Hamoye_StageB_TagAlongProject.ipynb

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      "name": "Hamoye_StageB_TagAlongProject.ipynb",
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Abdullah2020/Hamoye_StageB/blob/master/Hamoye_StageB_TagAlongProject.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Predicting Energy Efficiency of Buildings**\n",
        "\n",
        "Hamoye Stage B Tag Along Project"
      ],
      "metadata": {
        "id": "-uEnPHzEm4TL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Import all necessary libraries"
      ],
      "metadata": {
        "id": "5uBdMeGeoxVw"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jnxisWFbmL1r",
        "outputId": "61b02216-46cf-4599-c84d-763b33f6c180"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Setup Completed!\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np                            #mathematical operation\n",
        "import matplotlib.pyplot as plt               #data visualisation\n",
        "import seaborn as sns                         #data visualisation\n",
        "\n",
        "from sklearn.linear_model import LinearRegression          #used for linear regression model\n",
        "from sklearn.metrics import mean_absolute_error            #used for cal. MAE\n",
        "from sklearn.preprocessing import MinMaxScaler             #used for normalising dataset into a common scale using Min and Max.\n",
        "from sklearn.model_selection import train_test_split       #used for splitting dataset into training and testing df\n",
        "\n",
        "from sklearn.metrics import mean_squared_error            #used for Residual Sum of Squares(RSS)\n",
        "from sklearn.metrics import r2_score                      #used for R- Squares\n",
        "\n",
        "#Penalisation Methods\n",
        "from sklearn.linear_model import Ridge                    #used for L2 Regularisation\n",
        "from sklearn.linear_model import Lasso                    #used for LASSO \n",
        "\n",
        "print('Setup Completed!')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Import our dataset"
      ],
      "metadata": {
        "id": "zYGUOGCOsHiC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "energy_df = pd.read_csv(\"/content/energydata_complete.csv\")"
      ],
      "metadata": {
        "id": "NAbuOrMIsGD7"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Let know our data**"
      ],
      "metadata": {
        "id": "3aJYiluHsfuA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#display the first 5 rows\n",
        "\n",
        "energy_df.head()"
      ],
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              "                  date  Appliances  lights     T1       RH_1    T2       RH_2  \\\n",
              "0  2016-01-11 17:00:00          60      30  19.89  47.596667  19.2  44.790000   \n",
              "1  2016-01-11 17:10:00          60      30  19.89  46.693333  19.2  44.722500   \n",
              "2  2016-01-11 17:20:00          50      30  19.89  46.300000  19.2  44.626667   \n",
              "3  2016-01-11 17:30:00          50      40  19.89  46.066667  19.2  44.590000   \n",
              "4  2016-01-11 17:40:00          60      40  19.89  46.333333  19.2  44.530000   \n",
              "\n",
              "      T3       RH_3         T4  ...         T9   RH_9     T_out  Press_mm_hg  \\\n",
              "0  19.79  44.730000  19.000000  ...  17.033333  45.53  6.600000        733.5   \n",
              "1  19.79  44.790000  19.000000  ...  17.066667  45.56  6.483333        733.6   \n",
              "2  19.79  44.933333  18.926667  ...  17.000000  45.50  6.366667        733.7   \n",
              "3  19.79  45.000000  18.890000  ...  17.000000  45.40  6.250000        733.8   \n",
              "4  19.79  45.000000  18.890000  ...  17.000000  45.40  6.133333        733.9   \n",
              "\n",
              "   RH_out  Windspeed  Visibility  Tdewpoint        rv1        rv2  \n",
              "0    92.0   7.000000   63.000000        5.3  13.275433  13.275433  \n",
              "1    92.0   6.666667   59.166667        5.2  18.606195  18.606195  \n",
              "2    92.0   6.333333   55.333333        5.1  28.642668  28.642668  \n",
              "3    92.0   6.000000   51.500000        5.0  45.410389  45.410389  \n",
              "4    92.0   5.666667   47.666667        4.9  10.084097  10.084097  \n",
              "\n",
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              "      <td>19.79</td>\n",
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              "      <td>18.926667</td>\n",
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              "      <td>45.50</td>\n",
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              "      <td>55.333333</td>\n",
              "      <td>5.1</td>\n",
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              "      <th>3</th>\n",
              "      <td>2016-01-11 17:30:00</td>\n",
              "      <td>50</td>\n",
              "      <td>40</td>\n",
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      "cell_type": "code",
      "source": [
        "#display the last 5 rows\n",
        "\n",
        "energy_df.tail()"
      ],
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              "                      date  Appliances  lights         T1       RH_1  \\\n",
              "19730  2016-05-27 17:20:00         100       0  25.566667  46.560000   \n",
              "19731  2016-05-27 17:30:00          90       0  25.500000  46.500000   \n",
              "19732  2016-05-27 17:40:00         270      10  25.500000  46.596667   \n",
              "19733  2016-05-27 17:50:00         420      10  25.500000  46.990000   \n",
              "19734  2016-05-27 18:00:00         430      10  25.500000  46.600000   \n",
              "\n",
              "              T2       RH_2         T3       RH_3    T4  ...    T9     RH_9  \\\n",
              "19730  25.890000  42.025714  27.200000  41.163333  24.7  ...  23.2  46.7900   \n",
              "19731  25.754000  42.080000  27.133333  41.223333  24.7  ...  23.2  46.7900   \n",
              "19732  25.628571  42.768571  27.050000  41.690000  24.7  ...  23.2  46.7900   \n",
              "19733  25.414000  43.036000  26.890000  41.290000  24.7  ...  23.2  46.8175   \n",
              "19734  25.264286  42.971429  26.823333  41.156667  24.7  ...  23.2  46.8450   \n",
              "\n",
              "           T_out  Press_mm_hg     RH_out  Windspeed  Visibility  Tdewpoint  \\\n",
              "19730  22.733333        755.2  55.666667   3.333333   23.666667  13.333333   \n",
              "19731  22.600000        755.2  56.000000   3.500000   24.500000  13.300000   \n",
              "19732  22.466667        755.2  56.333333   3.666667   25.333333  13.266667   \n",
              "19733  22.333333        755.2  56.666667   3.833333   26.166667  13.233333   \n",
              "19734  22.200000        755.2  57.000000   4.000000   27.000000  13.200000   \n",
              "\n",
              "             rv1        rv2  \n",
              "19730  43.096812  43.096812  \n",
              "19731  49.282940  49.282940  \n",
              "19732  29.199117  29.199117  \n",
              "19733   6.322784   6.322784  \n",
              "19734  34.118851  34.118851  \n",
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              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>25.566667</td>\n",
              "      <td>46.560000</td>\n",
              "      <td>25.890000</td>\n",
              "      <td>42.025714</td>\n",
              "      <td>27.200000</td>\n",
              "      <td>41.163333</td>\n",
              "      <td>24.7</td>\n",
              "      <td>...</td>\n",
              "      <td>23.2</td>\n",
              "      <td>46.7900</td>\n",
              "      <td>22.733333</td>\n",
              "      <td>755.2</td>\n",
              "      <td>55.666667</td>\n",
              "      <td>3.333333</td>\n",
              "      <td>23.666667</td>\n",
              "      <td>13.333333</td>\n",
              "      <td>43.096812</td>\n",
              "      <td>43.096812</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19731</th>\n",
              "      <td>2016-05-27 17:30:00</td>\n",
              "      <td>90</td>\n",
              "      <td>0</td>\n",
              "      <td>25.500000</td>\n",
              "      <td>46.500000</td>\n",
              "      <td>25.754000</td>\n",
              "      <td>42.080000</td>\n",
              "      <td>27.133333</td>\n",
              "      <td>41.223333</td>\n",
              "      <td>24.7</td>\n",
              "      <td>...</td>\n",
              "      <td>23.2</td>\n",
              "      <td>46.7900</td>\n",
              "      <td>22.600000</td>\n",
              "      <td>755.2</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>24.500000</td>\n",
              "      <td>13.300000</td>\n",
              "      <td>49.282940</td>\n",
              "      <td>49.282940</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19732</th>\n",
              "      <td>2016-05-27 17:40:00</td>\n",
              "      <td>270</td>\n",
              "      <td>10</td>\n",
              "      <td>25.500000</td>\n",
              "      <td>46.596667</td>\n",
              "      <td>25.628571</td>\n",
              "      <td>42.768571</td>\n",
              "      <td>27.050000</td>\n",
              "      <td>41.690000</td>\n",
              "      <td>24.7</td>\n",
              "      <td>...</td>\n",
              "      <td>23.2</td>\n",
              "      <td>46.7900</td>\n",
              "      <td>22.466667</td>\n",
              "      <td>755.2</td>\n",
              "      <td>56.333333</td>\n",
              "      <td>3.666667</td>\n",
              "      <td>25.333333</td>\n",
              "      <td>13.266667</td>\n",
              "      <td>29.199117</td>\n",
              "      <td>29.199117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19733</th>\n",
              "      <td>2016-05-27 17:50:00</td>\n",
              "      <td>420</td>\n",
              "      <td>10</td>\n",
              "      <td>25.500000</td>\n",
              "      <td>46.990000</td>\n",
              "      <td>25.414000</td>\n",
              "      <td>43.036000</td>\n",
              "      <td>26.890000</td>\n",
              "      <td>41.290000</td>\n",
              "      <td>24.7</td>\n",
              "      <td>...</td>\n",
              "      <td>23.2</td>\n",
              "      <td>46.8175</td>\n",
              "      <td>22.333333</td>\n",
              "      <td>755.2</td>\n",
              "      <td>56.666667</td>\n",
              "      <td>3.833333</td>\n",
              "      <td>26.166667</td>\n",
              "      <td>13.233333</td>\n",
              "      <td>6.322784</td>\n",
              "      <td>6.322784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19734</th>\n",
              "      <td>2016-05-27 18:00:00</td>\n",
              "      <td>430</td>\n",
              "      <td>10</td>\n",
              "      <td>25.500000</td>\n",
              "      <td>46.600000</td>\n",
              "      <td>25.264286</td>\n",
              "      <td>42.971429</td>\n",
              "      <td>26.823333</td>\n",
              "      <td>41.156667</td>\n",
              "      <td>24.7</td>\n",
              "      <td>...</td>\n",
              "      <td>23.2</td>\n",
              "      <td>46.8450</td>\n",
              "      <td>22.200000</td>\n",
              "      <td>755.2</td>\n",
              "      <td>57.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>13.200000</td>\n",
              "      <td>34.118851</td>\n",
              "      <td>34.118851</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 29 columns</p>\n",
              "</div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#display the number of rows and columns\n",
        "\n",
        "print(\"There are {} rows and {} columns in the energy dataset.\".format(energy_df.shape[0], energy_df.shape[1]))"
      ],
      "metadata": {
        "id": "40aG6FR0soTH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "39df504a-29c7-4969-97f1-2a6d1b4f37dd"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 19735 rows and 29 columns in the energy dataset.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#display the details info of the dataset\n",
        "\n",
        "energy_df.info()"
      ],
      "metadata": {
        "id": "9nmC8uwHsoWE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9b5bd9c4-a0f6-46c4-83f7-af6a7ca8ce75"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 19735 entries, 0 to 19734\n",
            "Data columns (total 29 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   date         19735 non-null  object \n",
            " 1   Appliances   19735 non-null  int64  \n",
            " 2   lights       19735 non-null  int64  \n",
            " 3   T1           19735 non-null  float64\n",
            " 4   RH_1         19735 non-null  float64\n",
            " 5   T2           19735 non-null  float64\n",
            " 6   RH_2         19735 non-null  float64\n",
            " 7   T3           19735 non-null  float64\n",
            " 8   RH_3         19735 non-null  float64\n",
            " 9   T4           19735 non-null  float64\n",
            " 10  RH_4         19735 non-null  float64\n",
            " 11  T5           19735 non-null  float64\n",
            " 12  RH_5         19735 non-null  float64\n",
            " 13  T6           19735 non-null  float64\n",
            " 14  RH_6         19735 non-null  float64\n",
            " 15  T7           19735 non-null  float64\n",
            " 16  RH_7         19735 non-null  float64\n",
            " 17  T8           19735 non-null  float64\n",
            " 18  RH_8         19735 non-null  float64\n",
            " 19  T9           19735 non-null  float64\n",
            " 20  RH_9         19735 non-null  float64\n",
            " 21  T_out        19735 non-null  float64\n",
            " 22  Press_mm_hg  19735 non-null  float64\n",
            " 23  RH_out       19735 non-null  float64\n",
            " 24  Windspeed    19735 non-null  float64\n",
            " 25  Visibility   19735 non-null  float64\n",
            " 26  Tdewpoint    19735 non-null  float64\n",
            " 27  rv1          19735 non-null  float64\n",
            " 28  rv2          19735 non-null  float64\n",
            "dtypes: float64(26), int64(2), object(1)\n",
            "memory usage: 4.4+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#display all the basic statistical values \n",
        "\n",
        "energy_df.describe()"
      ],
      "metadata": {
        "id": "Xt75GrnDsoZG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "d335cfb3-e726-4735-ab2e-6e017546751e"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Appliances        lights            T1          RH_1            T2  \\\n",
              "count  19735.000000  19735.000000  19735.000000  19735.000000  19735.000000   \n",
              "mean      97.694958      3.801875     21.686571     40.259739     20.341219   \n",
              "std      102.524891      7.935988      1.606066      3.979299      2.192974   \n",
              "min       10.000000      0.000000     16.790000     27.023333     16.100000   \n",
              "25%       50.000000      0.000000     20.760000     37.333333     18.790000   \n",
              "50%       60.000000      0.000000     21.600000     39.656667     20.000000   \n",
              "75%      100.000000      0.000000     22.600000     43.066667     21.500000   \n",
              "max     1080.000000     70.000000     26.260000     63.360000     29.856667   \n",
              "\n",
              "               RH_2            T3          RH_3            T4          RH_4  \\\n",
              "count  19735.000000  19735.000000  19735.000000  19735.000000  19735.000000   \n",
              "mean      40.420420     22.267611     39.242500     20.855335     39.026904   \n",
              "std        4.069813      2.006111      3.254576      2.042884      4.341321   \n",
              "min       20.463333     17.200000     28.766667     15.100000     27.660000   \n",
              "25%       37.900000     20.790000     36.900000     19.530000     35.530000   \n",
              "50%       40.500000     22.100000     38.530000     20.666667     38.400000   \n",
              "75%       43.260000     23.290000     41.760000     22.100000     42.156667   \n",
              "max       56.026667     29.236000     50.163333     26.200000     51.090000   \n",
              "\n",
              "       ...            T9          RH_9         T_out   Press_mm_hg  \\\n",
              "count  ...  19735.000000  19735.000000  19735.000000  19735.000000   \n",
              "mean   ...     19.485828     41.552401      7.411665    755.522602   \n",
              "std    ...      2.014712      4.151497      5.317409      7.399441   \n",
              "min    ...     14.890000     29.166667     -5.000000    729.300000   \n",
              "25%    ...     18.000000     38.500000      3.666667    750.933333   \n",
              "50%    ...     19.390000     40.900000      6.916667    756.100000   \n",
              "75%    ...     20.600000     44.338095     10.408333    760.933333   \n",
              "max    ...     24.500000     53.326667     26.100000    772.300000   \n",
              "\n",
              "             RH_out     Windspeed    Visibility     Tdewpoint           rv1  \\\n",
              "count  19735.000000  19735.000000  19735.000000  19735.000000  19735.000000   \n",
              "mean      79.750418      4.039752     38.330834      3.760707     24.988033   \n",
              "std       14.901088      2.451221     11.794719      4.194648     14.496634   \n",
              "min       24.000000      0.000000      1.000000     -6.600000      0.005322   \n",
              "25%       70.333333      2.000000     29.000000      0.900000     12.497889   \n",
              "50%       83.666667      3.666667     40.000000      3.433333     24.897653   \n",
              "75%       91.666667      5.500000     40.000000      6.566667     37.583769   \n",
              "max      100.000000     14.000000     66.000000     15.500000     49.996530   \n",
              "\n",
              "                rv2  \n",
              "count  19735.000000  \n",
              "mean      24.988033  \n",
              "std       14.496634  \n",
              "min        0.005322  \n",
              "25%       12.497889  \n",
              "50%       24.897653  \n",
              "75%       37.583769  \n",
              "max       49.996530  \n",
              "\n",
              "[8 rows x 28 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d8b4e943-bdce-4015-ab2c-1250cb35bc88\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Appliances</th>\n",
              "      <th>lights</th>\n",
              "      <th>T1</th>\n",
              "      <th>RH_1</th>\n",
              "      <th>T2</th>\n",
              "      <th>RH_2</th>\n",
              "      <th>T3</th>\n",
              "      <th>RH_3</th>\n",
              "      <th>T4</th>\n",
              "      <th>RH_4</th>\n",
              "      <th>...</th>\n",
              "      <th>T9</th>\n",
              "      <th>RH_9</th>\n",
              "      <th>T_out</th>\n",
              "      <th>Press_mm_hg</th>\n",
              "      <th>RH_out</th>\n",
              "      <th>Windspeed</th>\n",
              "      <th>Visibility</th>\n",
              "      <th>Tdewpoint</th>\n",
              "      <th>rv1</th>\n",
              "      <th>rv2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>19735.000000</td>\n",
              "      <td>19735.000000</td>\n",
              "      <td>19735.000000</td>\n",
              "      <td>19735.000000</td>\n",
              "      <td>19735.000000</td>\n",
              "      <td>19735.000000</td>\n",
              "      <td>19735.000000</td>\n",
              "      <td>19735.000000</td>\n",
              "      <td>19735.000000</td>\n",
              "      <td>19735.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>19735.000000</td>\n",
              "      <td>19735.000000</td>\n",
              "      <td>19735.000000</td>\n",
              "      <td>19735.000000</td>\n",
              "      <td>19735.000000</td>\n",
              "      <td>19735.000000</td>\n",
              "      <td>19735.000000</td>\n",
              "      <td>19735.000000</td>\n",
              "      <td>19735.000000</td>\n",
              "      <td>19735.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>97.694958</td>\n",
              "      <td>3.801875</td>\n",
              "      <td>21.686571</td>\n",
              "      <td>40.259739</td>\n",
              "      <td>20.341219</td>\n",
              "      <td>40.420420</td>\n",
              "      <td>22.267611</td>\n",
              "      <td>39.242500</td>\n",
              "      <td>20.855335</td>\n",
              "      <td>39.026904</td>\n",
              "      <td>...</td>\n",
              "      <td>19.485828</td>\n",
              "      <td>41.552401</td>\n",
              "      <td>7.411665</td>\n",
              "      <td>755.522602</td>\n",
              "      <td>79.750418</td>\n",
              "      <td>4.039752</td>\n",
              "      <td>38.330834</td>\n",
              "      <td>3.760707</td>\n",
              "      <td>24.988033</td>\n",
              "      <td>24.988033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>102.524891</td>\n",
              "      <td>7.935988</td>\n",
              "      <td>1.606066</td>\n",
              "      <td>3.979299</td>\n",
              "      <td>2.192974</td>\n",
              "      <td>4.069813</td>\n",
              "      <td>2.006111</td>\n",
              "      <td>3.254576</td>\n",
              "      <td>2.042884</td>\n",
              "      <td>4.341321</td>\n",
              "      <td>...</td>\n",
              "      <td>2.014712</td>\n",
              "      <td>4.151497</td>\n",
              "      <td>5.317409</td>\n",
              "      <td>7.399441</td>\n",
              "      <td>14.901088</td>\n",
              "      <td>2.451221</td>\n",
              "      <td>11.794719</td>\n",
              "      <td>4.194648</td>\n",
              "      <td>14.496634</td>\n",
              "      <td>14.496634</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>16.790000</td>\n",
              "      <td>27.023333</td>\n",
              "      <td>16.100000</td>\n",
              "      <td>20.463333</td>\n",
              "      <td>17.200000</td>\n",
              "      <td>28.766667</td>\n",
              "      <td>15.100000</td>\n",
              "      <td>27.660000</td>\n",
              "      <td>...</td>\n",
              "      <td>14.890000</td>\n",
              "      <td>29.166667</td>\n",
              "      <td>-5.000000</td>\n",
              "      <td>729.300000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-6.600000</td>\n",
              "      <td>0.005322</td>\n",
              "      <td>0.005322</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>50.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>20.760000</td>\n",
              "      <td>37.333333</td>\n",
              "      <td>18.790000</td>\n",
              "      <td>37.900000</td>\n",
              "      <td>20.790000</td>\n",
              "      <td>36.900000</td>\n",
              "      <td>19.530000</td>\n",
              "      <td>35.530000</td>\n",
              "      <td>...</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>38.500000</td>\n",
              "      <td>3.666667</td>\n",
              "      <td>750.933333</td>\n",
              "      <td>70.333333</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>12.497889</td>\n",
              "      <td>12.497889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>60.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>21.600000</td>\n",
              "      <td>39.656667</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>40.500000</td>\n",
              "      <td>22.100000</td>\n",
              "      <td>38.530000</td>\n",
              "      <td>20.666667</td>\n",
              "      <td>38.400000</td>\n",
              "      <td>...</td>\n",
              "      <td>19.390000</td>\n",
              "      <td>40.900000</td>\n",
              "      <td>6.916667</td>\n",
              "      <td>756.100000</td>\n",
              "      <td>83.666667</td>\n",
              "      <td>3.666667</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>3.433333</td>\n",
              "      <td>24.897653</td>\n",
              "      <td>24.897653</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>100.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>22.600000</td>\n",
              "      <td>43.066667</td>\n",
              "      <td>21.500000</td>\n",
              "      <td>43.260000</td>\n",
              "      <td>23.290000</td>\n",
              "      <td>41.760000</td>\n",
              "      <td>22.100000</td>\n",
              "      <td>42.156667</td>\n",
              "      <td>...</td>\n",
              "      <td>20.600000</td>\n",
              "      <td>44.338095</td>\n",
              "      <td>10.408333</td>\n",
              "      <td>760.933333</td>\n",
              "      <td>91.666667</td>\n",
              "      <td>5.500000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>6.566667</td>\n",
              "      <td>37.583769</td>\n",
              "      <td>37.583769</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1080.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>26.260000</td>\n",
              "      <td>63.360000</td>\n",
              "      <td>29.856667</td>\n",
              "      <td>56.026667</td>\n",
              "      <td>29.236000</td>\n",
              "      <td>50.163333</td>\n",
              "      <td>26.200000</td>\n",
              "      <td>51.090000</td>\n",
              "      <td>...</td>\n",
              "      <td>24.500000</td>\n",
              "      <td>53.326667</td>\n",
              "      <td>26.100000</td>\n",
              "      <td>772.300000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>15.500000</td>\n",
              "      <td>49.996530</td>\n",
              "      <td>49.996530</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 28 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d8b4e943-bdce-4015-ab2c-1250cb35bc88')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d8b4e943-bdce-4015-ab2c-1250cb35bc88 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d8b4e943-bdce-4015-ab2c-1250cb35bc88');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#display the different data types contains on our dataframe\n",
        "\n",
        "energy_df.dtypes"
      ],
      "metadata": {
        "id": "xjkcwkOHsob0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5d1a8424-9fa5-4f7b-fa1b-a24c2f9b3091"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date            object\n",
              "Appliances       int64\n",
              "lights           int64\n",
              "T1             float64\n",
              "RH_1           float64\n",
              "T2             float64\n",
              "RH_2           float64\n",
              "T3             float64\n",
              "RH_3           float64\n",
              "T4             float64\n",
              "RH_4           float64\n",
              "T5             float64\n",
              "RH_5           float64\n",
              "T6             float64\n",
              "RH_6           float64\n",
              "T7             float64\n",
              "RH_7           float64\n",
              "T8             float64\n",
              "RH_8           float64\n",
              "T9             float64\n",
              "RH_9           float64\n",
              "T_out          float64\n",
              "Press_mm_hg    float64\n",
              "RH_out         float64\n",
              "Windspeed      float64\n",
              "Visibility     float64\n",
              "Tdewpoint      float64\n",
              "rv1            float64\n",
              "rv2            float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#display the unique values\n",
        "\n",
        "energy_df.nunique()"
      ],
      "metadata": {
        "id": "izZgLiWVsoec",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "840b2a95-ca00-45e3-ada2-b3fdf2002341"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date           19735\n",
              "Appliances        92\n",
              "lights             8\n",
              "T1               722\n",
              "RH_1            2547\n",
              "T2              1650\n",
              "RH_2            3376\n",
              "T3              1426\n",
              "RH_3            2618\n",
              "T4              1390\n",
              "RH_4            2987\n",
              "T5              2263\n",
              "RH_5            7571\n",
              "T6              4446\n",
              "RH_6            9709\n",
              "T7              1955\n",
              "RH_7            5891\n",
              "T8              2228\n",
              "RH_8            6649\n",
              "T9               924\n",
              "RH_9            3388\n",
              "T_out           1730\n",
              "Press_mm_hg     2189\n",
              "RH_out           566\n",
              "Windspeed        189\n",
              "Visibility       413\n",
              "Tdewpoint       1409\n",
              "rv1            19735\n",
              "rv2            19735\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#display the columns \n",
        "\n",
        "energy_df.columns"
      ],
      "metadata": {
        "id": "B3_fOB2psohc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "393ea69b-798c-44cc-d4a6-17a8dcf726aa"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['date', 'Appliances', 'lights', 'T1', 'RH_1', 'T2', 'RH_2', 'T3',\n",
              "       'RH_3', 'T4', 'RH_4', 'T5', 'RH_5', 'T6', 'RH_6', 'T7', 'RH_7', 'T8',\n",
              "       'RH_8', 'T9', 'RH_9', 'T_out', 'Press_mm_hg', 'RH_out', 'Windspeed',\n",
              "       'Visibility', 'Tdewpoint', 'rv1', 'rv2'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "EDA"
      ],
      "metadata": {
        "id": "FPlo3TbHY5jr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "missing_values = pd.DataFrame({'Count':energy_df.isna().sum(),'Percentage':energy_df.isna().sum()/len(energy_df)*100})\n",
        "missing_values"
      ],
      "metadata": {
        "id": "oVH-jhASsokN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 959
        },
        "outputId": "6ab54389-6437-4335-c2ee-af5b1cebc5c3"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Count  Percentage\n",
              "date             0         0.0\n",
              "Appliances       0         0.0\n",
              "lights           0         0.0\n",
              "T1               0         0.0\n",
              "RH_1             0         0.0\n",
              "T2               0         0.0\n",
              "RH_2             0         0.0\n",
              "T3               0         0.0\n",
              "RH_3             0         0.0\n",
              "T4               0         0.0\n",
              "RH_4             0         0.0\n",
              "T5               0         0.0\n",
              "RH_5             0         0.0\n",
              "T6               0         0.0\n",
              "RH_6             0         0.0\n",
              "T7               0         0.0\n",
              "RH_7             0         0.0\n",
              "T8               0         0.0\n",
              "RH_8             0         0.0\n",
              "T9               0         0.0\n",
              "RH_9             0         0.0\n",
              "T_out            0         0.0\n",
              "Press_mm_hg      0         0.0\n",
              "RH_out           0         0.0\n",
              "Windspeed        0         0.0\n",
              "Visibility       0         0.0\n",
              "Tdewpoint        0         0.0\n",
              "rv1              0         0.0\n",
              "rv2              0         0.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-db4043da-ac7e-475c-a835-b6493b5ff392\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Count</th>\n",
              "      <th>Percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Appliances</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lights</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>T1</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RH_1</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>T2</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RH_2</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>T3</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RH_3</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>T4</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RH_4</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>T5</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RH_5</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>T6</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RH_6</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>T7</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RH_7</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>T8</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RH_8</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>T9</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RH_9</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>T_out</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Press_mm_hg</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RH_out</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Windspeed</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Visibility</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tdewpoint</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rv1</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rv2</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "          const element = document.querySelector('#df-db4043da-ac7e-475c-a835-b6493b5ff392');\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The above dataframe shows that our dataset have no missing values"
      ],
      "metadata": {
        "id": "r8767EDwZJj3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question 12:**\n",
        "\n",
        "From the dataset, fit a linear model on the relationship between the temperature in the living room in Celsius (x = T2) and the temperature outside the building (y = T6). What is the R^2 value in two d.p.?"
      ],
      "metadata": {
        "id": "7c39MtLhCqMW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#let assign x to T2 and y to T6\n",
        "\n",
        "x = energy_df[\"T2\"]\n",
        "y = energy_df[\"T6\"]"
      ],
      "metadata": {
        "id": "IltF9dwZsond"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#in order to fit our linear model, we have to reshape our x variable\n",
        "\n",
        "arr = np.array(x)\n",
        "x_feature = arr.reshape(-1, 1)"
      ],
      "metadata": {
        "id": "-xv8EZIkEPOl"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#we can now split our data into train and test set\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x_feature, y, train_size=0.7, test_size = 0.3, random_state=42)\n",
        "\n",
        "#Define our model\n",
        "linear_m = LinearRegression()\n",
        "\n",
        "#fit the model\n",
        "linear_m.fit(x_train, y_train)\n",
        "\n",
        "#predict the model\n",
        "preds = linear_m.predict(x_test)"
      ],
      "metadata": {
        "id": "RfJ-m8ByEpOk"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#finally, we can now compute for R_squared value\n",
        "\n",
        "r2 = r2_score(y_test, preds)\n",
        "print('The value of R_squared is :', round(r2, 2))"
      ],
      "metadata": {
        "id": "iLMiOF_lFMvU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9ff54a01-d09d-404f-d524-68ff711d019a"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The value of R_squared is : 0.64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question 13:**\n",
        "\n",
        "Normalize the dataset using the MinMaxScaler after removing the following columns: [“date”, “lights”]. The target variable is “Appliances”. Use a 70-30 train-test set split with a random state of 42 (for reproducibility). Run a multiple linear regression using the training set and evaluate your model on the test set. Answer the following questions:\n",
        "\n",
        "What is the Mean Absolute Error (in two decimal places)?"
      ],
      "metadata": {
        "id": "-R3Tj9QdJgk3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#let remove date and lights columns\n",
        "\n",
        "new_energy_df = energy_df.drop(['lights', 'date'], axis = 1)"
      ],
      "metadata": {
        "id": "0oXrtjcYJdix"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_energy_df.head()"
      ],
      "metadata": {
        "id": "xFvJGc9eJdlQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "7c2f2c11-fc5c-49e4-df87-1e7f9005a5a7"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Appliances     T1       RH_1    T2       RH_2     T3       RH_3         T4  \\\n",
              "0          60  19.89  47.596667  19.2  44.790000  19.79  44.730000  19.000000   \n",
              "1          60  19.89  46.693333  19.2  44.722500  19.79  44.790000  19.000000   \n",
              "2          50  19.89  46.300000  19.2  44.626667  19.79  44.933333  18.926667   \n",
              "3          50  19.89  46.066667  19.2  44.590000  19.79  45.000000  18.890000   \n",
              "4          60  19.89  46.333333  19.2  44.530000  19.79  45.000000  18.890000   \n",
              "\n",
              "        RH_4         T5  ...         T9   RH_9     T_out  Press_mm_hg  RH_out  \\\n",
              "0  45.566667  17.166667  ...  17.033333  45.53  6.600000        733.5    92.0   \n",
              "1  45.992500  17.166667  ...  17.066667  45.56  6.483333        733.6    92.0   \n",
              "2  45.890000  17.166667  ...  17.000000  45.50  6.366667        733.7    92.0   \n",
              "3  45.723333  17.166667  ...  17.000000  45.40  6.250000        733.8    92.0   \n",
              "4  45.530000  17.200000  ...  17.000000  45.40  6.133333        733.9    92.0   \n",
              "\n",
              "   Windspeed  Visibility  Tdewpoint        rv1        rv2  \n",
              "0   7.000000   63.000000        5.3  13.275433  13.275433  \n",
              "1   6.666667   59.166667        5.2  18.606195  18.606195  \n",
              "2   6.333333   55.333333        5.1  28.642668  28.642668  \n",
              "3   6.000000   51.500000        5.0  45.410389  45.410389  \n",
              "4   5.666667   47.666667        4.9  10.084097  10.084097  \n",
              "\n",
              "[5 rows x 27 columns]"
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              "      <th></th>\n",
              "      <th>Appliances</th>\n",
              "      <th>T1</th>\n",
              "      <th>RH_1</th>\n",
              "      <th>T2</th>\n",
              "      <th>RH_2</th>\n",
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              "      <th>RH_4</th>\n",
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              "      <th>...</th>\n",
              "      <th>T9</th>\n",
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              "      <th>RH_out</th>\n",
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              "      <td>19.000000</td>\n",
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              "      <td>6.600000</td>\n",
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              "      <td>19.89</td>\n",
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              "      <td>19.2</td>\n",
              "      <td>44.722500</td>\n",
              "      <td>19.79</td>\n",
              "      <td>44.790000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>45.992500</td>\n",
              "      <td>17.166667</td>\n",
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              "      <td>17.066667</td>\n",
              "      <td>45.56</td>\n",
              "      <td>6.483333</td>\n",
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              "      <td>44.933333</td>\n",
              "      <td>18.926667</td>\n",
              "      <td>45.890000</td>\n",
              "      <td>17.166667</td>\n",
              "      <td>...</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>45.50</td>\n",
              "      <td>6.366667</td>\n",
              "      <td>733.7</td>\n",
              "      <td>92.0</td>\n",
              "      <td>6.333333</td>\n",
              "      <td>55.333333</td>\n",
              "      <td>5.1</td>\n",
              "      <td>28.642668</td>\n",
              "      <td>28.642668</td>\n",
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              "      <td>45.723333</td>\n",
              "      <td>17.166667</td>\n",
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              "      <td>45.410389</td>\n",
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              "      <td>45.530000</td>\n",
              "      <td>17.200000</td>\n",
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              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#we normalise our dataset to a common scale using the Min Max Scaler\n",
        "\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "normalised_df = pd.DataFrame(scaler.fit_transform(new_energy_df), columns = new_energy_df.columns)\n",
        "\n",
        "features_df = normalised_df.drop(columns = ['Appliances'], axis=1)\n",
        "energy_target = normalised_df['Appliances']"
      ],
      "metadata": {
        "id": "84I-R4A8Jdnx"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Use a 70-30 train-test set split with a random state of 42 (for reproducibility)\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(features_df, energy_target, train_size=0.7, test_size = 0.3, random_state=42)\n",
        "\n",
        "#Define the model\n",
        "linear_model = LinearRegression()\n",
        "\n",
        "#fit our model\n",
        "linear_model.fit(x_train, y_train)\n",
        "\n",
        "#prediction\n",
        "predicted_values = linear_model.predict(x_test)"
      ],
      "metadata": {
        "id": "AjhCZbjlJds5"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#The Mean Absolute Error(MAE) in two decimal places\n",
        "\n",
        "df_mae = mean_absolute_error(y_test, predicted_values)\n",
        "\n",
        "print(\"The Mean Absolute Error (MAE) is: \", round(df_mae,2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JO8fHnjwOu-_",
        "outputId": "96f0db5f-4009-4859-b5bd-530bf7eaea2f"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Mean Absolute Error (MAE) is:  0.05\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question 14:**\n",
        "\n",
        "What is the Residual Sum of Squares (in two decimal places)?"
      ],
      "metadata": {
        "id": "02VU5AOYJj-Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "residual_ss = np.sum(np.square(y_test - predicted_values))\n",
        "\n",
        "print(\"The Residual Sum Squares (RSS) is: \", round(residual_ss, 2))"
      ],
      "metadata": {
        "id": "wIa5-gghJdwp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "135a9d6f-e406-4f13-8311-267bbb0b4aa0"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Residual Sum Squares (RSS) is:  45.35\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question 15:**\n",
        "\n",
        "What is the Root Mean Squared Error (in three decimal places)?"
      ],
      "metadata": {
        "id": "4eJ5zVkdSR_I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "result_ms_err = np.sqrt(mean_squared_error(y_test, predicted_values))\n",
        "\n",
        "print(\"The Root Mean Squared Error is : \", round(result_ms_err, 3))"
      ],
      "metadata": {
        "id": "qZzjM45TJnby",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7500519b-4230-4910-bc50-6531dbd5b352"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Root Mean Squared Error is :  0.088\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question 16:**\n",
        "\n",
        "What is the Coefficient of Determination (in two decimal places)?"
      ],
      "metadata": {
        "id": "2XYAsSlmJntg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "R_square = r2_score(y_test, predicted_values)\n",
        "\n",
        "print(\"The Coefficient of Determination is \", round(R_square,2))"
      ],
      "metadata": {
        "id": "Pa-SC3qEJrf5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "65399f52-f7e4-417d-caef-e7023fc85042"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Coefficient of Determination is  0.15\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question 17:**\n",
        "\n",
        "Obtain the feature weights from your linear model above. Which features have the lowest and highest weights respectively?"
      ],
      "metadata": {
        "id": "ZbogWn-lYlnJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_weights_df(model, feat, col_name):\n",
        "\n",
        "  weights = pd.Series(model.coef_, feat.columns).sort_values()\n",
        "\n",
        "  weights_df = pd.DataFrame(weights).reset_index()\n",
        "\n",
        "  weights_df.columns = ['Features', col_name]\n",
        "  \n",
        "  weights_df[col_name].round(3)\n",
        "  return weights_df"
      ],
      "metadata": {
        "id": "tG4QOwkmJrjY"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "linear_model_weights = get_weights_df(linear_model, x_train, 'Linear_Model_Weight')"
      ],
      "metadata": {
        "id": "uMJEsSmvY3Zl"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "linear_model_weights"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 864
        },
        "id": "LPsVw2SkY3cO",
        "outputId": "da8fd146-e9a5-4e04-c7bf-9e964e402cfc"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Features  Linear_Model_Weight\n",
              "0          RH_2            -0.456698\n",
              "1         T_out            -0.321860\n",
              "2            T2            -0.236178\n",
              "3            T9            -0.189941\n",
              "4          RH_8            -0.157595\n",
              "5        RH_out            -0.077671\n",
              "6          RH_7            -0.044614\n",
              "7          RH_9            -0.039800\n",
              "8            T5            -0.015657\n",
              "9            T1            -0.003281\n",
              "10          rv1             0.000770\n",
              "11          rv2             0.000770\n",
              "12  Press_mm_hg             0.006839\n",
              "13           T7             0.010319\n",
              "14   Visibility             0.012307\n",
              "15         RH_5             0.016006\n",
              "16         RH_4             0.026386\n",
              "17           T4             0.028981\n",
              "18    Windspeed             0.029183\n",
              "19         RH_6             0.038049\n",
              "20         RH_3             0.096048\n",
              "21           T8             0.101995\n",
              "22    Tdewpoint             0.117758\n",
              "23           T6             0.236425\n",
              "24           T3             0.290627\n",
              "25         RH_1             0.553547"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cd8361a0-6c28-4257-95dc-cec55d896ae4\">\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Features</th>\n",
              "      <th>Linear_Model_Weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>RH_2</td>\n",
              "      <td>-0.456698</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>T_out</td>\n",
              "      <td>-0.321860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>T2</td>\n",
              "      <td>-0.236178</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>T9</td>\n",
              "      <td>-0.189941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>RH_8</td>\n",
              "      <td>-0.157595</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>RH_out</td>\n",
              "      <td>-0.077671</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>RH_7</td>\n",
              "      <td>-0.044614</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>RH_9</td>\n",
              "      <td>-0.039800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>T5</td>\n",
              "      <td>-0.015657</td>\n",
              "    </tr>\n",
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              "      <th>9</th>\n",
              "      <td>T1</td>\n",
              "      <td>-0.003281</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>rv1</td>\n",
              "      <td>0.000770</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>rv2</td>\n",
              "      <td>0.000770</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Press_mm_hg</td>\n",
              "      <td>0.006839</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>T7</td>\n",
              "      <td>0.010319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Visibility</td>\n",
              "      <td>0.012307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>RH_5</td>\n",
              "      <td>0.016006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>RH_4</td>\n",
              "      <td>0.026386</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>T4</td>\n",
              "      <td>0.028981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Windspeed</td>\n",
              "      <td>0.029183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>RH_6</td>\n",
              "      <td>0.038049</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>RH_3</td>\n",
              "      <td>0.096048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>T8</td>\n",
              "      <td>0.101995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Tdewpoint</td>\n",
              "      <td>0.117758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>T6</td>\n",
              "      <td>0.236425</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>T3</td>\n",
              "      <td>0.290627</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>RH_1</td>\n",
              "      <td>0.553547</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#the feature with the highest weight\n",
        "\n",
        "print(linear_model_weights[linear_model_weights.Linear_Model_Weight == linear_model_weights.Linear_Model_Weight.max()])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d-XedZkEeIxr",
        "outputId": "b09b5889-020e-42d9-c577-83eea1d55ff8"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Features  Linear_Model_Weight\n",
            "25     RH_1             0.553547\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#the feature with the lowest weight\n",
        "\n",
        "print(linear_model_weights[linear_model_weights.Linear_Model_Weight == linear_model_weights.Linear_Model_Weight.min()])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IOkBHOwweI0i",
        "outputId": "2077de5d-b313-4e23-fce3-0384f632bce7"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Features  Linear_Model_Weight\n",
            "0     RH_2            -0.456698\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question 18:**\n",
        "\n",
        "Train a ridge regression model with an alpha value of 0.4. Is there any change to the root mean squared error (RMSE) when evaluated on the test set?"
      ],
      "metadata": {
        "id": "rbI_f3wRgWGo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Ridge Regression\n",
        "\n",
        "#train ridge regression model where alpha is 0.4\n",
        "l2_reg = Ridge(alpha=0.4, random_state=None)\n",
        "\n",
        "#fit the model\n",
        "l2_reg.fit(x_train, y_train)\n",
        "\n",
        "#predict the model\n",
        "l2_preds = l2_reg.predict(x_test)"
      ],
      "metadata": {
        "id": "NVaqvUYMgOIw"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#get the Root Mean Squared Error (RMSE)\n",
        "\n",
        "l2_rmse = np.sqrt(mean_squared_error(y_test, l2_preds))\n",
        "\n",
        "print(\"The Root Mean Squared Error (RMSE) is: \", round(l2_rmse, 3))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p7d9zI8EgOL3",
        "outputId": "8dac1d0a-0d70-4d7f-87a5-37c657fb67ec"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Root Mean Squared Error (RMSE) is:  0.088\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Root Mean Squared Error (RMSE) on Ridge Regression model and Linear model is the same and equal to 0.088. Thus, there is no changes in the RMSE"
      ],
      "metadata": {
        "id": "nWyGNScriCsQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question 19:\n",
        "\n",
        "Train a lasso regression model with an alpha value of 0.001 and obtain the new feature weights with it. How many of the features have non-zero feature weights?"
      ],
      "metadata": {
        "id": "ijKKUVuHik7m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Lesson Regression model with alpha = 0.001\n",
        "\n",
        "#Define the model\n",
        "lasso_reg = Lasso(alpha=0.001)\n",
        "\n",
        "#fit the model\n",
        "lasso_reg.fit(x_train, y_train)\n",
        "\n",
        "#predict the model\n",
        "l1_pred = lasso_reg.predict(x_test)"
      ],
      "metadata": {
        "id": "_gOoIEargOOo"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lasso_weights_df = get_weights_df(lasso_reg, x_train, 'Lasso_weight')"
      ],
      "metadata": {
        "id": "7yCNDOQXgORh"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lasso_weights_df"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 864
        },
        "id": "PtbBg31OjgtF",
        "outputId": "fcb6ee5b-19c4-4e55-a2f5-88571de8f853"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Features  Lasso_weight\n",
              "0        RH_out     -0.049557\n",
              "1          RH_8     -0.000110\n",
              "2            T1      0.000000\n",
              "3     Tdewpoint      0.000000\n",
              "4    Visibility      0.000000\n",
              "5   Press_mm_hg     -0.000000\n",
              "6         T_out      0.000000\n",
              "7          RH_9     -0.000000\n",
              "8            T9     -0.000000\n",
              "9            T8      0.000000\n",
              "10         RH_7     -0.000000\n",
              "11          rv1     -0.000000\n",
              "12           T7     -0.000000\n",
              "13           T6      0.000000\n",
              "14         RH_5      0.000000\n",
              "15           T5     -0.000000\n",
              "16         RH_4      0.000000\n",
              "17           T4     -0.000000\n",
              "18         RH_3      0.000000\n",
              "19           T3      0.000000\n",
              "20         RH_2     -0.000000\n",
              "21           T2      0.000000\n",
              "22         RH_6     -0.000000\n",
              "23          rv2     -0.000000\n",
              "24    Windspeed      0.002912\n",
              "25         RH_1      0.017880"
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Features</th>\n",
              "      <th>Lasso_weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>RH_8</td>\n",
              "      <td>-0.000110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>T1</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Tdewpoint</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Visibility</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Press_mm_hg</td>\n",
              "      <td>-0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>T_out</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>RH_9</td>\n",
              "      <td>-0.000000</td>\n",
              "    </tr>\n",
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              "      <th>8</th>\n",
              "      <td>T9</td>\n",
              "      <td>-0.000000</td>\n",
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              "      <th>9</th>\n",
              "      <td>T8</td>\n",
              "      <td>0.000000</td>\n",
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              "      <th>10</th>\n",
              "      <td>RH_7</td>\n",
              "      <td>-0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>rv1</td>\n",
              "      <td>-0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>T7</td>\n",
              "      <td>-0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>T6</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>RH_5</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>T5</td>\n",
              "      <td>-0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>RH_4</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>T4</td>\n",
              "      <td>-0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>RH_3</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>T3</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>RH_2</td>\n",
              "      <td>-0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>T2</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>RH_6</td>\n",
              "      <td>-0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>rv2</td>\n",
              "      <td>-0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Windspeed</td>\n",
              "      <td>0.002912</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>RH_1</td>\n",
              "      <td>0.017880</td>\n",
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          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#How many of the features have non-zero feature weights?\n",
        "\n",
        "non_z = lasso_weights_df[\"Lasso_weight\"].astype(bool).sum(axis=0)\n",
        "\n",
        "print(\"The total features that have non-zero feature weights is = \", non_z)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "THAmqr_Xjgy3",
        "outputId": "ddd3268e-e8b9-4761-a384-48e4b06c0706"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The total features that have non-zero feature weights is =  4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question 20:**\n",
        "\n",
        "What is the new RMSE with the lasso regression? (Answer should be in three (3) decimal places)"
      ],
      "metadata": {
        "id": "dbKb8W_9lZ2k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#The new Root Mean Squared Error (RMSE) with LASSO\n",
        "\n",
        "new_lasso_rmse = np.sqrt(mean_squared_error(y_test, l1_pred))\n",
        "\n",
        "print(\"The New Root Mean Squared Error (RMSE) on LASSO is: \", round(new_lasso_rmse, 3))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ddYZonUjg1W",
        "outputId": "934ce34b-fe32-427c-f694-0e980f86a5ff"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The New Root Mean Squared Error (RMSE) on LASSO is:  0.094\n"
          ]
        }
      ]
    }
  ]
}