Minihome_Prices_Multiple_Linear_Regression_Model.ipynb

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              "   area  bedrooms  age   price\n",
              "0  2600       3.0   20  550000\n",
              "1  3000       4.0   15  565000\n",
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        "import numpy as np\n",
        "import pandas as pd\n",
        "df=pd.read_csv('/content/minihomeprices.csv')\n",
        "df"
      ]
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              "   area  bedrooms  age   price\n",
              "0  2600       3.0   20  550000\n",
              "1  3000       4.0   15  565000\n",
              "2  3200       NaN   18  610000\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-cc19a913-c8f8-4fe9-977a-9d784810f63f 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-cc19a913-c8f8-4fe9-977a-9d784810f63f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-0c66ce85-098a-4019-95ff-9a0cc54b23ce\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0c66ce85-098a-4019-95ff-9a0cc54b23ce')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0c66ce85-098a-4019-95ff-9a0cc54b23ce button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "P3HrSLwCEuJK",
        "outputId": "06c5b81a-09b8-4262-cd5a-47ae730d546b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   area  bedrooms  age   price\n",
              "1  3000       4.0   15  565000\n",
              "2  3200       NaN   18  610000\n",
              "3  3600       3.0   30  595000\n",
              "4  4000       5.0    8  760000\n",
              "5  4100       6.0    8  810000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6d51f3d9-285b-4133-8e94-80806f8a39c1\" 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>area</th>\n",
              "      <th>bedrooms</th>\n",
              "      <th>age</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3000</td>\n",
              "      <td>4.0</td>\n",
              "      <td>15</td>\n",
              "      <td>565000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3200</td>\n",
              "      <td>NaN</td>\n",
              "      <td>18</td>\n",
              "      <td>610000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3600</td>\n",
              "      <td>3.0</td>\n",
              "      <td>30</td>\n",
              "      <td>595000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4000</td>\n",
              "      <td>5.0</td>\n",
              "      <td>8</td>\n",
              "      <td>760000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4100</td>\n",
              "      <td>6.0</td>\n",
              "      <td>8</td>\n",
              "      <td>810000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6d51f3d9-285b-4133-8e94-80806f8a39c1')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6d51f3d9-285b-4133-8e94-80806f8a39c1 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-6d51f3d9-285b-4133-8e94-80806f8a39c1');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-88b0b874-7c19-49f1-b342-4ae759fb2244\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-88b0b874-7c19-49f1-b342-4ae759fb2244')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-88b0b874-7c19-49f1-b342-4ae759fb2244 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e1I3Xm9hEz72",
        "outputId": "44a9ce95-4705-4149-9bfb-ed726ee6fe00"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['area', 'bedrooms', 'age', 'price'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iJVttc4gE5PW",
        "outputId": "3cc44a53-5617-453d-9693-910027aa9deb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "area          int64\n",
              "bedrooms    float64\n",
              "age           int64\n",
              "price         int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8RCDoJr3E7HN",
        "outputId": "a743c220-afb7-4bb2-d9a1-6642fea81f91"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "area        0\n",
              "bedrooms    1\n",
              "age         0\n",
              "price       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Filling Missing Values\n",
        "bd_data=df['bedrooms'].mean()\n",
        "df['bedrooms'].fillna(bd_data,inplace=True)"
      ],
      "metadata": {
        "id": "QL1G_GQXXK_p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2nB_YmJHYezt",
        "outputId": "23200446-c7dd-46dd-ec91-6f84dd65f47f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "area        0\n",
              "bedrooms    0\n",
              "age         0\n",
              "price       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=df.iloc[:,:-1].values\n",
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B6pae6HlJkNL",
        "outputId": "f8bd1d76-6e97-4ae7-9db9-6a4c95ed3d5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2.6e+03, 3.0e+00, 2.0e+01],\n",
              "       [3.0e+03, 4.0e+00, 1.5e+01],\n",
              "       [3.2e+03, 4.2e+00, 1.8e+01],\n",
              "       [3.6e+03, 3.0e+00, 3.0e+01],\n",
              "       [4.0e+03, 5.0e+00, 8.0e+00],\n",
              "       [4.1e+03, 6.0e+00, 8.0e+00]])"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y=df.iloc[:,-1].values\n",
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KNTK9uy-KDi6",
        "outputId": "214a3a01-14c4-45b7-ba2d-9d6ecbbd0e37"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([550000, 565000, 610000, 595000, 760000, 810000])"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "sns.regplot(x=df['area'],y=y,color='red')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "2Y_6n1Hlm_7G",
        "outputId": "b43b28b9-9913-403c-f149-aae305c6efeb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='area'>"
            ]
          },
          "metadata": {},
          "execution_count": 99
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.regplot(x=df['bedrooms'],y=y,color='green')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "fNPP9YmaT9XF",
        "outputId": "70f4dca4-2eca-4abc-d5a7-598263be5a91"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='bedrooms'>"
            ]
          },
          "metadata": {},
          "execution_count": 100
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.regplot(x=df['age'],y=y,color='blue')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "yqPKprN1ULvA",
        "outputId": "036e34d7-5cbb-404b-a57d-40178ae840ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='age'>"
            ]
          },
          "metadata": {},
          "execution_count": 101
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Tranining and Testing data splitting\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.30,random_state=42)\n",
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JWq4h1gmUfeh",
        "outputId": "6a971657-d8e1-4eac-cc2c-c164f15124fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[4.1e+03, 6.0e+00, 8.0e+00],\n",
              "       [3.2e+03, 4.2e+00, 1.8e+01],\n",
              "       [4.0e+03, 5.0e+00, 8.0e+00],\n",
              "       [3.6e+03, 3.0e+00, 3.0e+01]])"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WwrBFfd0WupS",
        "outputId": "2be53a63-4002-48b4-905d-39c53f907705"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2600.,    3.,   20.],\n",
              "       [3000.,    4.,   15.]])"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hfIow4S8Wwvu",
        "outputId": "5e0c1514-5d59-4bbb-853d-e0a328c734bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([810000, 610000, 760000, 595000])"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qfji0tJaWyOY",
        "outputId": "fa889778-314c-47a8-900f-f27c92eb3a48"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([550000, 565000])"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "model=LinearRegression()\n",
        "model.fit(x_train,y_train)\n",
        "y_pred=model.predict(x_test)\n",
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b1KDQVqTVS7w",
        "outputId": "a1187b75-2d7b-4562-9fe6-5027e467ef11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([485028.9017341 , 582355.49132948])"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(model.predict([[3500,4.0,15]]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MhjdkS5ZYog2",
        "outputId": "ebb0d8ee-b1f9-45dd-f5de-9f0f3ce35702"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[646300.57803468]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1=pd.DataFrame({'Actual_Value':y_test,'Predicted_Value':y_pred,'Difference':y_test-y_pred})\n",
        "df1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "id": "1pc_ipSsa-Wg",
        "outputId": "03164be4-9513-4f8b-c305-5f5d4be4b9af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Actual_Value  Predicted_Value    Difference\n",
              "0        550000    485028.901734  64971.098266\n",
              "1        565000    582355.491329 -17355.491329"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-597e03af-4bcc-4c40-99f3-388a4a9e2fce\" 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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Actual_Value</th>\n",
              "      <th>Predicted_Value</th>\n",
              "      <th>Difference</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>550000</td>\n",
              "      <td>485028.901734</td>\n",
              "      <td>64971.098266</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>565000</td>\n",
              "      <td>582355.491329</td>\n",
              "      <td>-17355.491329</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-597e03af-4bcc-4c40-99f3-388a4a9e2fce')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-597e03af-4bcc-4c40-99f3-388a4a9e2fce 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-597e03af-4bcc-4c40-99f3-388a4a9e2fce');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-c5334773-b0b0-49bd-ba81-093326f3a9a9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c5334773-b0b0-49bd-ba81-093326f3a9a9')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c5334773-b0b0-49bd-ba81-093326f3a9a9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_6cba5008-94c2-4b28-b0cc-4a4dc7a63894\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df1')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_6cba5008-94c2-4b28-b0cc-4a4dc7a63894 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df1');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Slope is : \",model.coef_)\n",
        "print(list(zip(x,model.coef_)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1aIV16NbbrIK",
        "outputId": "2d5a3f26-8c68-43ea-facf-da82f74b3c8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Slope is :  [  127.89017341 37210.98265896 -1791.90751445]\n",
            "[(array([2600.,    3.,   20.]), 127.89017341040702), (array([3000.,    4.,   15.]), 37210.98265895953), (array([3200. ,    4.2,   18. ]), -1791.907514450868)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Constant is : \",model.intercept_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZTK_Gn9pcQra",
        "outputId": "c108b2ba-b0ed-4d60-b2ac-1c0ab77643e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Constant is :  76719.65317918186\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Performance Evaluation\n",
        "from sklearn.metrics import mean_absolute_error,mean_absolute_percentage_error,mean_squared_error,r2_score\n",
        "print(\"Error is : \",mean_absolute_error(y_test,y_pred))\n",
        "print(\"Error percentage is : \",mean_absolute_percentage_error(y_test,y_pred))\n",
        "print(\"Error is : \",mean_squared_error(y_test,y_pred))\n",
        "print(\"Error is : \",r2_score(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N-p2dxxEcYCI",
        "outputId": "46603a4d-6975-4b77-da02-e6a59326f97b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Error is :  41163.294797688315\n",
            "Error percentage is :  0.07442347667167439\n",
            "Error is :  2261228344.5823216\n",
            "Error is :  -39.19961501479683\n"
          ]
        }
      ]
    }
  ]
}