linear_regression_intro.ipynb

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        "<a href=\"https://colab.research.google.com/github/samarthm04/ComputerVision_0_to_Hero/blob/main/linear_regression_intro.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "execution_count": 1,
      "metadata": {
        "id": "E51qtSt9rtbN"
      },
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import linear_model"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv(\"/content/canada_per_capita_income.csv\")"
      ],
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        "id": "66kfALz4r6pe"
      },
      "execution_count": 3,
      "outputs": []
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        "df"
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      "execution_count": 4,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "    year  per capita income (US$)\n",
              "0   1970              3399.299037\n",
              "1   1971              3768.297935\n",
              "2   1972              4251.175484\n",
              "3   1973              4804.463248\n",
              "4   1974              5576.514583\n",
              "5   1975              5998.144346\n",
              "6   1976              7062.131392\n",
              "7   1977              7100.126170\n",
              "8   1978              7247.967035\n",
              "9   1979              7602.912681\n",
              "10  1980              8355.968120\n",
              "11  1981              9434.390652\n",
              "12  1982              9619.438377\n",
              "13  1983             10416.536590\n",
              "14  1984             10790.328720\n",
              "15  1985             11018.955850\n",
              "16  1986             11482.891530\n",
              "17  1987             12974.806620\n",
              "18  1988             15080.283450\n",
              "19  1989             16426.725480\n",
              "20  1990             16838.673200\n",
              "21  1991             17266.097690\n",
              "22  1992             16412.083090\n",
              "23  1993             15875.586730\n",
              "24  1994             15755.820270\n",
              "25  1995             16369.317250\n",
              "26  1996             16699.826680\n",
              "27  1997             17310.757750\n",
              "28  1998             16622.671870\n",
              "29  1999             17581.024140\n",
              "30  2000             18987.382410\n",
              "31  2001             18601.397240\n",
              "32  2002             19232.175560\n",
              "33  2003             22739.426280\n",
              "34  2004             25719.147150\n",
              "35  2005             29198.055690\n",
              "36  2006             32738.262900\n",
              "37  2007             36144.481220\n",
              "38  2008             37446.486090\n",
              "39  2009             32755.176820\n",
              "40  2010             38420.522890\n",
              "41  2011             42334.711210\n",
              "42  2012             42665.255970\n",
              "43  2013             42676.468370\n",
              "44  2014             41039.893600\n",
              "45  2015             35175.188980\n",
              "46  2016             34229.193630"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>year</th>\n",
              "      <th>per capita income (US$)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1970</td>\n",
              "      <td>3399.299037</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1971</td>\n",
              "      <td>3768.297935</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1972</td>\n",
              "      <td>4251.175484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1973</td>\n",
              "      <td>4804.463248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1974</td>\n",
              "      <td>5576.514583</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1975</td>\n",
              "      <td>5998.144346</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1976</td>\n",
              "      <td>7062.131392</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1977</td>\n",
              "      <td>7100.126170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1978</td>\n",
              "      <td>7247.967035</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1979</td>\n",
              "      <td>7602.912681</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1980</td>\n",
              "      <td>8355.968120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1981</td>\n",
              "      <td>9434.390652</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1982</td>\n",
              "      <td>9619.438377</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1983</td>\n",
              "      <td>10416.536590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1984</td>\n",
              "      <td>10790.328720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1985</td>\n",
              "      <td>11018.955850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1986</td>\n",
              "      <td>11482.891530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1987</td>\n",
              "      <td>12974.806620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>1988</td>\n",
              "      <td>15080.283450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1989</td>\n",
              "      <td>16426.725480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1990</td>\n",
              "      <td>16838.673200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>1991</td>\n",
              "      <td>17266.097690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>1992</td>\n",
              "      <td>16412.083090</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>1993</td>\n",
              "      <td>15875.586730</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>1994</td>\n",
              "      <td>15755.820270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>1995</td>\n",
              "      <td>16369.317250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>1996</td>\n",
              "      <td>16699.826680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>1997</td>\n",
              "      <td>17310.757750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>1998</td>\n",
              "      <td>16622.671870</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>1999</td>\n",
              "      <td>17581.024140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>2000</td>\n",
              "      <td>18987.382410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>2001</td>\n",
              "      <td>18601.397240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>2002</td>\n",
              "      <td>19232.175560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>2003</td>\n",
              "      <td>22739.426280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>2004</td>\n",
              "      <td>25719.147150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>2005</td>\n",
              "      <td>29198.055690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>2006</td>\n",
              "      <td>32738.262900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>2007</td>\n",
              "      <td>36144.481220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>2008</td>\n",
              "      <td>37446.486090</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>2009</td>\n",
              "      <td>32755.176820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>2010</td>\n",
              "      <td>38420.522890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>2011</td>\n",
              "      <td>42334.711210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>2012</td>\n",
              "      <td>42665.255970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>2013</td>\n",
              "      <td>42676.468370</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>2014</td>\n",
              "      <td>41039.893600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>2015</td>\n",
              "      <td>35175.188980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>2016</td>\n",
              "      <td>34229.193630</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "      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-5b161d29-91a5-48a9-ad53-a17598c2553f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_879d5aa7-de59-4bc2-8c06-3013641ed278\">\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('df')\"\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_879d5aa7-de59-4bc2-8c06-3013641ed278 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 47,\n  \"fields\": [\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 1970,\n        \"max\": 2016,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          1997,\n          2009,\n          1996\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"per capita income (US$)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12034.679437576675,\n        \"min\": 3399.299037,\n        \"max\": 42676.46837,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          17310.75775,\n          32755.17682,\n          16699.82668\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Simple scatter plot of the data"
      ],
      "metadata": {
        "id": "-DEVKzxQvOHA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(df['year'], df['per capita income (US$)'], color= 'green', marker='*')\n",
        "plt.xlabel('year')\n",
        "plt.ylabel('income')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "SUd6xO6ouRs2",
        "outputId": "fa0c957f-75d1-4fd7-d264-b8a6b25e3bba"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'income')"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "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": [
        "reg=linear_model.LinearRegression()\n",
        "reg.fit(df[['year']], df['per capita income (US$)'])#so what will be the value in x year, reg.predict x!"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "J4-AV746uhg5",
        "outputId": "f3208f1b-1857-46cc-f9b7-03c96803c141"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-3 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-3 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-3 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-3 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-3 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-3 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-3 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-3 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-3\" 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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg.predict([[2000]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SyDgIvCKJMp5",
        "outputId": "eeaa0457-5cd3-46fb-f22b-2199236cdbff"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([24719.39258996])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#y=mx+b, m=coeff,b=intercept"
      ],
      "metadata": {
        "id": "WA2dn6CnLPee"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "reg.coef_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bKhm5SrqLEmo",
        "outputId": "7138f386-20e9-457a-84d8-921dec93bb00"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([828.46507522])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg.intercept_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2PeJWCcCLJ1M",
        "outputId": "8343e211-209d-426e-af5f-5a13dfc86659"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-1632210.7578554575"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#y=mx+b- proof of linear regression calculation\n",
        "y=828.46507522*2000-1632210.7578554575\n",
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4AJlG26pLTGv",
        "outputId": "d9b85463-4f25-4e27-9ff5-48ccc4a2d629"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "24719.39258454251"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#representation of the plot"
      ],
      "metadata": {
        "id": "j7iYrG1mMGEr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(df['year'], df['per capita income (US$)'], color= 'green', marker='*')\n",
        "plt.xlabel('year')\n",
        "plt.ylabel('income')\n",
        "plt.plot(df['year'], reg.predict(df[['year']]), color='blue')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "pZ_AQFPuLYDb",
        "outputId": "0dcf17ba-fc1a-4f08-b790-a408fa1b7c17"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7df59d260be0>]"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "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": "2Qv3ikblLwKS"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}