wave_energy_farm.ipynb

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        "<a href=\"https://colab.research.google.com/github/ChamsToure/wave_energy_farm/blob/main/wave_energy_farm.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "# General information\n",
        "\n",
        "Wave energy is a rapidly advancing and promising renewable energy source that holds great potential for addressing the challenges of global warming and climate change. However, optimizing energy output in large wave farms presents a complex problem due to the expensive calculations required to account for hydrodynamic interactions between wave energy converters (WECs). Developing a fast and accurate surrogate model is crucial to overcome these challenges. In light of this, we have compiled an extensive WEC dataset that includes 54,000 and 9,600 configurations involving 49 and 100 WECs, coordination, power, q-factor, and total farm power output. The dataset was derived from a study published at the GECCO conference and received the prestigious Best Paper award. We want to acknowledge the support of the University of Adelaide Phoenix HPC service in conducting this research. For more details, please refer to the following link: https://dl.acm.org/doi/abs/10.1145/3377930.3390235.\n",
        "\n",
        "## Features\n",
        "The dataset includes 4 CSV files for 49 and 100 wave energy converters based on Perth and Sydney wave scenarios. The main goal is predicting the total power output of the wave farm based on the coordination of WECs (X1, Y1, X2, Y2,..., Xn, Yn). As the second plan, predicting the power output of each converter in the wave farm can be interesting.\n",
        "\n",
        "* X and Y - coordinates\n",
        "* Power - individual power\n",
        "* total power - total power of all the WECs\n",
        "* qW - q-factor\n",
        "\n",
        "## Findings\n",
        "There are many duplicates in the dataset (4540).\n",
        "\n",
        "## Goal\n",
        "* Predicting the total power output of the wave farm based on the coordination of WECs\n",
        "* Predicting the power ouput of each converter in the wave farm\n",
        "\n",
        "## Candidates for inaccuracy\n",
        "* To small dataset -> need more data\n",
        "* Not the right regression model -> Test many different regression models and measure their accuracy\n",
        "\n",
        "## Potential Problems\n",
        "* Cannot inference based on geodata from only 4 locations, especially with a relatively small dataset.\n",
        "* In what timeframe was the data collected? Becuase you cannot predict the total power over the year if it is also dependent of the seasons\n",
        "* Why are there so many duplicates?\n",
        "\n",
        "## Instances\n",
        "Each instance represents the coordination of wave energy converters in a wave farm plus the total power output and individual power of each converter and q-factor.\n",
        "\n",
        "## Source\n",
        "https://archive.ics.uci.edu/dataset/882/large-scale+wave+energy+farm\n",
        "Neshat, Mehdi, Bradley Alexander, Nataliia Y. Sergiienko, and Markus Wagner. \"Optimisation of large wave farms using a multi-strategy evolutionary framework.\" In Proceedings of the 2020 Genetic and Evolutionary Computation Conference, pp. 1150-1158. 2020.\n",
        "Link to research: https://dl.acm.org/doi/abs/10.1145/3377930.3390235."
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split,cross_val_score\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import mean_squared_error\n"
      ]
    },
    {
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      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
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        "id": "ZBGQiDRblGJo",
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      "id": "ZBGQiDRblGJo",
      "execution_count": 3,
      "outputs": [
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          "output_type": "stream",
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          "text": [
            "Mounted at /content/drive\n"
          ]
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      "source": [
        "dataset = pd.read_csv(\"/content/drive/MyDrive/Ai/WEC_Perth_100.csv\")"
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      "source": [
        "## Preprocessing"
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        "dataset.shape"
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              "       X1   Y1      X2     Y2      X3     Y3      X4      Y4      X5   Y5  \\\n",
              "0  1000.0  0.0  946.08  37.42  889.67  74.76  832.02  112.10  1250.0  1.0   \n",
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              "2   600.0  0.0  545.98  37.50  489.87  74.95  432.52  112.15   650.0  0.0   \n",
              "3   600.0  0.0  546.09  37.41  489.70  74.76  432.35  111.90   800.0  0.0   \n",
              "4   600.0  0.0  545.95  37.52  489.69  74.99  432.46  112.16   800.0  0.0   \n",
              "\n",
              "   ...   Power93    Power94    Power95    Power96   Power97    Power98  \\\n",
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              "3  100835.43  98915.38  0.75   7187769.87  \n",
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              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-65106a80-9472-448d-9233-b187a7676c48 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "dataset.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "16a88bd3-fe0a-48a9-b060-ccc3bd14126c",
      "metadata": {
        "id": "16a88bd3-fe0a-48a9-b060-ccc3bd14126c"
      },
      "source": [
        "### Remove Duplicates"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "c6826635-d9e8-4a2a-aaf0-7a7ed4ed384a",
      "metadata": {
        "id": "c6826635-d9e8-4a2a-aaf0-7a7ed4ed384a"
      },
      "outputs": [],
      "source": [
        "dataset = dataset.drop_duplicates()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "fda1d395-c5de-4a1d-98a0-8f5695cab1e3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fda1d395-c5de-4a1d-98a0-8f5695cab1e3",
        "outputId": "014f4eb5-03b5-4cac-851d-626cb0060389"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2737, 302)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "dataset.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c9f54f69-0af6-40d1-805a-3d1ca1dab90a",
      "metadata": {
        "id": "c9f54f69-0af6-40d1-805a-3d1ca1dab90a"
      },
      "source": [
        "### Check for null values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "fff37c03-f4da-42a5-88ee-c20bda0ac92e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fff37c03-f4da-42a5-88ee-c20bda0ac92e",
        "outputId": "8b15e540-1331-4b9f-a1ca-5ae6cae60663"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "#Check how many null values are in the entire dataset\n",
        "dataset.isnull().sum().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8fa58e91-adfc-4d1b-bdff-7292c1e1fadc",
      "metadata": {
        "id": "8fa58e91-adfc-4d1b-bdff-7292c1e1fadc"
      },
      "source": [
        "## Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "7008cd62-6183-4251-8011-8e4f5f08c7cb",
      "metadata": {
        "id": "7008cd62-6183-4251-8011-8e4f5f08c7cb"
      },
      "outputs": [],
      "source": [
        "columns = list(dataset)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a3fcbaf4-cc2a-4d46-abb3-95a46ac0587c",
      "metadata": {
        "id": "a3fcbaf4-cc2a-4d46-abb3-95a46ac0587c"
      },
      "source": [
        "### Check for outliers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "745f46ca-c4e2-45d4-9395-2f6910fd4dc6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "745f46ca-c4e2-45d4-9395-2f6910fd4dc6",
        "outputId": "331e07d2-97c4-4ce2-e391-4247634c1e1c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                X1           Y1           X2           Y2           X3  \\\n",
              "count  2737.000000  2737.000000  2737.000000  2737.000000  2737.000000   \n",
              "mean    352.430815    28.443796   376.587603    48.513548   390.824538   \n",
              "std     293.777257    97.276123   271.069835    59.577117   282.745074   \n",
              "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
              "25%     129.200000     0.000000   146.150000    37.390000    89.980000   \n",
              "50%     200.000000     0.000000   316.740000    37.490000   324.570000   \n",
              "75%     600.000000     0.000000   546.040000    37.550000   489.820000   \n",
              "max    1400.000000  1353.550000  1400.000000  1277.640000  1414.000000   \n",
              "\n",
              "                Y3           X4           Y4          X5           Y5  ...  \\\n",
              "count  2737.000000  2737.000000  2737.000000  2737.00000  2737.000000  ...   \n",
              "mean     67.395784   382.368316   100.883800   748.81297    31.299488  ...   \n",
              "std      45.932165   299.046854    57.995795   285.37046    73.050856  ...   \n",
              "min       0.000000     0.000000     0.000000     0.00000     0.000000  ...   \n",
              "25%      65.990000    32.640000    98.430000   600.00000     0.000000  ...   \n",
              "50%      74.880000   432.240000   112.070000   661.08000     0.000000  ...   \n",
              "75%      75.010000   514.460000   112.240000  1000.00000    40.990000  ...   \n",
              "max     698.010000  1400.000000  1035.090000  1413.39000   910.420000  ...   \n",
              "\n",
              "             Power93        Power94        Power95        Power96  \\\n",
              "count    2737.000000    2737.000000    2737.000000    2737.000000   \n",
              "mean    88784.392488   94574.215331   97805.710833   97620.391436   \n",
              "std     10396.969817    7717.499315    6250.371796    5975.969268   \n",
              "min     34236.100000   32447.850000   30658.290000   35208.350000   \n",
              "25%     76698.160000   87986.780000   98477.180000   98429.200000   \n",
              "50%     95014.870000   97237.470000   98785.020000   99601.280000   \n",
              "75%     98339.870000  102029.820000  101089.720000  100267.940000   \n",
              "max    103541.860000  104047.690000  104403.690000  104206.780000   \n",
              "\n",
              "             Power97        Power98        Power99       Power100  \\\n",
              "count    2737.000000    2737.000000    2737.000000    2737.000000   \n",
              "mean    97511.043405   98655.269839   98920.618290   97713.614468   \n",
              "std      5045.909333    6408.359410    4689.778147    3966.695051   \n",
              "min     29411.690000   36525.750000   42148.330000   33879.920000   \n",
              "25%     98614.520000   98350.170000   98118.470000   96904.240000   \n",
              "50%     98840.130000  101312.440000  100905.720000   98796.000000   \n",
              "75%     99017.440000  101438.470000  101027.360000   98959.650000   \n",
              "max    103678.570000  102543.340000  102219.050000  101692.770000   \n",
              "\n",
              "                qW   Total_Power  \n",
              "count  2737.000000  2.737000e+03  \n",
              "mean      0.721995  6.959157e+06  \n",
              "std       0.020921  1.998767e+05  \n",
              "min       0.630000  6.099480e+06  \n",
              "25%       0.710000  6.806309e+06  \n",
              "50%       0.720000  6.941249e+06  \n",
              "75%       0.740000  7.129514e+06  \n",
              "max       0.760000  7.364528e+06  \n",
              "\n",
              "[8 rows x 302 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-aeee4a5b-b752-4166-9010-242f744777f3\" 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>X1</th>\n",
              "      <th>Y1</th>\n",
              "      <th>X2</th>\n",
              "      <th>Y2</th>\n",
              "      <th>X3</th>\n",
              "      <th>Y3</th>\n",
              "      <th>X4</th>\n",
              "      <th>Y4</th>\n",
              "      <th>X5</th>\n",
              "      <th>Y5</th>\n",
              "      <th>...</th>\n",
              "      <th>Power93</th>\n",
              "      <th>Power94</th>\n",
              "      <th>Power95</th>\n",
              "      <th>Power96</th>\n",
              "      <th>Power97</th>\n",
              "      <th>Power98</th>\n",
              "      <th>Power99</th>\n",
              "      <th>Power100</th>\n",
              "      <th>qW</th>\n",
              "      <th>Total_Power</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2737.000000</td>\n",
              "      <td>2737.000000</td>\n",
              "      <td>2737.000000</td>\n",
              "      <td>2737.000000</td>\n",
              "      <td>2737.000000</td>\n",
              "      <td>2737.000000</td>\n",
              "      <td>2737.000000</td>\n",
              "      <td>2737.000000</td>\n",
              "      <td>2737.00000</td>\n",
              "      <td>2737.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>2737.000000</td>\n",
              "      <td>2737.000000</td>\n",
              "      <td>2737.000000</td>\n",
              "      <td>2737.000000</td>\n",
              "      <td>2737.000000</td>\n",
              "      <td>2737.000000</td>\n",
              "      <td>2737.000000</td>\n",
              "      <td>2737.000000</td>\n",
              "      <td>2737.000000</td>\n",
              "      <td>2.737000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>352.430815</td>\n",
              "      <td>28.443796</td>\n",
              "      <td>376.587603</td>\n",
              "      <td>48.513548</td>\n",
              "      <td>390.824538</td>\n",
              "      <td>67.395784</td>\n",
              "      <td>382.368316</td>\n",
              "      <td>100.883800</td>\n",
              "      <td>748.81297</td>\n",
              "      <td>31.299488</td>\n",
              "      <td>...</td>\n",
              "      <td>88784.392488</td>\n",
              "      <td>94574.215331</td>\n",
              "      <td>97805.710833</td>\n",
              "      <td>97620.391436</td>\n",
              "      <td>97511.043405</td>\n",
              "      <td>98655.269839</td>\n",
              "      <td>98920.618290</td>\n",
              "      <td>97713.614468</td>\n",
              "      <td>0.721995</td>\n",
              "      <td>6.959157e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>293.777257</td>\n",
              "      <td>97.276123</td>\n",
              "      <td>271.069835</td>\n",
              "      <td>59.577117</td>\n",
              "      <td>282.745074</td>\n",
              "      <td>45.932165</td>\n",
              "      <td>299.046854</td>\n",
              "      <td>57.995795</td>\n",
              "      <td>285.37046</td>\n",
              "      <td>73.050856</td>\n",
              "      <td>...</td>\n",
              "      <td>10396.969817</td>\n",
              "      <td>7717.499315</td>\n",
              "      <td>6250.371796</td>\n",
              "      <td>5975.969268</td>\n",
              "      <td>5045.909333</td>\n",
              "      <td>6408.359410</td>\n",
              "      <td>4689.778147</td>\n",
              "      <td>3966.695051</td>\n",
              "      <td>0.020921</td>\n",
              "      <td>1.998767e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>34236.100000</td>\n",
              "      <td>32447.850000</td>\n",
              "      <td>30658.290000</td>\n",
              "      <td>35208.350000</td>\n",
              "      <td>29411.690000</td>\n",
              "      <td>36525.750000</td>\n",
              "      <td>42148.330000</td>\n",
              "      <td>33879.920000</td>\n",
              "      <td>0.630000</td>\n",
              "      <td>6.099480e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>129.200000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>146.150000</td>\n",
              "      <td>37.390000</td>\n",
              "      <td>89.980000</td>\n",
              "      <td>65.990000</td>\n",
              "      <td>32.640000</td>\n",
              "      <td>98.430000</td>\n",
              "      <td>600.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>76698.160000</td>\n",
              "      <td>87986.780000</td>\n",
              "      <td>98477.180000</td>\n",
              "      <td>98429.200000</td>\n",
              "      <td>98614.520000</td>\n",
              "      <td>98350.170000</td>\n",
              "      <td>98118.470000</td>\n",
              "      <td>96904.240000</td>\n",
              "      <td>0.710000</td>\n",
              "      <td>6.806309e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>200.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>316.740000</td>\n",
              "      <td>37.490000</td>\n",
              "      <td>324.570000</td>\n",
              "      <td>74.880000</td>\n",
              "      <td>432.240000</td>\n",
              "      <td>112.070000</td>\n",
              "      <td>661.08000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>95014.870000</td>\n",
              "      <td>97237.470000</td>\n",
              "      <td>98785.020000</td>\n",
              "      <td>99601.280000</td>\n",
              "      <td>98840.130000</td>\n",
              "      <td>101312.440000</td>\n",
              "      <td>100905.720000</td>\n",
              "      <td>98796.000000</td>\n",
              "      <td>0.720000</td>\n",
              "      <td>6.941249e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>600.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>546.040000</td>\n",
              "      <td>37.550000</td>\n",
              "      <td>489.820000</td>\n",
              "      <td>75.010000</td>\n",
              "      <td>514.460000</td>\n",
              "      <td>112.240000</td>\n",
              "      <td>1000.00000</td>\n",
              "      <td>40.990000</td>\n",
              "      <td>...</td>\n",
              "      <td>98339.870000</td>\n",
              "      <td>102029.820000</td>\n",
              "      <td>101089.720000</td>\n",
              "      <td>100267.940000</td>\n",
              "      <td>99017.440000</td>\n",
              "      <td>101438.470000</td>\n",
              "      <td>101027.360000</td>\n",
              "      <td>98959.650000</td>\n",
              "      <td>0.740000</td>\n",
              "      <td>7.129514e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1400.000000</td>\n",
              "      <td>1353.550000</td>\n",
              "      <td>1400.000000</td>\n",
              "      <td>1277.640000</td>\n",
              "      <td>1414.000000</td>\n",
              "      <td>698.010000</td>\n",
              "      <td>1400.000000</td>\n",
              "      <td>1035.090000</td>\n",
              "      <td>1413.39000</td>\n",
              "      <td>910.420000</td>\n",
              "      <td>...</td>\n",
              "      <td>103541.860000</td>\n",
              "      <td>104047.690000</td>\n",
              "      <td>104403.690000</td>\n",
              "      <td>104206.780000</td>\n",
              "      <td>103678.570000</td>\n",
              "      <td>102543.340000</td>\n",
              "      <td>102219.050000</td>\n",
              "      <td>101692.770000</td>\n",
              "      <td>0.760000</td>\n",
              "      <td>7.364528e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 302 columns</p>\n",
              "</div>\n",
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              "    }\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-aeee4a5b-b752-4166-9010-242f744777f3 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-aeee4a5b-b752-4166-9010-242f744777f3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-7a8a33d8-451e-46ea-bb0a-eb0b27e09104\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7a8a33d8-451e-46ea-bb0a-eb0b27e09104')\"\n",
              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7a8a33d8-451e-46ea-bb0a-eb0b27e09104 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "dataset.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "3c539393-d1cd-4b5d-9823-5dcffebbfaa1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3c539393-d1cd-4b5d-9823-5dcffebbfaa1",
        "outputId": "48c314cf-fb9f-45b4-e92e-e92bad976ba8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    2.737000e+03\n",
              "mean     6.959157e+06\n",
              "std      1.998767e+05\n",
              "min      6.099480e+06\n",
              "25%      6.806309e+06\n",
              "50%      6.941249e+06\n",
              "75%      7.129514e+06\n",
              "max      7.364528e+06\n",
              "Name: Total_Power, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "dataset[\"Total_Power\"].describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "7f994a81-50b4-468f-8a2e-3439aacc366a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "7f994a81-50b4-468f-8a2e-3439aacc366a",
        "outputId": "f410bc4a-4f10-496a-fdcc-dd9fd61c8720"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "graph = plt.hist(x = dataset[\"Total_Power\"], bins=500)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d42c7628-e5ee-47c6-b27e-9ef8b90c80d2",
      "metadata": {
        "id": "d42c7628-e5ee-47c6-b27e-9ef8b90c80d2"
      },
      "source": [
        "# Train models to predict Total_Power output of wave energy farms based on coordinates"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "38460c83-c95e-4b7b-96ad-1a4c460c5fbc",
      "metadata": {
        "id": "38460c83-c95e-4b7b-96ad-1a4c460c5fbc"
      },
      "source": [
        "## Drop the power columns for individual WECs and qW"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "da4123dc-ba42-4df2-8977-c4f435f661c8",
      "metadata": {
        "id": "da4123dc-ba42-4df2-8977-c4f435f661c8"
      },
      "outputs": [],
      "source": [
        "dataset_copy = dataset.copy(deep=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "5a415ef5-bad6-4d77-9219-67c81b181a0d",
      "metadata": {
        "id": "5a415ef5-bad6-4d77-9219-67c81b181a0d"
      },
      "outputs": [],
      "source": [
        "for num in range(1, 101):\n",
        "    dataset_copy.drop(f\"Power{num}\", axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "628c21ec-6171-4b45-8b84-e2961c068a25",
      "metadata": {
        "id": "628c21ec-6171-4b45-8b84-e2961c068a25"
      },
      "outputs": [],
      "source": [
        "dataset_copy.drop(\"qW\", inplace=True, axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "1b2c6065-1e78-43d6-b267-b95a66352e4a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1b2c6065-1e78-43d6-b267-b95a66352e4a",
        "outputId": "86ce8790-9cf1-4ebc-8fcd-3fefcd62f6b2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2737, 201)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "dataset_copy.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "d7685d2e-9f5e-416b-a7a2-7d2e650eaa4c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d7685d2e-9f5e-416b-a7a2-7d2e650eaa4c",
        "outputId": "10978268-ee5b-4269-f156-4668747702ec"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2737, 302)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "dataset.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "fae9d3a3-dbf5-4692-ad9d-2f7ff8f8b680",
      "metadata": {
        "id": "fae9d3a3-dbf5-4692-ad9d-2f7ff8f8b680"
      },
      "outputs": [],
      "source": [
        "y = dataset_copy[\"Total_Power\"]\n",
        "X = dataset_copy.drop(\"Total_Power\", axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "f0511b60-3963-4761-923d-0c6796acca51",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "f0511b60-3963-4761-923d-0c6796acca51",
        "outputId": "3bf4090d-df79-49ef-b6fd-fee4c0b49100"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       X1   Y1      X2     Y2      X3     Y3      X4      Y4      X5   Y5  \\\n",
              "0  1000.0  0.0  946.08  37.42  889.67  74.76  832.02  112.10  1250.0  1.0   \n",
              "1   800.0  0.0  746.04  37.38  689.81  74.79  632.43  111.97  1200.0  0.0   \n",
              "2   600.0  0.0  545.98  37.50  489.87  74.95  432.52  112.15   650.0  0.0   \n",
              "3   600.0  0.0  546.09  37.41  489.70  74.76  432.35  111.90   800.0  0.0   \n",
              "4   600.0  0.0  545.95  37.52  489.69  74.99  432.46  112.16   800.0  0.0   \n",
              "\n",
              "   ...     X96      Y96     X97     Y97      X98      Y98      X99      Y99  \\\n",
              "0  ...  432.02  1312.10  1200.0  1200.0  1146.08  1237.42  1089.67  1274.76   \n",
              "1  ...  232.43  1311.97   800.0  1200.0   746.04  1237.38   689.81  1274.79   \n",
              "2  ...  432.52  1312.15  1000.0  1200.0   945.98  1237.50   889.87  1274.95   \n",
              "3  ...  832.35  1311.90  1200.0  1200.0  1146.09  1237.41  1089.70  1274.76   \n",
              "4  ...  432.46  1312.16  1200.0  1200.0  1145.95  1237.52  1089.69  1274.99   \n",
              "\n",
              "      X100     Y100  \n",
              "0  1032.02  1312.10  \n",
              "1   632.43  1311.97  \n",
              "2   832.52  1312.15  \n",
              "3  1032.35  1311.90  \n",
              "4  1032.46  1312.16  \n",
              "\n",
              "[5 rows x 200 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7fb60d0f-7654-4883-8839-e67aee850d1b\" 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>X1</th>\n",
              "      <th>Y1</th>\n",
              "      <th>X2</th>\n",
              "      <th>Y2</th>\n",
              "      <th>X3</th>\n",
              "      <th>Y3</th>\n",
              "      <th>X4</th>\n",
              "      <th>Y4</th>\n",
              "      <th>X5</th>\n",
              "      <th>Y5</th>\n",
              "      <th>...</th>\n",
              "      <th>X96</th>\n",
              "      <th>Y96</th>\n",
              "      <th>X97</th>\n",
              "      <th>Y97</th>\n",
              "      <th>X98</th>\n",
              "      <th>Y98</th>\n",
              "      <th>X99</th>\n",
              "      <th>Y99</th>\n",
              "      <th>X100</th>\n",
              "      <th>Y100</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>946.08</td>\n",
              "      <td>37.42</td>\n",
              "      <td>889.67</td>\n",
              "      <td>74.76</td>\n",
              "      <td>832.02</td>\n",
              "      <td>112.10</td>\n",
              "      <td>1250.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>432.02</td>\n",
              "      <td>1312.10</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>1146.08</td>\n",
              "      <td>1237.42</td>\n",
              "      <td>1089.67</td>\n",
              "      <td>1274.76</td>\n",
              "      <td>1032.02</td>\n",
              "      <td>1312.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>800.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>746.04</td>\n",
              "      <td>37.38</td>\n",
              "      <td>689.81</td>\n",
              "      <td>74.79</td>\n",
              "      <td>632.43</td>\n",
              "      <td>111.97</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>232.43</td>\n",
              "      <td>1311.97</td>\n",
              "      <td>800.0</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>746.04</td>\n",
              "      <td>1237.38</td>\n",
              "      <td>689.81</td>\n",
              "      <td>1274.79</td>\n",
              "      <td>632.43</td>\n",
              "      <td>1311.97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>600.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>545.98</td>\n",
              "      <td>37.50</td>\n",
              "      <td>489.87</td>\n",
              "      <td>74.95</td>\n",
              "      <td>432.52</td>\n",
              "      <td>112.15</td>\n",
              "      <td>650.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>432.52</td>\n",
              "      <td>1312.15</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>945.98</td>\n",
              "      <td>1237.50</td>\n",
              "      <td>889.87</td>\n",
              "      <td>1274.95</td>\n",
              "      <td>832.52</td>\n",
              "      <td>1312.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>600.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>546.09</td>\n",
              "      <td>37.41</td>\n",
              "      <td>489.70</td>\n",
              "      <td>74.76</td>\n",
              "      <td>432.35</td>\n",
              "      <td>111.90</td>\n",
              "      <td>800.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>832.35</td>\n",
              "      <td>1311.90</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>1146.09</td>\n",
              "      <td>1237.41</td>\n",
              "      <td>1089.70</td>\n",
              "      <td>1274.76</td>\n",
              "      <td>1032.35</td>\n",
              "      <td>1311.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>600.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>545.95</td>\n",
              "      <td>37.52</td>\n",
              "      <td>489.69</td>\n",
              "      <td>74.99</td>\n",
              "      <td>432.46</td>\n",
              "      <td>112.16</td>\n",
              "      <td>800.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>432.46</td>\n",
              "      <td>1312.16</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>1145.95</td>\n",
              "      <td>1237.52</td>\n",
              "      <td>1089.69</td>\n",
              "      <td>1274.99</td>\n",
              "      <td>1032.46</td>\n",
              "      <td>1312.16</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 200 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7fb60d0f-7654-4883-8839-e67aee850d1b')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-7fb60d0f-7654-4883-8839-e67aee850d1b 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-7fb60d0f-7654-4883-8839-e67aee850d1b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-2a812d37-e07c-42e8-b213-d07bdf2f2026\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2a812d37-e07c-42e8-b213-d07bdf2f2026')\"\n",
              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2a812d37-e07c-42e8-b213-d07bdf2f2026 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "X.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f34d22a6-f6c3-4952-981c-8c8739d06d53",
      "metadata": {
        "id": "f34d22a6-f6c3-4952-981c-8c8739d06d53"
      },
      "source": [
        "## Create train and test set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "894d8858-2e7c-426d-af1e-5d6a811adaf6",
      "metadata": {
        "id": "894d8858-2e7c-426d-af1e-5d6a811adaf6"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.2 ,random_state=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "675f9f70-41d7-4558-b207-10b2e44e4e1d",
      "metadata": {
        "id": "675f9f70-41d7-4558-b207-10b2e44e4e1d"
      },
      "source": [
        "## Try Decision Tree Regressor\n",
        "It is more powerful for nonlinear relationships in data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "773f2c66-7099-4d23-b8fd-4f25a1ea5df0",
      "metadata": {
        "id": "773f2c66-7099-4d23-b8fd-4f25a1ea5df0"
      },
      "outputs": [],
      "source": [
        "tree_reg_1 = DecisionTreeRegressor(max_depth=2,random_state=42).fit(X_train, y_train)\n",
        "tree_reg_2 = DecisionTreeRegressor(max_depth=5, random_state=42).fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "59b75cc1-ab16-448b-bc51-4cfb9b6a02bf",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "59b75cc1-ab16-448b-bc51-4cfb9b6a02bf",
        "outputId": "321ef279-b09f-4556-e85d-7ab9c11d31ec"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.24867754203147185"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "tree_reg_1.score(X_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3a679024-4a1d-4cd3-b6e2-80c40e4dd046",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3a679024-4a1d-4cd3-b6e2-80c40e4dd046",
        "outputId": "1d014b89-ec25-416c-8921-8e055b677b4e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.44036272433181334"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "tree_reg_2.score(X_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2c9fca52-18fb-4327-b56e-cb3793b28a41",
      "metadata": {
        "id": "2c9fca52-18fb-4327-b56e-cb3793b28a41"
      },
      "outputs": [],
      "source": [
        "cross_vals = -cross_val_score(tree_reg_1, X,y, scoring=\"neg_root_mean_squared_error\", cv=10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c6a8c985-f88b-415b-a4fb-c54096f0aaa9",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c6a8c985-f88b-415b-a4fb-c54096f0aaa9",
        "outputId": "e994563f-3d32-4141-a134-674ab2e08cde"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count        10.000000\n",
              "mean     173455.032255\n",
              "std       11567.575123\n",
              "min      157648.456309\n",
              "25%      164704.534676\n",
              "50%      171671.588416\n",
              "75%      182107.010265\n",
              "max      190399.689784\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "pd.Series(cross_vals).describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "82823a47-94a6-4f68-9686-bdf3e3ef9981",
      "metadata": {
        "id": "82823a47-94a6-4f68-9686-bdf3e3ef9981"
      },
      "source": [
        "## Try Random Forest Regressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fd58339f-6c1b-4682-90e2-390677bb75b5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fd58339f-6c1b-4682-90e2-390677bb75b5",
        "outputId": "cbb68936-34a8-4490-a130-020b1900c329"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7172245433317017"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "forest_reg = RandomForestRegressor()\n",
        "forest_reg.fit(X_train, y_train).score(X_test,y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "adc93d10-55c9-4ef6-a18b-9744b5a50eda",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "adc93d10-55c9-4ef6-a18b-9744b5a50eda",
        "outputId": "fcf82d32-3767-4342-d381-ace304e302a0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.7835241 , 0.7567162 , 0.68311678, 0.65717309, 0.64243909])"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "cross_vals_forest = cross_val_score(RandomForestRegressor(), X,y,cv=5)\n",
        "cross_vals_forest"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "27f75391-ac4e-4922-adb4-74ed98d9f786",
      "metadata": {
        "id": "27f75391-ac4e-4922-adb4-74ed98d9f786"
      },
      "source": [
        "## Conclusion\n",
        "Random forest model looks the most promising to predict the total_power"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "03e88369-bcc2-4e3f-8e1f-f07f0d178d39",
      "metadata": {
        "id": "03e88369-bcc2-4e3f-8e1f-f07f0d178d39"
      },
      "source": [
        "# Train models to predict the power of individual wave energy converters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "015e13fc-4c3f-44fd-852e-6b713b5977b9",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "015e13fc-4c3f-44fd-852e-6b713b5977b9",
        "outputId": "e113bd6d-8279-4f44-83ec-e421569d83d4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       X1   Y1      X2     Y2      X3     Y3      X4      Y4      X5   Y5  \\\n",
              "0  1000.0  0.0  946.08  37.42  889.67  74.76  832.02  112.10  1250.0  1.0   \n",
              "1   800.0  0.0  746.04  37.38  689.81  74.79  632.43  111.97  1200.0  0.0   \n",
              "2   600.0  0.0  545.98  37.50  489.87  74.95  432.52  112.15   650.0  0.0   \n",
              "3   600.0  0.0  546.09  37.41  489.70  74.76  432.35  111.90   800.0  0.0   \n",
              "4   600.0  0.0  545.95  37.52  489.69  74.99  432.46  112.16   800.0  0.0   \n",
              "\n",
              "   ...   Power93    Power94    Power95    Power96   Power97    Power98  \\\n",
              "0  ...  98711.68  102872.82  100743.44   99259.87  98909.46  101388.37   \n",
              "1  ...  96351.38  102253.02  101744.20   99482.45  99304.59  101953.23   \n",
              "2  ...  96985.29  102472.80  101757.63   99429.45  98709.14  101312.44   \n",
              "3  ...  76823.85   88005.41   98779.92  100260.30  98744.25  101144.58   \n",
              "4  ...  98195.95  102874.16  100256.15   99221.12  98969.54  101389.77   \n",
              "\n",
              "     Power99  Power100    qW  Total_Power  \n",
              "0  101025.35  98676.66  0.75   7257985.04  \n",
              "1  100878.42  99508.49  0.74   7103374.61  \n",
              "2  100979.86  99024.16  0.76   7335380.64  \n",
              "3  100835.43  98915.38  0.75   7187769.87  \n",
              "4  100924.02  98796.00  0.75   7260222.61  \n",
              "\n",
              "[5 rows x 302 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4618fdbf-8a33-4fb2-a970-422f8fb22741\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "        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>X1</th>\n",
              "      <th>Y1</th>\n",
              "      <th>X2</th>\n",
              "      <th>Y2</th>\n",
              "      <th>X3</th>\n",
              "      <th>Y3</th>\n",
              "      <th>X4</th>\n",
              "      <th>Y4</th>\n",
              "      <th>X5</th>\n",
              "      <th>Y5</th>\n",
              "      <th>...</th>\n",
              "      <th>Power93</th>\n",
              "      <th>Power94</th>\n",
              "      <th>Power95</th>\n",
              "      <th>Power96</th>\n",
              "      <th>Power97</th>\n",
              "      <th>Power98</th>\n",
              "      <th>Power99</th>\n",
              "      <th>Power100</th>\n",
              "      <th>qW</th>\n",
              "      <th>Total_Power</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>946.08</td>\n",
              "      <td>37.42</td>\n",
              "      <td>889.67</td>\n",
              "      <td>74.76</td>\n",
              "      <td>832.02</td>\n",
              "      <td>112.10</td>\n",
              "      <td>1250.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>98711.68</td>\n",
              "      <td>102872.82</td>\n",
              "      <td>100743.44</td>\n",
              "      <td>99259.87</td>\n",
              "      <td>98909.46</td>\n",
              "      <td>101388.37</td>\n",
              "      <td>101025.35</td>\n",
              "      <td>98676.66</td>\n",
              "      <td>0.75</td>\n",
              "      <td>7257985.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>800.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>746.04</td>\n",
              "      <td>37.38</td>\n",
              "      <td>689.81</td>\n",
              "      <td>74.79</td>\n",
              "      <td>632.43</td>\n",
              "      <td>111.97</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>96351.38</td>\n",
              "      <td>102253.02</td>\n",
              "      <td>101744.20</td>\n",
              "      <td>99482.45</td>\n",
              "      <td>99304.59</td>\n",
              "      <td>101953.23</td>\n",
              "      <td>100878.42</td>\n",
              "      <td>99508.49</td>\n",
              "      <td>0.74</td>\n",
              "      <td>7103374.61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>600.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>545.98</td>\n",
              "      <td>37.50</td>\n",
              "      <td>489.87</td>\n",
              "      <td>74.95</td>\n",
              "      <td>432.52</td>\n",
              "      <td>112.15</td>\n",
              "      <td>650.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>96985.29</td>\n",
              "      <td>102472.80</td>\n",
              "      <td>101757.63</td>\n",
              "      <td>99429.45</td>\n",
              "      <td>98709.14</td>\n",
              "      <td>101312.44</td>\n",
              "      <td>100979.86</td>\n",
              "      <td>99024.16</td>\n",
              "      <td>0.76</td>\n",
              "      <td>7335380.64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>600.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>546.09</td>\n",
              "      <td>37.41</td>\n",
              "      <td>489.70</td>\n",
              "      <td>74.76</td>\n",
              "      <td>432.35</td>\n",
              "      <td>111.90</td>\n",
              "      <td>800.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>76823.85</td>\n",
              "      <td>88005.41</td>\n",
              "      <td>98779.92</td>\n",
              "      <td>100260.30</td>\n",
              "      <td>98744.25</td>\n",
              "      <td>101144.58</td>\n",
              "      <td>100835.43</td>\n",
              "      <td>98915.38</td>\n",
              "      <td>0.75</td>\n",
              "      <td>7187769.87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>600.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>545.95</td>\n",
              "      <td>37.52</td>\n",
              "      <td>489.69</td>\n",
              "      <td>74.99</td>\n",
              "      <td>432.46</td>\n",
              "      <td>112.16</td>\n",
              "      <td>800.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>98195.95</td>\n",
              "      <td>102874.16</td>\n",
              "      <td>100256.15</td>\n",
              "      <td>99221.12</td>\n",
              "      <td>98969.54</td>\n",
              "      <td>101389.77</td>\n",
              "      <td>100924.02</td>\n",
              "      <td>98796.00</td>\n",
              "      <td>0.75</td>\n",
              "      <td>7260222.61</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 302 columns</p>\n",
              "</div>\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
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              "\n",
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              "\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6092e5dd-4d30-40d3-ba3f-8a5b2d40892d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "dataset.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "ed8599b8-7a98-4526-a0ff-67545eeeb180",
      "metadata": {
        "id": "ed8599b8-7a98-4526-a0ff-67545eeeb180"
      },
      "outputs": [],
      "source": [
        "individual_wec_data = dataset.copy(deep=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "480c285b-1a15-4615-941d-0072bc65b650",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "480c285b-1a15-4615-941d-0072bc65b650",
        "outputId": "204c58c7-4c11-47b4-e37a-2ee9306fd698"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['X1', 'Y1', 'X2', 'Y2', 'X3', 'Y3', 'X4', 'Y4', 'X5', 'Y5',\n",
              "       ...\n",
              "       'Power93', 'Power94', 'Power95', 'Power96', 'Power97', 'Power98',\n",
              "       'Power99', 'Power100', 'qW', 'Total_Power'],\n",
              "      dtype='object', length=302)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "individual_wec_data.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e96007b2-6539-43ad-9c03-9779a6719487",
      "metadata": {
        "id": "e96007b2-6539-43ad-9c03-9779a6719487"
      },
      "source": [
        "## Prepare the data\n",
        "Change the dataframe, so that we have the x,y coordinates and the power for each WEC in each row."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "2d8bc1e8-120e-488a-b85f-dd69fd36c88c",
      "metadata": {
        "id": "2d8bc1e8-120e-488a-b85f-dd69fd36c88c"
      },
      "outputs": [],
      "source": [
        "individual_wec_data.drop(columns=['Total_Power', 'qW'],axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "86ffee46-28b7-46ef-b64a-f534a53af47f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "86ffee46-28b7-46ef-b64a-f534a53af47f",
        "outputId": "95f44ae4-b7ad-4740-9aad-b24d5b056d71"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['X1', 'Y1', 'X2', 'Y2', 'X3', 'Y3', 'X4', 'Y4', 'X5', 'Y5',\n",
              "       ...\n",
              "       'Power91', 'Power92', 'Power93', 'Power94', 'Power95', 'Power96',\n",
              "       'Power97', 'Power98', 'Power99', 'Power100'],\n",
              "      dtype='object', length=300)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "individual_wec_data.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fa028aa8-a557-4f70-b99e-7b9a8e658c95",
      "metadata": {
        "id": "fa028aa8-a557-4f70-b99e-7b9a8e658c95"
      },
      "source": [
        "### Transform to individual WECs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "cfaeb517-97ef-40bb-8f83-9126d352a360",
      "metadata": {
        "id": "cfaeb517-97ef-40bb-8f83-9126d352a360"
      },
      "outputs": [],
      "source": [
        "def to_individual_WEC(dataset):\n",
        "    new_dataset = {\"X\":[], \"Y\":[], \"Power\":[]}\n",
        "    for num in range(0, len(dataset)):\n",
        "        current_row = dataset.iloc[num]\n",
        "        for index in range(1,101):\n",
        "            new_dataset[\"X\"].append(current_row.loc[f\"X{index}\"])\n",
        "            new_dataset[\"Y\"].append(current_row.loc[f\"Y{index}\"])\n",
        "            new_dataset[\"Power\"].append(current_row.loc[f\"Power{index}\"])\n",
        "\n",
        "    new_dataset = pd.DataFrame(new_dataset)\n",
        "    return new_dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "abc93450-4553-4e76-91a4-4089338cdfe5",
      "metadata": {
        "id": "abc93450-4553-4e76-91a4-4089338cdfe5"
      },
      "outputs": [],
      "source": [
        "individual_wec_data = to_individual_WEC(individual_wec_data)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "individual_wec_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z2LRdOSgmX2C",
        "outputId": "33b2582e-b75e-4c64-c804-1c35aa88df40"
      },
      "id": "Z2LRdOSgmX2C",
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(273700, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "individual_wec_data.drop_duplicates(inplace=True)"
      ],
      "metadata": {
        "id": "ZCqYYitmm7qe"
      },
      "id": "ZCqYYitmm7qe",
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "61d38391-b26b-4603-a0be-d6be7a6348c5",
      "metadata": {
        "id": "61d38391-b26b-4603-a0be-d6be7a6348c5"
      },
      "outputs": [],
      "source": [
        "y = individual_wec_data[\"Power\"]\n",
        "X = individual_wec_data.drop(\"Power\", axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "797f1f01-1097-47cc-8f0d-fb88336e9d47",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "797f1f01-1097-47cc-8f0d-fb88336e9d47",
        "outputId": "07a544fc-dc9a-4030-d42a-c53ed940ad42"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(273555, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "individual_wec_data.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3e4ee1e7-1d53-4b0b-8d15-0c26ecbd2f46",
      "metadata": {
        "id": "3e4ee1e7-1d53-4b0b-8d15-0c26ecbd2f46"
      },
      "source": [
        "### Create train and test set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "4e17ca29-f333-4de9-b896-b51a20021823",
      "metadata": {
        "id": "4e17ca29-f333-4de9-b896-b51a20021823"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.2 ,random_state=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "58c3db85-41d9-4c24-8cf1-bc684a73d117",
      "metadata": {
        "id": "58c3db85-41d9-4c24-8cf1-bc684a73d117"
      },
      "source": [
        "### Test with Decision Tree Regressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 107,
      "id": "28385836-69fd-4278-ae4c-246353ab0977",
      "metadata": {
        "id": "28385836-69fd-4278-ae4c-246353ab0977"
      },
      "outputs": [],
      "source": [
        "dtr = DecisionTreeRegressor(max_depth=5).fit(X_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 108,
      "id": "592a4ab0-160d-47bf-8701-5b69cea221de",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "592a4ab0-160d-47bf-8701-5b69cea221de",
        "outputId": "f4879203-36fc-406e-a8b2-b89252b6f850"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8179964931411378"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ],
      "source": [
        "dtr.score(X_test,y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 109,
      "id": "9570d715-2205-4405-a175-084b87a70764",
      "metadata": {
        "id": "9570d715-2205-4405-a175-084b87a70764"
      },
      "outputs": [],
      "source": [
        "cvs_dtr = cross_val_score(DecisionTreeRegressor(), X,y,cv=5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 110,
      "id": "28af4018-c72f-4432-835c-f8b49c89e9ea",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "28af4018-c72f-4432-835c-f8b49c89e9ea",
        "outputId": "6c0f2e6c-cf9a-44e2-b7be-29d522cb688a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.8420308 , 0.8458494 , 0.84369502, 0.83539277, 0.83827556])"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ],
      "source": [
        "cvs_dtr"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2e95bf12-994f-404d-83a8-389b15fa5529",
      "metadata": {
        "id": "2e95bf12-994f-404d-83a8-389b15fa5529"
      },
      "source": [
        "### Test with Random Forest Regressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 111,
      "id": "03819cb5-ca2f-433e-b914-a679b20f90fc",
      "metadata": {
        "id": "03819cb5-ca2f-433e-b914-a679b20f90fc"
      },
      "outputs": [],
      "source": [
        "rtr = RandomForestRegressor().fit(X_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 112,
      "id": "fbafa2be-07ca-4916-9465-6c14144bbb66",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fbafa2be-07ca-4916-9465-6c14144bbb66",
        "outputId": "3962a7fd-65a8-4015-aa0b-c0f85df9b647"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8547784601685058"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ],
      "source": [
        "rtr.score(X_test,y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 113,
      "id": "7d0066ab-93da-4002-9285-358199adbd5c",
      "metadata": {
        "id": "7d0066ab-93da-4002-9285-358199adbd5c"
      },
      "outputs": [],
      "source": [
        "cvs_rtr = cross_val_score(RandomForestRegressor(), X,y,cv=5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 114,
      "id": "9734934b-22f3-491a-a7d3-61e3a4e00bad",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9734934b-22f3-491a-a7d3-61e3a4e00bad",
        "outputId": "251aa36f-f235-484f-f458-f94348f7f8fd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.85452133, 0.8592236 , 0.85717353, 0.84866646, 0.84995573])"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ],
      "source": [
        "cvs_rtr"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a097a711-ad2b-4e81-bfcc-2439c7b80250",
      "metadata": {
        "id": "a097a711-ad2b-4e81-bfcc-2439c7b80250"
      },
      "source": [
        "## Conclusion\n",
        "And again the random forest model makes the best predictions. And it is more accurate to predict individual WECs than the entire farm."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training a neural net"
      ],
      "metadata": {
        "id": "sY2PPZOZQB1I"
      },
      "id": "sY2PPZOZQB1I"
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf"
      ],
      "metadata": {
        "id": "B7RzBAs15UBw"
      },
      "id": "B7RzBAs15UBw",
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_tensor = tf.convert_to_tensor(X)\n",
        "y_tensor = tf.convert_to_tensor(X)"
      ],
      "metadata": {
        "id": "NITtykOdw76a"
      },
      "id": "NITtykOdw76a",
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q6jSC9HkxVjc",
        "outputId": "6b6a5c30-42cb-4262-a86c-1a820c5f8bc0"
      },
      "id": "q6jSC9HkxVjc",
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(273555, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ap7JG_-SxW9h",
        "outputId": "c31f1349-104d-45dc-d1c7-e114982dfe48"
      },
      "id": "ap7JG_-SxW9h",
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(273555,)"
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_tensor = tf.convert_to_tensor(X_train)\n",
        "X_test_tensor = tf.convert_to_tensor(X_test)\n",
        "y_train_tensor = tf.convert_to_tensor(y_train)\n",
        "y_test_tensor = tf.convert_to_tensor(y_test)"
      ],
      "metadata": {
        "id": "KASGoBcjxYHq"
      },
      "id": "KASGoBcjxYHq",
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_tensor.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yC1ynvXXxpGA",
        "outputId": "d0de0bf8-eb04-4f16-8413-c6c89251e691"
      },
      "id": "yC1ynvXXxpGA",
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([218844, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_tensor.shape[1:]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P__w4iTlt9_J",
        "outputId": "e809a9aa-1bcd-480f-f8c1-00a51226d0bc"
      },
      "id": "P__w4iTlt9_J",
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([2])"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.random.set_seed(42)\n",
        "norm_layer = tf.keras.layers.Normalization(input_shape=X_train_tensor.shape[1:])\n",
        "model = tf.keras.Sequential([\n",
        "    norm_layer,\n",
        "    tf.keras.layers.Dense(50, activation=\"relu\"),\n",
        "    tf.keras.layers.Dense(50, activation=\"relu\"),\n",
        "    tf.keras.layers.Dense(50, activation=\"relu\"),\n",
        "    tf.keras.layers.Dense(1)\n",
        "])"
      ],
      "metadata": {
        "id": "-NqIVLUMxq7J"
      },
      "id": "-NqIVLUMxq7J",
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OPHG-3AE0Jkv",
        "outputId": "aa47e8a4-de8d-4c98-b77b-6c79b21625ab"
      },
      "id": "OPHG-3AE0Jkv",
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " normalization (Normalizati  (None, 2)                 5         \n",
            " on)                                                             \n",
            "                                                                 \n",
            " dense (Dense)               (None, 50)                150       \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 50)                2550      \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 50)                2550      \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 1)                 51        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 5306 (20.73 KB)\n",
            "Trainable params: 5301 (20.71 KB)\n",
            "Non-trainable params: 5 (24.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "optimizer = tf.keras.optimizers.Adam(learning_rate=1e-3)\n",
        "model.compile(loss=\"mse\", optimizer=optimizer,metrics=[\"RootMeanSquaredError\",tf.keras.metrics.MeanAbsolutePercentageError(),tf.keras.metrics.R2Score()])\n",
        "norm_layer.adapt(X_train_tensor)"
      ],
      "metadata": {
        "id": "XJ8cHrKe0Lb7"
      },
      "id": "XJ8cHrKe0Lb7",
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "history = model.fit(X_train_tensor, y_train_tensor, epochs=20,validation_data=(X_test_tensor, y_test_tensor))"
      ],
      "metadata": {
        "id": "j-Zta8yy0kZS"
      },
      "id": "j-Zta8yy0kZS",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mse_test, rmse_test = model.evaluate(X_test_tensor, y_test_tensor)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        },
        "id": "cTlunNlH2Glr",
        "outputId": "9b5dbf1e-5e2a-44d8-b734-6483c605443b"
      },
      "id": "cTlunNlH2Glr",
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1710/1710 [==============================] - 4s 3ms/step - loss: 69161744.0000 - root_mean_squared_error: 8316.3545 - mean_absolute_percentage_error: 9.5958 - r2_score: 0.8234\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-60-471d1b365d68>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmse_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrmse_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test_tensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m: too many values to unpack (expected 2)"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mse_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8RVALgfT4BCR",
        "outputId": "92d61631-4ba9-41b7-eb0c-f8bf363d5665"
      },
      "id": "8RVALgfT4BCR",
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "69899032.0"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rmse_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FGaiqyPr-MuM",
        "outputId": "bbef99f8-68d9-4d0c-98e6-3488ec24d45d"
      },
      "id": "FGaiqyPr-MuM",
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8360.564453125"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(X_test_tensor, y_test_tensor)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nlVSL0GO-N1Z",
        "outputId": "423b8cb0-07f1-48df-b56e-38cc5b3a17eb"
      },
      "id": "nlVSL0GO-N1Z",
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1710/1710 [==============================] - 4s 2ms/step - loss: 69161744.0000 - root_mean_squared_error: 8316.3545 - mean_absolute_percentage_error: 9.5958 - r2_score: 0.8234\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[69161744.0, 8316.3544921875, 9.595788955688477, 0.8234137296676636]"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_new = X_test_tensor[:3]\n",
        "y_pred = model.predict(X_new)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pESs19B--fTG",
        "outputId": "8b57d5df-6336-46ec-cc8c-3fcff89fc650"
      },
      "id": "pESs19B--fTG",
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 82ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bqbyP0_-CxtX",
        "outputId": "82df3d46-8420-4362-909e-3c3fdd5f2e86"
      },
      "id": "bqbyP0_-CxtX",
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[90670.05 ],\n",
              "       [43477.277],\n",
              "       [71901.16 ]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "abs(y_pred - y_test_tensor[:3])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RYHeS2WyCzZx",
        "outputId": "d1d18cc3-6e56-4356-e32b-ce2f35e8f132"
      },
      "id": "RYHeS2WyCzZx",
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(3, 3), dtype=float64, numpy=\n",
              "array([[  818.376875  , 40969.256875  , 17593.536875  ],\n",
              "       [46374.39265625,  6223.51265625, 29599.23265625],\n",
              "       [17950.51375   , 22200.36625   ,  1175.35375   ]])>"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_tensor[:3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FEvuBzY4C60N",
        "outputId": "e63b7f70-949f-456e-9004-257235ecfdee"
      },
      "id": "FEvuBzY4C60N",
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(3, 2), dtype=float64, numpy=\n",
              "array([[1346.22,  237.63],\n",
              "       [   0.  ,  300.  ],\n",
              "       [1137.96,  496.43]])>"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history.history"
      ],
      "metadata": {
        "id": "GXOJnDemFVzl"
      },
      "id": "GXOJnDemFVzl",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_loss(history):\n",
        "  plt.plot(history.history['loss'], label='loss')\n",
        "  plt.plot(history.history['val_loss'], label='val_loss')\n",
        "  plt.ylim([0, 1000])\n",
        "  plt.xlabel('Epoch')\n",
        "  plt.ylabel('Error [Power]')\n",
        "  plt.legend()\n",
        "  plt.grid(True)"
      ],
      "metadata": {
        "id": "cRcSd_XoJODH"
      },
      "execution_count": 72,
      "outputs": [],
      "id": "cRcSd_XoJODH"
    },
    {
      "cell_type": "code",
      "source": [
        "plot_loss(history)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "eaqAhMLCJObF",
        "outputId": "ca456b7a-142e-4450-9e2c-fca913f7003b"
      },
      "id": "eaqAhMLCJObF",
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(history.history).plot(\n",
        "figsize=(8, 5), xlim=[0, 29], ylim=[0, 100000], grid=True, xlabel=\"Epoch\", style=[\"r--\", \"r--.\", \"b-\", \"b-*\"])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "OWM9fN5aJQBF",
        "outputId": "fc10d0ca-ac86-457e-ba34-8784cbd613b3"
      },
      "id": "OWM9fN5aJQBF",
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.random.set_seed(42)\n",
        "norm_layer = tf.keras.layers.Normalization(input_shape=X_train_tensor.shape[1:])\n",
        "model2 = tf.keras.Sequential([\n",
        "    norm_layer,\n",
        "    tf.keras.layers.Dense(300, activation=\"relu\"),\n",
        "    tf.keras.layers.Dense(100, activation=\"relu\"),\n",
        "    tf.keras.layers.Dense(10, activation=\"relu\"),\n",
        "    tf.keras.layers.Dense(1)])"
      ],
      "metadata": {
        "id": "h08a0f7oNS1d"
      },
      "id": "h08a0f7oNS1d",
      "execution_count": 140,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "optimizer = tf.keras.optimizers.Adam(learning_rate=1e-3)\n",
        "model.compile(loss=\"mse\", optimizer=optimizer,metrics=[\"RootMeanSquaredError\",tf.keras.metrics.MeanAbsolutePercentageError(),tf.keras.metrics.R2Score()])\n",
        "norm_layer.adapt(X_train_tensor)"
      ],
      "metadata": {
        "id": "ejcJzJr0wNWx"
      },
      "id": "ejcJzJr0wNWx",
      "execution_count": 141,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train_tensor, y_train_tensor, epochs=30,validation_data=(X_test_tensor, y_test_tensor))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yffvMx9vwUZO",
        "outputId": "45bf0184-4603-4a65-c5cf-b97b1b2a97ac"
      },
      "id": "yffvMx9vwUZO",
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "6839/6839 [==============================] - 25s 4ms/step - loss: 78270040.0000 - root_mean_squared_error: 8847.0352 - mean_absolute_percentage_error: 10.4469 - r2_score: 0.7990 - val_loss: 77300696.0000 - val_root_mean_squared_error: 8792.0811 - val_mean_absolute_percentage_error: 10.3884 - val_r2_score: 0.8028\n",
            "Epoch 2/30\n",
            "6839/6839 [==============================] - 25s 4ms/step - loss: 77935384.0000 - root_mean_squared_error: 8828.1016 - mean_absolute_percentage_error: 10.4154 - r2_score: 0.7999 - val_loss: 77480112.0000 - val_root_mean_squared_error: 8802.2783 - val_mean_absolute_percentage_error: 10.1683 - val_r2_score: 0.8023\n",
            "Epoch 3/30\n",
            "6839/6839 [==============================] - 25s 4ms/step - loss: 77554744.0000 - root_mean_squared_error: 8806.5166 - mean_absolute_percentage_error: 10.3790 - r2_score: 0.8008 - val_loss: 76389840.0000 - val_root_mean_squared_error: 8740.1279 - val_mean_absolute_percentage_error: 10.2859 - val_r2_score: 0.8051\n",
            "Epoch 4/30\n",
            "6839/6839 [==============================] - 25s 4ms/step - loss: 77188608.0000 - root_mean_squared_error: 8785.7051 - mean_absolute_percentage_error: 10.3376 - r2_score: 0.8018 - val_loss: 77185928.0000 - val_root_mean_squared_error: 8785.5518 - val_mean_absolute_percentage_error: 10.5782 - val_r2_score: 0.8031\n",
            "Epoch 5/30\n",
            "6839/6839 [==============================] - 25s 4ms/step - loss: 76653856.0000 - root_mean_squared_error: 8755.2188 - mean_absolute_percentage_error: 10.2962 - r2_score: 0.8031 - val_loss: 75884192.0000 - val_root_mean_squared_error: 8711.1533 - val_mean_absolute_percentage_error: 10.2414 - val_r2_score: 0.8064\n",
            "Epoch 6/30\n",
            "6839/6839 [==============================] - 25s 4ms/step - loss: 76262008.0000 - root_mean_squared_error: 8732.8125 - mean_absolute_percentage_error: 10.2559 - r2_score: 0.8042 - val_loss: 76279576.0000 - val_root_mean_squared_error: 8733.8184 - val_mean_absolute_percentage_error: 10.3602 - val_r2_score: 0.8054\n",
            "Epoch 7/30\n",
            "6839/6839 [==============================] - 25s 4ms/step - loss: 75781080.0000 - root_mean_squared_error: 8705.2324 - mean_absolute_percentage_error: 10.2150 - r2_score: 0.8054 - val_loss: 74568896.0000 - val_root_mean_squared_error: 8635.3281 - val_mean_absolute_percentage_error: 10.0750 - val_r2_score: 0.8098\n",
            "Epoch 8/30\n",
            "6839/6839 [==============================] - 25s 4ms/step - loss: 75266192.0000 - root_mean_squared_error: 8675.6094 - mean_absolute_percentage_error: 10.1607 - r2_score: 0.8067 - val_loss: 74332400.0000 - val_root_mean_squared_error: 8621.6240 - val_mean_absolute_percentage_error: 10.2386 - val_r2_score: 0.8104\n",
            "Epoch 9/30\n",
            "6839/6839 [==============================] - 25s 4ms/step - loss: 74649424.0000 - root_mean_squared_error: 8639.9893 - mean_absolute_percentage_error: 10.1093 - r2_score: 0.8083 - val_loss: 74061360.0000 - val_root_mean_squared_error: 8605.8906 - val_mean_absolute_percentage_error: 10.0219 - val_r2_score: 0.8111\n",
            "Epoch 10/30\n",
            "6839/6839 [==============================] - 25s 4ms/step - loss: 74162360.0000 - root_mean_squared_error: 8611.7568 - mean_absolute_percentage_error: 10.0628 - r2_score: 0.8095 - val_loss: 73735336.0000 - val_root_mean_squared_error: 8586.9277 - val_mean_absolute_percentage_error: 9.7625 - val_r2_score: 0.8119\n",
            "Epoch 11/30\n",
            "6839/6839 [==============================] - 25s 4ms/step - loss: 73751608.0000 - root_mean_squared_error: 8587.8750 - mean_absolute_percentage_error: 10.0197 - r2_score: 0.8106 - val_loss: 73743120.0000 - val_root_mean_squared_error: 8587.3809 - val_mean_absolute_percentage_error: 10.1777 - val_r2_score: 0.8119\n",
            "Epoch 12/30\n",
            "6839/6839 [==============================] - 25s 4ms/step - loss: 73325240.0000 - root_mean_squared_error: 8563.0156 - mean_absolute_percentage_error: 9.9827 - r2_score: 0.8117 - val_loss: 74373936.0000 - val_root_mean_squared_error: 8624.0322 - val_mean_absolute_percentage_error: 9.7697 - val_r2_score: 0.8103\n",
            "Epoch 13/30\n",
            "6839/6839 [==============================] - 25s 4ms/step - loss: 73052632.0000 - root_mean_squared_error: 8547.0830 - mean_absolute_percentage_error: 9.9411 - r2_score: 0.8124 - val_loss: 72895992.0000 - val_root_mean_squared_error: 8537.9150 - val_mean_absolute_percentage_error: 9.8703 - val_r2_score: 0.8140\n",
            "Epoch 14/30\n",
            "6839/6839 [==============================] - 25s 4ms/step - loss: 72684688.0000 - root_mean_squared_error: 8525.5312 - mean_absolute_percentage_error: 9.9031 - r2_score: 0.8133 - val_loss: 73290488.0000 - val_root_mean_squared_error: 8560.9863 - val_mean_absolute_percentage_error: 10.1587 - val_r2_score: 0.8130\n",
            "Epoch 15/30\n",
            "6839/6839 [==============================] - 25s 4ms/step - loss: 72385360.0000 - root_mean_squared_error: 8507.9580 - mean_absolute_percentage_error: 9.8763 - r2_score: 0.8141 - val_loss: 70770936.0000 - val_root_mean_squared_error: 8412.5459 - val_mean_absolute_percentage_error: 9.7942 - val_r2_score: 0.8195\n",
            "Epoch 16/30\n",
            "6839/6839 [==============================] - 25s 4ms/step - loss: 72398224.0000 - root_mean_squared_error: 8508.7148 - mean_absolute_percentage_error: 9.8700 - r2_score: 0.8141 - val_loss: 71401952.0000 - val_root_mean_squared_error: 8449.9678 - val_mean_absolute_percentage_error: 9.7354 - val_r2_score: 0.8178\n",
            "Epoch 17/30\n",
            "6839/6839 [==============================] - 25s 4ms/step - loss: 72176248.0000 - root_mean_squared_error: 8495.6602 - mean_absolute_percentage_error: 9.8430 - r2_score: 0.8147 - val_loss: 71092608.0000 - val_root_mean_squared_error: 8431.6436 - val_mean_absolute_percentage_error: 9.5673 - val_r2_score: 0.8186\n",
            "Epoch 18/30\n",
            "6839/6839 [==============================] - 25s 4ms/step - loss: 72293792.0000 - root_mean_squared_error: 8502.5762 - mean_absolute_percentage_error: 9.8511 - r2_score: 0.8143 - val_loss: 73316328.0000 - val_root_mean_squared_error: 8562.4951 - val_mean_absolute_percentage_error: 9.5031 - val_r2_score: 0.8130\n",
            "Epoch 19/30\n",
            "6839/6839 [==============================] - 25s 4ms/step - loss: 72029272.0000 - root_mean_squared_error: 8487.0059 - mean_absolute_percentage_error: 9.8279 - r2_score: 0.8150 - val_loss: 72321368.0000 - val_root_mean_squared_error: 8504.1973 - val_mean_absolute_percentage_error: 9.8149 - val_r2_score: 0.8155\n",
            "Epoch 20/30\n",
            "6839/6839 [==============================] - 25s 4ms/step - loss: 71987712.0000 - root_mean_squared_error: 8484.5576 - mean_absolute_percentage_error: 9.8163 - r2_score: 0.8151 - val_loss: 70989304.0000 - val_root_mean_squared_error: 8425.5146 - val_mean_absolute_percentage_error: 9.9819 - val_r2_score: 0.8189\n",
            "Epoch 21/30\n",
            "6839/6839 [==============================] - 25s 4ms/step - loss: 71763616.0000 - root_mean_squared_error: 8471.3408 - mean_absolute_percentage_error: 9.8052 - r2_score: 0.8157 - val_loss: 70952688.0000 - val_root_mean_squared_error: 8423.3418 - val_mean_absolute_percentage_error: 9.7988 - val_r2_score: 0.8190\n",
            "Epoch 22/30\n",
            "6839/6839 [==============================] - 25s 4ms/step - loss: 71711224.0000 - root_mean_squared_error: 8468.2480 - mean_absolute_percentage_error: 9.7951 - r2_score: 0.8158 - val_loss: 70968776.0000 - val_root_mean_squared_error: 8424.2969 - val_mean_absolute_percentage_error: 9.6097 - val_r2_score: 0.8190\n",
            "Epoch 23/30\n",
            "6839/6839 [==============================] - 25s 4ms/step - loss: 71666792.0000 - root_mean_squared_error: 8465.6240 - mean_absolute_percentage_error: 9.7862 - r2_score: 0.8159 - val_loss: 78553696.0000 - val_root_mean_squared_error: 8863.0527 - val_mean_absolute_percentage_error: 10.3322 - val_r2_score: 0.7996\n",
            "Epoch 24/30\n",
            "6839/6839 [==============================] - 25s 4ms/step - loss: 71568576.0000 - root_mean_squared_error: 8459.8213 - mean_absolute_percentage_error: 9.7714 - r2_score: 0.8162 - val_loss: 71700712.0000 - val_root_mean_squared_error: 8467.6270 - val_mean_absolute_percentage_error: 10.1270 - val_r2_score: 0.8171\n",
            "Epoch 25/30\n",
            "6839/6839 [==============================] - 25s 4ms/step - loss: 71454352.0000 - root_mean_squared_error: 8453.0674 - mean_absolute_percentage_error: 9.7661 - r2_score: 0.8165 - val_loss: 72608312.0000 - val_root_mean_squared_error: 8521.0508 - val_mean_absolute_percentage_error: 10.0905 - val_r2_score: 0.8148\n",
            "Epoch 26/30\n",
            "6839/6839 [==============================] - 25s 4ms/step - loss: 71393352.0000 - root_mean_squared_error: 8449.4580 - mean_absolute_percentage_error: 9.7546 - r2_score: 0.8167 - val_loss: 71396440.0000 - val_root_mean_squared_error: 8449.6416 - val_mean_absolute_percentage_error: 10.0096 - val_r2_score: 0.8179\n",
            "Epoch 27/30\n",
            "6839/6839 [==============================] - 25s 4ms/step - loss: 71322888.0000 - root_mean_squared_error: 8445.2881 - mean_absolute_percentage_error: 9.7493 - r2_score: 0.8168 - val_loss: 72446256.0000 - val_root_mean_squared_error: 8511.5361 - val_mean_absolute_percentage_error: 9.9385 - val_r2_score: 0.8152\n",
            "Epoch 28/30\n",
            "6839/6839 [==============================] - 25s 4ms/step - loss: 71272240.0000 - root_mean_squared_error: 8442.2891 - mean_absolute_percentage_error: 9.7435 - r2_score: 0.8170 - val_loss: 71767160.0000 - val_root_mean_squared_error: 8471.5498 - val_mean_absolute_percentage_error: 9.6593 - val_r2_score: 0.8169\n",
            "Epoch 29/30\n",
            "6839/6839 [==============================] - 25s 4ms/step - loss: 71222568.0000 - root_mean_squared_error: 8439.3467 - mean_absolute_percentage_error: 9.7425 - r2_score: 0.8171 - val_loss: 72893504.0000 - val_root_mean_squared_error: 8537.7695 - val_mean_absolute_percentage_error: 9.6986 - val_r2_score: 0.8140\n",
            "Epoch 30/30\n",
            "6839/6839 [==============================] - 25s 4ms/step - loss: 71223960.0000 - root_mean_squared_error: 8439.4287 - mean_absolute_percentage_error: 9.7363 - r2_score: 0.8171 - val_loss: 71558136.0000 - val_root_mean_squared_error: 8459.2041 - val_mean_absolute_percentage_error: 9.7676 - val_r2_score: 0.8175\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%pip install -q -U keras-tuner"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FPrc2khPwdNM",
        "outputId": "7a6cceca-f1f0-453d-a659-2fe979cdba0a"
      },
      "id": "FPrc2khPwdNM",
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/129.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m \u001b[32m92.2/129.5 kB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m129.5/129.5 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/950.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m950.8/950.8 kB\u001b[0m \u001b[31m39.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import keras_tuner as kt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gOAjFKBx94hb",
        "outputId": "83d6f489-2371-4c8e-d9c7-0ca7c3245216"
      },
      "id": "gOAjFKBx94hb",
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using TensorFlow backend\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def model_builder(hp):\n",
        "\n",
        "  model = tf.keras.Sequential()\n",
        "\n",
        "  # Tune the number of units in the first Dense layer\n",
        "  # Choose an optimal value between 32-512\n",
        "  hp_units = hp.Int('units', min_value=32, max_value=512, step=32)\n",
        "  model.add(tf.keras.layers.Dense(units=hp_units, activation='relu'))\n",
        "  model.add(tf.keras.layers.Dense(units=hp_units, activation='relu'))\n",
        "  model.add(tf.keras.layers.Dense(1))\n",
        "\n",
        "  # Tune the learning rate for the optimizer\n",
        "  # Choose an optimal value from 0.01, 0.001, or 0.0001\n",
        "  hp_learning_rate = hp.Choice('learning_rate', values=[1e-2, 1e-3])\n",
        "\n",
        "  model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=hp_learning_rate),\n",
        "                loss=\"mean_absolute_error\",\n",
        "                metrics=['RootMeanSquaredError',tf.keras.metrics.R2Score(),\"mean_absolute_error\"])\n",
        "  return model"
      ],
      "metadata": {
        "id": "6h8G7svxVLv3"
      },
      "id": "6h8G7svxVLv3",
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tuner = kt.Hyperband(model_builder,\n",
        "                     objective=kt.Objective(\"val_r2_score\", direction=\"max\"),\n",
        "                     max_epochs=20,\n",
        "                     factor=3,\n",
        "                    overwrite=True,\n",
        "                     directory='./tuner',\n",
        "                     project_name='intro_to_kt')"
      ],
      "metadata": {
        "id": "G79ItXyC_EaR"
      },
      "id": "G79ItXyC_EaR",
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tuner.search(X_train_tensor, y_train_tensor, epochs=20, validation_data=(X_test_tensor, y_test_tensor))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BN-neITkXks6",
        "outputId": "1b4b0027-19a8-4f79-fb54-5e8773845afa"
      },
      "id": "BN-neITkXks6",
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Trial 30 Complete [00h 08m 04s]\n",
            "val_r2_score: 0.8178933262825012\n",
            "\n",
            "Best val_r2_score So Far: 0.8190782070159912\n",
            "Total elapsed time: 01h 30m 57s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best_models = tuner.get_best_models(num_models=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c27JXI40haBG",
        "outputId": "18e93060-f214-4ef0-939d-a2c48b8465a6"
      },
      "id": "c27JXI40haBG",
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Detecting that an object or model or tf.train.Checkpoint is being deleted with unrestored values. See the following logs for the specific values in question. To silence these warnings, use `status.expect_partial()`. See https://www.tensorflow.org/api_docs/python/tf/train/Checkpoint#restorefor details about the status object returned by the restore function.\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).layer_with_weights-0.kernel\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).layer_with_weights-0.bias\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).layer_with_weights-1.kernel\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).layer_with_weights-1.bias\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).layer_with_weights-2.kernel\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).layer_with_weights-2.bias\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.1\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.2\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.3\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.4\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.5\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.6\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.7\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.8\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.9\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.10\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.11\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.12\n",
            "WARNING:tensorflow:Detecting that an object or model or tf.train.Checkpoint is being deleted with unrestored values. See the following logs for the specific values in question. To silence these warnings, use `status.expect_partial()`. See https://www.tensorflow.org/api_docs/python/tf/train/Checkpoint#restorefor details about the status object returned by the restore function.\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).layer_with_weights-0.kernel\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).layer_with_weights-0.bias\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).layer_with_weights-1.kernel\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).layer_with_weights-1.bias\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).layer_with_weights-2.kernel\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).layer_with_weights-2.bias\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.1\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.2\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.3\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.4\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.5\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.6\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.7\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.8\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.9\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.10\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.11\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.12\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best_model1 = best_models[0]\n",
        "best_model2 = best_models[1]"
      ],
      "metadata": {
        "id": "T2ZwZKzv7STY"
      },
      "id": "T2ZwZKzv7STY",
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "best_model2.evaluate(X_test_tensor, y_test_tensor)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DKqKWNPC7c3c",
        "outputId": "65ef7a78-a934-4f00-cdb6-ced68b056c6c"
      },
      "id": "DKqKWNPC7c3c",
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1710/1710 [==============================] - 4s 2ms/step - loss: 6385.4541 - root_mean_squared_error: 8412.0078 - r2_score: 0.8183 - mean_absolute_error: 6385.4541\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[6385.4541015625, 8412.0078125, 0.818329930305481, 6385.4541015625]"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best_model1.evaluate(X_test_tensor, y_test_tensor)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u4xIzEbB71GP",
        "outputId": "5d685e02-8a58-410c-dd26-af8bb9a684a9"
      },
      "id": "u4xIzEbB71GP",
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1710/1710 [==============================] - 4s 2ms/step - loss: 6376.5039 - root_mean_squared_error: 8394.6660 - r2_score: 0.8191 - mean_absolute_error: 6376.5039\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[6376.50390625, 8394.666015625, 0.8190782070159912, 6376.50390625]"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vCtChQA975S2"
      },
      "id": "vCtChQA975S2",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Summary\n",
        "The Random Forest Regressor had the highest r2_score with 0.85. Even the neural net with hypertuning couldn't beat."
      ],
      "metadata": {
        "id": "IJJTdS7i9fqF"
      },
      "id": "IJJTdS7i9fqF"
    }
  ],
  "metadata": {
    "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": "3.10.9"
    },
    "colab": {
      "provenance": [],
      "machine_shape": "hm",
      "gpuType": "T4",
      "include_colab_link": true
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 5
}