House_price_prediction.ipynb

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        "<a href=\"https://colab.research.google.com/github/Pavani-Banothu/Bharath/blob/main/House_price_prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "Importing Libraries\n",
        "\n",
        "\n",
        "\n"
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        "dataset_link = \"/content/drive/MyDrive/data.csv\""
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      "outputs": []
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        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "dataset = pd.read_csv(dataset_link)"
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      "outputs": []
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        "dataset.head()"
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              "                  date      price  bedrooms  bathrooms  sqft_living  sqft_lot  \\\n",
              "0  2014-05-02 00:00:00   313000.0       3.0       1.50         1340      7912   \n",
              "1  2014-05-02 00:00:00  2384000.0       5.0       2.50         3650      9050   \n",
              "2  2014-05-02 00:00:00   342000.0       3.0       2.00         1930     11947   \n",
              "3  2014-05-02 00:00:00   420000.0       3.0       2.25         2000      8030   \n",
              "4  2014-05-02 00:00:00   550000.0       4.0       2.50         1940     10500   \n",
              "\n",
              "   floors  waterfront  view  condition  sqft_above  sqft_basement  yr_built  \\\n",
              "0     1.5           0     0          3        1340              0      1955   \n",
              "1     2.0           0     4          5        3370            280      1921   \n",
              "2     1.0           0     0          4        1930              0      1966   \n",
              "3     1.0           0     0          4        1000           1000      1963   \n",
              "4     1.0           0     0          4        1140            800      1976   \n",
              "\n",
              "   yr_renovated                    street       city  statezip country  \n",
              "0          2005      18810 Densmore Ave N  Shoreline  WA 98133     USA  \n",
              "1             0           709 W Blaine St    Seattle  WA 98119     USA  \n",
              "2             0  26206-26214 143rd Ave SE       Kent  WA 98042     USA  \n",
              "3             0           857 170th Pl NE   Bellevue  WA 98008     USA  \n",
              "4          1992         9105 170th Ave NE    Redmond  WA 98052     USA  "
            ],
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              "      <td>2014-05-02 00:00:00</td>\n",
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              "      <td>2014-05-02 00:00:00</td>\n",
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4600 entries, 0 to 4599\n",
            "Data columns (total 18 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   date           4600 non-null   object \n",
            " 1   price          4600 non-null   float64\n",
            " 2   bedrooms       4600 non-null   float64\n",
            " 3   bathrooms      4600 non-null   float64\n",
            " 4   sqft_living    4600 non-null   int64  \n",
            " 5   sqft_lot       4600 non-null   int64  \n",
            " 6   floors         4600 non-null   float64\n",
            " 7   waterfront     4600 non-null   int64  \n",
            " 8   view           4600 non-null   int64  \n",
            " 9   condition      4600 non-null   int64  \n",
            " 10  sqft_above     4600 non-null   int64  \n",
            " 11  sqft_basement  4600 non-null   int64  \n",
            " 12  yr_built       4600 non-null   int64  \n",
            " 13  yr_renovated   4600 non-null   int64  \n",
            " 14  street         4600 non-null   object \n",
            " 15  city           4600 non-null   object \n",
            " 16  statezip       4600 non-null   object \n",
            " 17  country        4600 non-null   object \n",
            "dtypes: float64(4), int64(9), object(5)\n",
            "memory usage: 647.0+ KB\n"
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        "dataset.isna().sum()"
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            "text/plain": [
              "date             0\n",
              "price            0\n",
              "bedrooms         0\n",
              "bathrooms        0\n",
              "sqft_living      0\n",
              "sqft_lot         0\n",
              "floors           0\n",
              "waterfront       0\n",
              "view             0\n",
              "condition        0\n",
              "sqft_above       0\n",
              "sqft_basement    0\n",
              "yr_built         0\n",
              "yr_renovated     0\n",
              "street           0\n",
              "city             0\n",
              "statezip         0\n",
              "country          0\n",
              "dtype: int64"
            ]
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          "metadata": {},
          "execution_count": 5
        }
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      "cell_type": "code",
      "source": [
        "# Replacing missing values\n",
        "dataset = dataset.fillna(dataset.mean())\n",
        "# print(dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dgvxLA_0VUbx",
        "outputId": "551e31a8-a789-4600-c1f3-e76142e1a289"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-6-073c94af99f9>:2: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  dataset = dataset.fillna(dataset.mean())\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 808
        },
        "id": "xOaZUw93VZbM",
        "outputId": "39926f27-8014-4c50-81d7-ea4b32348cea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     date         price  bedrooms  bathrooms  sqft_living  \\\n",
              "0     2014-05-02 00:00:00  3.130000e+05       3.0       1.50         1340   \n",
              "1     2014-05-02 00:00:00  2.384000e+06       5.0       2.50         3650   \n",
              "2     2014-05-02 00:00:00  3.420000e+05       3.0       2.00         1930   \n",
              "3     2014-05-02 00:00:00  4.200000e+05       3.0       2.25         2000   \n",
              "4     2014-05-02 00:00:00  5.500000e+05       4.0       2.50         1940   \n",
              "...                   ...           ...       ...        ...          ...   \n",
              "4595  2014-07-09 00:00:00  3.081667e+05       3.0       1.75         1510   \n",
              "4596  2014-07-09 00:00:00  5.343333e+05       3.0       2.50         1460   \n",
              "4597  2014-07-09 00:00:00  4.169042e+05       3.0       2.50         3010   \n",
              "4598  2014-07-10 00:00:00  2.034000e+05       4.0       2.00         2090   \n",
              "4599  2014-07-10 00:00:00  2.206000e+05       3.0       2.50         1490   \n",
              "\n",
              "      sqft_lot  floors  waterfront  view  condition  sqft_above  \\\n",
              "0         7912     1.5           0     0          3        1340   \n",
              "1         9050     2.0           0     4          5        3370   \n",
              "2        11947     1.0           0     0          4        1930   \n",
              "3         8030     1.0           0     0          4        1000   \n",
              "4        10500     1.0           0     0          4        1140   \n",
              "...        ...     ...         ...   ...        ...         ...   \n",
              "4595      6360     1.0           0     0          4        1510   \n",
              "4596      7573     2.0           0     0          3        1460   \n",
              "4597      7014     2.0           0     0          3        3010   \n",
              "4598      6630     1.0           0     0          3        1070   \n",
              "4599      8102     2.0           0     0          4        1490   \n",
              "\n",
              "      sqft_basement  yr_built  yr_renovated                    street  \\\n",
              "0                 0      1955          2005      18810 Densmore Ave N   \n",
              "1               280      1921             0           709 W Blaine St   \n",
              "2                 0      1966             0  26206-26214 143rd Ave SE   \n",
              "3              1000      1963             0           857 170th Pl NE   \n",
              "4               800      1976          1992         9105 170th Ave NE   \n",
              "...             ...       ...           ...                       ...   \n",
              "4595              0      1954          1979            501 N 143rd St   \n",
              "4596              0      1983          2009          14855 SE 10th Pl   \n",
              "4597              0      2009             0          759 Ilwaco Pl NE   \n",
              "4598           1020      1974             0         5148 S Creston St   \n",
              "4599              0      1990             0         18717 SE 258th St   \n",
              "\n",
              "           city  statezip country  \n",
              "0     Shoreline  WA 98133     USA  \n",
              "1       Seattle  WA 98119     USA  \n",
              "2          Kent  WA 98042     USA  \n",
              "3      Bellevue  WA 98008     USA  \n",
              "4       Redmond  WA 98052     USA  \n",
              "...         ...       ...     ...  \n",
              "4595    Seattle  WA 98133     USA  \n",
              "4596   Bellevue  WA 98007     USA  \n",
              "4597     Renton  WA 98059     USA  \n",
              "4598    Seattle  WA 98178     USA  \n",
              "4599  Covington  WA 98042     USA  \n",
              "\n",
              "[4600 rows x 18 columns]"
            ],
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2014-05-02 00:00:00</td>\n",
              "      <td>3.130000e+05</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.50</td>\n",
              "      <td>1340</td>\n",
              "      <td>7912</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1340</td>\n",
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              "      <td>1955</td>\n",
              "      <td>2005</td>\n",
              "      <td>18810 Densmore Ave N</td>\n",
              "      <td>Shoreline</td>\n",
              "      <td>WA 98133</td>\n",
              "      <td>USA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2014-05-02 00:00:00</td>\n",
              "      <td>2.384000e+06</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.50</td>\n",
              "      <td>3650</td>\n",
              "      <td>9050</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>3370</td>\n",
              "      <td>280</td>\n",
              "      <td>1921</td>\n",
              "      <td>0</td>\n",
              "      <td>709 W Blaine St</td>\n",
              "      <td>Seattle</td>\n",
              "      <td>WA 98119</td>\n",
              "      <td>USA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2014-05-02 00:00:00</td>\n",
              "      <td>3.420000e+05</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.00</td>\n",
              "      <td>1930</td>\n",
              "      <td>11947</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1930</td>\n",
              "      <td>0</td>\n",
              "      <td>1966</td>\n",
              "      <td>0</td>\n",
              "      <td>26206-26214 143rd Ave SE</td>\n",
              "      <td>Kent</td>\n",
              "      <td>WA 98042</td>\n",
              "      <td>USA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2014-05-02 00:00:00</td>\n",
              "      <td>4.200000e+05</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.25</td>\n",
              "      <td>2000</td>\n",
              "      <td>8030</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1000</td>\n",
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              "      <td>1963</td>\n",
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              "      <td>857 170th Pl NE</td>\n",
              "      <td>Bellevue</td>\n",
              "      <td>WA 98008</td>\n",
              "      <td>USA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014-05-02 00:00:00</td>\n",
              "      <td>5.500000e+05</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.50</td>\n",
              "      <td>1940</td>\n",
              "      <td>10500</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1140</td>\n",
              "      <td>800</td>\n",
              "      <td>1976</td>\n",
              "      <td>1992</td>\n",
              "      <td>9105 170th Ave NE</td>\n",
              "      <td>Redmond</td>\n",
              "      <td>WA 98052</td>\n",
              "      <td>USA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>4595</th>\n",
              "      <td>2014-07-09 00:00:00</td>\n",
              "      <td>3.081667e+05</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.75</td>\n",
              "      <td>1510</td>\n",
              "      <td>6360</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1510</td>\n",
              "      <td>0</td>\n",
              "      <td>1954</td>\n",
              "      <td>1979</td>\n",
              "      <td>501 N 143rd St</td>\n",
              "      <td>Seattle</td>\n",
              "      <td>WA 98133</td>\n",
              "      <td>USA</td>\n",
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              "    <tr>\n",
              "      <th>4596</th>\n",
              "      <td>2014-07-09 00:00:00</td>\n",
              "      <td>5.343333e+05</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.50</td>\n",
              "      <td>1460</td>\n",
              "      <td>7573</td>\n",
              "      <td>2.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1460</td>\n",
              "      <td>0</td>\n",
              "      <td>1983</td>\n",
              "      <td>2009</td>\n",
              "      <td>14855 SE 10th Pl</td>\n",
              "      <td>Bellevue</td>\n",
              "      <td>WA 98007</td>\n",
              "      <td>USA</td>\n",
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              "    <tr>\n",
              "      <th>4597</th>\n",
              "      <td>2014-07-09 00:00:00</td>\n",
              "      <td>4.169042e+05</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.50</td>\n",
              "      <td>3010</td>\n",
              "      <td>7014</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
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              "      <td>3</td>\n",
              "      <td>3010</td>\n",
              "      <td>0</td>\n",
              "      <td>2009</td>\n",
              "      <td>0</td>\n",
              "      <td>759 Ilwaco Pl NE</td>\n",
              "      <td>Renton</td>\n",
              "      <td>WA 98059</td>\n",
              "      <td>USA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4598</th>\n",
              "      <td>2014-07-10 00:00:00</td>\n",
              "      <td>2.034000e+05</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.00</td>\n",
              "      <td>2090</td>\n",
              "      <td>6630</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>3</td>\n",
              "      <td>1070</td>\n",
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              "      <td>Seattle</td>\n",
              "      <td>WA 98178</td>\n",
              "      <td>USA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4599</th>\n",
              "      <td>2014-07-10 00:00:00</td>\n",
              "      <td>2.206000e+05</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.50</td>\n",
              "      <td>1490</td>\n",
              "      <td>8102</td>\n",
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              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1490</td>\n",
              "      <td>0</td>\n",
              "      <td>1990</td>\n",
              "      <td>0</td>\n",
              "      <td>18717 SE 258th St</td>\n",
              "      <td>Covington</td>\n",
              "      <td>WA 98042</td>\n",
              "      <td>USA</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>4600 rows × 18 columns</p>\n",
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              "    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-845a4cc9-9958-40db-825e-d82c884215fb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_eae21b39-54ed-4b46-ab9a-4f9de607a074\">\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('dataset')\"\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_eae21b39-54ed-4b46-ab9a-4f9de607a074 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('dataset');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "# Univariate Regression"
      ],
      "metadata": {
        "id": "LiR2DEY1VyK6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_data = dataset['sqft_living']\n",
        "y_data = dataset['price']"
      ],
      "metadata": {
        "id": "aHwE85_XVha4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2KYVuqtgV4ek",
        "outputId": "68c4429b-f519-4e96-acb4-8292016f4b0d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       1340\n",
              "1       3650\n",
              "2       1930\n",
              "3       2000\n",
              "4       1940\n",
              "        ... \n",
              "4595    1510\n",
              "4596    1460\n",
              "4597    3010\n",
              "4598    2090\n",
              "4599    1490\n",
              "Name: sqft_living, Length: 4600, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating each single feature as a vector\n",
        "x_data = np.array(x_data).reshape((-1,1))\n",
        "print(x_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YQy0KESFV7PO",
        "outputId": "35b7a5b7-b23c-4289-f6fa-1e5422b0c0c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1340]\n",
            " [3650]\n",
            " [1930]\n",
            " ...\n",
            " [3010]\n",
            " [2090]\n",
            " [1490]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.15, random_state=1)\n",
        "\n",
        "\n",
        "print(\"shape of training samples:\",x_train.shape)\n",
        "print(\"shape of test samples :\", x_test.shape)\n",
        "print(\"shape of train labels :\", y_train.shape)\n",
        "print(\"shape of test labels:\",y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dl9x3RsOV-yB",
        "outputId": "dffcce0d-7ac7-481c-fff6-f852e08740a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shape of training samples: (3910, 1)\n",
            "shape of test samples : (690, 1)\n",
            "shape of train labels : (3910,)\n",
            "shape of test labels: (690,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.linear_model import LinearRegression"
      ],
      "metadata": {
        "id": "8foMh5fiWDHv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy.ma.core import nonzero\n",
        "lre=LinearRegression()\n",
        "\n",
        "lre.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "2bUbvHrOWHnB",
        "outputId": "2650cf8e-40d2-4317-f648-e32ef33b9e7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Intercept is \",lre.intercept_,\"\\n\",\"Coef is\",lre.coef_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7uFBTZI7WKYz",
        "outputId": "d288981a-4ad1-49ca-f7de-b487eb158b97"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intercept is  17548.86186971469 \n",
            " Coef is [250.87701234]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "r_squared = lre.score(x_test, y_test)\n",
        "print(\"R Square value is \",r_squared)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LAzn7CRPWNVb",
        "outputId": "ab9d21c4-864b-4e1d-9813-c48acf563073"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R Square value is  0.42661862607668444\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = lre.predict(x_test)\n",
        "print(f\"predicted response:\\n{y_pred}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eXzMO7XsWQS9",
        "outputId": "f9f736b9-bf2d-49c8-a0dd-815074abc4b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "predicted response:\n",
            "[ 602092.30061768  702443.10555295  323618.8169223   694916.79518281\n",
            "  539373.04753314  614636.15123459  285987.26507157  416443.31148743\n",
            "  481671.33469535  311074.96630539  519302.88654608  381320.52976008\n",
            "  363759.13889641  815337.76110514  185636.4601363   604601.07074106\n",
            "  672337.86407237  506759.03592917  418952.08161081  536864.27740975\n",
            "  540627.43259483  479162.56457197  669829.09394899  346197.74803273\n",
            "  702443.10555295  371285.44926655  263408.33396113  288496.03519495\n",
            "  627180.0018515   551916.89815004  388846.84013023  679864.17444252\n",
            "  280969.72482481  343688.97790935  584530.90975401  441531.01272124\n",
            "  240829.4028507   411425.77124066  720004.49641663  243338.17297408\n",
            "  933249.95690408  393864.38037699  592057.22012415  554425.66827343\n",
            "  545644.97284159  609618.61098783  516794.1164227   807811.45073499\n",
            "  434004.7023511   720004.49641663  903144.7154235   662302.78357885\n",
            "  491706.41518888  328636.35716906  511776.57617594  649758.93296194\n",
            "  383829.29988346  260899.56383775  408917.00111728  612127.38111121\n",
            "  373794.21938993  516794.1164227   428987.16210433  699934.33542957\n",
            "  321110.04679892  373794.21938993  336162.66753921  489197.6450655\n",
            "  431495.93222772  687390.48481266  471636.25420183  255882.02359099\n",
            "  637215.08234503  306057.42605863  602092.30061768  398881.92062375\n",
            "  333653.89741583  719502.74239195  459092.40358492  687390.48481266\n",
            "  220759.24186364  306057.42605863  439022.24259786  677355.40431914\n",
            "  564460.74876695  672337.86407237  915688.56604041  293513.57544172\n",
            "  371285.44926655  722513.26654001  449057.32309139  306057.42605863\n",
            "  368776.67914317 1056179.69294979  737565.8872803   318601.27667553\n",
            "  378811.7596367   632197.54209826  476653.79444859  371285.44926655\n",
            "  494215.18531226  546899.35790328  213232.9314935   413934.54136404\n",
            "  391355.61025361  521811.65666946 1540372.32676249  531846.73716299\n",
            "  258390.79371437  747600.96777383  790250.05987132  549408.12802666\n",
            "  855478.08307925  539373.04753314  604601.07074106  659794.01345546\n",
            "  321110.04679892  654776.4732087   544390.5877799   890600.86480659\n",
            "  534355.50728637  388846.84013023  336162.66753921  506759.03592917\n",
            "  935758.72702746  273443.41445466  293513.57544172  692408.02505943\n",
            "  559443.20852019  408917.00111728  765162.3586375   460346.78864661\n",
            "  275952.18457804  436513.47247448  868021.93369616  857986.85320263\n",
            "  810320.22085837  449057.32309139  321110.04679892  486688.87494212\n",
            " 1409916.28034663  205706.62112335 1077504.23899854  464109.94383168\n",
            "  689899.25493605  521811.65666946  474145.02432521  747600.96777383\n",
            "  772688.66900765  614636.15123459  812828.99098176  358741.59864964\n",
            "  469127.48407844  421460.85173419  919451.72122548  657285.24333208\n",
            "  574495.82926048  579513.36950724  624671.23172812  539373.04753314\n",
            "  434004.7023511   978658.69613729  367773.17109382  283478.49494819\n",
            "  506759.03592917  408917.00111728  564460.74876695  730039.57691015\n",
            "  449057.32309139  629688.77197488  657285.24333208  519302.88654608\n",
            "  692408.02505943  677355.40431914  401390.69074713  714986.95616986\n",
            "  820355.3013519   817846.53122852  577004.59938386  506759.03592917\n",
            "  391355.61025361  770430.7758966   386338.07000684  223268.01198702\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "MSE = mean_squared_error(y_test, y_pred)\n",
        "RMSE = mean_squared_error(y_test, y_pred, squared=False)\n",
        "print(\"MSE is:\", MSE)\n",
        "print(\"RMSE is:\", RMSE)\n",
        "print(\"R_squared is:\", r_squared)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QJhykFK-WX2Y",
        "outputId": "cfed2f75-c3aa-439e-b272-167e2a4b3e5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE is: 78142113488.84763\n",
            "RMSE is: 279539.10905067937\n",
            "R_squared is: 0.42661862607668444\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Multivariate Regression"
      ],
      "metadata": {
        "id": "wWIidwCQWg1L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_data = dataset[[ 'bedrooms','bathrooms','sqft_living']]\n",
        "y_data = dataset['price']"
      ],
      "metadata": {
        "id": "y5onVxnjWdAS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "hsmXq67XWnvk",
        "outputId": "52c223b5-26ae-401a-df3e-7972b22bc0db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   bedrooms  bathrooms  sqft_living\n",
              "0       3.0       1.50         1340\n",
              "1       5.0       2.50         3650\n",
              "2       3.0       2.00         1930\n",
              "3       3.0       2.25         2000\n",
              "4       4.0       2.50         1940"
            ],
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              "        <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-513d067d-41f9-4f0c-8287-59e0c18e3074 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": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.15, random_state=1)\n",
        "\n",
        "\n",
        "print(\"shape of training samples:\",x_train.shape)\n",
        "print(\"shape of test samples :\", x_test.shape)\n",
        "print(\"shape of train labels :\", y_train.shape)\n",
        "print(\"shape of test labels:\",y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_dNar94TWqsV",
        "outputId": "68eceeea-bba7-461e-8e72-eb4fbe04b967"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shape of training samples: (3910, 3)\n",
            "shape of test samples : (690, 3)\n",
            "shape of train labels : (3910,)\n",
            "shape of test labels: (690,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.linear_model import LinearRegression\n",
        "lre=LinearRegression()\n",
        "\n",
        "lre.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "iAqGR-PYWvMr",
        "outputId": "598e2fd2-2d2f-4496-8141-571ea0caaf2d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Intercept is \",lre.intercept_,\"\\n\",\"Coefficients are\",lre.coef_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LYnWcHu1Wy7U",
        "outputId": "4bb6dcbf-fd4e-4f2d-d73d-97da1dc56d11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intercept is  120811.6314257487 \n",
            " Coefficients are [-52657.08805055  14542.51392979    271.69893737]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "r_squared = lre.score(x_test, y_test)\n",
        "print(\"R Square value is \",r_squared)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JJ0hlaNqW2oj",
        "outputId": "8e400af3-c745-4369-9426-3cabbc3edd86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R Square value is  0.4450186747118554\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = lre.predict(x_test)\n",
        "print(f\"predicted response:\\n{y_pred}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rL7lnfHaW5yb",
        "outputId": "f27b3651-73b1-4923-9ec3-d24d2f548941"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "predicted response:\n",
            "[ 519669.74311355  740934.75112906  323398.09872973  669219.80950996\n",
            "  586144.21273077  645840.12304823  268100.74419387  304070.01552715\n",
            "  501839.68624012  358834.61142914  489937.43879564  318689.25234537\n",
            "  359598.67174464  757885.09684618  212078.25729489  634972.16555328\n",
            "  655673.79059366  529009.5799775   359444.09295144  547070.93853257\n",
            "  502124.96302507  499122.69686638  656592.42970237  287922.65807793\n",
            "  699184.54852586  427677.9848813   250919.09679512  270817.7335676\n",
            "  610403.61034882  510715.78672445  439425.65353257  678367.27264466\n",
            "  262666.76544639  330591.49978984  556943.53403038  472910.3701901\n",
            "  271852.02351713  418492.72681056  669182.01457392  274569.01289087\n",
            "  952783.19939221  384931.2872646   621387.21868459  580632.37807852\n",
            "  518465.82721989  685791.97538641  528970.65202511  806026.84525796\n",
            "  375746.02919386  665546.38609148  927450.58387224  592148.74504816\n",
            "  460050.55568452  287081.874874    530807.93024253  529542.33861137\n",
            "  268749.15366856  293587.93850703  363118.64938627  590466.04562395\n",
            "  370466.62923959  536241.90899     422969.13849694  692831.93066967\n",
            "  327952.36632088  374102.25772204  375097.61971917  457333.56631078\n",
            "  432957.38483557  724632.81488664  434679.01221217  288153.95975956\n",
            "  610364.68239643  364307.518129    575962.45964655  464836.12495731\n",
            "  330630.42774223  706753.19081994  473751.15339403  720997.18640419\n",
            "  250116.10852722  289836.65918377  399358.15035358  664743.39782358\n",
            "  595135.96405592  708330.87864421  884742.81420794  276251.71231508\n",
            "  375020.89683075  710013.57806842  399319.22240119  300743.54463111\n",
            "  357761.39352722 1085915.67870537  775336.97387895  303421.60605246\n",
            "  375900.60798707  608566.33213141  489134.45052775  378656.5253132\n",
            "  515424.63310881  572481.40995731  241965.14040601  424845.34466674\n",
            "  404028.06878555  489018.79968693 1568544.42523361  496251.12869944\n",
            "  343528.03718384  786204.9313739   797914.80508914  571562.7708486\n",
            "  854014.01487654  553423.55638875  624065.28010594  642088.84372497\n",
            "  268024.02130544  636654.86497749  573400.04906601  906594.38003866\n",
            "  558896.46308862  401311.07941181  322440.53166863  472716.8634445\n",
            "  993614.7628867   261787.05429007  276251.71231508  624752.61753302\n",
            "  533409.26877545  423046.99440171  756202.39742197  475109.6480809\n",
            "  309889.87474946  287691.3563963   882141.47567502  856731.00425028\n",
            "  805108.20614925  410226.10784853  358795.68347675  552659.49607325\n",
            " 1469011.18040244  297378.14578268 1123552.60231193  468278.24669416\n",
            "  723714.17577793  489018.79968693  490053.08963646  688162.0122377\n",
            "  767988.99402563  476961.97344925  909503.74314164  354164.69299716\n",
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            "  400392.4403031   709250.98705627  384051.57610828  252833.09790096\n",
            "  751610.33489478  651961.43922279  818005.81559087  805911.19441715\n",
            "  671975.72683609  676606.71731567  389524.48280815  629538.1868058\n",
            "  402954.85088363  205725.63943871  501800.75828773  374102.25772204\n",
            "  831282.7378895   349649.3533584   524609.89117954  566013.14126031\n",
            "  435713.30216169  730066.79363411  851297.0255028   724632.81488664\n",
            "  505320.73592936  889334.87673513  430279.32341422  341459.45728479\n",
            "  243647.83983022  287119.66981003  277286.00226461  712691.63948977\n",
            "  572365.75911649  171323.41668882  410341.75868934  277286.00226461\n",
            "  720155.27018391  838785.29653602  494684.08011604  675727.00615935\n",
            "  664859.0486644   448379.60992168  836029.3792099   319762.47024728\n",
            "  549787.9279063   220229.22541611  511789.00462636  508917.43645942\n",
            "  191639.16125437  486417.46115401  388165.98812128  526253.66265137\n",
            "  522541.3112805   328832.0774772   680126.6949573   379420.5856287\n",
            "  462651.89421744  907358.44035416  640406.14430076  799558.57656096\n",
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            "  409423.11958063  572211.18032329  334342.7791131   887575.45442249\n",
            "  219194.93546658  798794.51624546  519824.32190676  443825.34233052\n",
            " 1139336.82707142  553307.90554794  489937.43879564  795197.8157154\n",
            "  568691.20268166  768104.64486645  609446.04328773  377853.5370453\n",
            "  273534.72294134  574164.10938152  835913.72836908  576996.74959607\n",
            "  648518.18446958  449182.59818957  250764.51800191  604050.99249264\n",
            "  660073.47939161  439348.93064414  772504.3336644   552659.49607325\n",
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            " 1148406.43430135  727349.80426037  718164.54618964  547989.57764127\n",
            "  529773.640293    639371.85435123  586869.34509389 1210973.91278575\n",
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            "  382137.57500244  702820.17700831  350182.11199227  545272.58826754\n",
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            "  748063.53784498  719198.83613916  461524.70121221  626821.19743207\n",
            "  250116.10852722  461733.25510874  552428.19439162  396949.18554989\n",
            " 1128507.79752886  394039.82244691  270856.66151999  415011.67712131\n",
            "  437125.77195189  373183.61861333  486301.81031319  539722.95867925\n",
            "  531687.64139885  387648.27663834  273573.65089373  263701.05539591\n",
            "  806142.49609878  400276.78946228  762709.59407136  714413.26686637\n",
            "  591500.33557347  341382.73439637]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "MSE = mean_squared_error(y_test, y_pred)\n",
        "RMSE = mean_squared_error(y_test, y_pred, squared=False)\n",
        "print(\"MSE is:\", MSE)\n",
        "print(\"RMSE is:\", RMSE)\n",
        "print(\"R_squared is:\", r_squared)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5qc5nmhPW_RY",
        "outputId": "ad13f893-87c8-4f0c-c93c-d1eb487148a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE is: 75634500311.92892\n",
            "RMSE is: 275017.2727519654\n",
            "R_squared is: 0.4450186747118554\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Observe Variance Inflation Factor(VIF)"
      ],
      "metadata": {
        "id": "gdSwRnq1XNR6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install statsmodels --upgrade"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qaEUdY70XEl6",
        "outputId": "70212737-7350-491b-f90f-9ea511aab6b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: statsmodels in /usr/local/lib/python3.10/dist-packages (0.14.1)\n",
            "Requirement already satisfied: numpy<2,>=1.18 in /usr/local/lib/python3.10/dist-packages (from statsmodels) (1.23.5)\n",
            "Requirement already satisfied: scipy!=1.9.2,>=1.4 in /usr/local/lib/python3.10/dist-packages (from statsmodels) (1.11.4)\n",
            "Requirement already satisfied: pandas!=2.1.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from statsmodels) (1.5.3)\n",
            "Requirement already satisfied: patsy>=0.5.4 in /usr/local/lib/python3.10/dist-packages (from statsmodels) (0.5.6)\n",
            "Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.10/dist-packages (from statsmodels) (23.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas!=2.1.0,>=1.0->statsmodels) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas!=2.1.0,>=1.0->statsmodels) (2023.3.post1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from patsy>=0.5.4->statsmodels) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
        "import statsmodels.api as sm\n",
        "\n",
        "X = dataset[['sqft_living', 'bedrooms', 'bathrooms']]\n",
        "y = dataset['price']\n",
        "\n",
        "# Adding constant to predictor variables for intercept term\n",
        "X = sm.add_constant(X)\n",
        "\n",
        "# Computing VIF for each predictor variable\n",
        "vif = pd.DataFrame()\n",
        "vif[\"VIF Factor\"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
        "vif[\"predictor\"] = X.columns\n",
        "\n",
        "print(vif)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "opynPBKiXWLC",
        "outputId": "d4117426-ad0e-4dd3-8ccb-12b31534db66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   VIF Factor    predictor\n",
            "0   16.269620        const\n",
            "1    2.667951  sqft_living\n",
            "2    1.598727     bedrooms\n",
            "3    2.455652    bathrooms\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Implement Ridge Regression Model"
      ],
      "metadata": {
        "id": "lOJ4fgTMXmA5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_data = dataset['sqft_living']\n",
        "y_data = dataset['price']\n",
        "# Creating each single feature as a vector\n",
        "x_data = np.array(x_data).reshape((-1,1))\n",
        "x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.15, random_state=1)"
      ],
      "metadata": {
        "id": "mklH9-AJXgIv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import Ridge\n",
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "ridge_model = Ridge(alpha=0.0)\n",
        "\n",
        "ridge_model.fit(x_train, y_train)\n",
        "\n",
        "y_pred = ridge_model.predict(x_test)\n",
        "\n",
        "print(\"RSquared Error : \", ridge_model.score(x_test,y_test))\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "print(\"Mean Squared Error: \", mse)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VskffjWFXtGr",
        "outputId": "19de7a29-c1fa-48ad-8f9b-9e4ae3122ef5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RSquared Error :  0.4266186260766843\n",
            "Mean Squared Error:  78142113488.84764\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# LASSO Regression Model"
      ],
      "metadata": {
        "id": "VfC0uc6JX2Zj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import datasets\n",
        "from sklearn.linear_model import Lasso\n",
        "\n",
        "# Creating an instance of Lasso Regression implementation\n",
        "lasso = Lasso(alpha=0.2)\n",
        "\n",
        "# Fitting the Lasso model\n",
        "lasso.fit(x_train, y_train)\n",
        "y_pred = lasso.predict(x_test)\n",
        "\n",
        "# Creating the model score\n",
        "\n",
        "print(\"Model Score : \",lasso.score(x_test, y_test))\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "print(\"Mean Squared Error: \", mse)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r1AhnR4PXw02",
        "outputId": "3d6110f5-cffb-4116-fdc6-fdbb13c53a38"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Score :  0.426618626055833\n",
            "Mean Squared Error:  78142113491.68932\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "# Observation on above models"
      ],
      "metadata": {
        "id": "EGfk4PR-YCCj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "For Univariate model"
      ],
      "metadata": {
        "id": "ycs69hozYFDz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = dataset['sqft_living']\n",
        "y = dataset['price']"
      ],
      "metadata": {
        "id": "BXeW6yrRX7tB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# calculating the slope and intercept of the best-fit line\n",
        "slope = np.sum((X - np.mean(X)) * (y - np.mean(y))) / np.sum((X - np.mean(X)) ** 2)\n",
        "intercept = np.mean(y) - slope * np.mean(X)"
      ],
      "metadata": {
        "id": "Ag2VjPhBYMPN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(slope, intercept)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pVWlqXTLYPIO",
        "outputId": "009f587a-27b1-4bcf-e61e-6a7895cb8861"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "251.95013105923712 12954.2423963818\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lre=LinearRegression()\n",
        "lre.fit(np.array(X).reshape((-1,1)),y)\n",
        "pred_slope = lre.coef_\n",
        "pred_intercept = lre.intercept_"
      ],
      "metadata": {
        "id": "Jn6mSWd0YSGJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(pred_slope, pred_intercept)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sQLCOY7rYVS4",
        "outputId": "58e29e34-3bc0-4a82-8cba-7b97351e09eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[251.95013106] 12954.242396381567\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ploting the scatter plot and the best-fit line\n",
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(X, y)\n",
        "plt.plot(X, slope * X + intercept, color='red')\n",
        "plt.xlabel('sqft')\n",
        "plt.ylabel('price')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "2ZCwsy8qYX39",
        "outputId": "e881a613-9b36-4e72-d28d-d75ef51b85bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ploting the scatter plot and the best-fit line\n",
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(X, y)\n",
        "plt.plot(X, slope * X + intercept, color='red')\n",
        "plt.plot(X, pred_slope * X + pred_intercept, color='green')\n",
        "plt.legend(['sample','actual','predicted'])\n",
        "plt.xlabel('sqft')\n",
        "plt.ylabel('price')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "o77Ogo8RYax4",
        "outputId": "e2545077-9ae6-4a89-cd08-e04d37c0665e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For Multivariate model"
      ],
      "metadata": {
        "id": "g_sXztzNYmXb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# input features\n",
        "sqft = dataset['sqft_living']\n",
        "bedrooms = dataset['bedrooms']\n",
        "bathrooms = dataset['bathrooms']\n",
        "\n",
        "# target variable\n",
        "price = dataset['price']\n",
        "\n",
        "# combining input features into a single matrix\n",
        "X = np.column_stack((sqft, bedrooms, bathrooms))\n",
        "\n",
        "# adding a column of ones for the intercept term\n",
        "X = np.hstack((np.ones((len(X), 1)), X))\n",
        "\n",
        "# calculating the least squares solution for the model coefficients\n",
        "w = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(price)\n",
        "\n",
        "# calculating predicted values of the target variable\n",
        "y_pred = X.dot(w)\n",
        "\n",
        "# ploting the scatter plot and best-fit line\n",
        "fig, ax = plt.subplots()\n",
        "ax.scatter(sqft, price)\n",
        "ax.plot(sqft, y_pred, color='red')\n",
        "ax.set_xlabel('Square Feet')\n",
        "ax.set_ylabel('Price')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "3xS_MyC0Yfe5",
        "outputId": "4ce92650-2818-4462-a207-4dfa4de1f26f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9N3ufGfCYrxm"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}