Energy Efficiency.ipynb

{
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      "cell_type": "code",
      "execution_count": 286,
      "metadata": {
        "id": "UJoMW9ycguKD"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "# from google.colab import drive\n",
        "import seaborn as sns\n",
        "from scipy import stats\n",
        "import warnings\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 287,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tvc8_coWhJq1",
        "outputId": "c6117568-a772-4146-f4ad-b0ed91b42472"
      },
      "outputs": [],
      "source": [
        "# drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 288,
      "metadata": {
        "id": "GP8zLSoWga6N"
      },
      "outputs": [],
      "source": [
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 289,
      "metadata": {
        "id": "ejU3UlZgiREt"
      },
      "outputs": [],
      "source": [
        "# df1=pd.read_csv('/content/drive/MyDrive/ENB2012.csv')\n",
        "df1=pd.read_csv('https://raw.githubusercontent.com/thevirengarg/Energy-Efficiency-Regression-Analysis-Project/main/ENB2012.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 290,
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        },
        "id": "TsKbfyCviWVW",
        "outputId": "89256a50-0776-4fb4-cd13-cd1ac62ad34e"
      },
      "outputs": [
        {
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              "      <th>Y1</th>\n",
              "      <th>Y2</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.98</td>\n",
              "      <td>514.5</td>\n",
              "      <td>294.0</td>\n",
              "      <td>110.25</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>15.55</td>\n",
              "      <td>21.33</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.98</td>\n",
              "      <td>514.5</td>\n",
              "      <td>294.0</td>\n",
              "      <td>110.25</td>\n",
              "      <td>7.0</td>\n",
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              "      <td>15.55</td>\n",
              "      <td>21.33</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.98</td>\n",
              "      <td>514.5</td>\n",
              "      <td>294.0</td>\n",
              "      <td>110.25</td>\n",
              "      <td>7.0</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>15.55</td>\n",
              "      <td>21.33</td>\n",
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              "      <th>3</th>\n",
              "      <td>0.98</td>\n",
              "      <td>514.5</td>\n",
              "      <td>294.0</td>\n",
              "      <td>110.25</td>\n",
              "      <td>7.0</td>\n",
              "      <td>5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>15.55</td>\n",
              "      <td>21.33</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.90</td>\n",
              "      <td>563.5</td>\n",
              "      <td>318.5</td>\n",
              "      <td>122.50</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>20.84</td>\n",
              "      <td>28.28</td>\n",
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              "     X1     X2     X3      X4   X5  X6   X7  X8     Y1     Y2\n",
              "0  0.98  514.5  294.0  110.25  7.0   2  0.0   0  15.55  21.33\n",
              "1  0.98  514.5  294.0  110.25  7.0   3  0.0   0  15.55  21.33\n",
              "2  0.98  514.5  294.0  110.25  7.0   4  0.0   0  15.55  21.33\n",
              "3  0.98  514.5  294.0  110.25  7.0   5  0.0   0  15.55  21.33\n",
              "4  0.90  563.5  318.5  122.50  7.0   2  0.0   0  20.84  28.28"
            ]
          },
          "execution_count": 290,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 291,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "LJZJQgV-K0Y-",
        "outputId": "d205f868-9b7e-4d0d-d4be-cbf7d34b56ed"
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      "outputs": [
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              "      <th></th>\n",
              "      <th>X1</th>\n",
              "      <th>X2</th>\n",
              "      <th>X3</th>\n",
              "      <th>X4</th>\n",
              "      <th>X5</th>\n",
              "      <th>X6</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>763</th>\n",
              "      <td>0.64</td>\n",
              "      <td>784.0</td>\n",
              "      <td>343.0</td>\n",
              "      <td>220.5</td>\n",
              "      <td>3.5</td>\n",
              "      <td>5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>5</td>\n",
              "      <td>17.88</td>\n",
              "      <td>21.40</td>\n",
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              "    <tr>\n",
              "      <th>764</th>\n",
              "      <td>0.62</td>\n",
              "      <td>808.5</td>\n",
              "      <td>367.5</td>\n",
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              "      <td>16.54</td>\n",
              "      <td>16.88</td>\n",
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              "    <tr>\n",
              "      <th>765</th>\n",
              "      <td>0.62</td>\n",
              "      <td>808.5</td>\n",
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              "      <td>16.44</td>\n",
              "      <td>17.11</td>\n",
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              "      <th>766</th>\n",
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              "      <td>220.5</td>\n",
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              "      <td>0.4</td>\n",
              "      <td>5</td>\n",
              "      <td>16.48</td>\n",
              "      <td>16.61</td>\n",
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              "    <tr>\n",
              "      <th>767</th>\n",
              "      <td>0.62</td>\n",
              "      <td>808.5</td>\n",
              "      <td>367.5</td>\n",
              "      <td>220.5</td>\n",
              "      <td>3.5</td>\n",
              "      <td>5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>5</td>\n",
              "      <td>16.64</td>\n",
              "      <td>16.03</td>\n",
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            ],
            "text/plain": [
              "       X1     X2     X3     X4   X5  X6   X7  X8     Y1     Y2\n",
              "763  0.64  784.0  343.0  220.5  3.5   5  0.4   5  17.88  21.40\n",
              "764  0.62  808.5  367.5  220.5  3.5   2  0.4   5  16.54  16.88\n",
              "765  0.62  808.5  367.5  220.5  3.5   3  0.4   5  16.44  17.11\n",
              "766  0.62  808.5  367.5  220.5  3.5   4  0.4   5  16.48  16.61\n",
              "767  0.62  808.5  367.5  220.5  3.5   5  0.4   5  16.64  16.03"
            ]
          },
          "execution_count": 291,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1.tail(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 292,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pXOcTOTtiZFW",
        "outputId": "b7edcce2-2973-4ef1-9ae3-47f2e4ab439a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "X1    0\n",
              "X2    0\n",
              "X3    0\n",
              "X4    0\n",
              "X5    0\n",
              "X6    0\n",
              "X7    0\n",
              "X8    0\n",
              "Y1    0\n",
              "Y2    0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 292,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 293,
      "metadata": {
        "id": "Pz8eanTLieZR"
      },
      "outputs": [],
      "source": [
        "df1.rename(columns={'X1':'Relative Compactness','X2':'Surface Area','X3':'Wall Area','X4':'Roof Area','X5':'Overall Height','X6':'Orientation','X7':'Glazing Area','X8':'Glazing Area Distribution','Y1':'Heating Load','Y2':'Cooling Load'},inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 294,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9g4My0DOEai3",
        "outputId": "2adb19f0-f799-4e28-cf75-26f90dd79c79"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Relative Compactness         False\n",
              "Surface Area                 False\n",
              "Wall Area                    False\n",
              "Roof Area                    False\n",
              "Overall Height               False\n",
              "Orientation                  False\n",
              "Glazing Area                  True\n",
              "Glazing Area Distribution     True\n",
              "Heating Load                 False\n",
              "Cooling Load                 False\n",
              "dtype: bool"
            ]
          },
          "execution_count": 294,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(df1==0).any()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 295,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v63OGvdIFrBE",
        "outputId": "e8c08c62-8e51-4d5f-c713-6dd5c8387da3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 295,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Building with glazing area distribution equal to 0 are warehouses (with no windows)\n",
        "df1['Glazing Area Distribution'].min()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 296,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bm7_N6ZaFy9Z",
        "outputId": "9a159d40-02b3-4286-dd7b-7359aa2bba1c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "48"
            ]
          },
          "execution_count": 296,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(df1['Glazing Area']==0).sum().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 297,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zy4sgctDIKBg",
        "outputId": "d6103bd3-543b-44f8-adea-88a388d53aca"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "48"
            ]
          },
          "execution_count": 297,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(df1['Glazing Area Distribution']==0).sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 298,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_OXdR36DDw87",
        "outputId": "d60f1b6a-fc33-482a-a95e-91ecb40da997"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Relative Compactness         False\n",
              "Surface Area                 False\n",
              "Wall Area                    False\n",
              "Roof Area                    False\n",
              "Overall Height               False\n",
              "Orientation                  False\n",
              "Glazing Area                 False\n",
              "Glazing Area Distribution    False\n",
              "Heating Load                 False\n",
              "Cooling Load                 False\n",
              "dtype: bool"
            ]
          },
          "execution_count": 298,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(df1<0).any()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 299,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "98kpwkNvlSWh",
        "outputId": "338aba84-f62a-4d1f-d528-b8038963dd86"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Relative Compactness</th>\n",
              "      <th>Surface Area</th>\n",
              "      <th>Wall Area</th>\n",
              "      <th>Roof Area</th>\n",
              "      <th>Overall Height</th>\n",
              "      <th>Orientation</th>\n",
              "      <th>Glazing Area</th>\n",
              "      <th>Glazing Area Distribution</th>\n",
              "      <th>Heating Load</th>\n",
              "      <th>Cooling Load</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.00000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.00000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.764167</td>\n",
              "      <td>671.708333</td>\n",
              "      <td>318.500000</td>\n",
              "      <td>176.604167</td>\n",
              "      <td>5.25000</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>0.234375</td>\n",
              "      <td>2.81250</td>\n",
              "      <td>22.307201</td>\n",
              "      <td>24.587760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.105777</td>\n",
              "      <td>88.086116</td>\n",
              "      <td>43.626481</td>\n",
              "      <td>45.165950</td>\n",
              "      <td>1.75114</td>\n",
              "      <td>1.118763</td>\n",
              "      <td>0.133221</td>\n",
              "      <td>1.55096</td>\n",
              "      <td>10.090196</td>\n",
              "      <td>9.513306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.620000</td>\n",
              "      <td>514.500000</td>\n",
              "      <td>245.000000</td>\n",
              "      <td>110.250000</td>\n",
              "      <td>3.50000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>6.010000</td>\n",
              "      <td>10.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.682500</td>\n",
              "      <td>606.375000</td>\n",
              "      <td>294.000000</td>\n",
              "      <td>140.875000</td>\n",
              "      <td>3.50000</td>\n",
              "      <td>2.750000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>1.75000</td>\n",
              "      <td>12.992500</td>\n",
              "      <td>15.620000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.750000</td>\n",
              "      <td>673.750000</td>\n",
              "      <td>318.500000</td>\n",
              "      <td>183.750000</td>\n",
              "      <td>5.25000</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>3.00000</td>\n",
              "      <td>18.950000</td>\n",
              "      <td>22.080000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.830000</td>\n",
              "      <td>741.125000</td>\n",
              "      <td>343.000000</td>\n",
              "      <td>220.500000</td>\n",
              "      <td>7.00000</td>\n",
              "      <td>4.250000</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>4.00000</td>\n",
              "      <td>31.667500</td>\n",
              "      <td>33.132500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.980000</td>\n",
              "      <td>808.500000</td>\n",
              "      <td>416.500000</td>\n",
              "      <td>220.500000</td>\n",
              "      <td>7.00000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>5.00000</td>\n",
              "      <td>43.100000</td>\n",
              "      <td>48.030000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Relative Compactness  Surface Area   Wall Area   Roof Area  \\\n",
              "count            768.000000    768.000000  768.000000  768.000000   \n",
              "mean               0.764167    671.708333  318.500000  176.604167   \n",
              "std                0.105777     88.086116   43.626481   45.165950   \n",
              "min                0.620000    514.500000  245.000000  110.250000   \n",
              "25%                0.682500    606.375000  294.000000  140.875000   \n",
              "50%                0.750000    673.750000  318.500000  183.750000   \n",
              "75%                0.830000    741.125000  343.000000  220.500000   \n",
              "max                0.980000    808.500000  416.500000  220.500000   \n",
              "\n",
              "       Overall Height  Orientation  Glazing Area  Glazing Area Distribution  \\\n",
              "count       768.00000   768.000000    768.000000                  768.00000   \n",
              "mean          5.25000     3.500000      0.234375                    2.81250   \n",
              "std           1.75114     1.118763      0.133221                    1.55096   \n",
              "min           3.50000     2.000000      0.000000                    0.00000   \n",
              "25%           3.50000     2.750000      0.100000                    1.75000   \n",
              "50%           5.25000     3.500000      0.250000                    3.00000   \n",
              "75%           7.00000     4.250000      0.400000                    4.00000   \n",
              "max           7.00000     5.000000      0.400000                    5.00000   \n",
              "\n",
              "       Heating Load  Cooling Load  \n",
              "count    768.000000    768.000000  \n",
              "mean      22.307201     24.587760  \n",
              "std       10.090196      9.513306  \n",
              "min        6.010000     10.900000  \n",
              "25%       12.992500     15.620000  \n",
              "50%       18.950000     22.080000  \n",
              "75%       31.667500     33.132500  \n",
              "max       43.100000     48.030000  "
            ]
          },
          "execution_count": 299,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 300,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "HjQq7JWLlXWz",
        "outputId": "cc5e8281-4ebc-4fa6-cb35-f0149510b7b2"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 300x300 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 300x300 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 300x300 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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QkpLSrgsehBDS1dp1n5/JZGrRhx/QNHbvbwczIoSQ7qhd4Tdu3Di8+uqrKCoqMs+7ceMG5s+fj/Hjx1utOEII6SztCr8PP/wQWq0WgYGBCA4ORnBwMIKCgqDVavHBBx9Yu0ZCCLG6dp3z8/f3x+nTp7F//35cuHABABAaGmrxDC4hhHRnbTryO3DgAMLCwqDVasFxHB577DEkJSUhKSkJI0eOxKBBg/DTTz91Vq2EEGI1bQq/tWvX4sUXX4RcLm+xzNXVFS+99BINXkQIsQltCr+zZ89iwoQJd10eExODrKysDhdFCCGdrU3hV1JS0uotLs3EYjFu3rzZ4aIIIaSztSn8evfu3WKoyjv9/PPP6NWrV4eLIoSQztam8Hv88cexdOlSNDQ0tFhWX1+P5cuX4w9/+IPViiOEkM7SpltdlixZgq+++goDBgzAnDlzEBISAgC4cOECUlJSYDQa8be//a1TCiWEEGtqU/gpFAocO3YMs2fPxuLFi80dE3Ach9jYWKSkpLToUp4QQrqjNt/k3NyDyq1bt3D58mUwxtC/f3+4u7t3Rn2EENIp2t2Zqbu7O0aOHGnNWgghpMtYdfQ2QgixFRR+hBBBovAjhAgShR8hRJAo/AghgkThRwgRJAo/QoggUfgRQgSJwo8QIkgUfoQQQaLwI4QIEoUfIUSQKPwIIYJE4UcIESQKP0KIIFH4EUIEicKPECJI7e7JmXScWq1GWVmZ1bbn5eWFgIAAq22PEGtijEFdUYeCm7UorW5Ada0Ebo8+z1s9FH48UavVGBgaivq6Oqtt08HRERfy8igASbdzrbwWP10qQ3mt/o65HMQuXrzVROHHk7KyMtTX1WHaotVQBAR3eHsl6gJs/edClJWVUfiRbsNkYjh06SZ+vl4FAJCKRQhRuMDf3QF1Nwvx6brNAKbyUhuFH88UAcHw6z+I7zIIsTpdoxF7czS4VtH07SbSzw1RfT1gL7EDAFzXMhirrXfap60o/AghVqdrNOKr0zdQWq2DWMRhwmAlgr2d+S7LAoUfIcSqGk0m7P65GKXVOjhI7DAp0hcKuT3fZbVA4UcIsRrGGNLOl+D6rXpI7DhMjvSFTzcMPoDu8yOEWFF2YSUultRAxAFx4b26bfABFH6EECvRVDXgyOWmCxgP9/dGH08nniv6fRR+hJAO0zUasfdcMUwM6OftjAg/V75LuicKP0JIhx25XAZtQyPk9mJEh/mA4zi+S7onCj9CSIcUVtTh3A0tACA6VAGZ2I7niu4PhR8hpN0MRhPSL5QCAMJ7u8Lfw5Hniu4fhR8hpN1OXb2FqnoDnGVijOnnyXc5bcJr+B0+fBhPPPEEfH19wXEcvv76a4vljDEsW7YMvXr1goODA6Kjo3Hp0iWLNhUVFZg2bRrkcjnc3Nwwc+ZM1NTUdOFeECJMVfUGZKlvAQAeHuBlM193m/EafrW1tRgyZAhSUlJaXb5q1Sq8//772LBhA06cOAEnJyfExsaioaHB3GbatGnIzc1FWloadu/ejcOHD2PWrFldtQuECNZPl27CaGLwc3dAv2726Nr94PUJj4kTJ2LixImtLmOMYe3atViyZAkmTZoEAPjvf/8LhUKBr7/+GlOnTkVeXh727duHzMxMjBgxAgDwwQcf4PHHH8e7774LX1/fVret0+mg0+nMr7VarZX3jJCerfB2v3wcgEcGeNvE1d3f6rbn/K5cuQKNRoPo6GjzPFdXV0RFRSEjIwMAkJGRATc3N3PwAUB0dDREIhFOnDhx122vWLECrq6u5snf37/zdoSQHsbEgEMXbwIAIvxc4eUs47mi9um24afRaAAACoXCYr5CoTAv02g08PHxsVguFovh4eFhbtOaxYsXo6qqyjwVFhZauXpCeq6rNSKU1+phLxFhdF/bushxJ0F2bCCTySCT2eZvK0L4xIllyNM2XdiICvI0981ni7rtkZ9SqQQAlJSUWMwvKSkxL1MqlSgtLbVY3tjYiIqKCnMbQoj1uAyLQ4ORg9xejPDe3f8Rtt/TbcMvKCgISqUS6enp5nlarRYnTpyASqUCAKhUKlRWViIrK8vc5sCBAzCZTIiKiurymgnpyWr1JshH/wkAENXXE3Yi27vIcSdev/bW1NTg8uXL5tdXrlxBdnY2PDw8EBAQgHnz5uHtt99G//79ERQUhKVLl8LX1xeTJ08GAISGhmLChAl48cUXsWHDBhgMBsyZMwdTp06965VeQkj7fHuxFnYOcriIGQYqXfgup8N4Db9Tp05h7Nix5tcLFiwAACQkJCA1NRWvvfYaamtrMWvWLFRWVuLBBx/Evn37YG//ax9hW7duxZw5czB+/HiIRCLEx8fj/fff7/J9IaQnK6/RYdfFWgDAILdGiGzw1pbf4jX8Hn30UTDG7rqc4zgkJycjOTn5rm08PDywbdu2ziiPEHLb+oMFaGhk0BVfgq9/H77LsYpue86PENI9FFfV47/HrwEAKn/6BD3goA8AhR8h5B7eT78MfaMJYd5SNFw5zXc5VkPhRwi5q6tltfjiVNNDANMG2/5FjjsJ8iZnQsj9WbP/IowmhkdDvBHq3bPigo78CCGtyivWYtfZIgDAX2NCeK7G+ij8CCGt+tcPF8FY0xCUg238aY7WUPgRQlo4rb6F/XklEHHA/McG8F1Op+hZX+JJp2GMwWhiMDFAJALEIvq92ZO9+30+ACB+mB/6+dheR6X3g8KPmDHGUFzVgMyiBriqnsLxMjsYKwtRo2tEnb4RpjvuRxeLODjJxHB3lMDLWQY/dwf4ujlAYkehaOuOXi7DsYJySOw4vBrdn+9yOg2Fn4BV1RtwtrASZ9SVOK2+hZ+vV+JWnQEA4PbwdNyoA4CGVtdtNDFU1RtQVW/A1fI6nLp2C3YiDsFeTgj1laOPh6NN9u4rdIwxrL591Dctqg/83G1nNLa2ovATiAaDEQU3a5BzvQqn1bdwRl2Jyzdr8NunC+1EHHq72OH80R+gevBhBPj7w1kmhpPMDjKxHUQcYDQxNDSaUN1gQEWtHhptAwor6lGja8TF0hpcLK2Bu6MEIwM94HT3pxdJN/R9rgbZhZVwkNjhlbHBfJfTqSj8bFzzuTiDCRA5ueGXWwbcyiuBRtuA4soGFNysQX5JNa6W1Vp8bW3Wx9MRQ/3dMDTAHZH+bghRuuB8zlkMf+M9DHj8Qfi1cr5HbAfIJHZwdZDAz90REbfrKK3WIa9YiwuaatyqM+CH8yVwkYhh32dI538QpMMajSasun3U98JDQfBxsb/HGraNwq+N1Go1ysrKOrydvLw8iBzkuNnAobpYi+qGRtToGqE3mmBoNDX9aTTBaPr1QkPzz0bGYLr9569HblL4z/kUf00rA9B6fa4OEoT1kmNYHzcM9XdHZICb1cZf4DgOCrk9FHJ7qII98fPtI8xqA6CY+g7+lXELH4bo4e4ktcr7Eev7Mus6frlZC3dHCWY93JfvcjodhV8bqNVqDAwNRX1dXbvWl3j1gUPfYZD5DYJU0Rf+c7fhcCmA0pJ7rns/GDPB3UEMP09nKOUOULrKEOjphBClC0IULvB2kXXJeTiZ2A4jAz0Q3tsV+09fwmUtcLSwATFrD2NVfATGDvS590ZIl6rXG7F2f9OY2Ilj+8HFXsJzRZ2Pwq8NysrKUF9Xh2mLVkMRcO/zIYwBt/QcrtWKUFwvQr2xZfDYc43wdnOBs70YLjIJZBIRJHYiSOw4SO1EsBNxv04cB9Fvf+aaXhf/kod/z4nHzqxTGDZsWGfsfpvZS+wQ6WHEkZTXMDLpQ1zX6vB8aibmju+PeeP7Q2TjPQH3JKnHrkKjbUBvNwdMV/WMLqvuhcKvHRQBwfDrP+iuy/WNJuQWVSG3SIvyWr15vp2Ig5+7AwI8HFF56RS+W7MQ0//2b0QOC+1wTXYcAHTPqwt6zWWsjvbC3mIZ/ptxDe+nX0LO9UqsnToUrg49/wiju6uqM2D9waYe1Rc8NgAyse0OStQWFH5W1GAwIruwEmcLK9HQaALQFHj9vJ0xUOkCP3cHiG/fB5d1qR5MX89nuV1KJuaQPGkwIvzc8LedOfgx/yYmfXgEW/53FPp4OvFdnqCtO3QZ2oZGhChcMHlob77L6TIUflZgMJpwRl2JU9cqYDA2HX25OUow1N8NIQoXyGx4eD9r+9NwPwxUuuClT7JwtbwOU9Ydw+bnRyLCz43v0gSpqLIeqUevAgBemxBi84MStQXdjt8BjDFc0Gjx34xryPilHAYjg7ezDI8PVmL66D6I8HOj4GvF4N6u2Jn4AAb5ylFeq8fUj4/j0MWbfJclSCv2XoCu0YRRQR4YJ7ALURR+7XSrTo//O30D3+eWoEbXCBd7MSYOVuLpUf7or3DpEQO8dCYfF3tsnzUaD/bzQp3eiJmpmdj9cxHfZQnKySsV+PZsETgOWPaHMME9kUNfe9uKE+GiVoTz19UwmhjEIg6jgjww1N/NfD6P3B8Xewk2PTcSC788i2+yizD3szNoNDJBnXfii9HE8PdvcwEAU0cG9Mguq+6Fwq8NrlUaoHz2XeRUigEw+Hs4YPxABV2x7ACpWIT3/hwJmViEL05dx/wvsqE3mvDnEf58l9ajfXGqELlFWrjYi/HXmJ7ZZdW9UPjdp+oGA/72YzlkvgMg4RgeGahAWC+54L4qdAY7EYeVUyIgsRNh6wk1XvvyZzQaGZ6JCuC7tB6pqt5g7rJqXvQAeFrpKR9bQ9/T7pOLvQR/CnVG3cUMPNbLgEG+rhR8ViQScXh78mA890AgAOCNnTnYcuwqrzX1VKv2XUB5rR7B3k6YIZAbmltD4dcGT4Y44ebOd+BAx8udguM4LH8iDC/dfq50+a5cbDpyheeqepYTv5Rj6wk1AOCtyYMF3f+icPe8HegKbufjOA6vTxyIVx5tenwwefd5/L+ffuG5qp6hwWDE4q9yAABTR/rjgWAvniviF4Uf6XY4jsPC2BAkjesHAHj7uzx8fLiA56ps3/vpl/BLWS18XGRY/HjHH6m0dRR+pFviOA4LHhuAV8c3daP+jz0XsP4gBWB75RZV4aPDTUfQyZMG0x0KoPAj3RjHcZj/2ADMuz2OxD/3XUDKj5d5rsr2NBiMWPD5WRhNDBMGKTFhsJLvkroFCj/S7c2LHoC/3B4+cfX3+Xg//RLPFdmWlXsvIL+kGl7OUrw1eTDf5XQbFH7EJiSN74+FsSEAgPfSLmLt/os8V2QbDlwoQertW4be/Z8h8HYR5j19raHwIzYjcWw/vD5xIABg7f5L+NcP+WC/HYGJmBVW1GH+52cBAM+PCcSjIcLquOBeKPyITXn5kWD87faVyg8OXMbSb87B2NrITAJXrzfipU+yUFVvwBB/N/MvDfIrCj9ic158uC+SJw0CxwGfHldjzrbTaDAY+S6r22CM4fWvfsb5Yi08naTY8OwwwfTO3BYUfsQmzVAF4sOnh0FqJ8LecxokbDqJyjr9vVcUgNXf5+Ob7CKIRRw+fGYYerk68F1St0ThR2xWXEQvpP7vSDjLxDhxpQKTU47icmk132Xx6pPj17Du9v2QK6aEQxXsyXNF3ReFH7FpDwR7YcfLKvR2c8DV8jr8MeUYfswv5bssXnxxqhDLvjkHoGkgov+hbsF+F4UfsXmhveTYNWcMRgV6oFrXiJmpmVi7/6KgLoR8caoQi/7vZzAGPPdAoPnRQHJ3FH6kR/B0luHTF6Lw9Ch/mFjTrTBP/+c4iqt69gh5jDFsOFSA175sCr4EVR8sf0J4XdK3B4Uf6TGkYhFWTInA2qci4SS1w8krFZj475/w7dmiHnk/oMFowtJvzmHl3gsAgJkPBuHNJwdR8N0nCj/S40we2hvfzX0I4b1dUVlnQNJnZ/B8aiYKK+r4Ls1qiqvqMfXj4/j0uBocByz9QxiWCnAQoo6g8CM9UqCXE/5v9gOYHz0AUjsRDubfRMyaw/jwwCXU6Rv5Lq/dGGPY/XMR4t4/gqxrt+BiL8ZHzw7HzAeD+C7N5lD4kR5LKhbh1ej+2DvvIUQFeaDeYMS7P1zEw6sO4pOMq9A3mvgusU2u36rDS59kYc62M6io1WOQrxy7kx5EzCDqpaU9qEN20uMFeztj+6zR2HW2CP/64SLUFXVY+k0u1h8swHRVIJ4e5Q83RynfZd5VRa0eHx0qwOajV6E3miAWcUgc2w+vjA2mJzc6gMKPCALHcZgU2RsTB/fC55lqvH/gMoqqGvDPfRfw7/SLmDSkNyYN9UVUkCfsRN3jvNnl0mp8knENn58qRIOh6ShV1dcTy58Mw0ClnOfqbB+FHxEUqViE6apA/M8If3x7tgibjl5FXrEWn58qxOenCuHjIsPj4b3wyABvjArygJOsa/+LaKoasCenGDvP3EDOjSrz/EG+cvwlZgDGhvjQRQ0r6THhl5KSgtWrV0Oj0WDIkCH44IMPMGrUKL7LIt2UvcQO/zPCH38a7oeTVyrw1ekb2HuuGKXVOqQeu4rUY1chseMw1N8dkQFuGNzbFeG9XRHg4Wi1I0OjieFKWS3yirXIvFqBo5fLUHCz1rxcLOLwyABv/O+DQXgg2JNCz8p6RPh9/vnnWLBgATZs2ICoqCisXbsWsbGxyM/Ph48P9WFG7o7jOET19URUX0+8NXkwDl+8if15JfjpUhluVNbj5NUKnLxaYW4vtRPBz8MBfTwcoXR1gIeTBO6OUrg7SuFsL4bEjoOI4yAWNV1LrNU3ok7fiBqdEdUNBpRUNaCoqgE3btWj4GYNdL+56MJxQKS/GyYN8cUTQ3wFO6B4V+gR4ffee+/hxRdfxPPPPw8A2LBhA7777jts2rQJr7/+Os/VEVshFYsQHaZAdJgCjDGoK+pw/Jdy5NyoQs4NLfKKtdA3mvDLzVr8cscRWkc4SOwQonRBhJ8rHgj2gqqvJ1wdaXChrmDz4afX65GVlYXFixeb54lEIkRHRyMjI6PVdXQ6HXQ6nfl1VVXTuRWtVvu771VTUwMAuH4pF7r6jt0wW6Ju6nlDc/UiCpwcO7QtALh5vWlw76ysLHOd7ZWfnw/AOvtpzbqApr9bk8l6t6jca3u9APTyAGI8AOMgR1Q0GFFaa0JJjRFVOiNq9CZodSbU6E1oaGQwsqavsyYGmBhgL+ZgL+HgYMfBXiyCu4MIng528HS0Qy9nO3g72sFOZARQAVRU4GyF9ffTWtuy5r8L4Nd/GzU1Nb/7f695mdWf0mE27saNGwwAO3bsmMX8hQsXslGjRrW6zvLlyxkAmmiiyYamwsJCq2aHzR/5tcfixYuxYMEC82uTyYSKigp4enavk8parRb+/v4oLCyEXG57tzZQ/fzqKfWr1WpwHAdfX1+rbt/mw8/Lywt2dnYoKSmxmF9SUgKlsvU732UyGWQyyxPJbm5unVVih8nlcpv8x9uM6ueXrdfv6uraKfXb/ONtUqkUw4cPR3p6unmeyWRCeno6VCoVj5URQrozmz/yA4AFCxYgISEBI0aMwKhRo7B27VrU1taar/4SQshv9Yjwe+qpp3Dz5k0sW7YMGo0GkZGR2LdvHxQKBd+ldYhMJsPy5ctbfEW3FVQ/v6j+38cx1gN7eSSEkHuw+XN+hBDSHhR+hBBBovAjhAgShR8hRJAo/Hhw+PBhPPHEE/D19QXHcfj666/NywwGAxYtWoTw8HA4OTnB19cXM2bMQFFRkcU2AgMDwXGcxbRy5Ure6weA5557rkVtEyZMsGhTUVGBadOmQS6Xw83NDTNnzrTKs7/WqP+3tTdPq1evNrfh6/NfsWIFRo4cCRcXF/j4+GDy5MnmZ26bNTQ0IDExEZ6ennB2dkZ8fHyLhwDUajXi4uLg6OgIHx8fLFy4EI2NnT+2yb3qr6ioQFJSEkJCQuDg4ICAgADMnTvX/Px9s9b+frZv396mWij8eFBbW4shQ4YgJSWlxbK6ujqcPn0aS5cuxenTp/HVV18hPz8fTz75ZIu2ycnJKC4uNk9JSUldUf7v1t9swoQJFrV99tlnFsunTZuG3NxcpKWlYffu3Th8+DBmzZrV2aUDuHf9d9ZdXFyMTZs2geM4xMfHW7Tj4/M/dOgQEhMTcfz4caSlpcFgMCAmJga1tb/2MjN//nx8++232LFjBw4dOoSioiJMmTLFvNxoNCIuLg56vR7Hjh3Dli1bkJqaimXLlvFef1FREYqKivDuu+/i3LlzSE1Nxb59+zBz5swW29q8ebPF5z958uS2FWPVJ4VJmwFgO3fu/N02J0+eZADYtWvXzPP69OnD1qxZ07nF3YfW6k9ISGCTJk266zrnz59nAFhmZqZ53t69exnHcezGjRudVGnr7ufznzRpEhs3bpzFvO7y+ZeWljIA7NChQ4wxxiorK5lEImE7duwwt8nLy2MAWEZGBmOMsT179jCRSMQ0Go25zfr165lcLmc6nY7X+lvzxRdfMKlUygwGg3ne/fy93Qsd+dmAqqoqcBzX4vnjlStXwtPTE0OHDsXq1au75GvL/Tp48CB8fHwQEhKC2bNno7y83LwsIyMDbm5uGDFihHledHQ0RCIRTpw4wUe5d1VSUoLvvvuu1SOP7vD5N38d9PDwANDUdZjBYEB0dLS5zcCBAxEQEGDu4i0jIwPh4eEWDwHExsZCq9UiNze3C6tvWf/d2sjlcojFls9kJCYmwsvLC6NGjcKmTZva3OVVj3jCoydraGjAokWL8PTTT1s83D137lwMGzYMHh4eOHbsGBYvXozi4mK89957PFbbZMKECZgyZQqCgoJQUFCAN954AxMnTkRGRgbs7Oyg0Wha9LAtFovh4eEBjUbDU9Wt27JlC1xcXCy+NgLd4/M3mUyYN28exowZg8GDBwMANBoNpFJpi1+UCoXC/NlqNJoWTz81v+7Kz7+1+n+rrKwMb731VotTIsnJyRg3bhwcHR3xww8/4JVXXkFNTQ3mzp173+9P4deNGQwG/PnPfwZjDOvXr7dYdmeXXBEREZBKpXjppZewYsUK3h9nmjp1qvnn8PBwREREIDg4GAcPHsT48eN5rKztNm3ahGnTpsHe3t5ifnf4/BMTE3Hu3DkcOXKkS97P2u5Vv1arRVxcHMLCwvDmm29aLFu6dKn556FDh6K2tharV69uU/jR195uqjn4rl27hrS0tHt26RMVFYXGxkZcvXq1awpsg759+8LLywuXL18GACiVSpSWllq0aWxsREVFxV27IePDTz/9hPz8fLzwwgv3bNvVn/+cOXOwe/du/Pjjj/Dz8zPPVyqV0Ov1qKystGh/ZxdvSqWy1S7gmpd1hbvV36y6uhoTJkyAi4sLdu7cCYnk97v2j4qKwvXr1y16aL8XCr9uqDn4Ll26hP3798PT0/Oe62RnZ0MkEnXLAZuuX7+O8vJy9OrVCwCgUqlQWVmJrKwsc5sDBw7AZDIhKiqKrzJb2LhxI4YPH44hQ4bcs21Xff6MMcyZMwc7d+7EgQMHEBQUZLF8+PDhkEgkFl285efnQ61Wm7t4U6lUyMnJsfgF1PwLNiwsjNf6gaYjvpiYGEilUuzatavFUXdrsrOz4e7u3raj7g5dLiHtUl1dzc6cOcPOnDnDALD33nuPnTlzhl27do3p9Xr25JNPMj8/P5adnc2Ki4vNU/OVuGPHjrE1a9aw7OxsVlBQwD799FPm7e3NZsyYwXv91dXV7K9//SvLyMhgV65cYfv372fDhg1j/fv3Zw0NDeZtTJgwgQ0dOpSdOHGCHTlyhPXv3589/fTTvNffrKqqijk6OrL169e3WJ/Pz3/27NnM1dWVHTx40OLfRl1dnbnNyy+/zAICAtiBAwfYqVOnmEqlYiqVyry8sbGRDR48mMXExLDs7Gy2b98+5u3tzRYvXsx7/VVVVSwqKoqFh4ezy5cvW7RpbGxkjDG2a9cu9p///Ifl5OSwS5cusXXr1jFHR0e2bNmyNtVC4ceDH3/8sdUxChISEtiVK1fuOobBjz/+yBhjLCsri0VFRTFXV1dmb2/PQkND2T/+8Q+LcOGr/rq6OhYTE8O8vb2ZRCJhffr0YS+++KLFbRWMMVZeXs6efvpp5uzszORyOXv++edZdXU17/U3++ijj5iDgwOrrKxssT6fn//d/m1s3rzZ3Ka+vp698sorzN3dnTk6OrI//vGPrLi42GI7V69eZRMnTmQODg7My8uL/eUvf7G4lYSv+u/2dwOAXblyhTHWdFtUZGQkc3Z2Zk5OTmzIkCFsw4YNzGg0tqkW6tKKECJIdM6PECJIFH6EEEGi8COECBKFHyFEkCj8CCGCROFHCBEkCj9CiCBR+BFCBInCj9gsxhhmzZoFDw8PcByH7OxsvksiNoTCj1jdnWN4SCQSBAUF4bXXXkNDQ4NV32ffvn1ITU3F7t27UVxcfNc+4ZoNHDgQMpms2/UZSPhB4Uc6RfMYHr/88gvWrFmDjz76CMuXL7fqexQUFKBXr1544IEHoFQqW/T0e6cjR46gvr4ef/rTn7Bly5Z7bluv11uzVNINUfiRTiGTyaBUKuHv74/JkycjOjoaaWlp5uU6nQ5z586Fj48P7O3t8eCDDyIzM9NiG4cOHcKoUaMgk8nQq1cvvP766+au4p977jkkJSVBrVaD4zgEBgb+bj0bN27EM888g+nTp2PTpk0tlgcGBuKtt97CjBkzIJfLzT0HHzlyBA899BAcHBzg7++PuXPnWgwW9Mknn2DEiBFwcXGBUqnEM88806KvQtJNWbPHBkIYazmAUU5ODlMqlSwqKso8b+7cuczX15ft2bOH5ebmsoSEBObu7s7Ky8sZY4xdv36dOTo6sldeeYXl5eWxnTt3Mi8vL7Z8+XLGWNNAPcnJyczPz48VFxez0tLSu9aj1WqZk5MTO3fuHGtsbGQKhYIdPnzYok2fPn2YXC5n7777Lrt8+bJ5cnJyYmvWrGEXL15kR48eZUOHDmXPPfeceb2NGzeyPXv2sIKCApaRkcFUKhWbOHGiFT5F0tko/IjVJSQkMDs7O+bk5MRkMhkDwEQiEfvyyy8ZY4zV1NQwiUTCtm7dal5Hr9czX19ftmrVKsYYY2+88QYLCQlhJpPJ3CYlJYU5Ozubuy5as2YN69Onzz3r+fjjj1lkZKT59auvvmrRfRVjTeE3efJki3kzZ85ks2bNspj3008/MZFIxOrr61t9r8zMTAagy7rnIu1HX3tJpxg7diyys7Nx4sQJJCQk4PnnnzePe1tQUACDwYAxY8aY20skEowaNQp5eXkAgLy8PKhUKnAcZ24zZswY1NTU4Pr1622qZdOmTXj22WfNr5999lns2LED1dXVFu3uHE0OAM6ePYvU1FQ4Ozubp9jYWJhMJly5cgVA02hpTzzxBAICAuDi4oJHHnkEQNOg4KR7o/AjncLJyQn9+vXDkCFDsGnTJpw4cQIbN27s8jrOnz+P48eP47XXXoNYLIZYLMbo0aNRV1eH7du3t6j5TjU1NXjppZeQnZ1tns6ePYtLly4hODgYtbW1iI2NhVwux9atW5GZmYmdO3cCoAsmtoDCj3Q6kUiEN954A0uWLEF9fT2Cg4MhlUpx9OhRcxuDwYDMzEzzGBKhoaHIyMiwGIv16NGjcHFxaXXAm7vZuHEjHn74YZw9e9YixBYsWHDPMB42bBjOnz+Pfv36tZikUikuXLiA8vJyrFy5Eg899BAGDhxIFztsCd/fu0nP89sLHowxZjAYWO/evdnq1asZY03n3Xx9fdnevXstLnhUVFQwxn694JGYmMjy8vLY119/bXHBg7F7n/PT6/XM29u71XE4zp8/zwCwc+fOMcaazvmtWbPGos3Zs2eZg4MDS0xMZGfOnGEXL15kX3/9NUtMTGSMMVZaWsqkUilbuHAhKygoYN988w0bMGAAA8DOnDnTtg+NdDkKP2J1rYUfY4ytWLGCeXt7s5qaGlZfX8+SkpKYl5cXk8lkbMyYMezkyZMW7Q8ePMhGjhzJpFIpUyqVbNGiRRbjTNwr/L788ksmEolajB/SLDQ0lM2fP58x1nr4McbYyZMn2WOPPWYeLyIiIoK988475uXbtm1jgYGBTCaTMZVKxXbt2kXhZyNoDA9CiCDROT9CiCBR+BFCBInCjxAiSBR+hBBBovAjhAgShR8hRJAo/AghgkThRwgRJAo/QoggUfgRQgSJwo8QIkj/H0u5g3s/2khKAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 300x300 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 300x300 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 300x300 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 300x300 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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otVo6derE3LlzGTx4sE0KE0KIuqpXoK1atcrWdQghxA2r9wCPAImJiSQnJwPQpUsXevbsaZOihBCiPuoVaJmZmYwdO5Yff/wRPz8/oOqZnAMHDuSzzz6jZcuWtqxRCCFqpV6BNn36dC5dusSvv/5KREQEAEeOHGHixIk8+eSTfPrppzYtUjifgpJyLuSXUGSqxEWrwd/LjWA/D3Rax7obz1RRyfncEvKKy1GA3tOV1s098XR1sXdpVirNCmN+KdmFJsrNZrzcdATpb74eB/UKtM2bN7N161ZLmAHceuutLF68WC4KiGs6l1tM/KkczuWVVFvmptNyW2s9vcOa427nwMgvKSf+9EWOGQupvGLIdwCtBtq39KZfuwD8vdzsVGGV8koz+8/mcvBcPiXl1R/+7e+mw6NdbztUZh/1CjSz2VxtDDSoejbn7x+YIgRAhRl+OGIkOf2SZZ7B1wO/Zq6UV5ox5pdSVFZJwtlcjqQXMPjWQMICvBq9TqUUv5zLZ9eJbCrMVUHm18yVVt7uoIGLhWVcLCrjeGYhJ7MKiWwbQJ/w5o1eJ0B6fgnfHTZyqbQCAE9XF4L0HrjrtOSXlJNeUEpOmZbAh1/h/fg8FnepwNv9hk6bO7x6fbp77rmHGTNm8OmnnxIcHAzA+fPneeqppxg0aJBNCxT1Y8wv5VR2IQWlFei0Glp6u9O+pTfeHo3/C+3i25IfM3Tkl1eFWbfWevqEN8fH4z//KSqlOJ1dxK4T2eQWl7Mh6QJ3tA+gd1hzm46xdy0VlWa2Hc0k2VhVZ5vmnvRv3wLD7w7dsi6Z2H0ymzMXi4k7dZHMS6V0dW+UEoH/hO7O41mYFfh46OjfvgUdW3mj1f7nuyoyVbDj4AmO5cOOsyWMWPQTyyb0ppPBp/GKbWT1+u3+8MMPuf/++wkPD7eMI5aWlkbXrl35xz/+YdMCRd0UVcD6A+dJzSmutmzn8SxuDfIlqn0AzdwaJ9jO5JUT9KcF5Jdr8XR1YUS3IFo396y2nkajoV1Lb0IDmrEjJYvDFwrYffIiucVlREcEom3gUCurMLMh6Tzp+aVogDs7tqBniF+NYdrSx537uweTnH6JbUczOZlVRLarDm0zvwatEarCLO7URfadqXq6WodW3twbEYibrvq5Ry93Hbc1r+SnJS/Qdep7nL1YzCMfx7FqUh9uD228VqVSiowCExfySygtr6SZm46wgGYN8l71+q0OCQlh//79bN26laNHjwIQERFhNba/aHzuIV2JTXelXBWj1UCHlt608vWgrNJM6sVijAWlHL5QwMmsIoZ1NRDi3zC/VJcdz7jEKztycPHyQ+9q5sG+4fh6VD9VcSWdVsugiEBa+Xiw/VgmyemXMJth8K2B19zuRlSYYePBC6Tnl+Ku0zKiW9B1vxuNRsOtwb4093Jl0y/p5JdB4GNvkFtS/TyWLcWfzrGEWW1bsKbzybx7bwveP2Bif2oe4/8vnuV/6k3/Di0atFaAk1mF7D5xkZzismrLgj116PS2/bnW6ZLStm3buPXWWykoKECj0XDvvfcyffp0pk+fTp8+fejSpQs//fSTTQsUtZOcVUarh1+hXGkw+Hrwp6hwhnULoldYc6LaBfBonxAe6tWGAG83SsorWX/gPIlnc62ecWpLp7IK+eP/xVNgMmNKP84fAiuuG2ZX6tZGz7CuBrQaSMm4xPe/GjE3QKkanRu7s3SczyvBzUXL6J6t6xT0QXpPHu7VBk8XhVuLUF768SJZl0zX37Ae9p7OIf50DgB3dWxBn3D/Wh+O+7hr+cefI7mrYwuKyyqZvGYfcScvNkidAKXllWz+1cimX9LJKS7D1UVD+5ZedG+jJ9S/GRoNXCjR0uL+Z236O1inQFu4cCFTpkypcYRXvV7PX/7yF5s9IEXUXlpOMfN356J19SDQw8yY21uj96weHq39PBnbO4SIIB8UsOtENttTsjDbONTOXizij8vjybpkIkyvI/OLF3GrR2+Mjq18GNEtCK0GjmUWknjRBTS269ZRVqlo+cALZJm0uLpoGN0zGINv3bs6+DVz4w+tyqkoyOT8pUrG/188uUXVWyQ3IuFMDnGnqgKof4eAeh0yNnPT8X8TezOwU0tKy808sXofe38LSFvKKSpj3d5UUoyX0AC9wpoz+c623HdbMAM6teKBnq0Z1zeUVh5mcnessuk50jr9dhw8eJChQ4dedfngwYNJTEy84aJE7VVUmpnx2YGqlpDxBP1aVKBzufqPVeei5d6IQO6+parz86Hz+Xx32EiFja5On8st5o/L4zEWlNKxlTev3O2PubT+Y+S1a+nN8N9CLbXYhYCh020SwGUVZt7ZnYtnu164aBSjurcmSF/93F5tebtCxqfP09xDS0rGJSasjCe/pPyG6wTYn5rLz7+1pqLaBdA7zL/e+3LXubBkfC/u6tiCkvJKJq3aS+JZ24Xa+dwSvkhI41JpBXpPVx7pHcKdHVrgrrPuhhPg7c5drSowpR6y2XtDHQMtIyOjxu4al+l0OrKysm64KFF7y346xf7UPJq5asha/wY1nBuuRqPR0CPEz3JIdyKzkG+SLmCquLHzP8b8Uv64PJ7zeSW0beHF2j9Hove48f5k7Vt6M7SLAVB433YvyxILbugwpbzSzPRP95OQbsJcXsodLStqvFBRVxV5Rl4d4E+AlxuHzxcwadVeikwVN7TPpLQ8fjqeDUBkW3/6tq1/mF3m4erC8j/15o72ARSVVTJx5T6S0vJueL/HMi6x/sB5TBVmgvQePNo7pNoV4oZWp0Br3bp1tcfWXemXX36xevqSaFjG/FI+iD0BwBM9fKksyKzT9rcE+jCqR2tcXTScyy3hq/3n6/0HeD6vhEeXxZGaU0yIvyfrpkTSqh6Hb1fTMdCHPgGVKGXmh1PFvPrtkXqFmqmikr+u3c/3v2bgqoWsr1+nlYftDrnb+Lry98mR6D1d2Z+ax+Q1+ygpq99/FCcuadlxrKqB0Dfcn0gbhNllHq4urJjYh8i2/hSaKpiwIp5D5/LrtS+lFIlnc/nusJFKpWjf0osHe7bG063xO0fXKdCGDx/Oiy++SGlpabVlJSUlvPzyy9x33302K05c2/zNRykpr6R3WHMGhtevhRHq34wxt7fB09WFrEsmvkw8R1EdMy31YjGPLI3j7MXfwuzP/W7o8O2qtXqZufjv9wFYvfsMr21KxlyHKwXFZRX89z/2s+VIBu46Lc/d6U/pmSSb13lrsC9rnuiLt7uOPadyGL+ibufUlFLo+z/GwdyqTgi9wprTr13tLwDUlqebCysf70Of8OZcKq1g/Ip4fr1Qt1CrNCu2Hc1k14mqVmSPED+Gdwu65mmPhlSnd50zZw45OTnccsstvPXWW3zzzTd88803zJ8/n06dOpGTk8MLL7zQULWKKySezWX9gfNoNPDyyC439Mse6OvBw73b4OuhI7+knB+NrrgF3VKrbX8+kc2oxbssh5mfT41q0O4gRYdj+a9eVePxrfz5NFP/nsil0uufq0rLKWbMkji2Hc3Ew1XLysf70NPQcL1he4T4seaJPvh66Eg8m8uYpbs5e7HoutuVlFWyJCEfvzvHAdCvrT/92wc0WOdiL3cdqyb1pWeoH/kl5YxdtoetRzJqtW2RqYINSec5fKEAqLryevctLRu8z+C11CnQAgMD2b17N127dmX27Nk88MADPPDAAzz//PN07dqVXbt2ERho234l58+fZ/z48QQEBODp6Um3bt1ISEiwLFdK8dJLLxEUFISnpyfR0dEcP37cpjU4GqUU8/5dNWzTw73a0K2N/jpbXF/zZm483DuEAG83Ss0aDOPfZt2hS5RV1HyxoKzCzKLY40xYEU9ucTldW/vy2dR+BPvZvmX2e4PbN+Pdh7vjptOyNTmDUYt/vuqJbaUUGw9e4P4Pd5GcXkALbzf+MTmyUfpg9Qrz55//fQfBeg9OZRUxYtEuPtubetVW5eHz+Yz8cBdbT1fd59q9eQWR7RouzC7zdtex5om+9A6raqn9+ZME3vzuKKU13BsKoBSkGC/xjz1nOZdbgquLhpHdgxq1s+7V1LljbVhYGP/+97/Jzc3lxIkTKKXo2LEjzZvb/sPk5ubSv39/Bg4cyHfffUfLli05fvy41Xu99dZbLFq0iDVr1lieyzlkyBCOHDnitM832HMqh4SzubjptPzP4E4226+3u46Hb2/DpoTjnCt24Z/JhcS98yPj+4VxV8cWtPRxJ7vQxK7j2fz9t19mgId6teH10V3xaMQbysf0akP7Vt785e8JnMoqYsySOO6+pSX3dw/mlkAfys1mDp3L58vENA6fr2pBdGut5+MJvRoldC+7JdCH9TH9iVm7n4SzuTz39SFW7DrNo31C6Bnqh7vOhZNZhfzrl3S2JGegFDT30HJ0zfN0ePql67+Bjfh6uLJuSj/e+Hcyq3efYemOk3x78AITosIY2KkVQX4eZBRW4NX1HnZk6LiYZgSgpbc7g7sE0sK7Ee/9uoZ63//SvHlz+vTpY8taqpk/fz4hISFWI+S2bdvW8m+lFAsXLmTOnDmMGjUKgE8++YTAwEA2bNjA2LFjG7Q+e/lwe1UL9NHeIQTa8MQ7gLurC5EtKjmw6C06jn2B83klzN98lPmbq6/bwtudF0Z0ZnSP1o12v+WVeoT4sXnGH3jzu6N8mZjGjmNZlpPoV/J0deEvd7fjvwe0r9Z9oDEE+nrw2dR+rPr5DItij3M8s5DX/5Vc47ojuwczJrySga8mNW6RVI128sr9XejXzp9Xvz3C+bwS3vzuKG9+d9SyTosRs7hYBi5aDb3DmtMn3B8Xrf0OMX/PoW+937hxI0OGDOHhhx9mx44dtG7dmr/+9a9MmTIFgNOnT2M0Gq1uudLr9URGRhIXF3fVQDOZTJhM/+nNXVBQ0LAfxIYOpOby84mL6LQa/nJ3uwZ7n+KUn1kyvBVnacm/DqVz+Hw++SXl+Hq60jVYz7BuBh7s2cYuV7Ku1NzLjfkP3cZ/D2jPPxPPsftkNudyS3DRamjX0osBt7RiTK82dh/mR+eiZcof2vFo3xC+TjzHtpQsTmYWUl5Z1cUhsl0AD/Vqwy2BPuzfv9+utQ7tGsSATq3YcOA83/5ygaTUPIrKKnHRQHH6CXp2CueObh3xcsCROxyvoiucOnWKJUuWMGvWLJ5//nn27dvHk08+iZubGxMnTsRorGr2/v68XWBgoGVZTebNm8err77aoLU3lMXbTwLwQM/WtGnesPdiuus0jL09lLF9Qxv0fWwhvIUX/zukE2C7Q/CG4OvhyuP92/J4/7bXX9mOPFxdGNu36mevlMJUYebwL0n06X0fEYu/dsgwAxs/l9PWzGYzt99+O2+88QY9e/Zk6tSpTJkyhaVLl97QfmfPnk1+fr5lSktLs1HFDet0dhFbkzPQaOC/BrS3dzniJqHRaPBwdbHr1cvacuhACwoK4tZbb7WaFxERQWpqKgAGgwGouoPhShkZGZZlNXF3d8fX19dqago+iTsDwMBOrWjf0tu+xQjhgBw60Pr3709KSorVvGPHjhEWFgZUXSAwGAzExsZalhcUFBAfH09UVFSj1trQCk0VfJlwDoDH7wi3bzFCOCjHPBD+zVNPPcUdd9zBG2+8wSOPPMLevXtZtmwZy5YtA6qawjNnzuT111+nY8eOlm4bwcHBjB492r7F29hXiecoNFXQvqUXd3Vs+D5UQjRFDh1offr0Yf369cyePZu5c+fStm1bFi5cyLhx4yzrPPPMMxQVFTF16lTy8vK488472bx5s1P1QVNKsWb3GQAm3hFuly4SQjQFDh1oAPfdd9817w/VaDTMnTuXuXPnNmJVjSvu1EVOZRfh7a7jwdvb2LscIRyWQ59DE1U+21t1FXZUj2Cnf2qPEDdCAs3B5RSVsflwVZ+6x5pAfzAh7EkCzcF9vf8cZZVmurb2pWvrG78JXQhnJoHmwJRSfLq3qs+dtM6EuD4JNAeWcDaXk1lFeLq6cH/3YHuXI4TDk0BzYJ/GV7XO7u8ebPWUcSFEzSTQHFR+cTn/OpQOwNi+IXauRoimQQLNQW1Iqnp6TmeDDz1C/OxdjhBNggSaA/r9xQC5M0CI2pFAc0AH0vI4aryEu07L6J6t7V2OEE2GdDuvo9TUVLKzs22yrxYtWhAaWr07xrrfLgaMuC0IvadcDBCitiTQ6iA1NZXOERGUFBfbZH+ezZpxNDnZKtTyS8rZ9MsFAMZFSt8zIepCAq0OsrOzKSkuZtyzbxMYemMjxmaknmTt/KfJzs62CrT1+89RWm6mU6CPQzwWTIimRAKtHgJD29OmYxeb71cpxbrfLgb8MVIuBghRV3JRwIEkns3lWEYhHq5yMUCI+pBAcyCXLwaMvC1YLgYIUQ8SaA4it6iMTb/dGfBHuRggRL1IoDmIr/afo6zCTESQr9wZIEQ9SaA5gEqzYvVvzwyY0C9MLgYIUU9NKtDefPNNy5OeListLSUmJoaAgAC8vb0ZM2ZMted0OrqtyRmcyy3Br5krD8jFACHqrckE2r59+/j444+57bbbrOY/9dRTfPvtt3z55Zfs2LGDCxcu8OCDD9qpyvpZ9fNpoOq+TU83FztXI0TT1SQCrbCwkHHjxrF8+XKaN/9PZ9P8/HxWrFjBe++9xz333EOvXr1YtWoVu3fvZs+ePXasuPZO55az51QOLloNE/qF2bscIZq0JhFoMTExjBgxgujoaKv5iYmJlJeXW83v3LkzoaGhxMXFNXaZ9fLtsSIAhnU1EOznaedqhGjaHP5Ogc8++4z9+/ezb9++asuMRiNubm74+flZzQ8MDMRoNF51nyaTCZPJZHldUFBgs3rrQqcPZGdqCQBT7mpnlxqEcCYO3UJLS0tjxowZrF271qZPQp83bx56vd4yhYTYZ0RY38gxmBXc1bEF3aWrhhA3zKEDLTExkczMTG6//XZ0Oh06nY4dO3awaNEidDodgYGBlJWVkZeXZ7VdRkYGBoPhqvudPXs2+fn5liktLa2BP0l1JRXg3e1eAGIGdmj09xfCGTn0IeegQYM4dOiQ1bxJkybRuXNnnn32WUJCQnB1dSU2NpYxY8YAkJKSQmpqKlFRUVfdr7u7O+7u7g1a+/Ucu+SCRudC5xauRLb1t2stQjgLhw40Hx8funbtajXPy8uLgIAAy/zJkycza9Ys/P398fX1Zfr06URFRdGvXz97lFwrBSXlnLpU1Th+KMJbOtIKYSMOHWi1sWDBArRaLWPGjMFkMjFkyBA++ugje5d1TXGnLmJGQ+nZg/R8eIi9yxHCaTS5QPvxxx+tXnt4eLB48WIWL15sn4LqKOuSiaPGSwDk/rgKzdND7VyREM7DoS8KOBulFD8dzwKgTbNKyown7FyREM5FAq0RHTVeIi23BBethi5+lfYuRwinI4HWSIrLKtj5W+sssq0/3k3uYF8IxyeB1kh2Hs+mtNxMC283efiJEA1EAq0RHEkvIMV4CQ0wqHMgLlrppiFEQ5BAa2DZhSa2H80EILKdPwa97W7hEkJYk0BrQKXllfz7UDoVZkWofzP6hssdAUI0JAm0BlJeaWbjwQvkFpfj7a5jSJdAuSNAiAYmgdYAKirN/OtQOun5pbjrtIzuEUwzN7msKURDk78yGzNVVLLpYDrn8krQaTWM6hFMgLd9b4QX4mYhgWZD+SXl/OuXdLIKTbi5aLnvtiCC9DIKrRCNRQLNBpRSHDVe4seULMoqzXi6ujC6RzCtfOWKphCNSQLtBpWWV7LtaCbHMwsBCNJ7MLSLAV9PVztXJsTNRwLtBqTmFLPlSAaFpgq0GohsG0DvsOZopeOsEHYhgVYPlQp2Hs/iQGoeAH7NXBnSxYBBDjGFsCsJtDpybRHGNqOOgvI8ALq11nNXxxa4ukgPGCHsTQKtlsxmxbfHigiauICCci2eri5E39qKdi287V2aEOI3Emi1VFRWwbfHitDo3DB4mLmvd1u83OXrE8KRyHFSLfl4uDIjUs/F7xdzR8sKCTMhHJAEWh10aelOYdJ3yC2ZQjgmhw60efPm0adPH3x8fGjVqhWjR48mJSXFap3S0lJiYmIICAjA29ubMWPGkJGRYaeKhRD25NCBtmPHDmJiYtizZw9btmyhvLycwYMHU1RUZFnnqaee4ttvv+XLL79kx44dXLhwgQcffNCOVQsh7MWhTwRt3rzZ6vXq1atp1aoViYmJ/OEPfyA/P58VK1awbt067rnnHgBWrVpFREQEe/bsceiHDQshbM+hW2i/l5+fD4C/f9VAiYmJiZSXlxMdHW1Zp3PnzoSGhhIXF3fV/ZhMJgoKCqwmIUTT12QCzWw2M3PmTPr370/Xrl0BMBqNuLm54efnZ7VuYGAgRqPxqvuaN28eer3eMoWEhDRk6UKIRtJkAi0mJobDhw/z2Wef3fC+Zs+eTX5+vmVKS0uzQYVCCHtz6HNol02bNo1Nmzaxc+dO2rRpY5lvMBgoKysjLy/PqpWWkZGBwWC46v7c3d1xd5dBF4VwNg7dQlNKMW3aNNavX8+2bdto27at1fJevXrh6upKbGysZV5KSgqpqalERUU1drlCCDtz6BZaTEwM69at45tvvsHHx8dyXkyv1+Pp6Yler2fy5MnMmjULf39/fH19mT59OlFRUXKFU4ibkEMH2pIlSwAYMGCA1fxVq1bx+OOPA7BgwQK0Wi1jxozBZDIxZMgQPvroo0auVAjhCBw60JRS113Hw8ODxYsXs3jx4kaoSAjhyBz6HJoQQtSFBJoQwmlIoAkhnIYEmhDCaUigCSGchgSaEMJpSKAJIZyGBJoQwmlIoAkhnIYEmhDCaUigCSGchgSaEMJpSKAJIZyGBJoQwmlIoAkhnIYEmhDCaUigCSGchgSaEMJpSKAJIZyGBJoQwmk4TaAtXryY8PBwPDw8iIyMZO/evfYuSQjRyJwi0D7//HNmzZrFyy+/zP79++nevTtDhgwhMzPT3qUJIRqRUwTae++9x5QpU5g0aRK33norS5cupVmzZqxcudLepQkhGpFDP5ezNsrKykhMTGT27NmWeVqtlujoaOLi4mrcxmQyYTKZLK/z8/MBKCgouOZ7FRYWAnDu+K+YSopvqO6sc6cBSExMtOz3RqSkpDhkbY5alyPXdjPUdWVthYWF1/zbu7ysNs/pRTVx58+fV4DavXu31fynn35a9e3bt8ZtXn75ZQXIJJNMTWhKS0u7bh40+RZafcyePZtZs2ZZXpvNZnJycggICECj0Vx1u4KCAkJCQkhLS8PX17cxSnV68p3aljN+n0opLl26RHBw8HXXbfKB1qJFC1xcXMjIyLCan5GRgcFgqHEbd3d33N3dreb5+fnV+j19fX2d5pfFUch3alvO9n3q9fpardfkLwq4ubnRq1cvYmNjLfPMZjOxsbFERUXZsTIhRGNr8i00gFmzZjFx4kR69+5N3759WbhwIUVFRUyaNMnepQkhGpFTBNqjjz5KVlYWL730EkajkR49erB582YCAwNt+j7u7u68/PLL1Q5XRf3Jd2pbN/v3qVGqNtdChRDC8TX5c2hCCHGZBJoQwmlIoAkhnIYEmhDCaUig1YEMUWQb8+bNo0+fPvj4+NCqVStGjx5tuU9Q2Mabb76JRqNh5syZ9i6lUUmg1ZIMUWQ7O3bsICYmhj179rBlyxbKy8sZPHgwRUVF9i7NKezbt4+PP/6Y2267zd6lNL4bvz385tC3b18VExNjeV1ZWamCg4PVvHnz7FiVc8jMzFSA2rFjh71LafIuXbqkOnbsqLZs2aLuvvtuNWPGDHuX1KikhVYLl4coio6Otsy73hBFovYuD9/k7+9v50qavpiYGEaMGGH1u3ozcYo7BRpadnY2lZWV1e48CAwM5OjRo3aqyjmYzWZmzpxJ//796dq1q73LadI+++wz9u/fz759++xdit1IoAm7iomJ4fDhw+zatcvepTRpaWlpzJgxgy1btuDh4WHvcuxGAq0W6jNEkbi+adOmsWnTJnbu3EmbNm3sXU6TlpiYSGZmJrfffrtlXmVlJTt37uTDDz/EZDLh4uJixwobh5xDqwUZosi2lFJMmzaN9evXs23bNtq2bWvvkpq8QYMGcejQIZKSkixT7969GTduHElJSTdFmIG00GpNhiiynZiYGNatW8c333yDj48PRqMRqBrEz9PT087VNU0+Pj7VzkF6eXkREBBwU52blECrpcYaouhmsGTJEgAGDBhgNX/VqlU8/vjjjV+QcBoyfJAQwmnIOTQhhNOQQBNCOA0JNCGE05BAE0I4DQk0IYTTkEATQjgNCTQhhNOQQGtCNBoNGzZssMm+zpw5g0ajISkpySb7c1a2/M6vNGDAAKvRZMPDw1m4cKHN36em93JmEmgOwGg0MmPGDDp06ICHhweBgYH079+fJUuWUFxc3CDvGRISQnp6eqPeFjNkyBBcXFzsPrzN448/jkajQaPR4OrqSmBgIPfeey8rV67EbDZbrZuens6wYcNqtd+6hN/XX3/Na6+9VtfSr+nHH39Eo9GQl5fX4O/lqCTQ7OzUqVP07NmTH374gTfeeIMDBw4QFxfHM888w6ZNm9i6dWuDvK+LiwsGgwGdrnHufktNTWX37t1MmzaNlStXXnf9srKyBq1n6NChpKenc+bMGb777jsGDhzIjBkzuO+++6ioqLCsZzAYbPoU8sufy9/fHx8fH5vt91oa873szs4j5t70hgwZotq0aaMKCwtrXG42my3/BtT69estr5955hnVsWNH5enpqdq2bavmzJmjysrKLMvDwsIUUG1SSqnTp08rQB04cEAppdT27dsVoLZu3ap69eqlPD09VVRUlDp69KhVPa+99ppq2bKl8vb2VpMnT1bPPvus6t69+3U/5yuvvKLGjh2rkpOTlV6vV8XFxVbL7777bhUTE6NmzJihAgIC1IABA5RSSh06dEgNHTpUeXl5qVatWqnx48errKwsy3bfffed6t+/v9Lr9crf31+NGDFCnThx4pq1TJw4UY0aNara/NjYWAWo5cuXW+Zd+Z2bTCYVExOjDAaDcnd3V6GhoeqNN96o8bsOCwtTSin18ssvq+7du6vly5er8PBwpdFoLJ/3yuGxw8LC1Ny5c9XYsWNVs2bNVHBwsPrwww8ty3//81JKqdzcXAWo7du3W5ZfOU2cOLHG98rJyVETJkxQfn5+ytPTUw0dOlQdO3bMsnzVqlVKr9erzZs3q86dOysvLy81ZMgQdeHChWt+r45AWmh2dPHiRX744QdiYmLw8vKqcR2NRnPV7X18fFi9ejVHjhzh/fffZ/ny5SxYsMCyfN++faSnp5Oens65c+fo168fd9111zVreuGFF3j33XdJSEhAp9PxxBNPWJatXbuWv/3tb8yfP5/ExERCQ0MtN5pfi1KKVatWMX78eDp37kyHDh345z//WW29NWvW4Obmxs8//8zSpUvJy8vjnnvuoWfPniQkJLB582YyMjJ45JFHLNsUFRUxa9YsEhISiI2NRavV8sADD1Q7dKyNe+65h+7du/P111/XuHzRokVs3LiRL774gpSUFNauXUt4eDiA5TB61apVpKenWx1Wnzhxgq+++oqvv/76mucs3377bbp3786BAwd47rnnLAM21kZISAhfffUVACkpKaSnp/P+++/XuO7jjz9OQkICGzduJC4uDqUUw4cPp7y83LJOcXEx77zzDn//+9/ZuXMnqamp/O///m+tarEreyfqzWzPnj0KUF9//bXV/ICAAOXl5aW8vLzUM888Y5nP71pov/f222+rXr161bjsySefVGFhYSozM1Mpde0W2mX/+te/FKBKSkqUUkpFRkZaPShGKaX69+9/3RbaDz/8oFq2bKnKy8uVUkotWLBA3X333Vbr3H333apnz55W81577TU1ePBgq3lpaWkKUCkpKTW+V1ZWlgLUoUOHrlrP1VpoSin16KOPqoiICMvrK7/z6dOnq3vuuceq1Xylmn4+L7/8snJ1dbV875fV1EIbOnRotVqGDRumlLp+C02p//wMc3Nzr/pex44dU4D6+eefLcuzs7OVp6en+uKLL5RSVS00wKqlu3jxYhUYGFjj53Yk0kJzQHv37iUpKYkuXbpgMpmuut7nn39O//79MRgMeHt7M2fOHFJTU6utt2zZMlasWMHGjRtp2bLlNd/7ykefBQUFAVge1ZeSkkLfvn2t1v/965qsXLmSRx991HK+7rHHHuPnn3/m5MmTVuv16tXL6vXBgwfZvn073t7elqlz584Alm2PHz/OY489Rrt27fD19bW0mGr6HmpDKXXVVvHjjz9OUlISnTp14sknn+SHH36o1T7DwsKu+70D1QYLjYqKIjk5uVbvUVvJycnodDoiIyMt8wICAujUqZPVezVr1oz27dtbXgcFBTWJRzZKoNlRhw4d0Gg01R6y265dOzp06HDNwQ7j4uIYN24cw4cPZ9OmTRw4cIAXXnih2sn07du3M336dD755JNaPafR1dXV8u/Lf9j1OXy7LCcnh/Xr1/PRRx+h0+nQ6XS0bt2aioqKahcHfn/YXVhYyMiRI61GYU1KSuL48eP84Q9/AGDkyJHk5OSwfPly4uPjiY+PB+p/USE5OfmqI+jefvvtnD59mtdee42SkhIeeeQRHnrooevu82qnE+pCq636U1VXjPZ15SGirV35ewBVvwuqCYw0JoFmRwEBAdx77718+OGHdX7I7u7duwkLC+OFF16gd+/edOzYkbNnz1qtc+LECR566CGef/55HnzwwRuut1OnTtW6XFyvC8batWtp06YNBw8etAqld999l9WrV1NZWXnVbW+//XZ+/fVXwsPD6dChg9Xk5eXFxYsXSUlJYc6cOQwaNIiIiAhyc3Pr/fm2bdvGoUOHGDNmzFXX8fX15dFHH2X58uV8/vnnfPXVV+Tk5ABVIXCtz3M9e/bsqfY6IiICwNLCS09Ptyz//fk4Nzc3gGvWEBERQUVFhSX4Acv3eOutt9a7dkchgWZnH330ERUVFfTu3ZvPP/+c5ORkUlJS+Mc//sHRo0evOhZ8x44dSU1N5bPPPuPkyZMsWrSI9evXW5aXlJQwcuRIevbsydSpUzEajZapvqZPn86KFStYs2YNx48f5/XXX+eXX3655oWLFStW8NBDD9G1a1erafLkyWRnZ7N58+arbhsTE0NOTg6PPfYY+/bt4+TJk3z//fdMmjSJyspKmjdvTkBAAMuWLePEiRNs27aNWbNm1eqzmEwmjEYj58+fZ//+/bzxxhuMGjWK++67jz/96U81bvPee+/x6aefcvToUY4dO8aXX36JwWDAz88PqOocGxsbi9ForFew/vzzz7z11lscO3aMxYsX8+WXXzJjxgwAPD096devH2+++SbJycns2LGDOXPmWG0fFhaGRqNh06ZNZGVlUVhYWO09OnbsyKhRo5gyZQq7du3i4MGDjB8/ntatWzNq1Kg61+xw7HwOTyilLly4oKZNm6batm2rXF1dlbe3t+rbt696++23VVFRkWU9fnfS+emnn1YBAQHK29tbPfroo2rBggVKr9crpVSNl/EvT1cu//1FgStPKB84cEAB6vTp05Z5c+fOVS1atFDe3t7qiSeeUE8++aTq169fjZ8rISFBAWrv3r01Lh82bJh64IEHlFLVT5JfduzYMfXAAw9Yuhh07txZzZw503JifsuWLSoiIkK5u7ur2267Tf3444/XvXgyceJEy3eh0+lUy5YtVXR0tFq5cqWqrKy0WvfKfS1btkz16NFDeXl5KV9fXzVo0CC1f/9+y7obN25UHTp0UDqdrlq3jd+r6aLAq6++qh5++GHVrFkzZTAY1Pvvv2+1zZEjR1RUVJTy9PRUPXr0UD/88IPVRQGlqn4+BoNBaTSa63bb0Ov1ytPTUw0ZMqTGbhtXWr9+vWoKcSFDcIsbcu+992IwGPj73/9u71KEkIekiNorLi5m6dKllluYPv30U7Zu3VrrvlJCNDRpoYlau3xe7sCBA5SWltKpUyfmzJljkwsOQtiCBJoQwmnIVU4hhNOQQBNCOA0JNCGE05BAE0I4DQk0IYTTkEATQjgNCTQhhNOQQBNCOA0JNCGE0/h/6yBzQSfiTcQAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 300x300 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from scipy.stats import skew\n",
        "for i in ('Relative Compactness','Surface Area','Wall Area','Roof Area','Overall Height','Orientation','Glazing Area','Glazing Area Distribution'):\n",
        "  plt.subplots(figsize=(3,3))\n",
        "  sns.histplot(data=df1,x=i, kde=True)\n",
        "\n",
        "# Relative Compactness and Surface Area has variation and lean towards numerical data.\n",
        "# Wall Area, Roof Area, Overall Height, Orientation, Glazing Area, Glazing area distribution lean towards being categorical data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 301,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2XWf4CVWzPOW",
        "outputId": "92ca866c-2d47-4563-9b3d-a121421f999d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Skewness of all the features are as follows: \n",
            "    Relative Compactness : 0.4945441823526014\n",
            "    Surface Area : -0.12488635574227028\n",
            "    Wall Area : 0.5323750928990681\n",
            "    Roof Area : -0.16244592674971672\n",
            "    Overall Height : 0.0\n",
            "    Orientation : 0.0\n",
            "    Glazing Area : -0.060136480707452804\n",
            "    Glazing Area Distribution : -0.08851586032816593\n"
          ]
        }
      ],
      "source": [
        "from scipy.stats import skew\n",
        "print(\"Skewness of all the features are as follows: \")\n",
        "for i in ('Relative Compactness','Surface Area','Wall Area','Roof Area','Overall Height','Orientation','Glazing Area','Glazing Area Distribution'):\n",
        "  column_skewness = skew(df1[i])\n",
        "  print(\"   \",i,':',column_skewness)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 302,
      "metadata": {
        "id": "ZR8Bmiavu_cJ"
      },
      "outputs": [],
      "source": [
        "# 'Relative compactness' and 'Wall Area' are more skewed than other parameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 303,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JbjqgP4Pqm4A",
        "outputId": "c4a671ce-4cb2-4493-f30d-be865bb68e0d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.39046989341862925"
            ]
          },
          "execution_count": 303,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# we are using sqrt transformation instead of log transformation to keep all the values of relative compactness positive.\n",
        "df1['Relative Compactness'] = np.sqrt(df1['Relative Compactness'])\n",
        "skew(df1['Relative Compactness'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 304,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "uQ2bAVDU2K0V",
        "outputId": "af536008-df64-4503-966e-536d0c4a4aa1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: xlabel='Relative Compactness', ylabel='Count'>"
            ]
          },
          "execution_count": 304,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.histplot(data=df1, x='Relative Compactness', kde=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 305,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OCunZx_g2hgD",
        "outputId": "c31f3f55-7e3d-4939-98b7-4ee3b9987423"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.163024969656773"
            ]
          },
          "execution_count": 305,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# We will add a small constant (e.g.,1) to handle zero or negative values but 'Wall Area' do not require us to do that because there is no negative or zero value.\n",
        "# Performing log transformation to handle the skewness\n",
        "df1['Wall Area']=np.log(df1['Wall Area'])\n",
        "skew(df1['Wall Area'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 306,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "RcSTgwBkBuyj",
        "outputId": "930f4420-ea6e-4c66-ea0b-148f4c4b0a26"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<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>Relative Compactness</th>\n",
              "      <th>Surface Area</th>\n",
              "      <th>Wall Area</th>\n",
              "      <th>Roof Area</th>\n",
              "      <th>Overall Height</th>\n",
              "      <th>Orientation</th>\n",
              "      <th>Glazing Area</th>\n",
              "      <th>Glazing Area Distribution</th>\n",
              "      <th>Heating Load</th>\n",
              "      <th>Cooling Load</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.00000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.00000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.872121</td>\n",
              "      <td>671.708333</td>\n",
              "      <td>5.754459</td>\n",
              "      <td>176.604167</td>\n",
              "      <td>5.25000</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>0.234375</td>\n",
              "      <td>2.81250</td>\n",
              "      <td>22.307201</td>\n",
              "      <td>24.587760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.059808</td>\n",
              "      <td>88.086116</td>\n",
              "      <td>0.134990</td>\n",
              "      <td>45.165950</td>\n",
              "      <td>1.75114</td>\n",
              "      <td>1.118763</td>\n",
              "      <td>0.133221</td>\n",
              "      <td>1.55096</td>\n",
              "      <td>10.090196</td>\n",
              "      <td>9.513306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.787401</td>\n",
              "      <td>514.500000</td>\n",
              "      <td>5.501258</td>\n",
              "      <td>110.250000</td>\n",
              "      <td>3.50000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>6.010000</td>\n",
              "      <td>10.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.826098</td>\n",
              "      <td>606.375000</td>\n",
              "      <td>5.683580</td>\n",
              "      <td>140.875000</td>\n",
              "      <td>3.50000</td>\n",
              "      <td>2.750000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>1.75000</td>\n",
              "      <td>12.992500</td>\n",
              "      <td>15.620000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.866006</td>\n",
              "      <td>673.750000</td>\n",
              "      <td>5.763622</td>\n",
              "      <td>183.750000</td>\n",
              "      <td>5.25000</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>3.00000</td>\n",
              "      <td>18.950000</td>\n",
              "      <td>22.080000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.910994</td>\n",
              "      <td>741.125000</td>\n",
              "      <td>5.837730</td>\n",
              "      <td>220.500000</td>\n",
              "      <td>7.00000</td>\n",
              "      <td>4.250000</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>4.00000</td>\n",
              "      <td>31.667500</td>\n",
              "      <td>33.132500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.989949</td>\n",
              "      <td>808.500000</td>\n",
              "      <td>6.031886</td>\n",
              "      <td>220.500000</td>\n",
              "      <td>7.00000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>5.00000</td>\n",
              "      <td>43.100000</td>\n",
              "      <td>48.030000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Relative Compactness  Surface Area   Wall Area   Roof Area  \\\n",
              "count            768.000000    768.000000  768.000000  768.000000   \n",
              "mean               0.872121    671.708333    5.754459  176.604167   \n",
              "std                0.059808     88.086116    0.134990   45.165950   \n",
              "min                0.787401    514.500000    5.501258  110.250000   \n",
              "25%                0.826098    606.375000    5.683580  140.875000   \n",
              "50%                0.866006    673.750000    5.763622  183.750000   \n",
              "75%                0.910994    741.125000    5.837730  220.500000   \n",
              "max                0.989949    808.500000    6.031886  220.500000   \n",
              "\n",
              "       Overall Height  Orientation  Glazing Area  Glazing Area Distribution  \\\n",
              "count       768.00000   768.000000    768.000000                  768.00000   \n",
              "mean          5.25000     3.500000      0.234375                    2.81250   \n",
              "std           1.75114     1.118763      0.133221                    1.55096   \n",
              "min           3.50000     2.000000      0.000000                    0.00000   \n",
              "25%           3.50000     2.750000      0.100000                    1.75000   \n",
              "50%           5.25000     3.500000      0.250000                    3.00000   \n",
              "75%           7.00000     4.250000      0.400000                    4.00000   \n",
              "max           7.00000     5.000000      0.400000                    5.00000   \n",
              "\n",
              "       Heating Load  Cooling Load  \n",
              "count    768.000000    768.000000  \n",
              "mean      22.307201     24.587760  \n",
              "std       10.090196      9.513306  \n",
              "min        6.010000     10.900000  \n",
              "25%       12.992500     15.620000  \n",
              "50%       18.950000     22.080000  \n",
              "75%       31.667500     33.132500  \n",
              "max       43.100000     48.030000  "
            ]
          },
          "execution_count": 306,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 307,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yzG77v1EB0wR",
        "outputId": "6c18f541-73d2-4e49-c9b1-f307cfb5edbf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 768 entries, 0 to 767\n",
            "Data columns (total 10 columns):\n",
            " #   Column                     Non-Null Count  Dtype  \n",
            "---  ------                     --------------  -----  \n",
            " 0   Relative Compactness       768 non-null    float64\n",
            " 1   Surface Area               768 non-null    float64\n",
            " 2   Wall Area                  768 non-null    float64\n",
            " 3   Roof Area                  768 non-null    float64\n",
            " 4   Overall Height             768 non-null    float64\n",
            " 5   Orientation                768 non-null    int64  \n",
            " 6   Glazing Area               768 non-null    float64\n",
            " 7   Glazing Area Distribution  768 non-null    int64  \n",
            " 8   Heating Load               768 non-null    float64\n",
            " 9   Cooling Load               768 non-null    float64\n",
            "dtypes: float64(8), int64(2)\n",
            "memory usage: 60.1 KB\n"
          ]
        }
      ],
      "source": [
        "df1.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 308,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "WsMBm7XG3W18",
        "outputId": "6dc55a97-ccf6-4ed1-c77f-28c21a7f0fb6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: xlabel='Wall Area', ylabel='Count'>"
            ]
          },
          "execution_count": 308,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.histplot(data=df1,x='Wall Area',kde=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 309,
      "metadata": {
        "id": "rswIbTBojzB9"
      },
      "outputs": [],
      "source": [
        "def find_skewed_boundaries(df, variable, distance):\n",
        "\n",
        "    # The parameter \"distance\" gives us the option to estimate 1.5 times or 3 times the IQR when defining the boundaries.\n",
        "    # The more we increase the \"distance\", the boundaries will be more wider. Hence, flagging more datapoints as outliers.\n",
        "\n",
        "    IQR = df[variable].quantile(0.75) - df[variable].quantile(0.25)\n",
        "\n",
        "    lower_boundary = df[variable].quantile(0.25) - (IQR * distance)\n",
        "    upper_boundary = df[variable].quantile(0.75) + (IQR * distance)\n",
        "\n",
        "    return upper_boundary, lower_boundary"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 310,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SktOQx23lOUl",
        "outputId": "01debfb1-df8a-43c6-ffe9-2c70cc1e489c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1.0383392446171151, 0.6987528529653382)"
            ]
          },
          "execution_count": 310,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "upper_boundary, lower_boundary = find_skewed_boundaries(df1, 'Relative Compactness', 1.5)\n",
        "upper_boundary, lower_boundary"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 311,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wKFRpubBleb7",
        "outputId": "3a6f65a5-faf2-45f4-952a-d27271b2f87b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "total number of Data Entries: 768\n",
            "Relative compactness Entries with right end outliers: 0\n",
            "Relative compactness Entries with left end outliers: 0\n",
            "\n",
            "% right end outliers: 0.0\n",
            "% left end outliers: 0.0\n"
          ]
        }
      ],
      "source": [
        "# Inspect the number and percentage of outliers in Relative compactness.\n",
        "\n",
        "print('total number of Data Entries: {}'.format(len(df1)))\n",
        "\n",
        "print('Relative compactness Entries with right end outliers: {}'.format(\n",
        "    len(df1[df1['Relative Compactness'] > upper_boundary])))\n",
        "\n",
        "print('Relative compactness Entries with left end outliers: {}'.format(\n",
        "    len(df1[df1['Relative Compactness'] < lower_boundary])))\n",
        "print()\n",
        "print('% right end outliers: {}'.format(\n",
        "    len(df1[df1['Relative Compactness'] > upper_boundary]) / len(df1)))\n",
        "\n",
        "print('% left end outliers: {}'.format(\n",
        "    len(df1[df1['Relative Compactness'] < lower_boundary]) / len(df1)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 312,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 743
        },
        "id": "NC94dCGHrRhg",
        "outputId": "85355c8a-b3d3-4c64-e310-2a1e56708eea"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 312,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x650 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "corr_df1=df1.corr()\n",
        "plt.subplots(figsize=(8,6.5))\n",
        "sns.heatmap(corr_df1, annot=True, fmt='.3f')\n",
        "\n",
        "#Overall Height and relative compactness is highly correlated with each other and Outcomes(heating and cooling load)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 313,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q43k59v__ZOj",
        "outputId": "95fb4db7-5ca3-4096-d5d9-9b89bb6d7999"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(768, 10)"
            ]
          },
          "execution_count": 313,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 314,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "r3nYqKJ8CMbw",
        "outputId": "6893ce57-2a0a-4826-e40e-7ae6927ed9f3"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x500 with 8 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "n_rows=2\n",
        "n_cols=4\n",
        "\n",
        "fig, axes = plt.subplots(nrows=n_rows, ncols=n_cols)\n",
        "fig.set_size_inches(10, 5)\n",
        "for i, column in enumerate(df1.iloc[:,:-2].columns):\n",
        "    sns.regplot(x=df1[column], y=df1['Heating Load'], ax=axes[i//n_cols,i%n_cols], scatter_kws={\"color\": \"green\"}, line_kws={\"color\": \"red\"})\n",
        "plt.tight_layout()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 315,
      "metadata": {
        "id": "zSq5AsomCqbi"
      },
      "outputs": [],
      "source": [
        "# Most of the features are not linearly seprable with respect to Heating Load"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 316,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "x0pFLZ8wCR1h",
        "outputId": "f68edc8d-dfd9-4f27-af4f-5fb2cacaa883"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x500 with 8 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Create the subplots\n",
        "fig, axes = plt.subplots(nrows=n_rows, ncols=n_cols)\n",
        "fig.set_size_inches(10, 5)\n",
        "for i, column in enumerate(df1.iloc[:,:-2].columns):\n",
        "    sns.regplot(x = df1[column], y = df1['Cooling Load'],ax=axes[i//n_cols,i%n_cols], scatter_kws={\"color\": \"black\" , 'cmap':'jet'}, line_kws={\"color\": \"red\"})\n",
        "plt.tight_layout()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 317,
      "metadata": {
        "id": "w-C0Imb7C4c8"
      },
      "outputs": [],
      "source": [
        "# Same as of Heating Load relation, relationship betweeen Cooling Load and the features are not seprable linearly."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 318,
      "metadata": {
        "id": "6qr8r0x5vB_l"
      },
      "outputs": [],
      "source": [
        "# Orientation and Glazing Area is correlated to the outcomes and other features very weakly, so we can remove these columns from out dataset.\n",
        "\n",
        "df1=df1.drop('Orientation', axis =1)\n",
        "# df1=df1.drop('Overall Height', axis=1)\n",
        "df1=df1.drop('Glazing Area Distribution', axis =1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 319,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cq-sVl6KD3ru",
        "outputId": "f8ebed16-ab92-42be-f814-a654cccf8623"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Relative Compactness    False\n",
              "Surface Area            False\n",
              "Wall Area               False\n",
              "Roof Area               False\n",
              "Overall Height          False\n",
              "Glazing Area            False\n",
              "Heating Load            False\n",
              "Cooling Load            False\n",
              "dtype: bool"
            ]
          },
          "execution_count": 319,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(df1<0).any()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 320,
      "metadata": {
        "id": "UMBiXXgw_qLK"
      },
      "outputs": [],
      "source": [
        "heatOutcome=df1['Heating Load']\n",
        "df1=df1.drop('Heating Load', axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 321,
      "metadata": {
        "id": "So0dkvZCA_pS"
      },
      "outputs": [],
      "source": [
        "coolOutcome=df1['Cooling Load']\n",
        "df1=df1.drop('Cooling Load', axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 322,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kdG_vxHEOeYG",
        "outputId": "cd223e12-459a-4822-e9fa-f8cbd185a7cd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(768, 6)"
            ]
          },
          "execution_count": 322,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 323,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "KBeol1xABUoA",
        "outputId": "65c0fa68-6e18-4d5b-ab6e-bddd6898ff09"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<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>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.971395</td>\n",
              "      <td>-1.785875</td>\n",
              "      <td>-0.525411</td>\n",
              "      <td>-1.470077</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.760447</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.971395</td>\n",
              "      <td>-1.785875</td>\n",
              "      <td>-0.525411</td>\n",
              "      <td>-1.470077</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.760447</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.971395</td>\n",
              "      <td>-1.785875</td>\n",
              "      <td>-0.525411</td>\n",
              "      <td>-1.470077</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.760447</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.971395</td>\n",
              "      <td>-1.785875</td>\n",
              "      <td>-0.525411</td>\n",
              "      <td>-1.470077</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.760447</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.280971</td>\n",
              "      <td>-1.229239</td>\n",
              "      <td>0.067929</td>\n",
              "      <td>-1.198678</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.760447</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          0         1         2         3    4         5\n",
              "0  1.971395 -1.785875 -0.525411 -1.470077  1.0 -1.760447\n",
              "1  1.971395 -1.785875 -0.525411 -1.470077  1.0 -1.760447\n",
              "2  1.971395 -1.785875 -0.525411 -1.470077  1.0 -1.760447\n",
              "3  1.971395 -1.785875 -0.525411 -1.470077  1.0 -1.760447\n",
              "4  1.280971 -1.229239  0.067929 -1.198678  1.0 -1.760447"
            ]
          },
          "execution_count": 323,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# To make the data follow gaussian distribution, mean will be 0 and standardDeviation will be 1, it will help many algorithms to converge faster.\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(df1)\n",
        "ScaledX=pd.DataFrame(X_scaled)\n",
        "ScaledX.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 324,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "UobVwe4BE2p7",
        "outputId": "985a79c8-2565-45a9-c289-c578d7cdab99"
      },
      "outputs": [
        {
          "data": {
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              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>7.680000e+02</td>\n",
              "      <td>7.680000e+02</td>\n",
              "      <td>7.680000e+02</td>\n",
              "      <td>7.680000e+02</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>7.680000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-2.220446e-15</td>\n",
              "      <td>-4.163336e-16</td>\n",
              "      <td>-1.628327e-15</td>\n",
              "      <td>2.174187e-16</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.480297e-16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.000652e+00</td>\n",
              "      <td>1.000652e+00</td>\n",
              "      <td>1.000652e+00</td>\n",
              "      <td>1.000652e+00</td>\n",
              "      <td>1.000652</td>\n",
              "      <td>1.000652e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-1.417447e+00</td>\n",
              "      <td>-1.785875e+00</td>\n",
              "      <td>-1.876923e+00</td>\n",
              "      <td>-1.470077e+00</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.760447e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-7.700078e-01</td>\n",
              "      <td>-7.421818e-01</td>\n",
              "      <td>-5.254107e-01</td>\n",
              "      <td>-7.915797e-01</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.009323e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-1.023003e-01</td>\n",
              "      <td>2.319318e-02</td>\n",
              "      <td>6.792942e-02</td>\n",
              "      <td>1.583159e-01</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.173631e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.503970e-01</td>\n",
              "      <td>7.885681e-01</td>\n",
              "      <td>6.172765e-01</td>\n",
              "      <td>9.725122e-01</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.244049e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.971395e+00</td>\n",
              "      <td>1.553943e+00</td>\n",
              "      <td>2.056515e+00</td>\n",
              "      <td>9.725122e-01</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.244049e+00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  0             1             2             3           4  \\\n",
              "count  7.680000e+02  7.680000e+02  7.680000e+02  7.680000e+02  768.000000   \n",
              "mean  -2.220446e-15 -4.163336e-16 -1.628327e-15  2.174187e-16    0.000000   \n",
              "std    1.000652e+00  1.000652e+00  1.000652e+00  1.000652e+00    1.000652   \n",
              "min   -1.417447e+00 -1.785875e+00 -1.876923e+00 -1.470077e+00   -1.000000   \n",
              "25%   -7.700078e-01 -7.421818e-01 -5.254107e-01 -7.915797e-01   -1.000000   \n",
              "50%   -1.023003e-01  2.319318e-02  6.792942e-02  1.583159e-01    0.000000   \n",
              "75%    6.503970e-01  7.885681e-01  6.172765e-01  9.725122e-01    1.000000   \n",
              "max    1.971395e+00  1.553943e+00  2.056515e+00  9.725122e-01    1.000000   \n",
              "\n",
              "                  5  \n",
              "count  7.680000e+02  \n",
              "mean   1.480297e-16  \n",
              "std    1.000652e+00  \n",
              "min   -1.760447e+00  \n",
              "25%   -1.009323e+00  \n",
              "50%    1.173631e-01  \n",
              "75%    1.244049e+00  \n",
              "max    1.244049e+00  "
            ]
          },
          "execution_count": 324,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ScaledX.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 325,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sNnkrTJ6E7gA",
        "outputId": "ca7860a1-5035-439b-b479-482ea2124200"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    0\n",
              "1    0\n",
              "2    0\n",
              "3    0\n",
              "4    0\n",
              "5    0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 325,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ScaledX.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 326,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "rXagBs2OM9N5",
        "outputId": "3827a9b6-07bd-4032-a59f-e96fc38fae85"
      },
      "outputs": [
        {
          "data": {
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              "\n",
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              "\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.343596</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.343596</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.343596</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.343596</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.796265</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.494441</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.796265</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.494441</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.796265</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.494441</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.796265</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.494441</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.691000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.343596</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.691000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.343596</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          0         1         2         3    4    5\n",
              "0  1.000000  0.000000  0.343596  0.000000  1.0  0.0\n",
              "1  1.000000  0.000000  0.343596  0.000000  1.0  0.0\n",
              "2  1.000000  0.000000  0.343596  0.000000  1.0  0.0\n",
              "3  1.000000  0.000000  0.343596  0.000000  1.0  0.0\n",
              "4  0.796265  0.166667  0.494441  0.111111  1.0  0.0\n",
              "5  0.796265  0.166667  0.494441  0.111111  1.0  0.0\n",
              "6  0.796265  0.166667  0.494441  0.111111  1.0  0.0\n",
              "7  0.796265  0.166667  0.494441  0.111111  1.0  0.0\n",
              "8  0.691000  0.250000  0.343596  0.333333  1.0  0.0\n",
              "9  0.691000  0.250000  0.343596  0.333333  1.0  0.0"
            ]
          },
          "execution_count": 326,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "scaler=MinMaxScaler()\n",
        "X_scaled=scaler.fit_transform(df1)\n",
        "MinMaxX=pd.DataFrame(X_scaled)\n",
        "MinMaxX.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 327,
      "metadata": {
        "id": "B4qoeGmfGgDY"
      },
      "outputs": [],
      "source": [
        "def printPredictions(y_true, y_pred):\n",
        "    # Convert y_true and y_pred to 1-dimensional arrays\n",
        "    y_true = y_true.ravel()\n",
        "    y_pred = y_pred.ravel()\n",
        "\n",
        "    # Create a DataFrame with y_true and y_pred\n",
        "    predictions_df = pd.DataFrame({'y_true': y_true, 'y_pred': y_pred})\n",
        "\n",
        "    # Print the predictions\n",
        "    print(\"Predictions: \")\n",
        "    print(predictions_df.head(5).to_markdown(index=False))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 328,
      "metadata": {
        "id": "J3xoiOW7pHUw"
      },
      "outputs": [],
      "source": [
        "def printPredictions2(y_true, y_pred):\n",
        "    # Convert y_true Series to DataFrame\n",
        "    y_true_df = pd.DataFrame(y_true)\n",
        "\n",
        "    # Convert y_pred array to DataFrame\n",
        "    y_pred_df = pd.DataFrame(y_pred, columns=['Heating_pred', 'Cooling_pred'])\n",
        "\n",
        "    # Concatenate y_true and y_pred DataFrames horizontally\n",
        "    predictions_df = pd.concat([y_true_df, y_pred_df], axis=1)\n",
        "\n",
        "    # Print the predictions\n",
        "    print(\"Predictions: \")\n",
        "    print(predictions_df.head(5).to_markdown(index=False))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RF9oMM7Zjeko"
      },
      "source": [
        "Splitting the dataset\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 329,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "jtohz7nNMMl_",
        "outputId": "5a008df2-a79a-48c1-d829-a48f1fafbf2a"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<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>Relative Compactness</th>\n",
              "      <th>Surface Area</th>\n",
              "      <th>Wall Area</th>\n",
              "      <th>Roof Area</th>\n",
              "      <th>Overall Height</th>\n",
              "      <th>Glazing Area</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>766</th>\n",
              "      <td>0.787401</td>\n",
              "      <td>808.5</td>\n",
              "      <td>5.906723</td>\n",
              "      <td>220.5</td>\n",
              "      <td>3.5</td>\n",
              "      <td>0.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>748</th>\n",
              "      <td>0.842615</td>\n",
              "      <td>710.5</td>\n",
              "      <td>5.596568</td>\n",
              "      <td>220.5</td>\n",
              "      <td>3.5</td>\n",
              "      <td>0.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>0.800000</td>\n",
              "      <td>784.0</td>\n",
              "      <td>5.837730</td>\n",
              "      <td>220.5</td>\n",
              "      <td>3.5</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>485</th>\n",
              "      <td>0.948683</td>\n",
              "      <td>563.5</td>\n",
              "      <td>5.763622</td>\n",
              "      <td>122.5</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>543</th>\n",
              "      <td>0.905539</td>\n",
              "      <td>612.5</td>\n",
              "      <td>5.763622</td>\n",
              "      <td>147.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Relative Compactness  Surface Area  Wall Area  Roof Area  Overall Height  \\\n",
              "766              0.787401         808.5   5.906723      220.5             3.5   \n",
              "748              0.842615         710.5   5.596568      220.5             3.5   \n",
              "42               0.800000         784.0   5.837730      220.5             3.5   \n",
              "485              0.948683         563.5   5.763622      122.5             7.0   \n",
              "543              0.905539         612.5   5.763622      147.0             7.0   \n",
              "\n",
              "     Glazing Area  \n",
              "766          0.40  \n",
              "748          0.40  \n",
              "42           0.00  \n",
              "485          0.25  \n",
              "543          0.40  "
            ]
          },
          "execution_count": 329,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#coolOutcome split\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test= train_test_split(df1,coolOutcome,test_size=0.2, random_state=101)\n",
        "X_test.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 330,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "xAsVqwZIjmUS",
        "outputId": "8684e4c7-8920-43f6-a80f-3f45944ea553"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<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>Relative Compactness</th>\n",
              "      <th>Surface Area</th>\n",
              "      <th>Wall Area</th>\n",
              "      <th>Roof Area</th>\n",
              "      <th>Overall Height</th>\n",
              "      <th>Glazing Area</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>766</th>\n",
              "      <td>0.787401</td>\n",
              "      <td>808.5</td>\n",
              "      <td>5.906723</td>\n",
              "      <td>220.5</td>\n",
              "      <td>3.5</td>\n",
              "      <td>0.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>748</th>\n",
              "      <td>0.842615</td>\n",
              "      <td>710.5</td>\n",
              "      <td>5.596568</td>\n",
              "      <td>220.5</td>\n",
              "      <td>3.5</td>\n",
              "      <td>0.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>0.800000</td>\n",
              "      <td>784.0</td>\n",
              "      <td>5.837730</td>\n",
              "      <td>220.5</td>\n",
              "      <td>3.5</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>485</th>\n",
              "      <td>0.948683</td>\n",
              "      <td>563.5</td>\n",
              "      <td>5.763622</td>\n",
              "      <td>122.5</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>543</th>\n",
              "      <td>0.905539</td>\n",
              "      <td>612.5</td>\n",
              "      <td>5.763622</td>\n",
              "      <td>147.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Relative Compactness  Surface Area  Wall Area  Roof Area  Overall Height  \\\n",
              "766              0.787401         808.5   5.906723      220.5             3.5   \n",
              "748              0.842615         710.5   5.596568      220.5             3.5   \n",
              "42               0.800000         784.0   5.837730      220.5             3.5   \n",
              "485              0.948683         563.5   5.763622      122.5             7.0   \n",
              "543              0.905539         612.5   5.763622      147.0             7.0   \n",
              "\n",
              "     Glazing Area  \n",
              "766          0.40  \n",
              "748          0.40  \n",
              "42           0.00  \n",
              "485          0.25  \n",
              "543          0.40  "
            ]
          },
          "execution_count": 330,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#heatOutcome split\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train_heat, X_test_heat, y_train_heat, y_test_heat= train_test_split(ScaledX,heatOutcome,test_size=0.2, random_state=101)\n",
        "X_test.head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nafs5r2EZdib"
      },
      "source": [
        "Linear Regression (Base line)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 331,
      "metadata": {
        "id": "r-6T12qFJAV_"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "lr=LinearRegression()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 332,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "MgcSFtGENmnq",
        "outputId": "59c395df-9c0a-4c73-ea74-cae996559bb9"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-11 {\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-11 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-11 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-11 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-11 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-11 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-11 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-11 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-11 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-11 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-11 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-11 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-11 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-11 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-11 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-11 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-11 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-11 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-11 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-11 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-11 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-11 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-11 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-11 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-11 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-11 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-11 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-11 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-11 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-11 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-11 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-11 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-11 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-11 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-11 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-11 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-11 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-11 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-11 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-11 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-11 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-11 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-11\" 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-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" 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/1.4/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>"
            ],
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "execution_count": 332,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#coolOutcome model\n",
        "lr.fit(X_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 333,
      "metadata": {
        "id": "cIzoCZfJNx85"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import r2_score,mean_squared_error\n",
        "from sklearn.metrics import mean_absolute_error"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 334,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zu8SOeNXto_h",
        "outputId": "14e88f5f-cffd-4644-b547-6014060efc40"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Evaluation metrics for Training set\n",
            "r2_score:  0.8658138212203952\n",
            "mean_absolute_error:  2.3515291848260893\n",
            "mean_squared_error:  10.790971359341764\n"
          ]
        }
      ],
      "source": [
        "#coolOutcome\n",
        "print(\"Evaluation metrics for Training set\")\n",
        "print(\"r2_score: \", r2_score(lr.predict(X_train),y_train))\n",
        "print(\"mean_absolute_error: \",mean_absolute_error(lr.predict(X_train),y_train))\n",
        "print(\"mean_squared_error: \", mean_squared_error(lr.predict(X_train),y_train))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 335,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SelEcZHVOmoW",
        "outputId": "f5aa937a-730f-4a9f-e5c8-6f3a322c9e11"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Evaluation metrics of Testing set\n",
            "r2_score:  0.8968160278230966\n",
            "mean_absolute_error:  2.0110221201267886\n",
            "mean_squared_error:  7.590429780398145\n"
          ]
        }
      ],
      "source": [
        "# score for coolOutcome\n",
        "print(\"Evaluation metrics of Testing set\")\n",
        "print(\"r2_score: \",r2_score(lr.predict(X_test),y_test))\n",
        "print(\"mean_absolute_error: \", mean_absolute_error(lr.predict(X_test),y_test))\n",
        "print(\"mean_squared_error: \",mean_squared_error(lr.predict(X_test),y_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 336,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "es7kSJi5Edh-",
        "outputId": "7c0e8a52-58ab-4ed6-ac98-c9bbf2a6940a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Predictions: \n",
            "|   y_true |   y_pred |\n",
            "|---------:|---------:|\n",
            "|    16.61 |  19.7731 |\n",
            "|    15.59 |  17.9764 |\n",
            "|    16.75 |  13.694  |\n",
            "|    32    |  32.3731 |\n",
            "|    33.54 |  36.0387 |\n"
          ]
        }
      ],
      "source": [
        "printPredictions(y_test,lr.predict(X_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 337,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "AK_ICjJzkTQ_",
        "outputId": "d06094f9-d19d-4c27-f22f-6d926ca5df1d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-12 {\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-12 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-12 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-12 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-12 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-12 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-12 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-12 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-12 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-12 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-12 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-12 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-12 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-12 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-12 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-12 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-12 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-12 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-12 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-12 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-12 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-12 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-12 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-12 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-12 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-12 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-12 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-12 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-12 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-12 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-12 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-12 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-12 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-12 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-12 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-12 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-12 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-12 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-12 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-12 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-12 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-12 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-12\" 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-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" 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/1.4/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>"
            ],
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "execution_count": 337,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "lr.fit(X_train_heat, y_train_heat)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 338,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ds4D5vevuQP",
        "outputId": "16d5670d-3ae8-4c30-d52c-fca9af507e80"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Evaluation metrics of Training set\n",
            "r2_score:  0.9016160910930662\n",
            "mean_absolute_error:  2.1490447622488005\n",
            "mean_squared_error:  9.178092262967851\n"
          ]
        }
      ],
      "source": [
        "#heatOutcome\n",
        "print(\"Evaluation metrics of Training set\")\n",
        "print(\"r2_score: \",r2_score(lr.predict(X_train_heat),y_train_heat))\n",
        "print(\"mean_absolute_error: \",mean_absolute_error(lr.predict(X_train_heat),y_train_heat))\n",
        "print(\"mean_squared_error: \",mean_squared_error(lr.predict(X_train_heat),y_train_heat))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 339,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HRN1RKCOkhRc",
        "outputId": "5e74cacb-8c93-403f-93bb-75cd7af46094"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Evaluation metrics of Testing set\n",
            "r2_score:  0.9222656933490789\n",
            "mean_absolute_error:  1.7747809594402675\n",
            "mena_squared_error:  6.778634064006626\n"
          ]
        }
      ],
      "source": [
        "# score for heatOutcome\n",
        "print(\"Evaluation metrics of Testing set\")\n",
        "print(\"r2_score: \" , r2_score(lr.predict(X_test_heat),y_test_heat))\n",
        "print(\"mean_absolute_error: \", mean_absolute_error(lr.predict(X_test_heat),y_test_heat))\n",
        "print(\"mena_squared_error: \",mean_squared_error(lr.predict(X_test_heat),y_test_heat))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 340,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NIGEryQFEZE4",
        "outputId": "25e77825-2f27-465d-abbe-97c8fb93938c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Predictions: \n",
            "|   y_true |   y_pred |\n",
            "|---------:|---------:|\n",
            "|    16.48 | 19.0505  |\n",
            "|    12.43 | 15.5965  |\n",
            "|    10.77 |  9.89818 |\n",
            "|    32.31 | 30.4163  |\n",
            "|    28.65 | 34.6275  |\n"
          ]
        }
      ],
      "source": [
        "printPredictions(y_test_heat,lr.predict(X_test_heat))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JyOBXOhxauGe"
      },
      "source": [
        "Polynomial Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 341,
      "metadata": {
        "id": "0iVVGGWhOsFU"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "poly=PolynomialFeatures()\n",
        "lr=LinearRegression()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 342,
      "metadata": {
        "id": "ndukOsvKbF5A"
      },
      "outputs": [],
      "source": [
        "# coolOutcome\n",
        "poly_train=poly.fit_transform(X_train)\n",
        "poly_test=poly.fit_transform(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 343,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wrSQ_hMlipZA",
        "outputId": "710d385f-f842-4c46-9248-570f43961de0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(614, 6)"
            ]
          },
          "execution_count": 343,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 344,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lO8hdKpOeNLH",
        "outputId": "0b04ae88-70bc-4f68-943c-9b92c6f55802"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(614, 28)"
            ]
          },
          "execution_count": 344,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "poly_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 345,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "FCVpSMBkfJ9Z",
        "outputId": "90e5b43f-9599-47a1-c8d2-f4d94fbfe883"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-13 {\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-13 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-13 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-13 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-13 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-13 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-13 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-13 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-13 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-13 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-13 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-13 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-13 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-13 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-13 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-13 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-13 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-13 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-13 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-13 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-13 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-13 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-13 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-13 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-13 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-13 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-13 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-13 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-13 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-13 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-13 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-13 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-13 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-13 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-13 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-13 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-13 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-13 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-13 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-13 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-13 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-13 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-13\" 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-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" 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/1.4/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>"
            ],
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "execution_count": 345,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "lr.fit(poly_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 346,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zFTKCtTYuZSN",
        "outputId": "67cf1d56-83a9-4c11-8132-8d69f81e0f01"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Evaluation metrics for Training set\n",
            "r2_score:  0.9684448208476999\n",
            "mean_absolute_error:  1.1527189836791856\n",
            "mean_squared_error:  2.7900712320495478\n"
          ]
        }
      ],
      "source": [
        "#coolOutcome\n",
        "print(\"Evaluation metrics for Training set\")\n",
        "print(\"r2_score: \", r2_score(lr.predict(poly_train),y_train))\n",
        "print(\"mean_absolute_error: \",mean_absolute_error(lr.predict(poly_train),y_train))\n",
        "print(\"mean_squared_error: \", mean_squared_error(lr.predict(poly_train),y_train))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 347,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "52Lon8fJcFjb",
        "outputId": "3c1cbf29-fa54-493e-c227-9398240b064f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Evaluation metrics of Testing set\n",
            "r2_score:  0.9724952241780098\n",
            "mean_absolute_error:  0.965008392726954\n",
            "mean_squared_error:  2.1844719284445486\n"
          ]
        }
      ],
      "source": [
        "# coolOutcome\n",
        "print(\"Evaluation metrics of Testing set\")\n",
        "print(\"r2_score: \",r2_score(lr.predict(poly_test),y_test))\n",
        "print(\"mean_absolute_error: \", mean_absolute_error(lr.predict(poly_test),y_test))\n",
        "print(\"mean_squared_error: \",mean_squared_error(lr.predict(poly_test),y_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 348,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fJMqTVsLHR6t",
        "outputId": "e6d1a04b-b249-4f81-aa75-8af8e7cbd736"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Predictions: \n",
            "|   y_true |   y_pred |\n",
            "|---------:|---------:|\n",
            "|    16.61 |  16.7432 |\n",
            "|    15.59 |  16.6737 |\n",
            "|    16.75 |  17.0852 |\n",
            "|    32    |  34.4352 |\n",
            "|    33.54 |  30.2468 |\n"
          ]
        }
      ],
      "source": [
        "printPredictions(y_test,lr.predict(poly_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 349,
      "metadata": {
        "id": "LoBQ1UyWcijm"
      },
      "outputs": [],
      "source": [
        "#heatOutcome\n",
        "poly_train_heat=poly.fit_transform(X_train_heat)\n",
        "poly_test_heat=poly.fit_transform(X_test_heat)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 350,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "U_htkDP-l22w",
        "outputId": "82c5566b-d291-4580-b479-0838862adc25"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-14 {\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-14 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-14 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-14 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-14 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-14 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-14 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-14 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-14 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-14 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-14 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-14 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-14 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-14 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-14 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-14 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-14 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-14 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-14 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-14 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-14 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-14 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-14 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-14 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-14 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-14 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-14 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-14 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-14 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-14 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-14 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-14 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-14 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-14 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-14 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-14 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-14 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-14 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-14 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-14 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-14 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-14 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-14\" 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-14\" type=\"checkbox\" checked><label for=\"sk-estimator-id-14\" 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/1.4/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>"
            ],
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "execution_count": 350,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "lr.fit(poly_train_heat, y_train_heat)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 351,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fz15PTcgwi2m",
        "outputId": "d963953e-f7eb-4e0e-9a04-a6035aa97bd8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Evaluation metrics of Training set\n",
            "r2_score:  0.9917846078279805\n",
            "mean_absolute_error:  0.7228000305374593\n",
            "mean_squared_error:  0.831450947933632\n"
          ]
        }
      ],
      "source": [
        "print(\"Evaluation metrics of Training set\")\n",
        "print(\"r2_score: \",r2_score(lr.predict(poly_train_heat),y_train_heat))\n",
        "print(\"mean_absolute_error: \",mean_absolute_error(lr.predict(poly_train_heat),y_train_heat))\n",
        "print(\"mean_squared_error: \",mean_squared_error(lr.predict(poly_train_heat),y_train_heat))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 352,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wy650J2KmALT",
        "outputId": "62008841-1e7a-4673-e552-5ed059793a42"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Evaluation metrics of Testing set\n",
            "r2_score:  0.9925887756399274\n",
            "mean_absolute_error:  0.6870013189935064\n",
            "mean_squared_error:  0.6793732500745103\n"
          ]
        }
      ],
      "source": [
        "#heatOutcome\n",
        "print(\"Evaluation metrics of Testing set\")\n",
        "print(\"r2_score: \" , r2_score(lr.predict(poly_test_heat),y_test_heat))\n",
        "print(\"mean_absolute_error: \", mean_absolute_error(lr.predict(poly_test_heat),y_test_heat))\n",
        "print(\"mean_squared_error: \",mean_squared_error(lr.predict(poly_test_heat),y_test_heat))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 353,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dYXiQkwzHXUw",
        "outputId": "dde8fbb9-2a36-46ef-8a1e-036a773317b9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Predictions: \n",
            "|   y_true |   y_pred |\n",
            "|---------:|---------:|\n",
            "|    16.48 |  16.4414 |\n",
            "|    12.43 |  13.9883 |\n",
            "|    10.77 |  11.6426 |\n",
            "|    32.31 |  32.7949 |\n",
            "|    28.65 |  28.8672 |\n"
          ]
        }
      ],
      "source": [
        "printPredictions(y_test_heat,lr.predict(poly_test_heat))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vis-IRA9nliI"
      },
      "source": [
        "Decision tree"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 354,
      "metadata": {
        "id": "z9CLXf2fmfNU"
      },
      "outputs": [],
      "source": [
        "from sklearn.tree import DecisionTreeRegressor\n",
        "# not using polynomial features with decision tree because 1) decision tree has ability to capture non-linear features, 2) expanding number of features can lead to overfitting."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 355,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7iMw_yJ3fy4Z",
        "outputId": "428ff7b2-d9f5-4031-ee1a-b3825b286ed0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Best parameters: {'max_depth': 7, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'random_state': 101}\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "# Define Decision Tree model\n",
        "dt = DecisionTreeRegressor()\n",
        "\n",
        "# Define parameter grid for GridSearchCV\n",
        "param_grid = {\n",
        "    'max_depth': [5, 6, 7],\n",
        "    'min_samples_split': [2, 3],\n",
        "    'min_samples_leaf': [1, 2],\n",
        "    'max_features': ['auto', 'sqrt', 'log2'],\n",
        "    'random_state':[101]\n",
        "}\n",
        "\n",
        "# Initialize GridSearchCV\n",
        "grid_search = GridSearchCV(dt, param_grid, cv=5, scoring='neg_mean_squared_error')\n",
        "\n",
        "# Fit the grid search to the data\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "# Print the best parameters found\n",
        "print(\"Best parameters:\", grid_search.best_params_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 356,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DBqfh5uUumG9",
        "outputId": "69014af1-7fcf-4008-d3d5-0b27df0eb997"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Evaluation metrics for Training set\n",
            "r2_score:  0.9701125232444874\n",
            "mean_absolute_error:  1.0637797486910292\n",
            "mean_squared_error:  2.6468941797888497\n"
          ]
        }
      ],
      "source": [
        "#coolOutcome\n",
        "print(\"Evaluation metrics for Training set\")\n",
        "print(\"r2_score: \", r2_score(grid_search.predict(X_train),y_train))\n",
        "print(\"mean_absolute_error: \",mean_absolute_error(grid_search.predict(X_train),y_train))\n",
        "print(\"mean_squared_error: \", mean_squared_error(grid_search.predict(X_train),y_train))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 357,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OO5h-OJfpFbH",
        "outputId": "8d0a85ca-3cb1-4d65-b0f0-408ac7317a95"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Evaluation metrics of Testing set\n",
            "r2_score:  0.9726362028251077\n",
            "mean_absolute_error:  0.9221546627913833\n",
            "mean_squared_error:  2.1836041506231396\n"
          ]
        }
      ],
      "source": [
        "#coolOutcome\n",
        "print(\"Evaluation metrics of Testing set\")\n",
        "print(\"r2_score: \",r2_score(grid_search.predict(X_test),y_test))\n",
        "print(\"mean_absolute_error: \", mean_absolute_error(grid_search.predict(X_test),y_test))\n",
        "print(\"mean_squared_error: \",mean_squared_error(grid_search.predict(X_test),y_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 358,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JON2HIebHxbT",
        "outputId": "3b5fe5e7-1a1d-4a3d-fe64-2c72d695431a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Predictions: \n",
            "|   y_true |   y_pred |\n",
            "|---------:|---------:|\n",
            "|    16.61 |  16.9369 |\n",
            "|    15.59 |  16.664  |\n",
            "|    16.75 |  16.75   |\n",
            "|    32    |  34.4825 |\n",
            "|    33.54 |  30.4506 |\n"
          ]
        }
      ],
      "source": [
        "printPredictions(y_test, grid_search.predict(X_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 359,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ZHYE14hpX6n",
        "outputId": "4a24cd05-ddcb-43fb-db91-abc39063821a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Best parameters: {'max_depth': 7, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 3, 'random_state': 101}\n"
          ]
        }
      ],
      "source": [
        "#heatOutcome\n",
        "dt = DecisionTreeRegressor()\n",
        "\n",
        "# Define parameter grid for GridSearchCV\n",
        "param_grid = {\n",
        "    'max_depth': [6, 7, 8], #max depth of each tree\n",
        "    'min_samples_split': [3, 4], #min samples required to make a node split\n",
        "    'min_samples_leaf': [1, 2], #min samples required to consider a node leaf\n",
        "    'max_features': ['auto', 'sqrt', 'log2'], #considering number of features at each split\n",
        "    'random_state':[101]\n",
        "    \n",
        "}\n",
        "\n",
        "# Initialize GridSearchCV\n",
        "grid_search = GridSearchCV(dt, param_grid, cv=5, scoring='neg_mean_squared_error')\n",
        "\n",
        "# Fit the grid search to the data\n",
        "grid_search.fit(X_train_heat, y_train_heat)\n",
        "\n",
        "# Print the best parameters found\n",
        "print(\"Best parameters:\", grid_search.best_params_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 360,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iWGi3p7JwsD6",
        "outputId": "ff72f771-a718-4312-d953-ebf893c4a5cb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Evaluation metrics of Training set\n",
            "r2_score:  0.9978788574778066\n",
            "mean_absolute_error:  0.3375708298377354\n",
            "mean_squared_error:  0.21688630648736001\n"
          ]
        }
      ],
      "source": [
        "print(\"Evaluation metrics of Training set\")\n",
        "print(\"r2_score: \",r2_score(grid_search.predict(X_train_heat),y_train_heat))\n",
        "print(\"mean_absolute_error: \",mean_absolute_error(grid_search.predict(X_train_heat),y_train_heat))\n",
        "print(\"mean_squared_error: \",mean_squared_error(grid_search.predict(X_train_heat),y_train_heat))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 361,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uRPGwpt3p_yr",
        "outputId": "e2685d11-fa96-4c6c-8d88-65155425e3d2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Evaluation metrics of Testing set\n",
            "r2_score:  0.9982468785121635\n",
            "mean_absolute_error:  0.28361730575557403\n",
            "mean_squared_error:  0.16546639666593496\n"
          ]
        }
      ],
      "source": [
        "print(\"Evaluation metrics of Testing set\")\n",
        "print(\"r2_score: \",r2_score(grid_search.predict(X_test_heat),y_test_heat))\n",
        "print(\"mean_absolute_error: \", mean_absolute_error(grid_search.predict(X_test_heat),y_test_heat))\n",
        "print(\"mean_squared_error: \",mean_squared_error(grid_search.predict(X_test_heat),y_test_heat))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 362,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RpT6ib5rH-kF",
        "outputId": "71bd6916-3c15-41e5-eb66-a6d393d36cc4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Predictions: \n",
            "|   y_true |   y_pred |\n",
            "|---------:|---------:|\n",
            "|    16.48 |  16.7115 |\n",
            "|    12.43 |  14.0927 |\n",
            "|    10.77 |  10.65   |\n",
            "|    32.31 |  32.4375 |\n",
            "|    28.65 |  29.1744 |\n"
          ]
        }
      ],
      "source": [
        "printPredictions(y_test_heat, grid_search.predict(X_test_heat))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5zS9CdKu_zRQ"
      },
      "source": [
        "Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 363,
      "metadata": {
        "id": "a9F7lhO6whRn"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import RandomForestRegressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 364,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9AO_lwcHBjol",
        "outputId": "3242b64c-f6a9-4700-e401-6e852c221ab7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Best Hyperparameters: {'max_depth': 8, 'max_features': 'log2', 'min_samples_leaf': 1, 'min_samples_split': 4, 'n_estimators': 200}\n"
          ]
        }
      ],
      "source": [
        "param_grid = {\n",
        "    'n_estimators': [150, 175, 200], #number of trees in forest\n",
        "    'max_depth': [8, 9],  #max depth of each tree\n",
        "    'min_samples_split': [3, 4], #min number of samples required to split the node\n",
        "    'min_samples_leaf': [1, 2], #min number of samples required to consider a node leaf or not\n",
        "    'max_features': ['auto', 'log2', 'sqrt'],\n",
        "}\n",
        "\n",
        "rf_regressor = RandomForestRegressor(random_state=101)\n",
        "\n",
        "grid_search = GridSearchCV(rf_regressor, param_grid, cv=5, scoring='neg_mean_squared_error')\n",
        "\n",
        "# training the model\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "# Get the best estimator and its hyperparameters\n",
        "best_params = grid_search.best_params_\n",
        "\n",
        "# Train the best Random Forest Regressor model on the full training data\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "print(\"Best Hyperparameters:\", best_params)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 365,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CoCvsj_3uxX6",
        "outputId": "7f623589-05d7-464f-cfc5-360bc5b870d7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Evaluation metrics for Training set\n",
            "r2_score:  0.9702453343522084\n",
            "mean_absolute_error:  1.056292067030183\n",
            "mean_squared_error:  2.6321599997481817\n"
          ]
        }
      ],
      "source": [
        "#coolOutcome\n",
        "print(\"Evaluation metrics for Training set\")\n",
        "print(\"r2_score: \", r2_score(grid_search.predict(X_train),y_train))\n",
        "print(\"mean_absolute_error: \",mean_absolute_error(grid_search.predict(X_train),y_train))\n",
        "print(\"mean_squared_error: \", mean_squared_error(grid_search.predict(X_train),y_train))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 366,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AQnFcL-AC1Hr",
        "outputId": "adb6ce41-946a-4df4-ab7f-92da5c713d41"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Evaluation metrics of Testing set\n",
            "r2_score:  0.973316414185172\n",
            "mean_absolute_error:  0.8926857106335361\n",
            "mean_squared_error:  2.133734024776453\n"
          ]
        }
      ],
      "source": [
        "#coolOutcome\n",
        "print(\"Evaluation metrics of Testing set\")\n",
        "print(\"r2_score: \",r2_score(grid_search.predict(X_test),y_test))\n",
        "print(\"mean_absolute_error: \", mean_absolute_error(grid_search.predict(X_test),y_test))\n",
        "print(\"mean_squared_error: \",mean_squared_error(grid_search.predict(X_test),y_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 367,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I_4eMYnfEHl7",
        "outputId": "26e0228a-c0dc-4bcc-de49-560fe3ce2928"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Predictions: \n",
            "|   y_true |   y_pred |\n",
            "|---------:|---------:|\n",
            "|    16.61 |  16.945  |\n",
            "|    15.59 |  16.6482 |\n",
            "|    16.75 |  16.8738 |\n",
            "|    32    |  34.5117 |\n",
            "|    33.54 |  30.5057 |\n"
          ]
        }
      ],
      "source": [
        "printPredictions(y_test, grid_search.predict(X_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 368,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WqoXFloyEIV2",
        "outputId": "87e85895-4d0a-4f7d-ccf6-6106f537436d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Best Hyperparameters: {'max_depth': 9, 'max_features': 'log2', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 200}\n"
          ]
        }
      ],
      "source": [
        "param_grid = {\n",
        "    'n_estimators': [125, 175, 200],\n",
        "    'max_depth': [8, 9],\n",
        "    'min_samples_split': [2, 3],\n",
        "    'min_samples_leaf': [1, 2],\n",
        "    'max_features': ['auto', 'log2', 'sqrt'],\n",
        "}\n",
        "\n",
        "rf_regressor = RandomForestRegressor(random_state=101)\n",
        "\n",
        "grid_search = GridSearchCV(rf_regressor, param_grid, cv=5, scoring='neg_mean_squared_error')\n",
        "\n",
        "grid_search.fit(X_train_heat, y_train_heat)\n",
        "\n",
        "best_params = grid_search.best_params_\n",
        "\n",
        "print(\"Best Hyperparameters:\", best_params)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 369,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gyvN3hk7w0Qd",
        "outputId": "a0c04cb7-8887-4d85-ec14-985430366b79"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Evaluation metrics of Training set\n",
            "r2_score:  0.9978438741474206\n",
            "mean_absolute_error:  0.34377387598385084\n",
            "mean_squared_error:  0.21990349204832244\n"
          ]
        }
      ],
      "source": [
        "print(\"Evaluation metrics of Training set\")\n",
        "print(\"r2_score: \",r2_score(grid_search.predict(X_train_heat),y_train_heat))\n",
        "print(\"mean_absolute_error: \",mean_absolute_error(grid_search.predict(X_train_heat),y_train_heat))\n",
        "print(\"mean_squared_error: \",mean_squared_error(grid_search.predict(X_train_heat),y_train_heat))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 370,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OJCtcvkKEaLu",
        "outputId": "266383be-9346-4d44-ff02-fe15e020b6d0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Evaluation metrics of Testing set\n",
            "r2_score:  0.9981848041862403\n",
            "mean_absolute_error:  0.29425281945838294\n",
            "mean_squared_error:  0.1705246155680923\n"
          ]
        }
      ],
      "source": [
        "#heatOutcome\n",
        "print(\"Evaluation metrics of Testing set\")\n",
        "print(\"r2_score: \",r2_score(grid_search.predict(X_test_heat),y_test_heat))\n",
        "print(\"mean_absolute_error: \", mean_absolute_error(grid_search.predict(X_test_heat),y_test_heat))\n",
        "print(\"mean_squared_error: \",mean_squared_error(grid_search.predict(X_test_heat),y_test_heat))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 371,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rCBaDyGLFZht",
        "outputId": "ecea703a-ca72-44b9-db72-b87c6d6abfaf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Predictions: \n",
            "|   y_true |   y_pred |\n",
            "|---------:|---------:|\n",
            "|    16.48 |  16.6988 |\n",
            "|    12.43 |  14.077  |\n",
            "|    10.77 |  10.9267 |\n",
            "|    32.31 |  32.4199 |\n",
            "|    28.65 |  29.1508 |\n"
          ]
        }
      ],
      "source": [
        "printPredictions(y_test_heat, grid_search.predict(X_test_heat))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VQ-e6-igx-bZ"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}