviation\n",
        "def remove_pps_outliers(df):\n",
        "    df_out = pd.DataFrame()\n",
        "    for key, subdf in df.groupby('City'):\n",
        "        m = np.mean(subdf.Price_per_sqft)\n",
        "        st = np.std(subdf.Price_per_sqft)\n",
        "        reduced_df = subdf[(subdf.Price_per_sqft>(m-st)) & (subdf.Price_per_sqft<=(m+st))]\n",
        "        df_out = pd.concat([df_out,reduced_df],ignore_index=True)\n",
        "    return df_out\n",
        "\n",
        "df2 = remove_pps_outliers(df1)\n",
        "df2.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w6_qXGdPmE4t",
        "outputId": "f850af93-e8f5-46b3-cfcb-f2a926c340e3"
      },
      "execution_count": 329,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3699, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 329
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Mendeteksi bathroom outlier\n",
        "# 2 BHK dengan 4 kamar mandi itu tidak biasa jadi anggap saja batasnya kamar mandi tidak boleh melebihi jumlah BHK + 2\n",
        "\n",
        "df2[df2.Bathroom > df2.BHK + 2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "83gbI5L2cAMB",
        "outputId": "8d957a8d-4e12-402d-8586-1589468b1f94"
      },
      "execution_count": 330,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      BHK    Rent  Size    Area Type     City Furnishing Status  \\\n",
              "860     3   20000  1700  Carpet Area  Chennai         Furnished   \n",
              "1904    4  150000  4000  Carpet Area    Delhi    Semi-Furnished   \n",
              "3328    1   40000   680   Super Area   Mumbai         Furnished   \n",
              "\n",
              "      Tenant Preferred  Bathroom  Price_per_sqft  \n",
              "860   Bachelors/Family         6    11764.705882  \n",
              "1904         Bachelors         7    37500.000000  \n",
              "3328  Bachelors/Family         4    58823.529412  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8b195c04-66de-4f6a-bfaa-be8e3f6bb068\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>BHK</th>\n",
              "      <th>Rent</th>\n",
              "      <th>Size</th>\n",
              "      <th>Area Type</th>\n",
              "      <th>City</th>\n",
              "      <th>Furnishing Status</th>\n",
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              "      <th>860</th>\n",
              "      <td>3</td>\n",
              "      <td>20000</td>\n",
              "      <td>1700</td>\n",
              "      <td>Carpet Area</td>\n",
              "      <td>Chennai</td>\n",
              "      <td>Furnished</td>\n",
              "      <td>Bachelors/Family</td>\n",
              "      <td>6</td>\n",
              "      <td>11764.705882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1904</th>\n",
              "      <td>4</td>\n",
              "      <td>150000</td>\n",
              "      <td>4000</td>\n",
              "      <td>Carpet Area</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Semi-Furnished</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>7</td>\n",
              "      <td>37500.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3328</th>\n",
              "      <td>1</td>\n",
              "      <td>40000</td>\n",
              "      <td>680</td>\n",
              "      <td>Super Area</td>\n",
              "      <td>Mumbai</td>\n",
              "      <td>Furnished</td>\n",
              "      <td>Bachelors/Family</td>\n",
              "      <td>4</td>\n",
              "      <td>58823.529412</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8b195c04-66de-4f6a-bfaa-be8e3f6bb068')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
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              "\n",
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              "\n",
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              "\n",
              "      <script>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8b195c04-66de-4f6a-bfaa-be8e3f6bb068');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 330
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Menghapus bathroom outlier\n",
        "df2 = df2[~(df2.Bathroom > df2.BHK + 2)]\n",
        "df2.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ZF04FHb3cuPV",
        "outputId": "81b35cca-6e4d-4d6f-8ff3-b8304820f4a5"
      },
      "execution_count": 331,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   BHK   Rent  Size    Area Type       City Furnishing Status  \\\n",
              "0    3  70000  3354  Carpet Area  Bangalore         Furnished   \n",
              "1    2  10500   800   Super Area  Bangalore    Semi-Furnished   \n",
              "2    2  13000  1000   Super Area  Bangalore    Semi-Furnished   \n",
              "3    2  17000  1040   Super Area  Bangalore         Furnished   \n",
              "4    3  21000  1403   Super Area  Bangalore    Semi-Furnished   \n",
              "\n",
              "   Tenant Preferred  Bathroom  Price_per_sqft  \n",
              "0  Bachelors/Family         3    20870.602266  \n",
              "1  Bachelors/Family         2    13125.000000  \n",
              "2  Bachelors/Family         2    13000.000000  \n",
              "3  Bachelors/Family         2    16346.153846  \n",
              "4  Bachelors/Family         3    14967.925873  "
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>BHK</th>\n",
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              "      <th>Area Type</th>\n",
              "      <th>City</th>\n",
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              "      <td>10500</td>\n",
              "      <td>800</td>\n",
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              "      <td>Bangalore</td>\n",
              "      <td>Semi-Furnished</td>\n",
              "      <td>Bachelors/Family</td>\n",
              "      <td>2</td>\n",
              "      <td>13125.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>13000</td>\n",
              "      <td>1000</td>\n",
              "      <td>Super Area</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Semi-Furnished</td>\n",
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              "      <td>2</td>\n",
              "      <td>13000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>17000</td>\n",
              "      <td>1040</td>\n",
              "      <td>Super Area</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Furnished</td>\n",
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              "      <td>2</td>\n",
              "      <td>16346.153846</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>21000</td>\n",
              "      <td>1403</td>\n",
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              "      <td>14967.925873</td>\n",
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              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a95c3b9d-de91-4b66-8f9c-da975fe793af')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a95c3b9d-de91-4b66-8f9c-da975fe793af 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-a95c3b9d-de91-4b66-8f9c-da975fe793af');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 331
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1BgiFS2iAvky",
        "outputId": "8bbd02f3-7f3a-445d-8916-98308d332399"
      },
      "execution_count": 332,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3696, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 332
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Menghilangkan fitur price per sqft karena sudah tidak terpakai\n",
        "df3 = df2.drop(['Price_per_sqft'], axis = 'columns')"
      ],
      "metadata": {
        "id": "VL7c1hipq-Qq"
      },
      "execution_count": 333,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Melihat kolerasi antara fitur numerik dengan fitur target (harga)\n",
        "plt.figure(figsize=(10, 8))\n",
        "correlation_matrix = df3.corr().round(2)\n",
        " \n",
        "# Untuk menge-print nilai di dalam kotak, gunakan parameter anot=True\n",
        "sns.heatmap(data=correlation_matrix, annot=True, cmap='coolwarm', linewidths=0.5, )\n",
        "plt.title(\"Correlation Matrix untuk Fitur Numerik \", size=20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "cWu0Z4qtHjw-",
        "outputId": "f8651811-bb1e-4a2d-805f-6564148c2a64"
      },
      "execution_count": 334,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Correlation Matrix untuk Fitur Numerik ')"
            ]
          },
          "metadata": {},
          "execution_count": 334
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Melihat kolerasi antara fitur kategorik dengan fitur target (harga)\n",
        "cat_features = df2.select_dtypes(include='object').columns.to_list()\n",
        " \n",
        "for col in cat_features:\n",
        "  sns.catplot(x=col, y=\"Rent\", kind=\"bar\", dodge=False, height = 4, aspect = 3,  data=df2, palette=\"Set3\")\n",
        "  plt.title(\"Rata-rata 'Rent' Relatif terhadap - {}\".format(col))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Ue8M3M_jGRvp",
        "outputId": "f134b816-2ee9-44e9-cf72-9ff9f3e1f896"
      },
      "execution_count": 335,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ],
            "image/png": 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JtmmxOcCqgcNvarGJ4jeNER+rH8cnWZZk2dq1azf6vCRJkiRpLENNsKrqgao6AJgLHJRkP+Ak4GnAs4GdgDcPsw+tH6dV1fyqmj979uxhNydJkiRpC7VJZhGsqjuAC4EFVXVzGwZ4H/D3wEGt2Gpg94HD5rbYRPG5Y8QlSZIkaVoMcxbB2Ul2aOuPAV4IfL/dO0Wb8e9I4Kp2yFLg2Dab4MHAnVV1M3AecFiSHZPsCBwGnNf23ZXk4FbXscA5wzofSZIkSZrMMGcR3A04I8ksukTurKr6SpJvJJkNBLgceE0rfy5wBLAS+BnwKoCqWpfkncBlrdw7qmpdW38tcDrwGLrZA51BUJIkSdK0GVqCVVXLgWeOET90nPIFnDDOvsXA4jHiy4D9Nq6nkiRJktSPTXIPliRJkiRtCUywJEmSJKknJliSJEmS1BMTLEmSJEnqiQmWJEmSJPXEBEuSJEmSemKCJUmSJEk9McGSJEmSpJ6YYEmSJElST0ywJEmSJKknJliSJEmS1BMTLEmSJEnqiQmWJEmSJPXEBEuSJEmSemKCJUmSJEk9McGSJEmSpJ6YYEmSJElST4aWYCXZNsmlSa5IsiLJ21t8rySXJFmZ5MwkW7f4Nm17Zdu/50BdJ7X4tUleNBBf0GIrk5w4rHORJEmSpKkY5hWs+4BDq+oZwAHAgiQHA+8FPlBVTwFuB45r5Y8Dbm/xD7RyJNkHOBrYF1gAfDTJrCSzgI8AhwP7AMe0spIkSZI0LYaWYFXnnrb56LYUcChwdoufARzZ1he2bdr+5ydJiy+pqvuq6gZgJXBQW1ZW1fVV9QtgSSsrSZIkSdNiqPdgtStNlwO3AucDPwDuqKr7W5GbgDltfQ6wCqDtvxN44mB81DHjxcfqx/FJliVZtnbt2j5OTZIkSZIeZqgJVlU9UFUHAHPprjg9bZjtTdCP06pqflXNnz179nR0QZIkSdIWYJPMIlhVdwAXAs8FdkiyVds1F1jd1lcDuwO0/U8AfjIYH3XMeHFJkiRJmhbDnEVwdpId2vpjgBcC19AlWke1YouAc9r60rZN2/+NqqoWP7rNMrgXMA+4FLgMmNdmJdyabiKMpcM6H0mSJEmazFaTF9lguwFntNn+HgWcVVVfSXI1sCTJu4DvAZ9u5T8NfDbJSmAdXcJEVa1IchZwNXA/cEJVPQCQ5HXAecAsYHFVrRji+UiSJEnShIaWYFXVcuCZY8Svp7sfa3T858BLx6nrFOCUMeLnAududGclSZIkqQeb5B4sSZIkSdoSmGBJkiRJUk9MsCRJkiSpJyZYkiRJktQTEyxJkiRJ6okJliRJkiT1xARLkiRJknpigiVJkiRJPTHBkiRJkqSemGBJkiRJUk9MsCRJkiSpJyZYkiRJktQTEyxJkiRJ6okJliRJkiT1xARLkiRJknpigiVJkiRJPTHBkiRJkqSemGBJkiRJUk+GlmAl2T3JhUmuTrIiyetb/G1JVie5vC1HDBxzUpKVSa5N8qKB+IIWW5nkxIH4XkkuafEzk2w9rPORJEmSpMkM8wrW/cAbqmof4GDghCT7tH0fqKoD2nIuQNt3NLAvsAD4aJJZSWYBHwEOB/YBjhmo572trqcAtwPHDfF8JEmSJGlCQ0uwqurmqvq3tn43cA0wZ4JDFgJLquq+qroBWAkc1JaVVXV9Vf0CWAIsTBLgUODsdvwZwJHDORtJkiRJmtwmuQcryZ7AM4FLWuh1SZYnWZxkxxabA6waOOymFhsv/kTgjqq6f1R8rPaPT7IsybK1a9f2cEaSJEmS9HBDT7CSbA98EfirqroL+BiwN3AAcDPw/mH3oapOq6r5VTV/9uzZw25OkiRJ0hZqq2FWnuTRdMnV56vqSwBVdcvA/k8CX2mbq4HdBw6f22KME/8JsEOSrdpVrMHykiRJkrTJDXMWwQCfBq6pqr8biO82UOwlwFVtfSlwdJJtkuwFzAMuBS4D5rUZA7emmwhjaVUVcCFwVDt+EXDOsM5HkiRJkiYzzCtYvwP8GXBlkstb7C10swAeABRwI/AXAFW1IslZwNV0MxCeUFUPACR5HXAeMAtYXFUrWn1vBpYkeRfwPbqETpIkSZKmxdASrKr6FpAxdp07wTGnAKeMET93rOOq6nq6WQYlSZIkadpNaYjgyI8ETxaTJEmSpC3ZVO/BWjRG7JU99kOSJEmSZrwJhwgmOQb4U2CvJEsHdj0OWDfMjkmSJEnSTDPZPVj/SvdbVTvz0N+ruhtYPqxOSZIkSdJMNGGCVVU/BH4IPHfTdEeSJEmSZq6pTnLxH5Ncl+TOJHcluTvJXcPunCRJkiTNJFOdpv19wB9V1TXD7IwkSZIkzWRTnUXwFpMrSZIkSZrYVK9gLUtyJvBPwH0jwar60lB6JUmSJEkz0FQTrMcDPwMOG4gVYIIlSZIkSc2UEqyqetWwOyJJkiRJM91UZxF8apILklzVtvdP8j+H2zVJkiRJmlmmOsnFJ4GTgF8CVNVy4OhhdUqSJEmSZqKpJliPrapLR8Xu77szkiRJkjSTTTXBui3J3nQTW5DkKODmofVKkiRJkmagqc4ieAJwGvC0JKuBG4CXD61XkiRJkjQDTXUWweuBFyTZju6q18/o7sH64RD7JkmSJEkzyoRDBJM8PslJST6c5IV0idUiYCXwskmO3T3JhUmuTrIiyetbfKck5ye5rv3dscWT5NQkK5MsT/KsgboWtfLXJVk0ED8wyZXtmFOTZMMfCkmSJEnaOJPdg/VZ4DeBK4FXAxcCLwVeUlULJzn2fuANVbUPcDBwQpJ9gBOBC6pqHnBB2wY4HJjXluOBj0GXkAEnA88BDgJOHknKWplXDxy3YArnLEmSJElDMdkQwd+oqqcDJPkU3cQWT66qn09WcVXd3MpTVXcnuQaYAywEDmnFzgAuAt7c4p+pqgIuTrJDkt1a2fOral3rx/nAgiQXAY+vqotb/DPAkcBXp3TmkiRJktSzya5g/XJkpaoeAG6aSnI1WpI9gWcClwC7tOQLYA2wS1ufA6waOOymFpsoftMY8bHaPz7JsiTL1q5du77dlyRJkqQpmSzBekaSu9pyN7D/yHqSu6bSQJLtgS8Cf1VVDzmmXa2qDer5eqiq06pqflXNnz179rCbkyRJkrSFmnCIYFXN2pjKkzyaLrn6fFV9qYVvSbJbVd3chgDe2uKrgd0HDp/bYqt5cEjhSPyiFp87RnlJkiRJmhZT/aHh9dZm9Ps0cE1V/d3ArqV0MxHS/p4zED+2zSZ4MHBnG0p4HnBYkh3b5BaHAee1fXclObi1dexAXZIkSZK0yU31h4Y3xO8AfwZcmeTyFnsL8B7grCTH0f2O1sh07+cCR9BNAf8z4FUAVbUuyTuBy1q5d4xMeAG8FjgdeAzd5BZOcCFJkiRp2gwtwaqqbwHj/S7V88coX8AJ49S1GFg8RnwZsN9GdFOSJEmSejO0IYKSJEmStKUxwZIkSZKknphgSZIkSVJPTLAkSZIkqScmWJIkSZLUk2FO0y5JkqQZ7k1vehNr1qxh11135X3ve990d0fa7JlgSZIkaVxr1qxh9erV090NacZwiKAkSZIk9cQES5IkSZJ6YoIlSZIkST3xHixJkmaQe++9YLq7oC1M1b2//uvrT5vSYx7z/OnuwgbxCpYkSZIk9cQES5IkSZJ6YoIlSZIkST0xwZIkSZKknjjJhSRJksa1yy5PeMhfSRMzwZIkSdK43vnOY6a7C9KM4hBBSZIkSerJ0BKsJIuT3JrkqoHY25KsTnJ5W44Y2HdSkpVJrk3yooH4ghZbmeTEgfheSS5p8TOTbD2sc5EkSZKkqRjmFazTgQVjxD9QVQe05VyAJPsARwP7tmM+mmRWklnAR4DDgX2AY1pZgPe2up4C3A4cN8RzkSRJkqRJDS3BqqpvAuumWHwhsKSq7quqG4CVwEFtWVlV11fVL4AlwMIkAQ4Fzm7HnwEc2esJSJIkSdJ6mo57sF6XZHkbQrhji80BVg2UuanFxos/Ebijqu4fFR9TkuOTLEuybO3atX2dhyRJkiQ9xKZOsD4G7A0cANwMvH9TNFpVp1XV/KqaP3v27E3RpCRJkqQt0Cadpr2qbhlZT/JJ4CttczWw+0DRuS3GOPGfADsk2apdxRosL0mSJEnTYpNewUqy28DmS4CRGQaXAkcn2SbJXsA84FLgMmBemzFwa7qJMJZWVQEXAke14xcB52yKc5AkSZKk8QztClaSLwCHADsnuQk4GTgkyQFAATcCfwFQVSuSnAVcDdwPnFBVD7R6XgecB8wCFlfVitbEm4ElSd4FfA/49LDORZIkSZKmYmgJVlWN9bPf4yZBVXUKcMoY8XOBc8eIX083y6AkSZIkbRamYxZBSZIkSXpEMsGSJEmSpJ6YYEmSJElST0ywJEmSJKknJliSJEmS1BMTLEmSJEnqiQmWJEmSJPXEBEuSJEmSemKCJUmSJEk9McGSJEmSpJ6YYEmSJElST0ywJEmSJKknJliSJEmS1BMTLEmSJEnqiQmWJEmSJPXEBEuSJEmSemKCJUmSJEk9GVqClWRxkluTXDUQ2ynJ+Umua393bPEkOTXJyiTLkzxr4JhFrfx1SRYNxA9McmU75tQkGda5SJIkSdJUDPMK1unAglGxE4ELqmoecEHbBjgcmNeW44GPQZeQAScDzwEOAk4eScpamVcPHDe6LUmSJEnapIaWYFXVN4F1o8ILgTPa+hnAkQPxz1TnYmCHJLsBLwLOr6p1VXU7cD6woO17fFVdXFUFfGagLkmSJEmaFpv6Hqxdqurmtr4G2KWtzwFWDZS7qcUmit80RlySJEmSps20TXLRrjzVpmgryfFJliVZtnbt2k3RpCRJkqQt0KZOsG5pw/tof29t8dXA7gPl5rbYRPG5Y8THVFWnVdX8qpo/e/bsjT4JSZIkSRrLpk6wlgIjMwEuAs4ZiB/bZhM8GLizDSU8DzgsyY5tcovDgPPavruSHNxmDzx2oC5JkiRJmhZbDaviJF8ADgF2TnIT3WyA7wHOSnIc8EPgZa34ucARwErgZ8CrAKpqXZJ3Ape1cu+oqpGJM15LN1PhY4CvtkWSJEmSps3QEqyqOmacXc8fo2wBJ4xTz2Jg8RjxZcB+G9NHSZIkSerTtE1yIUmSJEmPNCZYkiRJktQTEyxJkiRJ6okJliRJkiT1xARLkiRJknpigiVJkiRJPTHBkiRJkqSemGBJkiRJUk9MsCRJkiSpJyZYkiRJktQTEyxJkiRJ6okJliRJkiT1xARLkiRJknpigiVJkiRJPTHBkiRJkqSemGBJkiRJUk9MsCRJkiSpJyZYkiRJktSTaUmwktyY5MoklydZ1mI7JTk/yXXt744tniSnJlmZZHmSZw3Us6iVvy7Jouk4F0mSJEkaMZ1XsP6gqg6oqvlt+0TggqqaB1zQtgEOB+a15XjgY9AlZMDJwHOAg4CTR5IySZIkSZoOm9MQwYXAGW39DODIgfhnqnMxsEOS3YAXAedX1bqquh04H1iwqTstSZIkSSOmK8Eq4OtJvpvk+BbbpapubutrgF3a+hxg1cCxN7XYePGHSXJ8kmVJlq1du7avc5AkSZKkh9hqmtr93apaneQ/AOcn+f7gzqqqJNVXY1V1GnAawPz583urV5IkSZIGTcsVrKpa3f7eCnyZ7h6qW9rQP9rfW1vx1cDuA4fPbbHx4pIkSZI0LTZ5gpVkuySPG1kHDgOuApYCIzMBLgLOaetLgWPbbIIHA3e2oYTnAYcl2bFNbnFYi0mSJEnStJiOIYK7AF9OMtL+P1TV15JcBpyV5Djgh8DLWvlzgSOAlcDPgFcBVNW6JO8ELmvl3lFV6zbdaUiSJEnSQ23yBKuqrgeeMUb8J8Dzx4gXcMI4dS0GFvfdR0mSJEnaEJvTNO2SJEmSNKOZYEmSJElST0ywJEmSJKknJliSJEmS1BMTLEmSJEnqiQmWJEmSJPXEBEuSJEmSemKCJUmSJEk9McGSJEmSpJ6YYEmSJElST0ywJEmSJKknJliSJEmS1BMTLEmSJEnqiQmWJEmSJPXEBEuSJEmSemKCJUmSJEk9McGSJEmSpJ6YYEmSJElST2Z8gpVkQZJrk6xMcuJ090eSJEnSlm4wREkAAAjlSURBVGtGJ1hJZgEfAQ4H9gGOSbLP9PZKkiRJ0pZqRidYwEHAyqq6vqp+ASwBFk5znyRJkiRtobaa7g5spDnAqoHtm4DnjC6U5Hjg+LZ5T5JrN0HfpJliZ+C26e6Etiyvne4OSFpfvldID/e1qlowOjjTE6wpqarTgNOmux/S5ijJsqqaP939kCRtvnyvkKZupg8RXA3sPrA9t8UkSZIkaZOb6QnWZcC8JHsl2Ro4Glg6zX2SJEmStIWa0UMEq+r+JK8DzgNmAYurasU0d0uaaRw+K0majO8V0hSlqqa7D5IkSZL0iDDThwhKkiRJ0mbDBEuSJEmSemKCJW1GkuyaZEmSHyT5bpJzkzx1yG0emWSfScpcnmTJMPshSVo/Sd6aZEWS5e3/6Yf9Fugm6MPOSX6Z5DWbum1pc2WCJW0mkgT4MnBRVe1dVQcCJwG7TPX4JBvyb/pIYNwEK8lv0U0i87wk241TZkZPmCNJM02S5wIvBp5VVfsDLwBWDbG98d5jXgpcDBwzwbGzhtUvaXNkgiVtPv4A+GVVfXwkUFVXVNW/JNk+yQVJ/i3JlUkWAiTZM8m1ST4DXAXsnuSeJB9o32pekGR2K7t3kq+1K2P/kuRpSX4b+GPgf7dvP/ceo1/HAJ8Fvg4sHAkmuSjJB5MsA16f5MAk/9zqPy/Jbq3cq5NcluSKJF9M8tjhPHyStEXZDbitqu4DqKrbqurHAEluTLJzW5+f5KK2/rYkn03ynSTXJXn1SGVJ3tj+r16e5O0t9rD3mDH6cQzwBmBOkrkD9d2T5P1JrgCem+QVSS5t7zWfGEm6knwsybL2nvX2/h8madMzwZI2H/sB3x1n38+Bl1TVs+gSsfe3K14A84CPVtW+VfVDYDtgWVXtC/wzcHIrdxrwX9qVsf/RjvlXut+Oe2NVHVBVPxij7T8BlgBf4OHfUG5dVfOBU4H/AxzV6l8MnNLKfKmqnl1VzwCuAY6b6gMiSRrX1+m+VPv3JB9N8vtTPG5/4FDgucBfJ3lSksPo3ksOAg4ADkzye6386PeYX0uyO7BbVV0KnEX3fjFiO+CS9n//T9q+36mqA4AHgJe3cm9t7yP7A7+fZP/1eRCkzZHDeqSZIcC72xver4A5PDh08IdVdfFA2V8BZ7b1zwFfSrI98NvAPz6Yl7HNpI0m8+m+If1RktXA4iQ7VdW6VmSknd+kSxDPb/XPAm5u+/ZL8i5gB2B7ut+tkyRthKq6J8mBwPPovng7M8mJVXX6JIeeU1X3AvcmuZAuqfpd4DDge63M9nSJ1Y94+HvMoD+hS6yg+yJuMfD+tv0A8MW2/nzgQOCy9h7xGODWtu9lSY6n+0y6G92Q9eWTnIO0WTPBkjYfK4Cjxtn3cmA2cGBV/TLJjcC2bd9PJ6m36K5W39G+OVwfxwBPa+0BPB74T8AnR7UdYEVVPXeMOk4HjqyqK5K8EjhkPfsgSRpDVT0AXARclORKYBHd/7n38+AopW1HHzbGdoC/qapPDO5IsicTv8ccA+yaZORq1JOSzKuq64Cft/7R6j+jqk4aVf9edCMqnl1Vtyc5fYz+SjOOQwSlzcc3gG3aN3kAJNk/yfOAJwC3tuTqD4A9JqjnUTyYqP0p8K2qugu4IclLW71J8oxW5m7gcaMraTczvwx4elXtWVV70t2DNdaNzNcCs9tN1yR5dJJ9277HATcneTQPDgmRJG2EJL+ZZN5A6ABgZAjfjXRXjKD7UmzQwiTbJnki3Rdel9GNLPjPbbQDSeYk+Q+TtP9UYPuqmjPwHvE3jP0ecQFw1EidSXZKsgfdl3Y/Be5Msgtw+ORnLm3+TLCkzURVFfAS4AXppmlfQfdmtQb4PDC/fUN5LPD9Car6KXBQkqvoxtm/o8VfDhzXbjhewYMTViwB3pjke6MmuXgesHrkpunmm8A+IxNYDPT9F3RJ3Xtb/ZfTDUkE+F/AJcC3J+m3JGnqtgfOSHJ1kuV0Q+ve1va9HfhQm4TogVHHLQcupJv5751V9eOq+jrwD8B32vvM2Yzxxdsox9DNfDvoi4yRYFXV1cD/BL7e+no+3b1bV9ANS/x+a//bk561NAOk+0wn6ZEiyT1Vtf1090OStHlJ8jbgnqr62+nui/RI5hUsSZIkSeqJV7AkSZIkqSdewZIkSZKknphgSZIkSVJPTLAkSZIkqScmWJKkGSnJkUkqydOGVP8lSS5P8qMka9v65e3HVyVJGpOTXEiSZqQkZwJPAr5RVSePsX+rqrq/h3ZeCcyvqtdtbF2SpEc+r2BJkmacJNsDvwscBxw9ED8kyb8kWQpcnWRWkv+d5LIky5P8xcjxSS5I8m9JrkyycOyWHtLmo5Jcl2T2wPbKJLOTnJ7k40mWJfn3JC9uZcZsX5L0yLXVdHdAkqQNsBD4WlX9e5KfJDmwqr7b9j0L2K+qbkhyPHBnVT07yTbAt5N8HVgFvKSq7kqyM3BxkqU1wbCOqvpVks8BLwc+CLwAuKKq1iYB2BM4CNgbuDDJU4Bjx2q/qm4YwmMiSdoMeAVLkjQTHQMsaetL2vaISwcSmMOAY5NcDlwCPBGYBwR4d5LlwP8D5gC7TKHdxXRJE8B/Bv5+YN9ZVfWrqroOuB542gTtS5IeobyCJUmaUZLsBBwKPD1JAbOASvLGVuSng8WB/1JV542q45XAbODAqvplkhuBbSdru6pWJbklyaF0V6tePrh7dPHx2pckPXJ5BUuSNNMcBXy2qvaoqj2ranfgBuB5Y5Q9D/jLJI8GSPLUJNsBTwBubcnVHwB7rEf7nwI+B/xjVT0wEH9puy9rb+A3gGsnaF+S9AhlgiVJmmmOAb48KvZFHjpMcMSngKuBf0tyFfAJutEbnwfmJ7mSbsjf99ej/aXA9jx0eCDAj4BLga8Cr6mqn0/QviTpEcpp2iVJWg9J5gMfqKrnDcROB75SVWdPW8ckSZsFv0WTJGmKkpwI/CUPvfdKkqRf8wqWJEmSJPXEe7AkSZIkqScmWJIkSZLUExMsSZIkSeqJCZYkSZIk9cQES5IkSZJ68v8BrGHPViy4XPQAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Preparation"
      ],
      "metadata": {
        "id": "p3bJgDR910AI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## One hot encoding"
      ],
      "metadata": {
        "id": "sk87IEuv16Lx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df3 = pd.get_dummies(data =  df3, columns = ['Area Type'])\n",
        "df3 = pd.get_dummies(data =  df3, columns = ['City'])\n",
        "df3 = pd.get_dummies(data =  df3, columns = ['Furnishing Status'])\n",
        "df3 = pd.get_dummies(data =  df3, columns = ['Tenant Preferred'])"
      ],
      "metadata": {
        "id": "wqQ5B47YIPQ5"
      },
      "execution_count": 336,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df3.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "id": "3gTFGdvuJYOo",
        "outputId": "1aa967aa-a291-4ddc-c228-08d0f7e20caa"
      },
      "execution_count": 337,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   BHK   Rent  Size  Bathroom  Area Type_Carpet Area  Area Type_Super Area  \\\n",
              "0    3  70000  3354         3                      1                     0   \n",
              "1    2  10500   800         2                      0                     1   \n",
              "2    2  13000  1000         2                      0                     1   \n",
              "3    2  17000  1040         2                      0                     1   \n",
              "4    3  21000  1403         3                      0                     1   \n",
              "\n",
              "   City_Bangalore  City_Chennai  City_Delhi  City_Hyderabad  City_Kolkata  \\\n",
              "0               1             0           0               0             0   \n",
              "1               1             0           0               0             0   \n",
              "2               1             0           0               0             0   \n",
              "3               1             0           0               0             0   \n",
              "4               1             0           0               0             0   \n",
              "\n",
              "   City_Mumbai  Furnishing Status_Furnished  Furnishing Status_Semi-Furnished  \\\n",
              "0            0                            1                                 0   \n",
              "1            0                            0                                 1   \n",
              "2            0                            0                                 1   \n",
              "3            0                            1                                 0   \n",
              "4            0                            0                                 1   \n",
              "\n",
              "   Furnishing Status_Unfurnished  Tenant Preferred_Bachelors  \\\n",
              "0                              0                           0   \n",
              "1                              0                           0   \n",
              "2                              0                           0   \n",
              "3                              0                           0   \n",
              "4                              0                           0   \n",
              "\n",
              "   Tenant Preferred_Bachelors/Family  Tenant Preferred_Family  \n",
              "0                                  1                        0  \n",
              "1                                  1                        0  \n",
              "2                                  1                        0  \n",
              "3                                  1                        0  \n",
              "4                                  1                        0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e8bb2418-b9c9-4503-88a7-63f1416b172d\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>BHK</th>\n",
              "      <th>Rent</th>\n",
              "      <th>Size</th>\n",
              "      <th>Bathroom</th>\n",
              "      <th>Area Type_Carpet Area</th>\n",
              "      <th>Area Type_Super Area</th>\n",
              "      <th>City_Bangalore</th>\n",
              "      <th>City_Chennai</th>\n",
              "      <th>City_Delhi</th>\n",
              "      <th>City_Hyderabad</th>\n",
              "      <th>City_Kolkata</th>\n",
              "      <th>City_Mumbai</th>\n",
              "      <th>Furnishing Status_Furnished</th>\n",
              "      <th>Furnishing Status_Semi-Furnished</th>\n",
              "      <th>Furnishing Status_Unfurnished</th>\n",
              "      <th>Tenant Preferred_Bachelors</th>\n",
              "      <th>Tenant Preferred_Bachelors/Family</th>\n",
              "      <th>Tenant Preferred_Family</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>70000</td>\n",
              "      <td>3354</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>10500</td>\n",
              "      <td>800</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>13000</td>\n",
              "      <td>1000</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>17000</td>\n",
              "      <td>1040</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>21000</td>\n",
              "      <td>1403</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e8bb2418-b9c9-4503-88a7-63f1416b172d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e8bb2418-b9c9-4503-88a7-63f1416b172d 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-e8bb2418-b9c9-4503-88a7-63f1416b172d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 337
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train Test Split"
      ],
      "metadata": {
        "id": "xdTaBzzy1_4N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        " \n",
        "X = df3.drop([\"Rent\"],axis =1)\n",
        "y = df3[\"Rent\"]\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.05, random_state=123)"
      ],
      "metadata": {
        "id": "1XQHLlRQJcQh"
      },
      "execution_count": 338,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Total # of sample in whole dataset: {len(X)}')\n",
        "print(f'Total # of sample in train dataset: {len(X_train)}')\n",
        "print(f'Total # of sample in test dataset: {len(X_test)}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L71hEo_YKrVV",
        "outputId": "b40e95bc-d5c5-4da0-82fb-268ebd7c6765"
      },
      "execution_count": 339,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total # of sample in whole dataset: 3696\n",
            "Total # of sample in train dataset: 3511\n",
            "Total # of sample in test dataset: 185\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Normalization"
      ],
      "metadata": {
        "id": "IFoMpxkV2Fiz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# Normalisasi data train\n",
        "numerical_features = ['BHK', 'Size', 'Bathroom']\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(X_train[numerical_features])\n",
        "X_train[numerical_features] = scaler.transform(X_train.loc[:, numerical_features])\n",
        "X_train[numerical_features].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "-ALMIqKGKkJB",
        "outputId": "9a6b697b-b7bc-4e75-d3ca-6d7da8095c50"
      },
      "execution_count": 340,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           BHK      Size  Bathroom\n",
              "1865  1.186385  0.454836  0.053982\n",
              "439  -0.071283 -0.376100  0.053982\n",
              "823  -0.071283  1.470424  0.053982\n",
              "3463 -0.071283 -0.560752  0.053982\n",
              "1519 -0.071283  0.194476  0.053982"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-67cf34d3-b31c-4042-9e34-39dac95b4a54\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>BHK</th>\n",
              "      <th>Size</th>\n",
              "      <th>Bathroom</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1865</th>\n",
              "      <td>1.186385</td>\n",
              "      <td>0.454836</td>\n",
              "      <td>0.053982</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>439</th>\n",
              "      <td>-0.071283</td>\n",
              "      <td>-0.376100</td>\n",
              "      <td>0.053982</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>823</th>\n",
              "      <td>-0.071283</td>\n",
              "      <td>1.470424</td>\n",
              "      <td>0.053982</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3463</th>\n",
              "      <td>-0.071283</td>\n",
              "      <td>-0.560752</td>\n",
              "      <td>0.053982</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1519</th>\n",
              "      <td>-0.071283</td>\n",
              "      <td>0.194476</td>\n",
              "      <td>0.053982</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-67cf34d3-b31c-4042-9e34-39dac95b4a54')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-67cf34d3-b31c-4042-9e34-39dac95b4a54 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-67cf34d3-b31c-4042-9e34-39dac95b4a54');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 340
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Normalisasi data test\n",
        "X_test.loc[:, numerical_features] = scaler.transform(X_test[numerical_features])"
      ],
      "metadata": {
        "id": "ECgZJlatLlDj"
      },
      "execution_count": 341,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Modeling"
      ],
      "metadata": {
        "id": "0SUoQq9A2cF_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Grid Search"
      ],
      "metadata": {
        "id": "Pwyvrj2gP9eu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.ensemble import AdaBoostRegressor"
      ],
      "metadata": {
        "id": "dsSB08wvCV2i"
      },
      "execution_count": 342,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.model_selection import ShuffleSplit\n",
        "\n",
        "def grid_search_model(X,y):\n",
        "    algos = {\n",
        "        'knn': {\n",
        "            'model': KNeighborsRegressor(),\n",
        "            'params': {\n",
        "                'n_neighbors': [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],\n",
        "            }\n",
        "        },\n",
        "        'boosting': {\n",
        "            'model': AdaBoostRegressor(),\n",
        "            'params': {\n",
        "                'learning_rate' : [0.1, 0.05, 0.01, 0.05, 0.001],\n",
        "                'n_estimators': [25, 50, 75, 100],\n",
        "                'random_state': [11, 33, 55, 77]\n",
        "            }\n",
        "        },\n",
        "        'random_forest': {\n",
        "            'model': RandomForestRegressor(),\n",
        "            'params': {\n",
        "                'n_estimators': [25, 50, 75, 100],\n",
        "                'max_depth' : [8, 16, 32, 64],\n",
        "                'random_state': [11, 33, 55, 77],\n",
        "            }\n",
        "        }\n",
        "        \n",
        "    }\n",
        "\n",
        "    scores = []\n",
        "    cv = ShuffleSplit(n_splits=5, test_size=0.05, random_state=123)\n",
        "    for algo_name, config in algos.items():\n",
        "        gs =  GridSearchCV(config['model'], config['params'], cv=cv, return_train_score=False)\n",
        "        gs.fit(X,y)\n",
        "        scores.append({\n",
        "            'model': algo_name,\n",
        "            'best_score': gs.best_score_,\n",
        "            'best_params': gs.best_params_\n",
        "        })\n",
        "\n",
        "    return pd.DataFrame(scores,columns=['model','best_score','best_params'])\n",
        "\n",
        "grid_search_model(X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "OSpZJLTH6g1g",
        "outputId": "a30cb590-4ffe-4d72-f34b-ae7908cb7539"
      },
      "execution_count": 343,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           model  best_score  \\\n",
              "0            knn    0.460230   \n",
              "1       boosting    0.856539   \n",
              "2  random_forest    0.893655   \n",
              "\n",
              "                                         best_params  \n",
              "0                                 {'n_neighbors': 7}  \n",
              "1  {'learning_rate': 0.1, 'n_estimators': 100, 'r...  \n",
              "2  {'max_depth': 8, 'n_estimators': 25, 'random_s...  "
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model</th>\n",
              "      <th>best_score</th>\n",
              "      <th>best_params</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>knn</td>\n",
              "      <td>0.460230</td>\n",
              "      <td>{'n_neighbors': 7}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>boosting</td>\n",
              "      <td>0.856539</td>\n",
              "      <td>{'learning_rate': 0.1, 'n_estimators': 100, 'r...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>random_forest</td>\n",
              "      <td>0.893655</td>\n",
              "      <td>{'max_depth': 8, 'n_estimators': 25, 'random_s...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-83d799c7-8a5b-4e2d-bac1-3712fe65925e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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-83d799c7-8a5b-4e2d-bac1-3712fe65925e 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-83d799c7-8a5b-4e2d-bac1-3712fe65925e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 343
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model with best parameter"
      ],
      "metadata": {
        "id": "rbSeK4vjVN4k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "acc = pd.DataFrame(index=['accuracy'])"
      ],
      "metadata": {
        "id": "dyqVpKnzR52I"
      },
      "execution_count": 344,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error"
      ],
      "metadata": {
        "id": "YOOpqaGVCbNZ"
      },
      "execution_count": 345,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn = KNeighborsRegressor(n_neighbors = 7)\n",
        "knn.fit(X_train, y_train)\n",
        "acc.loc['accuracy', 'knn'] = knn.score(X_test,y_test)\n",
        "knn.score(X_test,y_test)"
      ],
      "metadata": {
        "id": "khMDeczpLNpS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5e0a0250-b92c-476e-fb23-5db6275dfe3e"
      },
      "execution_count": 346,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7267752134903871"
            ]
          },
          "metadata": {},
          "execution_count": 346
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf = RandomForestRegressor(n_estimators = 50, max_depth = 8, random_state = 11)\n",
        "rf.fit(X_train, y_train)\n",
        "acc.loc['accuracy', 'rf'] = rf.score(X_test,y_test)\n",
        "rf.score(X_test,y_test)"
      ],
      "metadata": {
        "id": "kKOolVq4LboR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "147ea550-ee23-4437-a8a3-5c0351447de0"
      },
      "execution_count": 347,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9320574970694799"
            ]
          },
          "metadata": {},
          "execution_count": 347
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "boosting = AdaBoostRegressor(n_estimators = 25, learning_rate = 0.001, random_state = 11)                             \n",
        "boosting.fit(X_train, y_train)\n",
        "acc.loc['accuracy', 'boosting'] = boosting.score(X_test,y_test)\n",
        "boosting.score(X_test,y_test)"
      ],
      "metadata": {
        "id": "2GbH9oq_LfN9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "de7bec70-345b-4bf5-9880-8d2903c5271f"
      },
      "execution_count": 348,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8985561811074477"
            ]
          },
          "metadata": {},
          "execution_count": 348
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluation"
      ],
      "metadata": {
        "id": "Ep-VSSyRQSzG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Akurasi dari model\n",
        "acc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "X0qxmcPzTelA",
        "outputId": "d7d331ab-39fa-4339-be41-c82396b015a0"
      },
      "execution_count": 349,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               knn        rf  boosting\n",
              "accuracy  0.726775  0.932057  0.898556"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-22666ab5-0cf7-4d64-9210-180a5ffcc32c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>knn</th>\n",
              "      <th>rf</th>\n",
              "      <th>boosting</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>accuracy</th>\n",
              "      <td>0.726775</td>\n",
              "      <td>0.932057</td>\n",
              "      <td>0.898556</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-22666ab5-0cf7-4d64-9210-180a5ffcc32c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-22666ab5-0cf7-4d64-9210-180a5ffcc32c 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-22666ab5-0cf7-4d64-9210-180a5ffcc32c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 349
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Mean squared error dari model\n",
        "mse = pd.DataFrame(columns=['train', 'test'], index=['KNN','RF','Boosting'])\n",
        " \n",
        "model_dict = {'KNN': knn, 'RF': rf, 'Boosting': boosting}\n",
        "\n",
        "for name, model in model_dict.items():\n",
        "    mse.loc[name, 'train'] = mean_squared_error(y_true=y_train, y_pred=model.predict(X_train))/1e3 \n",
        "    mse.loc[name, 'test'] = mean_squared_error(y_true=y_test, y_pred=model.predict(X_test))/1e3\n",
        " \n",
        "mse"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "pPy7WYhWLl9U",
        "outputId": "b005ae92-7370-4960-c5fe-8c1508ef9de3"
      },
      "execution_count": 350,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  train           test\n",
              "KNN       159226.590016  656598.477133\n",
              "RF         63498.211157  163275.610997\n",
              "Boosting  171831.281366   243784.09386"
            ],
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              "\n",
              "  <div id=\"df-8a3fea92-91f8-432f-8d59-2dbec67c9bb8\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>train</th>\n",
              "      <th>test</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>KNN</th>\n",
              "      <td>159226.590016</td>\n",
              "      <td>656598.477133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RF</th>\n",
              "      <td>63498.211157</td>\n",
              "      <td>163275.610997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Boosting</th>\n",
              "      <td>171831.281366</td>\n",
              "      <td>243784.09386</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8a3fea92-91f8-432f-8d59-2dbec67c9bb8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8a3fea92-91f8-432f-8d59-2dbec67c9bb8 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-8a3fea92-91f8-432f-8d59-2dbec67c9bb8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 350
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "mse.sort_values(by='test', ascending=False).plot(kind='barh', ax=ax, zorder=3)\n",
        "ax.grid(zorder=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "YZhytVX-L0d9",
        "outputId": "3b9f86e1-5025-49a0-db24-d61abe0ae3ec"
      },
      "execution_count": 351,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAD4CAYAAAAUymoqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAATgklEQVR4nO3df5BV5X3H8fdXXIFl6YKgjErGRZMxSU2LP2okWmeJYwSTOqbNaDRpnDYttlpDayVgYhz9oy2tjUGmxh9Jickk2Bgt6qBWNGGrU40GCCpGFjCSYTWikkJYFIvk6R/3rLkuu8s+u5e9e+D9mrmz5z7nnOf5PniSz54fe2+klJAkKcdB9S5AklQ+hockKZvhIUnKZnhIkrIZHpKkbAfXu4ChMm7cuPTe97633mUM2I4dOxgzZky9yxgU51B/Za8fyj+HstW/cuXK11NKh3VvP2DCY9KkSaxYsaLeZQxYW1sbra2t9S5jUJxD/ZW9fij/HMpWf0T8oqd2L1tJkrIZHpKkbIaHJCnbAXPPQ5Jy7dq1i46ODnbu3FmzPpubm3n++edr1l+tjBo1ismTJ9PQ0NCv7Q0PSepFR0cHY8eOpaWlhYioSZ/bt29n7NixNemrVlJKbNmyhY6ODqZMmdKvfbxsJUm92LlzJxMmTKhZcAxXEcGECROyzrAMD0nqw/4eHF1y52l4SJKyec9DkvqpZd79Ne1v4/yP97l+69atLF68mEsvvTSr33POOYfFixczbty4wZTXJ888JGmY2rp1K1//+tf3aH/77bf73O+BBx7Yp8EBnnlI0rA1b948XnjhBaZOnUpDQwOjRo1i/PjxrF27lnXr1nHeeeexadMmdu7cyezZs5k1axYALS0trFixgs7OTmbOnMnpp5/O448/zlFHHcW9997L6NGjB12bZx6SNEzNnz+fY489ltWrV3P99dezatUqbrzxRtatWwfAokWLWLlyJStWrGDhwoVs2bJljz7Wr1/PZZddxnPPPce4ceO4++67a1KbZx6SVBKnnHLKu/4OY+HChSxZsgSATZs2sX79eiZMmPCufaZMmcLUqVMBOOmkk9i4cWNNajE8JKkkqj/Kva2tjUceeYQnnniCxsZGWltbe/w7jZEjR76zPGLECN58882a1OJlK0kapsaOHcv27dt7XLdt2zbGjx9PY2Mja9eu5cc//vGQ1uaZhyT1094ere2PnI8nmTBhAqeddhrHH388o0ePZtKkSe+smzFjBrfccgsf+MAHOO644zj11FMHXVuOAyY82i98Ba5trncZA9YK0Fanwa/dVqeBJS1evLjH9pEjR/Lggw/2uK7rvsbEiRNZs2bNO+1XXnllzeryspUkKZvhIUnKZnhIkrIZHpKkbIaHJCmb4SFJynbAPKorSYNWg8f93/UXHnt5DH6gH8kOsGDBAmbNmkVjY2P2vv3hmYckDVO9fSR7fyxYsIA33nijxhX9lmcekjRMVX8k+1lnncXhhx/OnXfeyVtvvcUnP/lJrrvuOnbs2MH5559PR0cHu3fv5itf+QqbN2/m5ZdfZvr06UycOJHly5fXvDbDQ5KGqfnz57NmzRpWr17NsmXLuOuuu3jqqadIKXHuuefy6KOP8tprr3HkkUdy//2Vbznctm0bzc3N3HDDDSxfvpyJEyfuk9q8bCVJJbBs2TKWLVvGCSecwIknnsjatWtZv349H/rQh3j44YeZO3cujz32GM3NQ/MxTJ55SFIJpJS46qqruOSSS/ZYt2rVKh544AGuvvpqzjzzTK655pp9Xo9nHpI0TFV/JPvZZ5/NokWL6OzsBOCll17i1Vdf5eWXX6axsZHPfvazzJkzh1WrVu2x777gmYck9VcNPmF6oB/JPnPmTC666CKmTZsGQFNTE9/97nfZsGEDc+bM4aCDDqKhoYGbb74ZgFmzZjFjxgyOPPLIA++GeUTsBp6lUueLwJ+mlLZGRAvwPNBetfkpKaX/G/IiJWkf6v6R7LNnz37X+2OPPZazzz57j/0uv/xyLr/88n1W13C/bPVmSmlqSul44FfAZVXrXijWdb0MDkkaIsM9PKo9ARxV7yIkSSUJj4gYAZwJ3FfVfGxErC5eN9WpNEn7uZRSvUsYErnzjOH8D1N1z+MoKvc4pqeUdhf3PJYWl7P62n8WMAvg6LlLT9q31fbu9hljBt1HZ2cnTU1NNaimfpxD/ZW9fhjaOTQ1NTFp0iSam5uJiJr0uXv3bkaMGFGTvmolpcS2bdvYvHnzO09zdZk+ffrKlNLJ3fcZ7uHRmVJqiohG4CHgBymlhf0Nj2ot8+6v20Q3zv/4oPtoa2ujtbV18MXUkXOov7LXD0M7h127dtHR0cHOnTtr1ufOnTsZNWpUzfqrlVGjRjF58mQaGhre1R4RPYbHsH7aqktK6Y2I+AJwT0QM7FPCJClTQ0MDU6ZMqWmfbW1tnHDCCTXtsx5Kcc8DIKX0U+AZ4MJ61yJJB7phfeaRUmrq9v6Pqt72+5KVJKm2SnPmIUkaPgwPSVI2w0OSlM3wkCRlMzwkSdkMD0lSNsNDkpTN8JAkZTM8JEnZDA9JUjbDQ5KUzfCQJGUzPCRJ2Yb1p+rW0sglV9De3l7vMiRpv+CZhyQpm+EhScpmeEiSshkekqRshockKZvhIUnKZnhIkrIZHpKkbIaHJCmb4SFJymZ4SJKyGR6SpGyGhyQpm+EhScpmeEiSshkekqRshockKZvhIUnKZnhIkrIZHpKkbIaHJCmb4SFJymZ4SJKyGR6SpGyGhyQpm+EhScpmeEiSshkekqRshockKZvhIUnKZnhIkrIZHpKkbIaHJCmb4SFJymZ4SJKyHVzvAoZK+4WvwLXN9S5jwFoB2upbw2C1Qu3ncO22GncoqT8885AkZTM8JEnZDA9JUjbDQ5KUzfCQJGUzPCRJ2QwPSVI2w0OSlM3wkCRlMzwkSdkMD0lSNsNDkpTN8JAkZTM8JEnZ9hoeEbE7IlZHxNMRsSoiPlLLAiLiS93eP17L/iVJtdefM483U0pTU0q/D1wF/FONa3hXeKSUahpOkqTay71s9TvA/wJExfURsSYino2IC/bSfkREPFqcxayJiD+MiPnA6KLte8V2ncXP1ohoi4i7ImJtRHwvIqJYd07RtjIiFkbE0hr9e0iS+iFSSn1vELEbeBYYBRwBfDSltDIi/gT4K2AGMBH4CfBh4CO9tF8EjEop/UNEjAAaU0rbI6IzpdRUNV5nSqkpIlqBe4HfBV4G/geYA6wA1gNnpJRejIg7gLEppU/0UPssYBbA0XOXnjSQfyDt6fYZYwa0X2dnJ01NTXvfcBgr+xzKXj+Ufw5lq3/69OkrU0ond2/vz9fQvplSmgoQEdOA70TE8cDpwB0ppd3A5oj4b+AP+mj/CbAoIhqAe1JKq/sx9lMppY5i7NVAC9AJ/Dyl9GKxzR0UAdFdSuk24DaAlnn3952S6rfW1tYB7dfW1jbgfYeLss+h7PVD+edQ9vq7ZF22Sik9QeVs4rDcgVJKjwJnAC8Bt0fE5/qx21tVy7s5gL5zXZKGs6zwiIj3AyOALcBjwAURMSIiDqMSDE/11h4RRwObU0rfAL4JnFh0u6s4G+mvduCYiGgp3l+QMwdJ0uD15zf50cUlI4AALk4p7Y6IJcA04GkgAV9MKb3SR/vFwJyI2EXl0lPXmcdtwDMRsSql9Jm9FZNSejMiLgX+KyJ2ULkcJkkaQnsNj5TSiF7aE5Ub2HP62f5t4Ns99DMXmFv1vqn42Qa0VbX/TdVuy1NK7y+evrqJyk10SdIQKetfmP9lcTb0HNAM3FrneiTpgFLKG9Appa8BX6t3HZJ0oCrrmYckqY4MD0lSNsNDkpTN8JAkZTM8JEnZDA9JUjbDQ5KUzfCQJGUzPCRJ2QwPSVK2Un48yUCMXHIF7e3t9S5jwPaXL5CRtH/wzEOSlM3wkCRlMzwkSdkMD0lSNsNDkpTN8JAkZTM8JEnZDA9JUjbDQ5KUzfCQJGUzPCRJ2QwPSVI2w0OSlM3wkCRlMzwkSdkMD0lSNsNDkpTN8JAkZTM8JEnZDA9JUjbDQ5KUzfCQJGUzPCRJ2QwPSVI2w0OSlM3wkCRlMzwkSdkMD0lSNsNDkpTN8JAkZTM8JEnZDA9JUjbDQ5KUzfCQJGUzPCRJ2SKlVO8ahsa1zQfIRCWpyrXbBrV7RKxMKZ3cvd0zD0lSNsNDkpTN8JAkZTM8JEnZDA9JUjbDQ5KUzfCQJGUzPCRJ2QwPSVI2w0OSlM3wkCRlMzwkSdkMD0lSNsNDkpStbuEREZ1Vy+dExLqIODoiro2INyLi8F62TRHx1ar3V0bEtUNWuCSp/mceEXEmsBCYmVL6RdH8OvD3vezyFvDHETFxKOqTJO2pruEREWcA3wA+kVJ6oWrVIuCCiDi0h93eBm4D/m4ISpQk9aCe4TESuAc4L6W0ttu6TioBMruXfW8CPhMRzfuwPklSLw6u49i7gMeBz9NzSCwEVkfEv3ZfkVL6dUR8B/gC8GZvA0TELGAWwNFzl9ai5gPK7TPG1LS/zs5OmpqaatrnUCv7HMpeP5R/DkNef1vbPum2nuHxG+B84IcR8aWU0j9Wr0wpbY2IxcBlvey/AFgFfKu3AVJKt1G5xEXLvPv9DvNMra2tNe2vra2t5n0OtbLPoez1Q/nnUPb6u9T1nkdK6Q3g41QuQX2+h01uAC6hh5BLKf0KuJPKmYskaQjV/WmrIgRmAFdHxLnd1r0OLKFyf6QnXwV86kqShljdLlullJqqljcBU4q393Xb7grgil722ww07ttKJUnd1f3MQ5JUPoaHJCmb4SFJymZ4SJKyGR6SpGyGhyQpm+EhScpmeEiSshkekqRshockKZvhIUnKZnhIkrIZHpKkbPX8MqghNXLJFbS3t9e7jAHbX75ARtL+wTMPSVI2w0OSlM3wkCRlMzwkSdkMD0lSNsNDkpTN8JAkZTM8JEnZDA9JUjbDQ5KUzfCQJGUzPCRJ2QwPSVI2w0OSlM3wkCRlMzwkSdkMD0lStkgp1buGIRER24HyfpUgTARer3cRg+Qc6q/s9UP551C2+o9OKR3WvfGA+RpaoD2ldHK9ixioiFhR5vrBOQwHZa8fyj+HstffxctWkqRshockKduBFB631buAQSp7/eAchoOy1w/ln0PZ6wcOoBvmkqTaOZDOPCRJNWJ4SJKy7ffhEREzIqI9IjZExLw61bAoIl6NiDVVbYdGxMMRsb74Ob5oj4hYWNT7TEScWLXPxcX26yPi4qr2kyLi2WKfhRERfY0xgPrfExHLI+JnEfFcRMwu0xwiYlREPBURTxf1X1e0T4mIJ4sxvx8RhxTtI4v3G4r1LVV9XVW0t0fE2VXtPR5nvY0xUBExIiJ+GhFLyziHiNhY/HdeHRErirZSHEdFP+Mi4q6IWBsRz0fEtDLVX1Mppf32BYwAXgCOAQ4BngY+WIc6zgBOBNZUtf0LMK9Yngf8c7F8DvAgEMCpwJNF+6HAz4uf44vl8cW6p4pto9h3Zl9jDKD+I4ATi+WxwDrgg2WZQ9FnU7HcADxZjHUn8Omi/Rbgr4vlS4FbiuVPA98vlj9YHEMjgSnFsTWir+OstzEGcSxdASwGlvbV/3CdA7ARmNitrRTHUbHvt4G/KJYPAcaVqf5avuo6+D6fHEwDHqp6fxVwVZ1qaeHd4dEOHFEsH0HljxgBbgUu7L4dcCFwa1X7rUXbEcDaqvZ3tuttjBrM5V7grDLOAWgEVgEfpvJXvgd3P1aAh4BpxfLBxXbR/fjp2q6346zYp8cxBlj7ZOCHwEeBpX31P4znsJE9w6MUxxHQDLxI8aBR2eqv9Wt/v2x1FLCp6n1H0TYcTEop/bJYfgWYVCz3VnNf7R09tPc1xoAVlz9OoPLbe2nmUFzuWQ28CjxM5bfsrSmlt3sY8506i/XbgAkDmNeEPsYYiAXAF4HfFO/76n+4ziEByyJiZUTMKtrKchxNAV4DvlVcOvxmRIwpUf01tb+HRymkyq8T+/SZ6VqMERFNwN3A36aUfl3r/vdmMGOklHanlKZS+e39FOD9taxtX4uITwCvppRW1ruWQTo9pXQiMBO4LCLOqF45zI+jg6lcfr45pXQCsIPKJaRa9N1vQzFGf+zv4fES8J6q95OLtuFgc0QcAVD8fLVo763mvton99De1xjZIqKBSnB8L6X0n2WcA0BKaSuwnMrll3ER0fX5btVjvlNnsb4Z2DKAeW3pY4xcpwHnRsRG4D+oXLq6sWRzIKX0UvHzVWAJlSAvy3HUAXSklJ4s3t9FJUzKUn9N7e/h8RPgfcXTIodQuXF4X51r6nIf0PWUxcVU7iN0tX+ueFLjVGBbcbr6EPCxiBhfPGnxMSrXnn8J/DoiTi2ezPhct756GiNL0e+/A8+nlG4o2xwi4rCIGFcsj6Zyv+Z5KiHyqV7q7xrzU8CPit/27gM+HZUnmaYA76Nyg7PH46zYp7cxsqSUrkopTU4ptRT9/yil9JkyzSEixkTE2K5lKv/911CS4yil9AqwKSKOK5rOBH5Wlvprrt43Xfb1i8oTD+uoXOP+cp1quAP4JbCLym8vn6dyLfmHwHrgEeDQYtsAbirqfRY4uaqfPwc2FK8/q2o/mcr/CF8A/o3ffnJAj2MMoP7TqZwmPwOsLl7nlGUOwO8BPy3qXwNcU7QfQ+X/ODcAPwBGFu2jivcbivXHVPX15aLGdoonYfo6znobY5DHUyu/fdqqNHMo+nm6eD3XNUZZjqOin6nAiuJYuofK01Klqb+WLz+eRJKUbX+/bCVJ2gcMD0lSNsNDkpTN8JAkZTM8JEnZDA9JUjbDQ5KU7f8BqwdJ75/0kNMAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediksi = X_test.iloc[5:10].copy()\n",
        "pred_dict = {'y_true':y_test[5:10]}\n",
        "for name, model in model_dict.items():\n",
        "    pred_dict['prediksi_'+name] = model.predict(prediksi).round(1)\n",
        " \n",
        "pd.DataFrame(pred_dict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "dnZyJtYpL4MB",
        "outputId": "3874ff79-c65c-4fd1-f02c-3110b5af3028"
      },
      "execution_count": 353,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      y_true  prediksi_KNN  prediksi_RF  prediksi_Boosting\n",
              "1733   11000       11642.9      10913.1            13168.5\n",
              "1442   13000       12071.4      13297.7            13168.5\n",
              "1911   18000       21571.4      22240.4            13517.4\n",
              "2003   22000       23428.6      17893.1            18242.7\n",
              "553    11000       16500.0      11114.7            13168.5"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b732d65d-88c2-453a-afdc-e146df2f1b81\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>y_true</th>\n",
              "      <th>prediksi_KNN</th>\n",
              "      <th>prediksi_RF</th>\n",
              "      <th>prediksi_Boosting</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1733</th>\n",
              "      <td>11000</td>\n",
              "      <td>11642.9</td>\n",
              "      <td>10913.1</td>\n",
              "      <td>13168.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1442</th>\n",
              "      <td>13000</td>\n",
              "      <td>12071.4</td>\n",
              "      <td>13297.7</td>\n",
              "      <td>13168.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1911</th>\n",
              "      <td>18000</td>\n",
              "      <td>21571.4</td>\n",
              "      <td>22240.4</td>\n",
              "      <td>13517.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003</th>\n",
              "      <td>22000</td>\n",
              "      <td>23428.6</td>\n",
              "      <td>17893.1</td>\n",
              "      <td>18242.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>553</th>\n",
              "      <td>11000</td>\n",
              "      <td>16500.0</td>\n",
              "      <td>11114.7</td>\n",
              "      <td>13168.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b732d65d-88c2-453a-afdc-e146df2f1b81')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b732d65d-88c2-453a-afdc-e146df2f1b81 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-b732d65d-88c2-453a-afdc-e146df2f1b81');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 353
        }
      ]
    }
  ]
}