Linear_Regression_by_appling_on_height_weight_dataset.ipynb

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        "<a href=\"https://colab.research.google.com/github/mostafizur1997/Machine-Learning-Project/blob/main/Linear_Regression_by_appling_on_height_weight_dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "#import library\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns "
      ]
    },
    {
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      "source": [
        "# read dataset\n",
        "df = pd.read_csv('/content/Height-Weight Dataset.csv')"
      ]
    },
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      "execution_count": null,
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        "outputId": "394df5c5-14a1-401c-d056-d8605ca82e79"
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      "outputs": [
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              "      <th>Height</th>\n",
              "      <th>Weight</th>\n",
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            "text/plain": [
              "      Height      Weight\n",
              "0  73.847017  241.893563\n",
              "1  68.781904  162.310473\n",
              "2  74.110105  212.740856\n",
              "3  71.730978  220.042470\n",
              "4  69.881796  206.349801"
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      "source": [
        "# chcek some data \n",
        "df.head()"
      ]
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        "outputId": "f033a472-37dc-47ef-c112-3142316f26cd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3501 entries, 0 to 3500\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   Height  3501 non-null   float64\n",
            " 1   Weight  3501 non-null   float64\n",
            "dtypes: float64(2)\n",
            "memory usage: 54.8 KB\n"
          ]
        }
      ],
      "source": [
        "# To know the data information\n",
        "df.info()"
      ]
    },
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              "<matplotlib.collections.PathCollection at 0x7f4342cabe50>"
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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# visualize the dataset\n",
        "plt.scatter(df.Height, df.Weight)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "dqPWjy7FdWdt",
        "outputId": "0bd4a3ad-8adb-4d1d-8897-1db6503ab471"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Weight plot')"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# define the label\n",
        "plt.scatter(df.Height, df.Weight, color='green', marker='*')\n",
        "plt.xlabel('Height')\n",
        "plt.ylabel('Weight')\n",
        "plt.title('Weight plot') "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lir9IuSKdwnI",
        "outputId": "5eb568f2-2c74-4ddd-881f-c39726c92437"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "69.02441087268781"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#mean of height \n",
        "df.Height.mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xUzPO1ifewmw",
        "outputId": "42853334-b2a8-4f07-83e2-60b63d84b45f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "186.891257979463"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#mean of weight\n",
        "df.Weight.mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w9eZ4A0ge7xZ"
      },
      "source": [
        "# Seperate dependent and independent variable"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "qGetroapfFJe",
        "outputId": "3b01393d-1d81-4615-dfc3-bbdc4fd61096"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-256eff6f-b6d1-4dd6-a7df-aa6121d1f872\">\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>Height</th>\n",
              "      <th>Weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>73.847017</td>\n",
              "      <td>241.893563</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>68.781904</td>\n",
              "      <td>162.310473</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>74.110105</td>\n",
              "      <td>212.740856</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>71.730978</td>\n",
              "      <td>220.042470</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>69.881796</td>\n",
              "      <td>206.349801</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-256eff6f-b6d1-4dd6-a7df-aa6121d1f872')\"\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-256eff6f-b6d1-4dd6-a7df-aa6121d1f872');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      Height      Weight\n",
              "0  73.847017  241.893563\n",
              "1  68.781904  162.310473\n",
              "2  74.110105  212.740856\n",
              "3  71.730978  220.042470\n",
              "4  69.881796  206.349801"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lcscR2xFfHWs"
      },
      "outputs": [],
      "source": [
        "# separate independet data \n",
        "x= df[['Height']] # x= df.drop('Weight', axis=1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rpTIhe4gffYZ",
        "outputId": "b085d5f8-1741-42ca-9407-bf0b1dfb6d32"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(3501, 1)"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "epQA-m9Rfzq5"
      },
      "outputs": [],
      "source": [
        "#separet dependent variable\n",
        "y = df['Weight']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "JsdAHEBRf60a",
        "outputId": "0ba8ac7a-a349-4536-ebe7-82ed09e35520"
      },
      "outputs": [
        {
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-76646cf1-40a3-4e20-9b04-63b26d7b5359');\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",
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              "  "
            ],
            "text/plain": [
              "      Height\n",
              "0  73.847017\n",
              "1  68.781904\n",
              "2  74.110105\n",
              "3  71.730978\n",
              "4  69.881796"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#check the independent data \n",
        "x.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "omVj3ntCgC9V",
        "outputId": "160af771-6631-4692-e11c-5efbfb39bc2f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    241.893563\n",
              "1    162.310473\n",
              "2    212.740856\n",
              "3    220.042470\n",
              "4    206.349801\n",
              "Name: Weight, dtype: float64"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# check the dependent data\n",
        "y.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qj3jaf_igi7q"
      },
      "source": [
        "#split the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zwB-2_DkgMug"
      },
      "outputs": [],
      "source": [
        "from sklearn .model_selection import train_test_split\n",
        "x_train, x_test, y_train, ytest = train_test_split(x,y,test_size=.30) # train data=70%, test data=30%"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "GFoEjg7ehPOr",
        "outputId": "282fd71c-1ac4-4f55-91f6-02e042afa48f"
      },
      "outputs": [
        {
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
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              "  "
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            "text/plain": [
              "         Height\n",
              "514   71.729060\n",
              "2747  68.568959\n",
              "2126  71.370013\n",
              "1598  69.725207\n",
              "1226  65.831654"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_train.head()"
      ]
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    {
      "cell_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "8MQ11FK5hSa0",
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              "\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>Height</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2476</th>\n",
              "      <td>69.587280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1746</th>\n",
              "      <td>68.164233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>925</th>\n",
              "      <td>64.909785</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2421</th>\n",
              "      <td>64.572897</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1693</th>\n",
              "      <td>66.703480</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-35ecee80-d111-4d5b-918c-21382d68a59f')\"\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",
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              "    <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",
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              "      \n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      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",
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              "\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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-35ecee80-d111-4d5b-918c-21382d68a59f 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-35ecee80-d111-4d5b-918c-21382d68a59f');\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",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         Height\n",
              "2476  69.587280\n",
              "1746  68.164233\n",
              "925   64.909785\n",
              "2421  64.572897\n",
              "1693  66.703480"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_test.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oK8mf5rQhZqm",
        "outputId": "d7f49a34-0f7f-4721-b20d-1c4028127f5b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(2450, 1)"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_train.shape#70%"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FEOT3SPohdSj",
        "outputId": "d0bebd46-3a9f-405b-8ce8-f7fd8b4f35c9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1051, 1)"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_test.shape # 30%"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yqXS_Qf5h-58"
      },
      "source": [
        "#Linear Regression with sikitlearn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G5J67RnDh2Yk"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LinearRegression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AYyc3l6jv1mz"
      },
      "outputs": [],
      "source": [
        "reg = LinearRegression()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "fD2Jmv0HwWdc",
        "outputId": "1d33b623-e9c1-445f-8247-c0a88f48f2a8"
      },
      "outputs": [
        {
          "data": {
            "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>"
            ],
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "reg.fit(x_train, y_train) #training the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_NK2kOVqwtXT",
        "outputId": "7f4651e3-6d9e-46ad-8cdb-443d1104ecac"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "array([215.79745195])"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "reg.predict([[73.847017]]) #xtrain, ytain"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "P0tEBnjOxyJF",
        "outputId": "de58ac4b-fc16-443a-d1d8-bdca8b22125f"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-c56d9834-619d-47d1-aebf-3f25170c9b60\">\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>Height</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>514</th>\n",
              "      <td>71.729060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2747</th>\n",
              "      <td>68.568959</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2126</th>\n",
              "      <td>71.370013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1598</th>\n",
              "      <td>69.725207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1226</th>\n",
              "      <td>65.831654</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c56d9834-619d-47d1-aebf-3f25170c9b60')\"\n",
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              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    <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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              "      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-c56d9834-619d-47d1-aebf-3f25170c9b60 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-c56d9834-619d-47d1-aebf-3f25170c9b60');\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",
              "  "
            ],
            "text/plain": [
              "         Height\n",
              "514   71.729060\n",
              "2747  68.568959\n",
              "2126  71.370013\n",
              "1598  69.725207\n",
              "1226  65.831654"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lPta_UMr-3cm",
        "outputId": "c71ff5b0-d31a-4249-b7c3-652bfc88fbe2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "514     188.595024\n",
              "2747    185.975825\n",
              "2126    205.538501\n",
              "1598    182.481687\n",
              "1226    165.592195\n",
              "Name: Weight, dtype: float64"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WOxhwO0C-5Rj"
      },
      "outputs": [],
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HHAQgZTa_OAe",
        "outputId": "0a6607ec-9e22-43cd-9e59-57a2e596c22a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([215.79745195])"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "reg.predict([[73.847017]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S8ssneXP_zQ2",
        "outputId": "b892f489-14d7-4f2d-8b70-c1620f4f4f7c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2476    194.498356\n",
              "1746    191.830235\n",
              "925     145.176136\n",
              "2421    176.718917\n",
              "1693    189.430029\n",
              "Name: Weight, dtype: float64"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ytest.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cEf303diAzlF",
        "outputId": "d5868efd-5bba-4ba1-9af2-0f9378608319"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([190.15470122, 181.58824555, 161.99713268, ..., 212.37319065,\n",
              "       182.91352856, 195.59715382])"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ypred =reg.predict(x_test)\n",
        "ypred"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3L07KrFtaS9F"
      },
      "source": [
        "Mean Squared Error (MSE) is a commonly used evaluation metric in machine learning and statistics. It measures the average squared difference between predicted and actual values. While MSE has its advantages, it also has some disadvantages when compared to other metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R2 score. Let's explore the advantages and disadvantages of MSE in comparison to these metrics:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oZrSsNkmCluB"
      },
      "source": [
        "#Mean squared error\n",
        "**Advantages of Mean Squared Error (MSE):**\n",
        "\n",
        "Emphasizes large errors: MSE squares the errors, giving more weight to larger errors compared to MAE. This can be beneficial in scenarios where large errors are considered more important than smaller ones, such as in outlier detection or anomaly detection tasks.\n",
        "\n",
        "Differentiable: MSE is a differentiable loss function, which makes it suitable for optimization algorithms that rely on gradient-based methods, such as gradient descent. This property is important for training neural networks.\n",
        "\n",
        "**Disadvantages of Mean Squared Error (MSE):**\n",
        "\n",
        "Sensitive to outliers: Squaring the errors in MSE amplifies the effect of outliers in the data, leading to a higher overall error. Outliers can disproportionately influence the model's performance evaluation, making MSE less robust in the presence of outliers compared to MAE or RMSE.\n",
        "\n",
        "Scale-dependent: MSE is affected by the scale of the target variable. When the scale of the target variable changes, the magnitude of the errors will also change accordingly. This scale dependence makes it difficult to compare MSE values across different datasets or models.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HCSV49hyBBkJ"
      },
      "outputs": [],
      "source": [
        "\n",
        "from sklearn.metrics import mean_squared_error"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U5hQcL2GCBcJ"
      },
      "outputs": [],
      "source": [
        "mse = mean_squared_error(ytest, ypred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MVAV1jESCfu-",
        "outputId": "a7dffb21-f4b7-4e0b-c511-f8beeab540e0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "99.61380726998675"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "mse"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PUHWEVk6CqSS"
      },
      "source": [
        "#Mean absolute error\n",
        "\n",
        "**Advantages:**\n",
        "\n",
        "Robust to outliers: MAE is not influenced by outliers as much as MSE, as it does not involve squaring the errors.\n",
        "\n",
        "Scale-invariant: MAE does not depend on the scale of the target variable, making it easier to interpret and compare across datasets.\n",
        "\n",
        "**Disadvantages:**\n",
        "\n",
        "Does not emphasize large errors: MAE treats all errors equally and does not emphasize large errors over smaller ones."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UGb85CwACjgi"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import mean_absolute_error"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3ilw5Lg4C3Qw"
      },
      "outputs": [],
      "source": [
        "mae= mean_absolute_error(ytest, ypred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RCZKXvQIDODE",
        "outputId": "46501c69-e815-4fe2-ec89-3d35a30682d4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "8.122746510936203"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "mae"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a6qCyVkEcrBQ"
      },
      "source": [
        "#Root mean squared error\n",
        "\n",
        "The mathematical formulation of RMSE is:\n",
        "\n",
        "RMSE = sqrt(1/n * sum((y_pred - y_actual)^2))\n",
        "\n",
        "\n",
        "**Advantages:**\n",
        "\n",
        "\n",
        "Similar to MSE: RMSE has similar advantages to MSE, such as emphasizing large errors and being differentiable.\n",
        "\n",
        "**Disadvantages:**\n",
        "\n",
        "Sensitive to outliers: RMSE is still influenced by outliers, albeit to a lesser extent than MSE."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gOoWJH_FcqJe"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import mean_squared_error"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DUKdvAMIdoZs"
      },
      "outputs": [],
      "source": [
        "rmse = np.sqrt(mean_squared_error(ytest, ypred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eNQH6b_zeTBP",
        "outputId": "3fa988a0-727b-4c91-a4f4-762519da6861"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "9.980671684309966"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rmse"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g4VbeImPdBXI"
      },
      "source": [
        "#R2 Score (Coefficient of Determination):\n",
        "**Advantages:**\n",
        "\n",
        "Provides an indication of model goodness-of-fit: R2 score measures the proportion of the variance in the dependent variable that is predictable from the independent variables.\n",
        "\n",
        "Scale-independent: R2 score is not affected by the scale of the target variable.\n",
        "\n",
        "**Disadvantages:**\n",
        "\n",
        "Can be misleading: R2 score can be misleading when applied to complex models or models with high dimensionality. It may artificially inflate the score, giving a false sense of model performance."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3o2wZ8ZtfECI"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import r2_score\n",
        "r2=r2_score(ytest, ypred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m82vC_oLnzrX",
        "outputId": "2818114f-8871-4f7d-8a4d-4deb5cc1d5ae"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.7463422562226918"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "r2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZuR24Ul_DW0y"
      },
      "outputs": [],
      "source": [
        "m = reg.coef_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z28n3NwODjQP",
        "outputId": "1a2c56a0-a850-4bdf-b1b4-4160ab93c321"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([6.01979681])"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "m"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c0b9UhT8Dkjb"
      },
      "outputs": [],
      "source": [
        "c= reg.intercept_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vRFkmNHoDovi",
        "outputId": "3a827f95-59b4-41a2-c980-ad62ab24e0bb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "-228.74658569239028"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "c"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_nHggUEvDppd"
      },
      "outputs": [],
      "source": [
        "result = m * 73.847017 + c"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fiB8suhDEDRo",
        "outputId": "69d0442d-c915-49bb-82b7-f26b872e2054"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([215.79745195])"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bpLBmuQdEEas",
        "outputId": "dcb070a6-1bdb-4d7c-d0d5-836b7164827c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([215.79745195])"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "reg.predict([[73.847017]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "euqZ8ECoETXp",
        "outputId": "a2a0b2e8-b193-43a2-8876-8096bfa501e4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f433fd9a080>]"
            ]
          },
          "execution_count": 48,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#Best fit line\n",
        "plt.plot(x_test, reg.predict(x_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "EUmXFXQ-EcBO",
        "outputId": "532c99a7-76aa-4665-8e11-348711204ce9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f433dad1a50>]"
            ]
          },
          "execution_count": 49,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.scatter(df.Height,df.Weight, color='red', marker='+')\n",
        "plt.xlabel('Height')\n",
        "plt.ylabel('Weight')\n",
        "plt.title('Weight plot')\n",
        "plt.plot(df.Height, reg.predict(df[['Height']]), color ='red') #best fit"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "qhky1boUHHUE",
        "outputId": "eca5df31-84ca-410d-ae29-10782b64b3e3"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
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              "      <th>Height</th>\n",
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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.plot(x_test, reg.predict(x_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "9BbSgy5FJ_0A",
        "outputId": "07602f47-aa41-4e1c-9c05-0164c6704da3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ff9a25e9510>]"
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "pred = reg.predict(x_test)\n",
        "plt.plot(np.squeeze(x_test), pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "hBe_0GVpKK_R"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPcE+jzdu1kN7tzn1jjY+1c",
      "include_colab_link": true
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}