Dhaka_Home_Prices.ipynb

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      "source": [
        "#importing library\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n"
      ]
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      "source": [
        "df = pd.read_csv('/content/dhaka homeprices.csv')\n",
        "df"
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              "   area   price\n",
              "0  2600   55000\n",
              "1  3000   56500\n",
              "2  3200   61000\n",
              "3  3600   68000\n",
              "4  4000   72000\n",
              "5  5000   71000\n",
              "6  2500   40000\n",
              "7  2700   38000\n",
              "8  1200   17000\n",
              "9  5000  100000"
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              "   area  price\n",
              "0  2600  55000\n",
              "1  3000  56500\n",
              "2  3200  61000"
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            "text/plain": [
              "(10, 2)"
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      "cell_type": "code",
      "source": [
        "df.isnull().values.any()"
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            "text/plain": [
              "False"
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        "df.isna().sum()"
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "area     0\n",
              "price    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = df[['area']]\n",
        "y = df['price']"
      ],
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        "id": "FL45XJCDcUqE"
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      "execution_count": null,
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      "source": [
        "##Visualization"
      ],
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        "id": "b2T-SxbYdE4P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(df['area'],df['price'], marker='+', color='red')\n",
        "plt.xlabel('Area in square ft')\n",
        "plt.ylabel('Price in taka')\n",
        "plt.title('Home prices in Dhaka')"
      ],
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        "id": "Bdg0uFB9dNrb",
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      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Home prices in Dhaka')"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "qzy6Vkmecnpv",
        "outputId": "f8de910f-992d-4220-8e41-d6d0174ea2ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   area\n",
              "0  2600\n",
              "1  3000\n",
              "2  3200\n",
              "3  3600\n",
              "4  4000\n",
              "5  5000\n",
              "6  2500\n",
              "7  2700\n",
              "8  1200\n",
              "9  5000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7df24d0a-90d5-4da6-9c50-1f24b3555456\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>5000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7df24d0a-90d5-4da6-9c50-1f24b3555456')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7df24d0a-90d5-4da6-9c50-1f24b3555456 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-7df24d0a-90d5-4da6-9c50-1f24b3555456');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z-1Sm7_Oc3_m",
        "outputId": "cee1253f-1e3f-4c08-ee70-7b73b678d647"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     55000\n",
              "1     56500\n",
              "2     61000\n",
              "3     68000\n",
              "4     72000\n",
              "5     71000\n",
              "6     40000\n",
              "7     38000\n",
              "8     17000\n",
              "9    100000\n",
              "Name: price, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "NPm3TSmQc4kz"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xtrain,xtest,ytrain,ytest=train_test_split(x,y, test_size = .30, random_state = 1)\n",
        "xtrain"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "4xn4yu7XinNB",
        "outputId": "d9d9b645-7370-4eaf-86ef-66afb05e2d18"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   area\n",
              "4  4000\n",
              "0  2600\n",
              "3  3600\n",
              "1  3000\n",
              "7  2700\n",
              "8  1200\n",
              "5  5000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-efdecb00-3ef5-4592-a0e9-04f2320d3192\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>area</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-efdecb00-3ef5-4592-a0e9-04f2320d3192')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-efdecb00-3ef5-4592-a0e9-04f2320d3192 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-efdecb00-3ef5-4592-a0e9-04f2320d3192');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xtest"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "Bb4Fxp7okOKB",
        "outputId": "a25becdd-16a7-4688-ee53-0078c788176c"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   area\n",
              "2  3200\n",
              "9  5000\n",
              "6  2500"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fc6133d6-0238-45ef-a6fb-ab83a43a9c08\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>area</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>5000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fc6133d6-0238-45ef-a6fb-ab83a43a9c08')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fc6133d6-0238-45ef-a6fb-ab83a43a9c08 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-fc6133d6-0238-45ef-a6fb-ab83a43a9c08');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ytrain"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RjxwT-0LkPwb",
        "outputId": "1d086796-a9d0-48d1-ad0e-424ea4634a39"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4    72000\n",
              "0    55000\n",
              "3    68000\n",
              "1    56500\n",
              "7    38000\n",
              "8    17000\n",
              "5    71000\n",
              "Name: price, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ytest"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o3LrJxvalhO0",
        "outputId": "a2f12f1c-b5aa-472c-f5f0-bc400341a109"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2     61000\n",
              "9    100000\n",
              "6     40000\n",
              "Name: price, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression"
      ],
      "metadata": {
        "id": "k7c0jjOOkWWr"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reg = LinearRegression()"
      ],
      "metadata": {
        "id": "K5qmLmxwkstz"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reg.fit(xtrain, ytrain)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k8kiJo9gk2vN",
        "outputId": "c3504174-2558-4e44-d769-2d65846dc451"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg.predict(xtest)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C4CpIpXglJmZ",
        "outputId": "5fc5a97f-84fd-49e9-e509-2a707ee0f5f8"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([54577.95521897, 81852.07441554, 43971.35330919])"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(df.area, reg.predict(df[['area']]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "VkvfTSHklPK8",
        "outputId": "93a66f68-95d4-4d60-b3f5-1cbf37253ea6"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fa486ef7a90>]"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(df['area'],df['price'], marker='+', color='red')\n",
        "plt.xlabel('Area in square ft')\n",
        "plt.ylabel('Price in taka')\n",
        "plt.title('Home prices in Dhaka')\n",
        "plt.plot(df.area, reg.predict(df[['area']]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "BJh5fPsBmski",
        "outputId": "b30e6dc1-1d14-42ed-c163-503219abaffd"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fa486a1cc90>]"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg.predict([[3500]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tjNr0ArDqVri",
        "outputId": "5dfc6ec4-ba0d-4d84-d0fd-7730605677da"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:451: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  \"X does not have valid feature names, but\"\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([59123.64175173])"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# y = mx + c\n",
        "reg.coef_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MX6KlF2cqz6E",
        "outputId": "3ad09797-74fd-4884-ab18-c694b16af9c2"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([15.15228844])"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg.intercept_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K39-Zku9q8CC",
        "outputId": "a96a6d2c-90d4-4737-dcb6-48a70fae79b2"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6090.632202831752"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    }
  ]
}