Fire_Prediction_final.ipynb

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      "metadata": {
        "id": "g_5u0OVMMgv9"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip3 install pickle5\n",
        "import pickle5 as pickle\n",
        "with open('dataset_time.pkl', \"rb\") as fh:\n",
        "  df = pickle.load(fh)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8ecXURdYNGSM",
        "outputId": "c88ae2ab-d1b8-4705-9b8c-83ed026163f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pickle5\n",
            "  Downloading pickle5-0.0.12-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl (256 kB)\n",
            "\u001b[?25l\r\u001b[K     |█▎                              | 10 kB 20.2 MB/s eta 0:00:01\r\u001b[K     |██▋                             | 20 kB 9.9 MB/s eta 0:00:01\r\u001b[K     |███▉                            | 30 kB 7.6 MB/s eta 0:00:01\r\u001b[K     |█████▏                          | 40 kB 7.1 MB/s eta 0:00:01\r\u001b[K     |██████▍                         | 51 kB 4.2 MB/s eta 0:00:01\r\u001b[K     |███████▊                        | 61 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |█████████                       | 71 kB 5.2 MB/s eta 0:00:01\r\u001b[K     |██████████▎                     | 81 kB 5.1 MB/s eta 0:00:01\r\u001b[K     |███████████▌                    | 92 kB 5.6 MB/s eta 0:00:01\r\u001b[K     |████████████▉                   | 102 kB 5.2 MB/s eta 0:00:01\r\u001b[K     |██████████████                  | 112 kB 5.2 MB/s eta 0:00:01\r\u001b[K     |███████████████▍                | 122 kB 5.2 MB/s eta 0:00:01\r\u001b[K     |████████████████▋               | 133 kB 5.2 MB/s eta 0:00:01\r\u001b[K     |██████████████████              | 143 kB 5.2 MB/s eta 0:00:01\r\u001b[K     |███████████████████▏            | 153 kB 5.2 MB/s eta 0:00:01\r\u001b[K     |████████████████████▌           | 163 kB 5.2 MB/s eta 0:00:01\r\u001b[K     |█████████████████████▊          | 174 kB 5.2 MB/s eta 0:00:01\r\u001b[K     |███████████████████████         | 184 kB 5.2 MB/s eta 0:00:01\r\u001b[K     |████████████████████████▎       | 194 kB 5.2 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▋      | 204 kB 5.2 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▉     | 215 kB 5.2 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▏   | 225 kB 5.2 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▍  | 235 kB 5.2 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▊ | 245 kB 5.2 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 256 kB 5.2 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 256 kB 5.2 MB/s \n",
            "\u001b[?25hInstalling collected packages: pickle5\n",
            "Successfully installed pickle5-0.0.12\n"
          ]
        }
      ]
    },
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              "   fire       veg        tair        tmax          prec      wind  year  month\n",
              "0   0.0  0.519537  281.850800  287.042877  0.000000e+00  2.924010  2001      1\n",
              "1   0.0  0.522642  282.009521  286.676941  1.428358e-07  2.436872  2001      1\n",
              "2   0.0  0.525748  280.715393  283.814606  1.779684e-05  1.660926  2001      1\n",
              "3   0.0  0.528854  281.651886  283.489288  9.013197e-05  2.029600  2001      1\n",
              "4   0.0  0.531959  280.482574  283.335846  1.318250e-04  3.849517  2001      1"
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              "      <th>4</th>\n",
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              "      <td>280.482574</td>\n",
              "      <td>283.335846</td>\n",
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              "(4932, 8)"
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              "fire     False\n",
              "veg      False\n",
              "tair     False\n",
              "tmax     False\n",
              "prec     False\n",
              "wind     False\n",
              "year     False\n",
              "month    False\n",
              "dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
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        "df.isnull().any() "
      ]
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              "               fire          veg         tair         tmax          prec  \\\n",
              "count  4.932000e+03  4932.000000  4932.000000  4932.000000  4.932000e+03   \n",
              "mean   4.851529e+05     0.615639   287.358276   291.962830  5.146567e-05   \n",
              "std    2.416577e+06     0.042578     4.900804     5.822189  1.056126e-04   \n",
              "min    0.000000e+00     0.418210   274.894012   276.355804  0.000000e+00   \n",
              "25%    0.000000e+00     0.593076   283.549400   287.063660  0.000000e+00   \n",
              "50%    0.000000e+00     0.619659   287.057938   291.222031  8.237752e-07   \n",
              "75%    0.000000e+00     0.647683   291.347519   296.355911  5.169595e-05   \n",
              "max    3.500581e+07     0.688896   301.290588   310.879608  1.005264e-03   \n",
              "\n",
              "              wind         year        month  \n",
              "count  4932.000000  4932.000000  4932.000000  \n",
              "mean      3.208490  2007.293390     6.427413  \n",
              "std       1.365396     3.905507     3.436565  \n",
              "min       0.936329  2001.000000     1.000000  \n",
              "25%       2.125525  2004.000000     3.000000  \n",
              "50%       2.891564  2007.000000     6.000000  \n",
              "75%       3.996434  2011.000000     9.000000  \n",
              "max       9.724447  2014.000000    12.000000  "
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              "      <th>veg</th>\n",
              "      <th>tair</th>\n",
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              "      <th>prec</th>\n",
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              "      <th>count</th>\n",
              "      <td>4.932000e+03</td>\n",
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              "      <th>mean</th>\n",
              "      <td>4.851529e+05</td>\n",
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              "      <th>std</th>\n",
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              "      <th>min</th>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.418210</td>\n",
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              "      <td>0.936329</td>\n",
              "      <td>2001.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "      <th>25%</th>\n",
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              "      <td>0.593076</td>\n",
              "      <td>283.549400</td>\n",
              "      <td>287.063660</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>2.125525</td>\n",
              "      <td>2004.000000</td>\n",
              "      <td>3.000000</td>\n",
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              "      <th>50%</th>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.619659</td>\n",
              "      <td>287.057938</td>\n",
              "      <td>291.222031</td>\n",
              "      <td>8.237752e-07</td>\n",
              "      <td>2.891564</td>\n",
              "      <td>2007.000000</td>\n",
              "      <td>6.000000</td>\n",
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              "      <th>75%</th>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.647683</td>\n",
              "      <td>291.347519</td>\n",
              "      <td>296.355911</td>\n",
              "      <td>5.169595e-05</td>\n",
              "      <td>3.996434</td>\n",
              "      <td>2011.000000</td>\n",
              "      <td>9.000000</td>\n",
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              "      <th>max</th>\n",
              "      <td>3.500581e+07</td>\n",
              "      <td>0.688896</td>\n",
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              "      <td>310.879608</td>\n",
              "      <td>1.005264e-03</td>\n",
              "      <td>9.724447</td>\n",
              "      <td>2014.000000</td>\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1c35f5e8-1e81-4faa-aed5-ce4b6b69b3c3 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-1c35f5e8-1e81-4faa-aed5-ce4b6b69b3c3');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2FaUZOn3MgwD",
        "outputId": "0731f6f8-1b65-410c-f39c-3d0451198fdd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.000000         3855\n",
              "241491.000000       3\n",
              "7790.032227         3\n",
              "23370.097656        3\n",
              "38950.160156        3\n",
              "                 ... \n",
              "96595.750000        1\n",
              "67974.585938        1\n",
              "52825.500000        1\n",
              "51148.500000        1\n",
              "48298.199219        1\n",
              "Name: fire, Length: 941, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "df['fire'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "fNeY8DtsMgwD",
        "outputId": "470639b4-222d-438c-9a73-7d2eea541e7e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'A plot of Fire against Years from 2001 to 2014')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "x = df['year']\n",
        "y = df['fire']\n",
        "plt.plot(x,y)\n",
        "plt.xlabel('Years')\n",
        "plt.ylabel('Fire')\n",
        "plt.title('A plot of Fire against Years from 2001 to 2014')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fglC663yMgwE",
        "outputId": "ad1e01fe-f385-4222-83f8-5a3958fcf5c5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4932 entries, 0 to 4931\n",
            "Data columns (total 8 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   fire    4932 non-null   float32\n",
            " 1   veg     4932 non-null   float32\n",
            " 2   tair    4932 non-null   float32\n",
            " 3   tmax    4932 non-null   float32\n",
            " 4   prec    4932 non-null   float32\n",
            " 5   wind    4932 non-null   float32\n",
            " 6   year    4932 non-null   int64  \n",
            " 7   month   4932 non-null   int64  \n",
            "dtypes: float32(6), int64(2)\n",
            "memory usage: 192.8 KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W3vxosCPMgwE"
      },
      "source": [
        "- No missing values nor dummy variables, we can therefore go ahead and use the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iIrxq5HvMgwF"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4iddpJtlMgwF"
      },
      "source": [
        "## Month when fire was maximum in all the years"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "__vi50DAMgwG",
        "outputId": "a03662bc-2470-4e1c-9406-f148610e1c2e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "fire     3.500581e+07\n",
              "veg      6.467643e-01\n",
              "tair     2.908929e+02\n",
              "tmax     2.958454e+02\n",
              "prec     0.000000e+00\n",
              "wind     3.282810e+00\n",
              "year     2.005000e+03\n",
              "month    7.000000e+00\n",
              "Name: 1659, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "# Worst month in all the years\n",
        "fire_df = df['fire']\n",
        "max_index = fire_df.idxmax() # to get index of maximum value\n",
        "df.loc[max_index]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4g2-IZ5tMgwG",
        "outputId": "bf374c3b-c0e5-4f6a-c3d1-f98f09bb0393"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Month 7 of the year 2005 is when the maximum value of fire was recorded throughout the years\n"
          ]
        }
      ],
      "source": [
        "print('Month {} of the year {} is when the maximum value of fire was recorded throughout the years'.format(int(df.loc[max_index].month), \n",
        "                                                                                          int(df.loc[max_index].year)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W8zvZpp1MgwH",
        "outputId": "608d9fd8-3738-4bac-e390-7fe02bb56fdd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The month 8 of the year 2001 is when the maximum value of fire was recorded. The value was 2639955.0\n",
            "The month 7 of the year 2002 is when the maximum value of fire was recorded. The value was 746977.1875\n",
            "The month 7 of the year 2003 is when the maximum value of fire was recorded. The value was 475191.96875\n",
            "The month 6 of the year 2004 is when the maximum value of fire was recorded. The value was 2663325.25\n",
            "The month 7 of the year 2005 is when the maximum value of fire was recorded. The value was 35005812.0\n",
            "The month 7 of the year 2006 is when the maximum value of fire was recorded. The value was 4540723.0\n",
            "The month 1 of the year 2007 is when the maximum value of fire was recorded. The value was 0.0\n",
            "The month 1 of the year 2008 is when the maximum value of fire was recorded. The value was 0.0\n",
            "The month 8 of the year 2009 is when the maximum value of fire was recorded. The value was 2865866.25\n",
            "The month 7 of the year 2010 is when the maximum value of fire was recorded. The value was 8826106.0\n",
            "The month 6 of the year 2011 is when the maximum value of fire was recorded. The value was 5335306.0\n",
            "The month 1 of the year 2012 is when the maximum value of fire was recorded. The value was 0.0\n",
            "The month 8 of the year 2013 is when the maximum value of fire was recorded. The value was 2871059.75\n",
            "The month 1 of the year 2014 is when the maximum value of fire was recorded. The value was 0.0\n"
          ]
        }
      ],
      "source": [
        "# Worst month in every year\n",
        "years = range(2001, 2015)\n",
        "for i in years:\n",
        "    df_new = df[df['year'].isin([i])].copy() # We work with a copy to avoid tempering with the original df\n",
        "    fire_df = df_new['fire']\n",
        "    max_index = fire_df.idxmax() # to get index of maximum value\n",
        "    print('The month {} of the year {} is when the maximum value of fire was recorded. The value was {}'.format(\n",
        "        int(df_new.loc[max_index].month),\n",
        "        int(df_new.loc[max_index].year),\n",
        "        df_new.loc[max_index].fire))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eErY1vkkMgwH"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "THjv-V5SMgwH"
      },
      "source": [
        "### Corresponding Features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7Il22BEvMgwI"
      },
      "outputs": [],
      "source": [
        "# Worst month in every year\n",
        "max_fire_features = []\n",
        "years = range(2001, 2015)\n",
        "for i in years:\n",
        "    df_new = df[df['year'].isin([i])].copy() # We work with a copy to avoid tempering with the original df\n",
        "    fire_df = df_new['fire']\n",
        "    max_index = fire_df.idxmax() # to get index of maximum value\n",
        "    features = df_new.loc[max_index]\n",
        "    max_fire_features.append(features)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 552
        },
        "id": "2wEZJ9UrMgwI",
        "outputId": "7a675f1d-b7d7-4791-8c19-dc767989a85d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              fire       veg        tair        tmax      prec      wind  \\\n",
              "229   2.639955e+06  0.645835  293.060852  297.353485  0.000000  2.638513   \n",
              "564   7.469772e+05  0.661517  290.333038  295.824219  0.000000  5.610419   \n",
              "928   4.751920e+05  0.658878  297.614136  305.025024  0.000000  3.495153   \n",
              "1264  2.663325e+06  0.652399  293.548615  299.065948  0.000000  3.970703   \n",
              "1659  3.500581e+07  0.646764  290.892914  295.845398  0.000000  3.282810   \n",
              "2024  4.540723e+06  0.626935  294.144836  298.045319  0.000056  2.076092   \n",
              "2179  0.000000e+00  0.587520  285.592468  287.404449  0.000189  3.142594   \n",
              "2544  0.000000e+00  0.498767  280.897888  283.869812  0.000095  4.922199   \n",
              "3151  2.865866e+06  0.660475  297.685028  305.441315  0.000000  2.071964   \n",
              "3485  8.826106e+06  0.663072  296.892090  303.947662  0.000000  3.178471   \n",
              "3820  5.335306e+06  0.597221  294.248291  302.581665  0.000000  3.546634   \n",
              "4005  0.000000e+00  0.590309  284.717010  285.462585  0.000329  4.667645   \n",
              "4612  2.871060e+06  0.655444  294.110748  300.852783  0.000000  2.765255   \n",
              "4736  0.000000e+00  0.500568  284.963287  287.124329  0.000269  7.254364   \n",
              "\n",
              "        year  month  \n",
              "229   2001.0    8.0  \n",
              "564   2002.0    7.0  \n",
              "928   2003.0    7.0  \n",
              "1264  2004.0    6.0  \n",
              "1659  2005.0    7.0  \n",
              "2024  2006.0    7.0  \n",
              "2179  2007.0    1.0  \n",
              "2544  2008.0    1.0  \n",
              "3151  2009.0    8.0  \n",
              "3485  2010.0    7.0  \n",
              "3820  2011.0    6.0  \n",
              "4005  2012.0    1.0  \n",
              "4612  2013.0    8.0  \n",
              "4736  2014.0    1.0  "
            ],
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              "      <th></th>\n",
              "      <th>fire</th>\n",
              "      <th>veg</th>\n",
              "      <th>tair</th>\n",
              "      <th>tmax</th>\n",
              "      <th>prec</th>\n",
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              "      <th>month</th>\n",
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              "      <th>229</th>\n",
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              "      <th>564</th>\n",
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              "      <td>6.0</td>\n",
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              "      <td>0.000056</td>\n",
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              "      <th>2179</th>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.587520</td>\n",
              "      <td>285.592468</td>\n",
              "      <td>287.404449</td>\n",
              "      <td>0.000189</td>\n",
              "      <td>3.142594</td>\n",
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              "      <td>1.0</td>\n",
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              "      <th>2544</th>\n",
              "      <td>0.000000e+00</td>\n",
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              "      <td>283.869812</td>\n",
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              "      <td>4.922199</td>\n",
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              "      <td>303.947662</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.178471</td>\n",
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              "      <td>7.0</td>\n",
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              "      <th>3820</th>\n",
              "      <td>5.335306e+06</td>\n",
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              "      <td>302.581665</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>6.0</td>\n",
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              "      <th>4005</th>\n",
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              "      <th>4612</th>\n",
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              "      <td>300.852783</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.765255</td>\n",
              "      <td>2013.0</td>\n",
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              "      <td>0.000269</td>\n",
              "      <td>7.254364</td>\n",
              "      <td>2014.0</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "max_fire_features_df = pd.DataFrame(max_fire_features)\n",
        "max_fire_features_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8aCH4JgeMgwI"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XwEhYFpoMgwJ"
      },
      "source": [
        "### Fire Threshold Values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i4_joziAMgwJ"
      },
      "outputs": [],
      "source": [
        "veg_mean = max_fire_features_df['veg'].mean()\n",
        "tair_mean = max_fire_features_df['tair'].mean()\n",
        "tmax_mean = max_fire_features_df['tmax'].mean()\n",
        "prec_mean = max_fire_features_df['prec'].mean()\n",
        "wind_mean = max_fire_features_df['wind'].mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vEGYt5oWMgwJ",
        "outputId": "040a5d70-55af-4241-941f-09fabf390362"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The threshold features that lead to fire are:\n",
            " veg: 0.6175503347601209\n",
            " tair: 291.33580017089844\n",
            " tmax: 296.27457101004467\n",
            " prec: 6.69785282038252e-05\n",
            " wind: 3.758772645677839\n"
          ]
        }
      ],
      "source": [
        "print('The threshold features that lead to fire are:\\n veg: {}\\n tair: {}\\n tmax: {}\\n prec: {}\\n wind: {}'.format(\n",
        "    veg_mean, tair_mean, tmax_mean,prec_mean,wind_mean))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "29OQqmsGMgwJ"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FZclIge-MgwK"
      },
      "source": [
        "## Year with worst fire from year 2001 -2014"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZryZBNHyMgwK"
      },
      "outputs": [],
      "source": [
        "fires_dictionary = {}\n",
        "for i in years:\n",
        "    df_new = df[df['year'].isin([i])].copy() # We work with a copy to avoid tempering with the original df\n",
        "    fire_df = df_new['fire']\n",
        "    fire_sum = fire_df.sum() # to get value of total fires\n",
        "    case = {i: fire_sum}\n",
        "    fires_dictionary.update(case)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eIOEy9Z9MgwK",
        "outputId": "4c6b3c4d-5600-4439-acba-53b9e0381de2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{2001: 124394670.0,\n",
              " 2002: 38021410.0,\n",
              " 2003: 14972442.0,\n",
              " 2004: 156244640.0,\n",
              " 2005: 1102970000.0,\n",
              " 2006: 143069980.0,\n",
              " 2007: 0.0,\n",
              " 2008: 0.0,\n",
              " 2009: 128312190.0,\n",
              " 2010: 294189660.0,\n",
              " 2011: 191931650.0,\n",
              " 2012: 0.0,\n",
              " 2013: 198666600.0,\n",
              " 2014: 0.0}"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "fires_dictionary"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S46mAJ9wMgwL",
        "outputId": "fd0699b2-a21b-42ce-fde2-ab65d4ca09e7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The year when maximum fire was experienced is 2005\n"
          ]
        }
      ],
      "source": [
        "max_year = max(fires_dictionary, key = fires_dictionary.get)\n",
        "print('The year when maximum fire was experienced is {}'.format(max_year))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qyE0gx-xMgwL"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yWvYoXTGMgwL"
      },
      "source": [
        "# Modeling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pZjs4JEyMgwL"
      },
      "source": [
        "### X-Y Split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nQKyPRFaMgwL",
        "outputId": "3e8d7b6a-9977-4e43-b651-a06641965e36"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "x = df.loc[:, df.columns != 'fire']\n",
        "type(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "wJQj3QlzMgwM",
        "outputId": "4bca5af9-350d-441d-84f5-e6601210fe06"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape:  (4932, 7)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        veg        tair        tmax          prec      wind  year  month\n",
              "0  0.519537  281.850800  287.042877  0.000000e+00  2.924010  2001      1\n",
              "1  0.522642  282.009521  286.676941  1.428358e-07  2.436872  2001      1\n",
              "2  0.525748  280.715393  283.814606  1.779684e-05  1.660926  2001      1\n",
              "3  0.528854  281.651886  283.489288  9.013197e-05  2.029600  2001      1\n",
              "4  0.531959  280.482574  283.335846  1.318250e-04  3.849517  2001      1"
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>veg</th>\n",
              "      <th>tair</th>\n",
              "      <th>tmax</th>\n",
              "      <th>prec</th>\n",
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              "      <th>0</th>\n",
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              "      <td>0.522642</td>\n",
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              "      <td>2001</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.525748</td>\n",
              "      <td>280.715393</td>\n",
              "      <td>283.814606</td>\n",
              "      <td>1.779684e-05</td>\n",
              "      <td>1.660926</td>\n",
              "      <td>2001</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.528854</td>\n",
              "      <td>281.651886</td>\n",
              "      <td>283.489288</td>\n",
              "      <td>9.013197e-05</td>\n",
              "      <td>2.029600</td>\n",
              "      <td>2001</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.531959</td>\n",
              "      <td>280.482574</td>\n",
              "      <td>283.335846</td>\n",
              "      <td>1.318250e-04</td>\n",
              "      <td>3.849517</td>\n",
              "      <td>2001</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c5556ebf-ce73-4197-b926-1d3d1488fb9d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c5556ebf-ce73-4197-b926-1d3d1488fb9d 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-c5556ebf-ce73-4197-b926-1d3d1488fb9d');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "print('Shape: ', x.shape)\n",
        "x.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W6ecd1JMMgwM",
        "outputId": "d90c1eb4-8ed0-427d-97cc-8d7d13fdccfd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "y = df[\"fire\"]\n",
        "type(y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dx5vxdapMgwM",
        "outputId": "abc648e9-cb03-41cc-9969-2bba1d08180c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape:  (4932,)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.0\n",
              "1    0.0\n",
              "2    0.0\n",
              "3    0.0\n",
              "4    0.0\n",
              "Name: fire, dtype: float32"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "print('Shape: ', y.shape)\n",
        "y.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wVFDQIi5MgwM"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "COP4rFQ8MgwN"
      },
      "source": [
        "### Train - Test Split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_2rogPu-MgwN"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4AT7Rjj3MgwN"
      },
      "outputs": [],
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "81f0i65WMgwN",
        "outputId": "365467a3-b6a9-49a7-ac81-5e7a42ecc661"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           veg        tair        tmax      prec      wind  year  month\n",
              "1024  0.593894  286.449646  288.544800  0.000024  3.239456  2003     11\n",
              "2572  0.523059  282.031647  288.318542  0.000000  1.282783  2008      1\n",
              "324   0.633246  282.010437  285.350708  0.000000  1.698439  2001     12\n",
              "1495  0.609453  279.666565  285.238007  0.000000  4.675715  2005      2\n",
              "998   0.645931  290.318542  296.683136  0.000000  3.857375  2003     10"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3b03f068-11f5-4470-91e1-31618e24d6ec\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>veg</th>\n",
              "      <th>tair</th>\n",
              "      <th>tmax</th>\n",
              "      <th>prec</th>\n",
              "      <th>wind</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1024</th>\n",
              "      <td>0.593894</td>\n",
              "      <td>286.449646</td>\n",
              "      <td>288.544800</td>\n",
              "      <td>0.000024</td>\n",
              "      <td>3.239456</td>\n",
              "      <td>2003</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2572</th>\n",
              "      <td>0.523059</td>\n",
              "      <td>282.031647</td>\n",
              "      <td>288.318542</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.282783</td>\n",
              "      <td>2008</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>324</th>\n",
              "      <td>0.633246</td>\n",
              "      <td>282.010437</td>\n",
              "      <td>285.350708</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.698439</td>\n",
              "      <td>2001</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1495</th>\n",
              "      <td>0.609453</td>\n",
              "      <td>279.666565</td>\n",
              "      <td>285.238007</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.675715</td>\n",
              "      <td>2005</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>0.645931</td>\n",
              "      <td>290.318542</td>\n",
              "      <td>296.683136</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.857375</td>\n",
              "      <td>2003</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3b03f068-11f5-4470-91e1-31618e24d6ec')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3b03f068-11f5-4470-91e1-31618e24d6ec 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-3b03f068-11f5-4470-91e1-31618e24d6ec');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "x_train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LDZXm54iMgwN",
        "outputId": "a7f7dc84-215a-4fc6-94de-0799a2b13c27"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3945, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "x_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gz93X0cHMgwO",
        "outputId": "689f848e-c1ec-4586-9726-c1a242276dd1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(987, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "x_test.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m_LhBW6AMgwO"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b-zCH9-eMgwO"
      },
      "source": [
        "### Standardizing the Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PzPKWrCrMgwO"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "94ZBMIamMgwO"
      },
      "outputs": [],
      "source": [
        "sc = StandardScaler().fit(x_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a8QdPaQ9MgwO"
      },
      "outputs": [],
      "source": [
        "x_train_std = sc.transform(x_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D5Ve25hKMgwP"
      },
      "outputs": [],
      "source": [
        "x_test_std = sc.transform(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vi-7YLI3MgwP",
        "outputId": "29b98899-a281-4e00-fad3-2f8e3482feef"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.28656076,  0.05102202, -0.25334862, ..., -0.1215275 ,\n",
              "        -1.3449707 ,  1.35057887],\n",
              "       [-0.36649918,  0.89094057,  0.83258676, ...,  0.72096061,\n",
              "         0.69743296,  0.76472658],\n",
              "       [ 1.12331738,  1.28791218,  1.56698082, ..., -0.86576922,\n",
              "        -0.83436978,  0.76472658],\n",
              "       ...,\n",
              "       [-0.74298   , -1.20497696, -0.89995221, ..., -1.03884503,\n",
              "        -0.06846841, -1.57868257],\n",
              "       [ 0.85522398,  0.31826601,  0.11871873, ...,  0.30431683,\n",
              "         0.69743296, -0.11405185],\n",
              "       [ 0.22940335, -2.17726076, -2.01195964, ...,  1.40359994,\n",
              "         0.69743296, -1.57868257]])"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "x_test_std"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "a3tUepGNMgwP",
        "outputId": "6c1916ba-0563-44b0-afdf-9ea4f6675926"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           veg        tair        tmax      prec      wind  year  month\n",
              "662   0.603843  287.699463  290.584686  0.000008  3.034380  2002     11\n",
              "3534  0.600441  291.829590  296.960510  0.000005  4.182255  2010      9\n",
              "1353  0.663849  293.781616  301.272339  0.000000  2.020365  2004      9\n",
              "4722  0.528906  283.136505  287.665955  0.000301  5.236522  2013     12\n",
              "3062  0.609876  292.690033  299.505585  0.000000  1.642647  2009      6\n",
              "...        ...         ...         ...       ...       ...   ...    ...\n",
              "4827  0.600992  284.266479  288.622559  0.000048  4.050830  2014      4\n",
              "3329  0.573021  284.198547  286.150635  0.000529  8.187447  2010      2\n",
              "2180  0.584418  281.523346  286.788300  0.000000  1.784552  2007      1\n",
              "3437  0.652438  289.013580  292.769196  0.000008  3.614586  2010      6\n",
              "3281  0.625803  276.742340  280.259399  0.000000  5.112339  2010      1\n",
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            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "x_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SafH6m4SMgwP"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4mTupMlTMgwP"
      },
      "source": [
        "### Training Regression Tree"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Vn3UgRiJMgwQ"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import RandomForestRegressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K6WA1j9oMgwQ"
      },
      "outputs": [],
      "source": [
        "rf_reg = RandomForestRegressor(n_estimators=1000, n_jobs = -1, random_state = 42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RaFpIC0bMgwQ"
      },
      "outputs": [],
      "source": [
        "#Gridsearch\n",
        "#from sklearn.model_selection import GridSearchCV\n",
        "#params_grid = {\"n_estimators\" : [600,700,800,900,1000],\n",
        "#              \"max_depth\" : [1,2,3,4,5],\n",
        "#               \"min_samples_split\" : [2,3,4,5]\n",
        "#              }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SbgDYyeMMgwQ"
      },
      "outputs": [],
      "source": [
        "#grid_search = GridSearchCV(rf_reg, params_grid, \n",
        " #                         n_jobs = -1, cv = 5, scoring = 'r2')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e5C-pq-6MgwQ",
        "outputId": "4f76391a-2556-4602-dd82-36723cef14bd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(n_estimators=1000, n_jobs=-1, random_state=42)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "rf_reg.fit(x_train_std, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PvLI-P4-MgwR"
      },
      "outputs": [],
      "source": [
        "#cvrf_reg = grid_search.best_estimator_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "flxDU-t9MgwR"
      },
      "outputs": [],
      "source": [
        "#cvrf_reg.fit(x_train_std1, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sRebRrK3MgwR"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xhuau3ONMgwR"
      },
      "source": [
        "### Predicting wih the trained model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GhsKjjI8MgwR"
      },
      "outputs": [],
      "source": [
        "y_test_pred = rf_reg.predict(x_test_std)\n",
        "y_train_pred = rf_reg.predict(x_train_std)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bvfh7lsgMgwR",
        "outputId": "4a8d4ccf-2393-4039-e9c4-4837e2de2a97"
      },
      "outputs": [
        {
          "output_type": "execute_result",
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              "       1.84470000e+01, 0.00000000e+00, 0.00000000e+00, 2.39314994e+05,\n",
              "       1.28030172e+05, 0.00000000e+00, 1.33081108e+06, 0.00000000e+00,\n",
              "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 5.14720726e+05,\n",
              "       8.10718102e+04, 3.51213062e+06, 1.01102415e+06, 0.00000000e+00,\n",
              "       0.00000000e+00, 0.00000000e+00, 2.91506415e+05, 0.00000000e+00,\n",
              "       0.00000000e+00, 6.53740571e+04, 0.00000000e+00, 1.45389389e+06,\n",
              "       2.82607562e+06, 3.67692209e+03, 5.54732076e+04, 6.42939701e+06,\n",
              "       0.00000000e+00, 2.25652103e+05, 2.86326955e+03, 3.14829330e+04,\n",
              "       1.38005346e+05, 2.58465624e+05, 6.49735783e+05, 4.38641794e+05,\n",
              "       0.00000000e+00, 0.00000000e+00, 5.86976703e+05, 1.59314517e+06,\n",
              "       0.00000000e+00, 2.48003956e+06, 6.53052854e+05, 1.21877845e+06,\n",
              "       0.00000000e+00, 3.10245000e+01, 0.00000000e+00, 3.27933957e+05,\n",
              "       2.58804779e+05, 2.33429132e+04, 0.00000000e+00, 0.00000000e+00,\n",
              "       3.56230086e+05, 0.00000000e+00, 2.95930820e+05, 0.00000000e+00,\n",
              "       0.00000000e+00, 1.39283481e+06, 0.00000000e+00, 1.63037385e+05,\n",
              "       3.83129413e+05, 4.25810656e+05, 5.99074892e+04, 5.86950000e+00,\n",
              "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 5.69736825e+05,\n",
              "       0.00000000e+00, 0.00000000e+00, 8.35923531e+04, 0.00000000e+00,\n",
              "       0.00000000e+00, 0.00000000e+00, 4.67959047e+05, 4.98536396e+06,\n",
              "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 4.13086809e+05,\n",
              "       1.59315000e+02, 1.12002807e+06, 8.53890287e+03, 9.78128133e+05,\n",
              "       5.85112556e+06, 9.23506287e+05, 3.03490098e+03, 7.62085074e+05,\n",
              "       4.90695425e+03, 0.00000000e+00, 0.00000000e+00, 4.54484650e+04,\n",
              "       0.00000000e+00, 2.14520112e+03, 0.00000000e+00, 0.00000000e+00,\n",
              "       2.54170131e+05, 4.40095396e+05, 5.86950000e+00, 4.18561558e+06,\n",
              "       6.73895647e+05, 0.00000000e+00, 1.54519101e+06, 1.42712700e+03,\n",
              "       0.00000000e+00, 1.02404183e+06, 0.00000000e+00, 0.00000000e+00,\n",
              "       2.13086832e+06, 5.78810680e+04, 2.89760800e+03, 0.00000000e+00,\n",
              "       1.46039867e+06, 5.35953033e+04, 1.17911535e+06, 2.73610893e+04,\n",
              "       8.50248750e+02, 0.00000000e+00, 0.00000000e+00, 1.29964422e+05,\n",
              "       1.87358894e+05, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
              "       1.99425131e+04, 2.97977364e+03, 0.00000000e+00, 2.28404054e+04,\n",
              "       0.00000000e+00, 3.48976338e+05, 2.92938391e+04, 0.00000000e+00,\n",
              "       3.88767984e+03, 8.13343096e+05, 3.92490522e+04, 0.00000000e+00,\n",
              "       3.24775064e+04, 0.00000000e+00, 0.00000000e+00, 1.36190914e+06,\n",
              "       1.11499324e+06, 1.17147036e+05, 3.99930314e+04, 0.00000000e+00,\n",
              "       0.00000000e+00, 1.89234447e+04, 0.00000000e+00, 3.94479102e+04,\n",
              "       1.19057655e+04, 4.25599675e+03, 0.00000000e+00, 6.16174180e+03,\n",
              "       3.80018600e+06, 0.00000000e+00, 1.51766264e+04, 0.00000000e+00,\n",
              "       7.15509552e+04, 0.00000000e+00, 0.00000000e+00, 8.98697547e+04,\n",
              "       0.00000000e+00, 3.05071983e+04, 5.98679203e+05, 7.45743559e+04,\n",
              "       0.00000000e+00, 8.64951268e+04, 5.43905312e+04, 0.00000000e+00,\n",
              "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 7.98094023e+04,\n",
              "       7.00022346e+05, 2.67926231e+03, 9.91948750e+02, 1.86568678e+05,\n",
              "       8.35308311e+03, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
              "       0.00000000e+00, 2.89036766e+03, 1.75448772e+03, 0.00000000e+00,\n",
              "       0.00000000e+00, 1.24087498e+04, 3.96175209e+03, 1.26530075e+03,\n",
              "       1.17325392e+06, 1.85197937e+05, 0.00000000e+00, 0.00000000e+00,\n",
              "       8.80533207e+03, 0.00000000e+00, 9.89817918e+04, 3.10055920e+05,\n",
              "       3.39613870e+04, 2.52451326e+05, 8.49690438e+02, 0.00000000e+00,\n",
              "       0.00000000e+00, 2.59585063e+05, 0.00000000e+00, 0.00000000e+00,\n",
              "       1.23680081e+03, 0.00000000e+00, 2.18010000e+01, 1.73111781e+02,\n",
              "       3.14620601e+05, 0.00000000e+00, 1.67046684e+05, 1.08828386e+05,\n",
              "       2.93929105e+06, 2.05395844e+04, 0.00000000e+00, 7.93954213e+03,\n",
              "       4.75369613e+04, 6.54428344e+03, 9.22350000e+00, 1.89894531e+02,\n",
              "       2.07737490e+06, 5.68051303e+05, 0.00000000e+00, 0.00000000e+00,\n",
              "       3.71952106e+04, 0.00000000e+00, 3.36299671e+04, 3.47186201e+06,\n",
              "       0.00000000e+00, 0.00000000e+00, 5.12664125e+03, 0.00000000e+00,\n",
              "       2.66446967e+05, 0.00000000e+00, 1.48248500e+03, 0.00000000e+00,\n",
              "       4.78753139e+04, 3.86953123e+05, 3.52437044e+03, 0.00000000e+00,\n",
              "       0.00000000e+00, 0.00000000e+00, 1.23273908e+06, 2.22438326e+04,\n",
              "       3.77042915e+06, 0.00000000e+00, 9.16009760e+05, 4.72821169e+05,\n",
              "       6.16982196e+05, 2.89216446e+05, 2.32594012e+05, 0.00000000e+00,\n",
              "       2.27211856e+06, 2.37569460e+07, 0.00000000e+00, 0.00000000e+00,\n",
              "       0.00000000e+00, 1.43801941e+04, 0.00000000e+00, 0.00000000e+00,\n",
              "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 3.25355793e+05,\n",
              "       2.74109237e+05, 0.00000000e+00, 4.44058077e+04, 0.00000000e+00,\n",
              "       1.88866887e+05, 0.00000000e+00, 5.18236173e+05, 5.33612339e+05,\n",
              "       9.47113734e+05, 8.19024490e+04, 1.54455002e+06, 2.29367947e+04,\n",
              "       8.44956448e+04, 8.75957695e+03, 9.89366331e+04, 0.00000000e+00,\n",
              "       1.32369343e+04, 0.00000000e+00, 0.00000000e+00, 8.81436203e+03,\n",
              "       0.00000000e+00, 3.15570772e+04, 5.04782375e+02, 2.28456607e+06,\n",
              "       3.88978759e+03, 0.00000000e+00, 8.65162549e+04, 3.46304188e+02,\n",
              "       0.00000000e+00, 0.00000000e+00, 1.47377264e+05, 2.85712773e+03,\n",
              "       0.00000000e+00, 1.31970657e+06, 3.95471035e+05, 0.00000000e+00,\n",
              "       2.20255611e+06, 0.00000000e+00, 8.78292880e+05, 3.43933751e+04,\n",
              "       1.09006250e+02, 9.54471038e+03, 1.58560350e+03, 3.73551750e+03,\n",
              "       0.00000000e+00, 0.00000000e+00, 1.64243658e+05, 0.00000000e+00,\n",
              "       5.05789047e+02, 2.15870425e+03, 4.48938012e+03, 7.94910360e+04,\n",
              "       2.80039997e+03, 0.00000000e+00, 9.19630654e+05, 0.00000000e+00,\n",
              "       7.06346296e+04, 0.00000000e+00, 1.20618498e+04, 1.49773779e+04,\n",
              "       0.00000000e+00, 3.73886804e+06, 0.00000000e+00, 1.34546504e+05,\n",
              "       4.71398461e+04, 0.00000000e+00, 0.00000000e+00, 1.90768139e+06,\n",
              "       9.22685812e+02, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
              "       1.74134334e+05, 3.53045099e+05, 4.75351062e+03, 1.87520446e+04,\n",
              "       3.11335050e+03, 9.55890000e+01, 1.76085000e+01, 1.35466288e+04,\n",
              "       4.77985830e+04, 1.05396261e+06, 4.02480000e+01, 6.02726297e+03,\n",
              "       0.00000000e+00, 0.00000000e+00, 3.32885688e+02, 1.28918657e+05,\n",
              "       3.93078172e+04, 7.50038694e+03, 0.00000000e+00, 0.00000000e+00,\n",
              "       1.83021494e+04, 3.80758101e+06, 0.00000000e+00, 9.02942422e+06,\n",
              "       1.09280194e+03, 6.05380635e+04, 1.91874118e+05, 0.00000000e+00,\n",
              "       1.42166636e+04, 1.00797495e+03, 1.05581019e+06, 2.11583972e+04,\n",
              "       0.00000000e+00, 3.14422828e+05, 3.66394342e+04, 4.36514748e+03,\n",
              "       3.41056248e+04, 0.00000000e+00, 0.00000000e+00, 1.29470596e+06,\n",
              "       5.03736064e+04, 1.45566530e+04, 3.71113681e+03, 0.00000000e+00,\n",
              "       4.97024030e+05, 5.32766180e+05, 0.00000000e+00, 0.00000000e+00,\n",
              "       1.90007519e+04, 0.00000000e+00, 0.00000000e+00, 6.02185689e+05,\n",
              "       4.35182687e+02, 5.37551594e+05, 1.76085000e+01, 9.83078202e+05,\n",
              "       1.09005000e+01, 0.00000000e+00, 7.67896369e+03, 0.00000000e+00,\n",
              "       0.00000000e+00, 1.28124263e+03, 4.11245018e+05, 0.00000000e+00,\n",
              "       1.06365926e+05, 2.21992800e+03, 3.42722183e+05, 3.99957939e+04,\n",
              "       0.00000000e+00, 2.86797581e+04, 0.00000000e+00, 6.51305751e+05,\n",
              "       3.61563793e+03, 0.00000000e+00, 6.48148073e+04, 4.92735701e+05,\n",
              "       0.00000000e+00, 8.31698335e+03, 0.00000000e+00, 0.00000000e+00,\n",
              "       9.43319938e+02, 0.00000000e+00, 5.52621973e+05, 0.00000000e+00,\n",
              "       0.00000000e+00, 6.23574467e+05, 3.42290855e+05, 7.89088057e+04,\n",
              "       0.00000000e+00, 0.00000000e+00, 1.03304471e+04, 2.01240000e+01,\n",
              "       0.00000000e+00, 0.00000000e+00, 2.81790672e+05, 2.99754237e+05,\n",
              "       9.52824750e+02, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
              "       2.76264659e+06, 5.76414585e+04, 4.99242426e+04, 2.83385610e+07,\n",
              "       1.50811953e+04, 8.82102000e+02, 0.00000000e+00, 0.00000000e+00,\n",
              "       2.00188705e+06, 0.00000000e+00, 0.00000000e+00, 3.46304188e+02,\n",
              "       3.18355872e+05, 4.83199043e+04, 0.00000000e+00, 6.28088180e+05,\n",
              "       2.95341068e+05, 1.09736244e+06, 5.36121619e+04, 0.00000000e+00,\n",
              "       1.78313507e+06, 0.00000000e+00, 0.00000000e+00, 2.50040911e+05,\n",
              "       0.00000000e+00, 0.00000000e+00, 2.10968846e+04, 0.00000000e+00,\n",
              "       6.99898270e+05, 1.16587233e+06, 0.00000000e+00, 8.57886469e+04,\n",
              "       4.11875904e+03, 0.00000000e+00, 0.00000000e+00, 2.43749229e+03,\n",
              "       1.27017174e+05, 8.38001502e+03, 0.00000000e+00, 3.35632666e+04,\n",
              "       8.69746281e+03, 5.81512077e+03, 3.09894867e+05, 0.00000000e+00,\n",
              "       8.64374992e+05, 1.23448256e+05, 0.00000000e+00, 0.00000000e+00,\n",
              "       1.00285750e+03, 7.80538716e+04, 3.38411836e+02, 9.92543019e+03,\n",
              "       0.00000000e+00, 2.05937241e+04, 0.00000000e+00, 0.00000000e+00,\n",
              "       2.11980443e+04, 0.00000000e+00, 0.00000000e+00, 5.64244497e+06,\n",
              "       4.96273094e+05, 7.36168731e+04, 3.52402965e+05, 0.00000000e+00,\n",
              "       0.00000000e+00, 1.50615584e+04, 0.00000000e+00, 1.51438296e+06,\n",
              "       1.01552677e+05, 7.08540625e+02, 2.57306393e+05, 6.74385813e+02,\n",
              "       0.00000000e+00, 1.96335970e+05, 0.00000000e+00, 7.62973540e+04,\n",
              "       1.95902217e+04, 0.00000000e+00, 2.69355053e+04, 0.00000000e+00,\n",
              "       1.09006250e+01, 6.03999965e+05, 2.59936425e+06, 2.09935476e+04,\n",
              "       5.00974645e+05, 4.68759676e+04, 0.00000000e+00, 0.00000000e+00,\n",
              "       1.85250983e+06, 1.32361794e+05, 5.95928769e+03, 5.45677954e+04,\n",
              "       3.31619497e+05, 4.88807665e+04, 2.79298551e+03, 2.21442866e+05,\n",
              "       1.76085000e+01, 8.24861027e+05, 1.66042867e+06, 1.45964402e+03,\n",
              "       1.21945961e+06, 2.18010000e+01, 1.17367075e+05, 1.76085000e+01,\n",
              "       3.42050167e+04, 8.24252375e+02, 0.00000000e+00, 2.03455991e+03,\n",
              "       4.91292649e+05, 9.22350000e+00, 0.00000000e+00, 6.33828888e+03,\n",
              "       3.52936953e+03, 0.00000000e+00, 3.14703730e+06, 0.00000000e+00,\n",
              "       4.98326506e+06, 5.12329375e+02, 0.00000000e+00, 0.00000000e+00,\n",
              "       0.00000000e+00, 1.05783197e+06, 4.38724709e+03, 0.00000000e+00,\n",
              "       0.00000000e+00, 2.54283398e+05, 2.13892509e+03, 2.57310834e+04,\n",
              "       6.93101288e+03, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
              "       1.17864319e+05, 1.54355023e+05, 0.00000000e+00, 0.00000000e+00,\n",
              "       6.80721763e+03, 0.00000000e+00, 7.29495000e+01, 9.61560109e+02,\n",
              "       2.15413289e+03, 6.63645445e+05, 5.89567475e+05, 0.00000000e+00,\n",
              "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.25664534e+03,\n",
              "       0.00000000e+00, 0.00000000e+00, 1.26220792e+06, 0.00000000e+00,\n",
              "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 9.65131405e+03,\n",
              "       1.36758306e+03, 5.43468190e+05, 9.11920250e+02, 2.29628339e+05,\n",
              "       1.30575795e+06, 4.02744678e+04, 7.46660563e+05, 0.00000000e+00,\n",
              "       8.72574713e+05, 1.11830251e+06, 0.00000000e+00, 0.00000000e+00,\n",
              "       5.43307229e+05, 0.00000000e+00, 1.09005000e+01, 2.71004892e+04,\n",
              "       0.00000000e+00, 0.00000000e+00, 2.79289662e+04, 1.47226560e+04,\n",
              "       0.00000000e+00, 0.00000000e+00, 1.62389272e+06, 0.00000000e+00,\n",
              "       2.70450646e+04, 0.00000000e+00, 0.00000000e+00, 1.76893769e+05,\n",
              "       1.51115105e+05, 2.69067404e+04, 8.69023693e+04, 0.00000000e+00,\n",
              "       0.00000000e+00, 1.84623813e+07, 2.47917131e+06, 7.39782806e+05,\n",
              "       8.94487493e+05, 4.65959744e+03, 0.00000000e+00, 2.31175500e+03,\n",
              "       0.00000000e+00, 4.25640077e+06, 2.79307319e+03, 2.82109486e+05,\n",
              "       2.66999165e+06, 2.96447213e+04, 0.00000000e+00, 7.59299966e+05,\n",
              "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
              "       4.43847594e+06, 5.95507130e+05, 0.00000000e+00, 5.10943919e+06,\n",
              "       1.93747316e+04, 0.00000000e+00, 0.00000000e+00, 5.10299367e+04,\n",
              "       0.00000000e+00, 1.27984854e+05, 5.77825695e+04, 0.00000000e+00,\n",
              "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.12790261e+06,\n",
              "       0.00000000e+00, 1.67657356e+04, 9.01068302e+04, 0.00000000e+00,\n",
              "       0.00000000e+00, 4.43534948e+03, 2.55549993e+06, 0.00000000e+00,\n",
              "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 4.93782612e+06,\n",
              "       0.00000000e+00, 0.00000000e+00, 3.48643882e+05, 0.00000000e+00,\n",
              "       1.11993468e+05, 2.79031279e+05, 1.59969716e+03, 1.38530648e+04,\n",
              "       1.08616915e+06, 0.00000000e+00, 8.44684152e+03, 2.26802937e+07,\n",
              "       6.09401874e+05, 0.00000000e+00, 4.37097485e+05, 4.56818617e+06,\n",
              "       1.66671281e+06, 0.00000000e+00, 2.28110636e+05, 0.00000000e+00,\n",
              "       6.26208867e+05, 1.14084029e+04, 1.86159629e+06, 2.07111875e+02,\n",
              "       1.80476167e+05, 0.00000000e+00, 7.37750529e+05, 1.08488988e+05,\n",
              "       6.22962592e+05, 0.00000000e+00, 0.00000000e+00, 8.59544720e+03,\n",
              "       0.00000000e+00, 1.06724061e+05, 2.51776010e+05, 2.24764764e+05,\n",
              "       4.56082325e+03, 1.86485840e+04, 1.94303605e+05, 0.00000000e+00,\n",
              "       1.23758513e+03, 3.01998613e+05, 2.76808279e+03, 1.95055806e+06,\n",
              "       1.23580129e+04, 0.00000000e+00, 6.07017482e+05, 0.00000000e+00,\n",
              "       0.00000000e+00, 0.00000000e+00, 3.48736445e+04, 7.47889791e+05,\n",
              "       9.62608250e+02, 1.84999083e+05, 8.41991769e+04, 5.86950000e+00,\n",
              "       0.00000000e+00, 0.00000000e+00, 3.15533869e+05, 1.25307678e+06,\n",
              "       0.00000000e+00, 5.31768150e+05, 0.00000000e+00, 0.00000000e+00,\n",
              "       0.00000000e+00, 2.76362477e+05, 0.00000000e+00])"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "y_test_pred"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MtOSfQ8lMgwS"
      },
      "source": [
        "### Model Perfomance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gc-ho2FPMgwS"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import mean_squared_error, r2_score"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Conv1D, Flatten,TimeDistributed\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.layers.convolutional_recurrent import ConvLSTM2D"
      ],
      "metadata": {
        "id": "LX-aBbdzJdlS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xtrain_reshaped = x_train.reshape(x_train.shape[0], x_train.shape[1], 1) "
      ],
      "metadata": {
        "id": "tjMcw9XAJdn9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Conv1D(32, 2, activation=\"relu\", input_shape=(7, 1)))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(64, activation=\"relu\"))\n",
        "model.add(Dense(1))\n",
        "model.compile(optimizer=keras.optimizers.Adam(learning_rate = 1e-1), loss = 'mean_squared_error',metrics = 'mae')"
      ],
      "metadata": {
        "id": "Ds-u-aZXJdqs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(xtrain_reshaped, y_train, batch_size = 10, epochs = 100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n60HKk8uJdsz",
        "outputId": "21dd2488-fee0-4e1f-b287-12fabd593493"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "278/278 [==============================] - 2s 5ms/step - loss: 5378367553536.0000 - mae: 654820.0625\n",
            "Epoch 2/100\n",
            "278/278 [==============================] - 1s 5ms/step - loss: 5223355514880.0000 - mae: 645133.8750\n",
            "Epoch 3/100\n",
            "278/278 [==============================] - 1s 4ms/step - loss: 5159621492736.0000 - mae: 646032.8125\n",
            "Epoch 4/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 5122862088192.0000 - mae: 631839.3125\n",
            "Epoch 5/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 5111053025280.0000 - mae: 626822.0625\n",
            "Epoch 6/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 5062684311552.0000 - mae: 619981.4375\n",
            "Epoch 7/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 5033137012736.0000 - mae: 612718.7500\n",
            "Epoch 8/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 5002944315392.0000 - mae: 614817.2500\n",
            "Epoch 9/100\n",
            "278/278 [==============================] - 1s 4ms/step - loss: 4972347916288.0000 - mae: 590341.8125\n",
            "Epoch 10/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 5031732969472.0000 - mae: 608216.0625\n",
            "Epoch 11/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 4948062371840.0000 - mae: 591802.0625\n",
            "Epoch 12/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 4941446381568.0000 - mae: 601184.6875\n",
            "Epoch 13/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 4897020313600.0000 - mae: 582426.1875\n",
            "Epoch 14/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 4893659627520.0000 - mae: 587547.8750\n",
            "Epoch 15/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 4854317580288.0000 - mae: 588424.0000\n",
            "Epoch 16/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 4873952690176.0000 - mae: 580656.7500\n",
            "Epoch 17/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 4842734485504.0000 - mae: 589489.8125\n",
            "Epoch 18/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 4854647881728.0000 - mae: 584219.7500\n",
            "Epoch 19/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 4826471596032.0000 - mae: 588592.2500\n",
            "Epoch 20/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 4807205060608.0000 - mae: 574689.2500\n",
            "Epoch 21/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 4799135744000.0000 - mae: 586439.6875\n",
            "Epoch 22/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 4781370769408.0000 - mae: 585999.5625\n",
            "Epoch 23/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 4768165527552.0000 - mae: 578867.8125\n",
            "Epoch 24/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 4746823860224.0000 - mae: 586522.5625\n",
            "Epoch 25/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 4735592562688.0000 - mae: 581115.1250\n",
            "Epoch 26/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 4687919054848.0000 - mae: 583121.4375\n",
            "Epoch 27/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 4656435560448.0000 - mae: 573834.6875\n",
            "Epoch 28/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 4526234402816.0000 - mae: 580440.2500\n",
            "Epoch 29/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 4609623457792.0000 - mae: 574595.0000\n",
            "Epoch 30/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 4503745593344.0000 - mae: 570872.4375\n",
            "Epoch 31/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 4447246221312.0000 - mae: 582228.8750\n",
            "Epoch 32/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 4422850052096.0000 - mae: 576444.3125\n",
            "Epoch 33/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 4275168870400.0000 - mae: 556527.1875\n",
            "Epoch 34/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 4244588986368.0000 - mae: 560407.8750\n",
            "Epoch 35/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 4249711017984.0000 - mae: 566252.3125\n",
            "Epoch 36/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 4172415500288.0000 - mae: 556107.1250\n",
            "Epoch 37/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 4029439279104.0000 - mae: 551254.8750\n",
            "Epoch 38/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 3949154795520.0000 - mae: 552809.7500\n",
            "Epoch 39/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 3997979639808.0000 - mae: 536750.3125\n",
            "Epoch 40/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 3822630731776.0000 - mae: 537850.1250\n",
            "Epoch 41/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 3732553334784.0000 - mae: 534157.1250\n",
            "Epoch 42/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 3626904322048.0000 - mae: 538035.8750\n",
            "Epoch 43/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 3569141940224.0000 - mae: 510767.5625\n",
            "Epoch 44/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 3727502868480.0000 - mae: 535999.8750\n",
            "Epoch 45/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 3412370391040.0000 - mae: 518915.1250\n",
            "Epoch 46/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 3320130568192.0000 - mae: 512017.5312\n",
            "Epoch 47/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 3264205815808.0000 - mae: 505210.9688\n",
            "Epoch 48/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 3095865851904.0000 - mae: 490151.8750\n",
            "Epoch 49/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 3045052907520.0000 - mae: 482560.6562\n",
            "Epoch 50/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 2966524264448.0000 - mae: 483742.9688\n",
            "Epoch 51/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 2991284551680.0000 - mae: 501616.5000\n",
            "Epoch 52/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 2860402343936.0000 - mae: 485307.0625\n",
            "Epoch 53/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 2737922375680.0000 - mae: 469428.6875\n",
            "Epoch 54/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 2696142913536.0000 - mae: 465771.9375\n",
            "Epoch 55/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 2770583945216.0000 - mae: 461078.1562\n",
            "Epoch 56/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 2692798218240.0000 - mae: 456075.6875\n",
            "Epoch 57/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 2556506144768.0000 - mae: 445980.6562\n",
            "Epoch 58/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 2385540546560.0000 - mae: 436645.4688\n",
            "Epoch 59/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 2374505332736.0000 - mae: 440178.4375\n",
            "Epoch 60/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 2407925284864.0000 - mae: 444252.0938\n",
            "Epoch 61/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 2349241991168.0000 - mae: 444828.7812\n",
            "Epoch 62/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 2192373579776.0000 - mae: 433237.3125\n",
            "Epoch 63/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 2198302752768.0000 - mae: 415505.9062\n",
            "Epoch 64/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 2130976571392.0000 - mae: 427298.4688\n",
            "Epoch 65/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 2119162789888.0000 - mae: 416708.1562\n",
            "Epoch 66/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 2264319918080.0000 - mae: 419517.6875\n",
            "Epoch 67/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 2029953089536.0000 - mae: 402775.8438\n",
            "Epoch 68/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 2185237233664.0000 - mae: 418558.4375\n",
            "Epoch 69/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 1977544474624.0000 - mae: 395798.8750\n",
            "Epoch 70/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 2120344797184.0000 - mae: 414789.0312\n",
            "Epoch 71/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 1821463674880.0000 - mae: 398488.6875\n",
            "Epoch 72/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 1833485336576.0000 - mae: 385361.3125\n",
            "Epoch 73/100\n",
            "278/278 [==============================] - 1s 4ms/step - loss: 1906047320064.0000 - mae: 402060.2188\n",
            "Epoch 74/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 1860154687488.0000 - mae: 387706.1250\n",
            "Epoch 75/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 1671703429120.0000 - mae: 380392.4688\n",
            "Epoch 76/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 1884568027136.0000 - mae: 391722.0312\n",
            "Epoch 77/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 1702725812224.0000 - mae: 377577.3750\n",
            "Epoch 78/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 1811367985152.0000 - mae: 383265.0938\n",
            "Epoch 79/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 1659730788352.0000 - mae: 372573.7500\n",
            "Epoch 80/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 1702034407424.0000 - mae: 371109.5000\n",
            "Epoch 81/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 1877356707840.0000 - mae: 394863.1562\n",
            "Epoch 82/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 1885860134912.0000 - mae: 373967.2500\n",
            "Epoch 83/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 1923448832000.0000 - mae: 388127.1875\n",
            "Epoch 84/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 1739544068096.0000 - mae: 371512.3750\n",
            "Epoch 85/100\n",
            "278/278 [==============================] - 1s 4ms/step - loss: 1703935213568.0000 - mae: 374385.1562\n",
            "Epoch 86/100\n",
            "278/278 [==============================] - 1s 5ms/step - loss: 1665661009920.0000 - mae: 368718.6875\n",
            "Epoch 87/100\n",
            "278/278 [==============================] - 1s 5ms/step - loss: 1470638325760.0000 - mae: 365303.5000\n",
            "Epoch 88/100\n",
            "278/278 [==============================] - 1s 4ms/step - loss: 1808846028800.0000 - mae: 366756.4688\n",
            "Epoch 89/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 1696976338944.0000 - mae: 366677.2188\n",
            "Epoch 90/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 1799685931008.0000 - mae: 373842.8438\n",
            "Epoch 91/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 1458405638144.0000 - mae: 352306.7188\n",
            "Epoch 92/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 1634059681792.0000 - mae: 367749.7812\n",
            "Epoch 93/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 1518439366656.0000 - mae: 349377.5625\n",
            "Epoch 94/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 1518566244352.0000 - mae: 346854.9375\n",
            "Epoch 95/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 1554443665408.0000 - mae: 352125.9375\n",
            "Epoch 96/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 1413373624320.0000 - mae: 341228.2812\n",
            "Epoch 97/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 1390772486144.0000 - mae: 358005.3750\n",
            "Epoch 98/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 1427862192128.0000 - mae: 345077.1562\n",
            "Epoch 99/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 1652742946816.0000 - mae: 357968.0312\n",
            "Epoch 100/100\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 1563169128448.0000 - mae: 360220.3438\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss = pd.DataFrame(history.history)\n",
        "loss"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "0XqqiMxkJdwb",
        "outputId": "f407570d-a98f-426f-db74-cd96dee2dc62"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            loss           mae\n",
              "0   5.378368e+12  654820.06250\n",
              "1   5.223356e+12  645133.87500\n",
              "2   5.159621e+12  646032.81250\n",
              "3   5.122862e+12  631839.31250\n",
              "4   5.111053e+12  626822.06250\n",
              "..           ...           ...\n",
              "95  1.413374e+12  341228.28125\n",
              "96  1.390772e+12  358005.37500\n",
              "97  1.427862e+12  345077.15625\n",
              "98  1.652743e+12  357968.03125\n",
              "99  1.563169e+12  360220.34375\n",
              "\n",
              "[100 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3389a4af-906c-43e7-ad31-e7a5adfbc820\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>loss</th>\n",
              "      <th>mae</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.378368e+12</td>\n",
              "      <td>654820.06250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.223356e+12</td>\n",
              "      <td>645133.87500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5.159621e+12</td>\n",
              "      <td>646032.81250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.122862e+12</td>\n",
              "      <td>631839.31250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.111053e+12</td>\n",
              "      <td>626822.06250</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>1.413374e+12</td>\n",
              "      <td>341228.28125</td>\n",
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              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>1.390772e+12</td>\n",
              "      <td>358005.37500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>1.427862e+12</td>\n",
              "      <td>345077.15625</td>\n",
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              "    <tr>\n",
              "      <th>98</th>\n",
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              "      <td>357968.03125</td>\n",
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              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>1.563169e+12</td>\n",
              "      <td>360220.34375</td>\n",
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              "<p>100 rows × 2 columns</p>\n",
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              "    [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-3389a4af-906c-43e7-ad31-e7a5adfbc820 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-3389a4af-906c-43e7-ad31-e7a5adfbc820');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "81RdlNlQMgwS",
        "outputId": "c5eac57b-238e-4e44-9254-61a83b090b2c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "388893005573.8062"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "mean_squared_error(y_test, y_test_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cP0qO4GnMgwS",
        "outputId": "7b8343c6-ec61-48b3-d551-8f2624313fc8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9728597258597328"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "r2_score(y_train, y_train_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TLI0J46oMgwT",
        "outputId": "9d284249-4dff-4b36-cbbc-f80ed3313db9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9195080668587611"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "r2_score(y_test, y_test_pred)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Feature Importance"
      ],
      "metadata": {
        "id": "k6h1H0UbVwDB"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "V90HY9QGMgwT",
        "outputId": "e724eaf8-3592-475f-d676-a22d738bf9ae"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'score')"
            ]
          },
          "metadata": {},
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sorted_idx = rf_reg.feature_importances_.argsort()\n",
        "plt.barh(list(x.columns[sorted_idx]), rf_reg.feature_importances_[sorted_idx])\n",
        "plt.title('Feature Importances')\n",
        "plt.ylabel('Feature')\n",
        "plt.xlabel('score')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kJcJZHhzMgwT"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wj-Y1QlvMgwT"
      },
      "source": [
        "## Linear Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hBsRll_xMgwT"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LinearRegression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tttIbZEzMgwU"
      },
      "outputs": [],
      "source": [
        "lr = LinearRegression(n_jobs=-1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_1xWlk5HMgwU",
        "outputId": "edadbe8b-5f8b-44d0-ef96-82c9c4ca0e37"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(n_jobs=-1)"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "lr.fit(x_train_std, y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-7Pa4VnXMgwU"
      },
      "source": [
        "### Predicting with the Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lAqZLkZiMgwU"
      },
      "outputs": [],
      "source": [
        "y_test_pred = lr.predict(x_test_std)\n",
        "y_train_pred = lr.predict(x_train_std)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2OqKDiqTMgwU",
        "outputId": "ff400503-7a01-4eaa-a8df-9aae28eae1d0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 6.94466985e+05,  1.09241839e+06,  1.32839974e+06, -3.98253534e+05,\n",
              "        9.57983488e+05, -2.70091658e+05, -4.32713216e+05,  3.08042031e+05,\n",
              "        1.00461655e+06,  2.28260585e+05,  5.62381221e+05,  3.28408152e+05,\n",
              "        1.02864603e+06,  7.57160013e+05,  3.45233192e+04,  1.32304885e+06,\n",
              "        6.80852867e+05, -2.43791601e+05,  3.50685843e+05,  4.54837090e+05,\n",
              "        1.28239374e+06, -5.91549379e+04,  3.85509000e+04,  1.09870445e+06,\n",
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              "        1.68064780e+06,  1.14689940e+05,  4.04918620e+04, -2.69342784e+05,\n",
              "        1.20764754e+06,  5.12739023e+05, -1.57165259e+05,  1.03428439e+04,\n",
              "        1.09063046e+06,  1.03442298e+06,  1.47295089e+06, -9.48412457e+05,\n",
              "        9.33723413e+05, -4.61512533e+05,  2.24974357e+05,  1.12111898e+06,\n",
              "       -9.06588199e+04, -3.28652018e+05,  2.81287927e+05,  8.48880431e+05,\n",
              "        1.02763361e+06,  7.28708876e+05, -2.11716826e+05,  2.48100303e+05,\n",
              "        9.87901171e+05, -3.82193092e+04,  2.53195336e+04,  7.51023989e+05,\n",
              "        8.94654356e+05,  6.53959380e+05, -2.05944910e+05,  9.02497995e+05,\n",
              "        4.84133348e+05, -4.38540147e+04,  1.45636581e+06,  1.28432497e+05,\n",
              "        1.18814697e+06,  8.08104768e+05, -4.45429950e+05, -5.97088541e+05,\n",
              "        6.24508375e+05,  5.90618998e+05,  3.01605955e+05, -2.13432883e+05,\n",
              "        8.18081000e+04,  5.24124169e+05,  1.17308460e+06, -3.15781883e+05,\n",
              "       -1.77446162e+05, -6.17326410e+04,  8.93033879e+04,  1.86416434e+06,\n",
              "        1.25575131e+06,  1.28449242e+06, -6.54753458e+05, -2.44884799e+04,\n",
              "        1.01363592e+05,  5.01969788e+05, -5.11729485e+05,  1.08883257e+06,\n",
              "        1.13569867e+06,  3.71681618e+04,  1.05510390e+06, -1.44492896e+05,\n",
              "       -1.60211178e+05,  7.81307329e+05, -1.60151396e+05,  1.12946612e+06,\n",
              "        9.85047918e+05,  1.28477641e+05,  7.98290742e+05, -4.92577679e+05,\n",
              "       -1.97701803e+05,  1.04582018e+06,  1.22830933e+06,  2.74275197e+05,\n",
              "        1.13337664e+06,  3.57955959e+05, -2.88800984e+05, -1.54265834e+05,\n",
              "        9.95546474e+05,  9.02397032e+05,  1.16861633e+06,  8.06019386e+05,\n",
              "        1.26350076e+06,  9.43537203e+04,  2.18279435e+05,  1.24391124e+06,\n",
              "        6.69192707e+05,  1.17117393e+06,  1.22481872e+06, -1.08155906e+05,\n",
              "        1.15710203e+06, -1.66797615e+05,  1.23917518e+06,  5.68288833e+05,\n",
              "        1.35289848e+06, -1.29199045e+05, -3.93907406e+05,  5.63108330e+05,\n",
              "        9.46159491e+05, -3.97275704e+04, -3.94801193e+05,  1.25725721e+05,\n",
              "        5.14561264e+05,  2.82475490e+05,  1.34796797e+06,  4.81107778e+05,\n",
              "        1.00584048e+06,  3.38420393e+04,  4.70690473e+05, -5.66245245e+05,\n",
              "        3.72292671e+05,  1.31925881e+06,  5.25774141e+05, -3.99792369e+04,\n",
              "       -6.59214567e+05,  5.53794491e+05,  3.40880071e+05,  1.33987440e+06,\n",
              "        1.26459304e+05,  3.73284420e+05, -3.18996508e+05,  1.94085707e+05,\n",
              "        6.56303666e+05,  6.46372062e+05,  4.03859882e+05,  5.58062080e+04,\n",
              "        1.69190858e+05, -3.38867497e+04,  3.64989238e+05,  1.16927999e+05,\n",
              "        6.61440383e+05,  1.36016022e+06,  1.22433023e+06, -5.43552204e+02,\n",
              "        3.33905501e+05, -2.50596436e+04, -1.79556196e+05, -1.24887451e+05,\n",
              "       -4.30739193e+05, -2.26336176e+05,  1.13006343e+06,  5.51517854e+05,\n",
              "       -6.21776910e+05, -8.09117428e+04, -3.04155371e+05,  6.04148198e+05,\n",
              "        2.23098432e+05,  1.23272356e+06, -1.10337707e+05,  1.45659296e+06,\n",
              "        1.33680873e+06,  1.07766946e+06,  6.96990073e+05,  1.90518345e+05,\n",
              "        7.87887149e+05,  7.85467690e+05,  2.66412103e+05,  7.09821576e+04,\n",
              "        1.19598373e+06,  3.07150180e+05,  2.46768145e+05, -2.35922105e+05,\n",
              "        6.59557073e+05,  1.96207126e+05,  8.91097903e+05,  4.98679105e+05,\n",
              "        1.84146732e+05, -2.61802852e+04,  8.98976144e+05, -2.72380385e+05,\n",
              "        7.82597891e+05, -6.23213833e+05,  6.24617635e+03,  9.77667027e+05,\n",
              "        1.22490242e+06,  5.72756892e+05,  1.05517974e+06, -1.65417991e+05,\n",
              "       -5.22936080e+05,  1.78755813e+06,  1.28035290e+06,  7.92843849e+05,\n",
              "        8.44819197e+05,  1.73116135e+05, -3.23719901e+05, -5.42293616e+05,\n",
              "        1.20669698e+04,  1.36232303e+06,  2.69645080e+05,  6.59809642e+05,\n",
              "        9.66956259e+05,  1.00401338e+06, -4.45685264e+05,  7.81622010e+05,\n",
              "       -3.97267470e+04, -3.18732431e+05, -5.25691142e+04,  2.39665377e+05,\n",
              "        1.63033157e+06,  5.11862701e+05, -3.53311413e+05,  1.46002399e+06,\n",
              "        8.45087337e+05, -1.65532342e+05, -2.45310523e+04,  9.62669881e+05,\n",
              "       -2.21759313e+04,  7.87616045e+05,  9.43381802e+05, -1.91029192e+05,\n",
              "       -3.98121672e+05, -1.21961016e+05, -2.33684870e+05,  5.53593000e+05,\n",
              "       -3.52148954e+05,  7.37076023e+05,  9.98800072e+05, -5.25934240e+05,\n",
              "       -4.92386655e+05, -3.07130237e+05,  1.02526499e+06, -7.51521781e+05,\n",
              "       -1.57928194e+05,  2.60695716e+05, -4.94447374e+05,  9.28892454e+05,\n",
              "        2.08498298e+05, -1.44228883e+05,  1.29184817e+06,  3.28648019e+05,\n",
              "        8.66631101e+05,  8.97128544e+05, -1.29894334e+05,  7.67812947e+05,\n",
              "        1.25009675e+06, -2.79062420e+05,  4.40639333e+05,  1.29588027e+06,\n",
              "        8.90017435e+05,  1.61147392e+05,  1.30801520e+06,  1.64318672e+06,\n",
              "        1.28781940e+06,  2.17334386e+05,  9.60522006e+05, -1.24860768e+05,\n",
              "       -3.99605374e+05,  1.02689052e+06,  1.16225495e+06,  3.05253982e+05,\n",
              "        5.63042208e+05,  1.51362269e+05,  5.92079370e+05,  5.43881910e+05,\n",
              "        1.85208815e+06, -2.13222043e+05,  2.22057144e+05,  6.60102595e+05,\n",
              "       -1.85844135e+05, -2.81584580e+05,  9.09695374e+05,  7.89477161e+05,\n",
              "        2.20572366e+05,  3.65458120e+05,  5.14163591e+05, -3.91756217e+04,\n",
              "        5.37236790e+05,  1.26139555e+06,  3.90786586e+05,  1.17030802e+06,\n",
              "        9.16020150e+05, -4.88591967e+05,  1.04798300e+06, -1.57447838e+05,\n",
              "       -1.41162842e+05,  3.22791573e+05,  8.18249659e+05,  1.05533570e+06,\n",
              "       -2.48666814e+05,  5.71816211e+05,  1.08179596e+06, -3.06389901e+04,\n",
              "        8.93992965e+04, -2.47048110e+05,  8.46862960e+05,  1.50704453e+06,\n",
              "        1.59843474e+05,  9.63809379e+05,  5.41417447e+04, -2.52336607e+05,\n",
              "       -2.75791766e+05,  7.64795351e+05, -4.70327858e+05])"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "y_test_pred"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9_GfSeBaMgwU"
      },
      "source": [
        "### Model Perfomance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EH6UqZqvMgwV",
        "outputId": "4edb23fe-4c30-4689-9ae3-b34b7efec246"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4464218360113.907"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ],
      "source": [
        "mean_squared_error(y_test, y_test_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GH80nOnPMgwV",
        "outputId": "30ca7a1e-afdc-4558-aa7a-ddf3638f9f6c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5705555244508.796"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ],
      "source": [
        "mean_squared_error(y_train, y_train_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N5-bLBUrMgwV",
        "outputId": "09c72f54-2fce-48bf-ee07-4f38db387241"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.06293392417116306"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ],
      "source": [
        "r2_score(y_train, y_train_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P5nr182jMgwV",
        "outputId": "b7204edd-27c3-4498-f40d-564475146410"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.07600918345140173"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ],
      "source": [
        "r2_score(y_test, y_test_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XLHx_ADWMgwV"
      },
      "outputs": [],
      "source": [
        "# The linear regression model performs very poorly"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "voBOu-NbMgwW"
      },
      "source": [
        "## LSTM Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aYOB0ZmjMgwW"
      },
      "outputs": [],
      "source": [
        "x_train_full, x_test, y_train_full, y_test = train_test_split(x, y)\n",
        "x_train, x_valid, y_train, y_valid = train_test_split(x_train_full, y_train_full, random_state = 42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jP4eWlYKMgwW"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "x_train = scaler.fit_transform(x_train)\n",
        "x_valid = scaler.fit_transform(x_valid)\n",
        "x_test = scaler.fit_transform(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VPIlfp8hMgwW"
      },
      "outputs": [],
      "source": [
        "np.random.seed(42)\n",
        "tf.random.set_seed(42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "78WFH2p7MgwX",
        "outputId": "4216bf13-d505-446b-8b62-10e01722557d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2774, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ],
      "source": [
        "x_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CYivb-0_MgwX"
      },
      "outputs": [],
      "source": [
        "from keras.layers import LSTM, Activation, Dense, Dropout, Input, Embedding\n",
        "from keras.callbacks import EarlyStopping\n",
        "from keras import Sequential"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lEZpjrS6MgwX"
      },
      "outputs": [],
      "source": [
        "model = Sequential()  \n",
        "model.add(LSTM(1, batch_input_shape=(None, 7, 1), return_sequences=False))\n",
        "model.add(Dense(100))\n",
        "model.compile(loss='mean_absolute_error', optimizer='adam',metrics=['mae'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wdxLTeMLMgwX",
        "outputId": "01754477-45ae-4cbc-8bc1-c4e628bfbd71"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "278/278 - 8s - loss: 482919.5000 - mae: 482919.5000 - val_loss: 375041.9688 - val_mae: 375041.9688 - 8s/epoch - 28ms/step\n",
            "Epoch 2/100\n",
            "278/278 - 2s - loss: 482919.4375 - mae: 482919.4375 - val_loss: 375041.9688 - val_mae: 375041.9688 - 2s/epoch - 6ms/step\n",
            "Epoch 3/100\n",
            "278/278 - 2s - loss: 482919.5000 - mae: 482919.5000 - val_loss: 375041.9688 - val_mae: 375041.9688 - 2s/epoch - 6ms/step\n",
            "Epoch 4/100\n",
            "278/278 - 2s - loss: 482919.4062 - mae: 482919.4062 - val_loss: 375041.9688 - val_mae: 375041.9688 - 2s/epoch - 6ms/step\n",
            "Epoch 5/100\n",
            "278/278 - 2s - loss: 482919.5312 - mae: 482919.5312 - val_loss: 375041.9688 - val_mae: 375041.9688 - 2s/epoch - 7ms/step\n",
            "Epoch 6/100\n",
            "278/278 - 2s - loss: 482919.4375 - mae: 482919.4375 - val_loss: 375041.9688 - val_mae: 375041.9688 - 2s/epoch - 7ms/step\n",
            "Epoch 7/100\n",
            "278/278 - 1s - loss: 482919.6250 - mae: 482919.6250 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 5ms/step\n",
            "Epoch 8/100\n",
            "278/278 - 1s - loss: 482919.4062 - mae: 482919.4062 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 9/100\n",
            "278/278 - 1s - loss: 482919.5938 - mae: 482919.5938 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 10/100\n",
            "278/278 - 1s - loss: 482919.5312 - mae: 482919.5312 - val_loss: 375041.9688 - val_mae: 375041.9688 - 993ms/epoch - 4ms/step\n",
            "Epoch 11/100\n",
            "278/278 - 1s - loss: 482919.5938 - mae: 482919.5938 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 12/100\n",
            "278/278 - 1s - loss: 482919.5312 - mae: 482919.5312 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 13/100\n",
            "278/278 - 1s - loss: 482919.5312 - mae: 482919.5312 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 14/100\n",
            "278/278 - 1s - loss: 482919.5000 - mae: 482919.5000 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 15/100\n",
            "278/278 - 1s - loss: 482919.5312 - mae: 482919.5000 - val_loss: 375041.9688 - val_mae: 375041.9688 - 962ms/epoch - 3ms/step\n",
            "Epoch 16/100\n",
            "278/278 - 1s - loss: 482919.6250 - mae: 482919.6250 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1000ms/epoch - 4ms/step\n",
            "Epoch 17/100\n",
            "278/278 - 1s - loss: 482919.5938 - mae: 482919.5938 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 18/100\n",
            "278/278 - 1s - loss: 482919.4375 - mae: 482919.4375 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1000ms/epoch - 4ms/step\n",
            "Epoch 19/100\n",
            "278/278 - 1s - loss: 482919.5312 - mae: 482919.5312 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 20/100\n",
            "278/278 - 1s - loss: 482919.5938 - mae: 482919.5938 - val_loss: 375041.9688 - val_mae: 375041.9688 - 983ms/epoch - 4ms/step\n",
            "Epoch 21/100\n",
            "278/278 - 1s - loss: 482919.5938 - mae: 482919.5938 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 22/100\n",
            "278/278 - 1s - loss: 482919.5938 - mae: 482919.5938 - val_loss: 375041.9688 - val_mae: 375041.9688 - 967ms/epoch - 3ms/step\n",
            "Epoch 23/100\n",
            "278/278 - 1s - loss: 482919.3750 - mae: 482919.3750 - val_loss: 375041.9688 - val_mae: 375041.9688 - 992ms/epoch - 4ms/step\n",
            "Epoch 24/100\n",
            "278/278 - 1s - loss: 482919.6250 - mae: 482919.6250 - val_loss: 375041.9688 - val_mae: 375041.9688 - 984ms/epoch - 4ms/step\n",
            "Epoch 25/100\n",
            "278/278 - 1s - loss: 482919.5000 - mae: 482919.5000 - val_loss: 375041.9688 - val_mae: 375041.9688 - 972ms/epoch - 3ms/step\n",
            "Epoch 26/100\n",
            "278/278 - 1s - loss: 482919.3125 - mae: 482919.3125 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 27/100\n",
            "278/278 - 1s - loss: 482919.5312 - mae: 482919.5312 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 28/100\n",
            "278/278 - 1s - loss: 482919.5312 - mae: 482919.5312 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 29/100\n",
            "278/278 - 1s - loss: 482919.2812 - mae: 482919.2812 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 30/100\n",
            "278/278 - 1s - loss: 482919.7188 - mae: 482919.7188 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 31/100\n",
            "278/278 - 1s - loss: 482919.6250 - mae: 482919.6250 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 32/100\n",
            "278/278 - 1s - loss: 482919.3750 - mae: 482919.3750 - val_loss: 375041.9688 - val_mae: 375041.9688 - 987ms/epoch - 4ms/step\n",
            "Epoch 33/100\n",
            "278/278 - 1s - loss: 482919.4062 - mae: 482919.4062 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 34/100\n",
            "278/278 - 1s - loss: 482919.6875 - mae: 482919.6875 - val_loss: 375041.9688 - val_mae: 375041.9688 - 983ms/epoch - 4ms/step\n",
            "Epoch 35/100\n",
            "278/278 - 1s - loss: 482919.6875 - mae: 482919.6875 - val_loss: 375041.9688 - val_mae: 375041.9688 - 996ms/epoch - 4ms/step\n",
            "Epoch 36/100\n",
            "278/278 - 1s - loss: 482919.4375 - mae: 482919.4375 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 37/100\n",
            "278/278 - 1s - loss: 482919.6250 - mae: 482919.6250 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 38/100\n",
            "278/278 - 1s - loss: 482919.5938 - mae: 482919.5938 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 39/100\n",
            "278/278 - 1s - loss: 482919.6875 - mae: 482919.6875 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 40/100\n",
            "278/278 - 1s - loss: 482919.5000 - mae: 482919.4375 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 41/100\n",
            "278/278 - 1s - loss: 482919.5000 - mae: 482919.5000 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 42/100\n",
            "278/278 - 1s - loss: 482919.5938 - mae: 482919.5938 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 43/100\n",
            "278/278 - 1s - loss: 482919.6875 - mae: 482919.6250 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 44/100\n",
            "278/278 - 1s - loss: 482919.5000 - mae: 482919.5000 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 45/100\n",
            "278/278 - 1s - loss: 482919.4062 - mae: 482919.4062 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 46/100\n",
            "278/278 - 1s - loss: 482919.5312 - mae: 482919.5312 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 47/100\n",
            "278/278 - 1s - loss: 482919.4375 - mae: 482919.4375 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 48/100\n",
            "278/278 - 1s - loss: 482919.5938 - mae: 482919.5938 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 49/100\n",
            "278/278 - 1s - loss: 482919.5938 - mae: 482919.5938 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 50/100\n",
            "278/278 - 1s - loss: 482919.5000 - mae: 482919.5000 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 51/100\n",
            "278/278 - 1s - loss: 482919.5000 - mae: 482919.5000 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 52/100\n",
            "278/278 - 1s - loss: 482919.8125 - mae: 482919.8125 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 53/100\n",
            "278/278 - 1s - loss: 482919.5000 - mae: 482919.5000 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 54/100\n",
            "278/278 - 1s - loss: 482919.5312 - mae: 482919.5312 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 55/100\n",
            "278/278 - 1s - loss: 482919.5312 - mae: 482919.5312 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 56/100\n",
            "278/278 - 1s - loss: 482919.4062 - mae: 482919.4062 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 57/100\n",
            "278/278 - 1s - loss: 482919.5938 - mae: 482919.5938 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 58/100\n",
            "278/278 - 1s - loss: 482919.5938 - mae: 482919.5938 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 59/100\n",
            "278/278 - 1s - loss: 482919.5000 - mae: 482919.5000 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 60/100\n",
            "278/278 - 1s - loss: 482919.5938 - mae: 482919.5938 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 61/100\n",
            "278/278 - 1s - loss: 482919.5938 - mae: 482919.5938 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 62/100\n",
            "278/278 - 1s - loss: 482919.4375 - mae: 482919.4375 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 63/100\n",
            "278/278 - 1s - loss: 482919.6875 - mae: 482919.6875 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 64/100\n",
            "278/278 - 1s - loss: 482919.6875 - mae: 482919.6875 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 65/100\n",
            "278/278 - 1s - loss: 482919.7188 - mae: 482919.7188 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 66/100\n",
            "278/278 - 1s - loss: 482919.5938 - mae: 482919.5938 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 67/100\n",
            "278/278 - 1s - loss: 482919.5312 - mae: 482919.5312 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 5ms/step\n",
            "Epoch 68/100\n",
            "278/278 - 2s - loss: 482919.4062 - mae: 482919.4062 - val_loss: 375041.9688 - val_mae: 375041.9688 - 2s/epoch - 7ms/step\n",
            "Epoch 69/100\n",
            "278/278 - 2s - loss: 482919.6875 - mae: 482919.6875 - val_loss: 375041.9688 - val_mae: 375041.9688 - 2s/epoch - 6ms/step\n",
            "Epoch 70/100\n",
            "278/278 - 2s - loss: 482919.6250 - mae: 482919.6250 - val_loss: 375041.9688 - val_mae: 375041.9688 - 2s/epoch - 6ms/step\n",
            "Epoch 71/100\n",
            "278/278 - 2s - loss: 482919.6250 - mae: 482919.6250 - val_loss: 375041.9688 - val_mae: 375041.9688 - 2s/epoch - 6ms/step\n",
            "Epoch 72/100\n",
            "278/278 - 2s - loss: 482919.6875 - mae: 482919.6875 - val_loss: 375041.9688 - val_mae: 375041.9688 - 2s/epoch - 7ms/step\n",
            "Epoch 73/100\n",
            "278/278 - 2s - loss: 482919.5938 - mae: 482919.5938 - val_loss: 375041.9688 - val_mae: 375041.9688 - 2s/epoch - 7ms/step\n",
            "Epoch 74/100\n",
            "278/278 - 2s - loss: 482919.5000 - mae: 482919.5000 - val_loss: 375041.9688 - val_mae: 375041.9688 - 2s/epoch - 6ms/step\n",
            "Epoch 75/100\n",
            "278/278 - 2s - loss: 482919.5938 - mae: 482919.5312 - val_loss: 375041.9688 - val_mae: 375041.9688 - 2s/epoch - 7ms/step\n",
            "Epoch 76/100\n",
            "278/278 - 2s - loss: 482919.6250 - mae: 482919.6250 - val_loss: 375041.9688 - val_mae: 375041.9688 - 2s/epoch - 7ms/step\n",
            "Epoch 77/100\n",
            "278/278 - 2s - loss: 482919.5312 - mae: 482919.5312 - val_loss: 375041.9688 - val_mae: 375041.9688 - 2s/epoch - 7ms/step\n",
            "Epoch 78/100\n",
            "278/278 - 2s - loss: 482919.5312 - mae: 482919.5312 - val_loss: 375041.9688 - val_mae: 375041.9688 - 2s/epoch - 7ms/step\n",
            "Epoch 79/100\n",
            "278/278 - 2s - loss: 482919.6875 - mae: 482919.6250 - val_loss: 375041.9688 - val_mae: 375041.9688 - 2s/epoch - 7ms/step\n",
            "Epoch 80/100\n",
            "278/278 - 2s - loss: 482919.5000 - mae: 482919.5000 - val_loss: 375041.9688 - val_mae: 375041.9688 - 2s/epoch - 7ms/step\n",
            "Epoch 81/100\n",
            "278/278 - 2s - loss: 482919.4375 - mae: 482919.4375 - val_loss: 375041.9688 - val_mae: 375041.9688 - 2s/epoch - 6ms/step\n",
            "Epoch 82/100\n",
            "278/278 - 2s - loss: 482919.4062 - mae: 482919.4062 - val_loss: 375041.9688 - val_mae: 375041.9688 - 2s/epoch - 6ms/step\n",
            "Epoch 83/100\n",
            "278/278 - 2s - loss: 482919.6250 - mae: 482919.6250 - val_loss: 375041.9688 - val_mae: 375041.9688 - 2s/epoch - 6ms/step\n",
            "Epoch 84/100\n",
            "278/278 - 2s - loss: 482919.7188 - mae: 482919.7188 - val_loss: 375041.9688 - val_mae: 375041.9688 - 2s/epoch - 7ms/step\n",
            "Epoch 85/100\n",
            "278/278 - 1s - loss: 482919.5312 - mae: 482919.5312 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 86/100\n",
            "278/278 - 1s - loss: 482919.5938 - mae: 482919.5938 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 87/100\n",
            "278/278 - 1s - loss: 482919.5312 - mae: 482919.5312 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 88/100\n",
            "278/278 - 1s - loss: 482919.6250 - mae: 482919.6250 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 89/100\n",
            "278/278 - 1s - loss: 482919.6250 - mae: 482919.6250 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 90/100\n",
            "278/278 - 1s - loss: 482919.5312 - mae: 482919.5312 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 5ms/step\n",
            "Epoch 91/100\n",
            "278/278 - 1s - loss: 482919.4062 - mae: 482919.4062 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 5ms/step\n",
            "Epoch 92/100\n",
            "278/278 - 1s - loss: 482919.7812 - mae: 482919.7812 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 93/100\n",
            "278/278 - 1s - loss: 482919.8125 - mae: 482919.8125 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 94/100\n",
            "278/278 - 1s - loss: 482919.4062 - mae: 482919.4062 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 95/100\n",
            "278/278 - 1s - loss: 482919.5938 - mae: 482919.5938 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 96/100\n",
            "278/278 - 1s - loss: 482919.6250 - mae: 482919.6250 - val_loss: 375041.9688 - val_mae: 375041.9688 - 1s/epoch - 4ms/step\n",
            "Epoch 97/100\n",
            "278/278 - 1s - loss: 482919.4375 - mae: 482919.4375 - val_loss: 375041.9688 - val_mae: 375041.9688 - 971ms/epoch - 3ms/step\n",
            "Epoch 98/100\n",
            "278/278 - 1s - loss: 482919.6875 - mae: 482919.6875 - val_loss: 375041.9688 - val_mae: 375041.9688 - 982ms/epoch - 4ms/step\n",
            "Epoch 99/100\n",
            "278/278 - 1s - loss: 482919.4062 - mae: 482919.4062 - val_loss: 375041.9688 - val_mae: 375041.9688 - 989ms/epoch - 4ms/step\n",
            "Epoch 100/100\n",
            "278/278 - 1s - loss: 482919.6875 - mae: 482919.6875 - val_loss: 375041.9688 - val_mae: 375041.9688 - 967ms/epoch - 3ms/step\n"
          ]
        }
      ],
      "source": [
        "history = model.fit(x_train, y_train, epochs=100, batch_size=10, verbose=2,validation_data=(x_valid, y_valid))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "7xpsUq4sMgwX",
        "outputId": "3bcec9b1-14d5-45f2-fbfb-8bb689b13651"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            loss           mae      val_loss       val_mae\n",
              "0   482919.50000  482919.50000  375041.96875  375041.96875\n",
              "1   482919.43750  482919.43750  375041.96875  375041.96875\n",
              "2   482919.50000  482919.50000  375041.96875  375041.96875\n",
              "3   482919.40625  482919.40625  375041.96875  375041.96875\n",
              "4   482919.53125  482919.53125  375041.96875  375041.96875\n",
              "..           ...           ...           ...           ...\n",
              "95  482919.62500  482919.62500  375041.96875  375041.96875\n",
              "96  482919.43750  482919.43750  375041.96875  375041.96875\n",
              "97  482919.68750  482919.68750  375041.96875  375041.96875\n",
              "98  482919.40625  482919.40625  375041.96875  375041.96875\n",
              "99  482919.68750  482919.68750  375041.96875  375041.96875\n",
              "\n",
              "[100 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9d3ed78b-2be1-45bd-b26e-d02e49d25a44\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>loss</th>\n",
              "      <th>mae</th>\n",
              "      <th>val_loss</th>\n",
              "      <th>val_mae</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>482919.50000</td>\n",
              "      <td>482919.50000</td>\n",
              "      <td>375041.96875</td>\n",
              "      <td>375041.96875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>482919.43750</td>\n",
              "      <td>482919.43750</td>\n",
              "      <td>375041.96875</td>\n",
              "      <td>375041.96875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>482919.50000</td>\n",
              "      <td>482919.50000</td>\n",
              "      <td>375041.96875</td>\n",
              "      <td>375041.96875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>482919.40625</td>\n",
              "      <td>482919.40625</td>\n",
              "      <td>375041.96875</td>\n",
              "      <td>375041.96875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>482919.53125</td>\n",
              "      <td>482919.53125</td>\n",
              "      <td>375041.96875</td>\n",
              "      <td>375041.96875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>482919.62500</td>\n",
              "      <td>482919.62500</td>\n",
              "      <td>375041.96875</td>\n",
              "      <td>375041.96875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>482919.43750</td>\n",
              "      <td>482919.43750</td>\n",
              "      <td>375041.96875</td>\n",
              "      <td>375041.96875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>482919.68750</td>\n",
              "      <td>482919.68750</td>\n",
              "      <td>375041.96875</td>\n",
              "      <td>375041.96875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>482919.40625</td>\n",
              "      <td>482919.40625</td>\n",
              "      <td>375041.96875</td>\n",
              "      <td>375041.96875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>482919.68750</td>\n",
              "      <td>482919.68750</td>\n",
              "      <td>375041.96875</td>\n",
              "      <td>375041.96875</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 4 columns</p>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9d3ed78b-2be1-45bd-b26e-d02e49d25a44 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-9d3ed78b-2be1-45bd-b26e-d02e49d25a44');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ],
      "source": [
        "loss = pd.DataFrame(history.history)\n",
        "loss"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(loss['loss'],label = 'loss')\n",
        "plt.legend()\n",
        "plt.title('Loss vs Epochs')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "A9Hh91nlh1sO",
        "outputId": "e5b03334-83a9-4e40-96fa-c2ad138c7005"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Loss vs Epochs')"
            ]
          },
          "metadata": {},
          "execution_count": 67
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(loss['mae'],label = 'mae')\n",
        "plt.legend()\n",
        "plt.title('mae vs epochs')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "tdD4JMT9h2-V",
        "outputId": "3571bce3-1bd2-49f8-f8af-83d8912f3275"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'mae vs epochs')"
            ]
          },
          "metadata": {},
          "execution_count": 68
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "PaO1SfRVpHLN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "WkgmIJ3Uh3H_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jxEgOywqMgwY"
      },
      "source": [
        "# ANN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "I7jJBuAQMgwY"
      },
      "outputs": [],
      "source": [
        "# Initialising the ANN\n",
        "model = Sequential()\n",
        "\n",
        "# Adding the input layer and the first hidden layer\n",
        "model.add(Dense(32, activation = 'relu', input_dim = 7))\n",
        "\n",
        "# Adding the second hidden layer\n",
        "model.add(Dense(units = 128, activation = 'relu'))\n",
        "\n",
        "# Adding the third hidden layer\n",
        "model.add(Dense(units = 128, activation = 'relu'))\n",
        "\n",
        "# Adding the output layer\n",
        "model.add(Dense(units = 1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0Wjpt9WBMgwY"
      },
      "outputs": [],
      "source": [
        "model.compile(optimizer = keras.optimizers.Adam(learning_rate = 1e-1),loss = 'mean_squared_error',metrics = 'mae')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wnrua4RogMV4",
        "outputId": "06103f17-5b36-4114-946c-28d09179dd69"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_1 (Dense)             (None, 32)                256       \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 128)               4224      \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 1)                 129       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 21,121\n",
            "Trainable params: 21,121\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CS3mZZDVMgwY",
        "outputId": "c9859370-da6d-4b75-92be-fc486d4cba6c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 5357524484096.0000 - mae: 607552.6250\n",
            "Epoch 2/277\n",
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            "Epoch 3/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 5308134981632.0000 - mae: 569305.9375\n",
            "Epoch 4/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 5142682271744.0000 - mae: 582279.9375\n",
            "Epoch 5/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 5113990086656.0000 - mae: 572383.1875\n",
            "Epoch 6/277\n",
            "278/278 [==============================] - 0s 2ms/step - loss: 5014073376768.0000 - mae: 579161.8750\n",
            "Epoch 7/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 4885606039552.0000 - mae: 562783.1250\n",
            "Epoch 8/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 4688203743232.0000 - mae: 547206.9375\n",
            "Epoch 9/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 4364686065664.0000 - mae: 532029.0000\n",
            "Epoch 10/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 4615757103104.0000 - mae: 549105.3750\n",
            "Epoch 11/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 4356261806080.0000 - mae: 531576.8750\n",
            "Epoch 12/277\n",
            "278/278 [==============================] - 0s 2ms/step - loss: 4055774789632.0000 - mae: 533149.0000\n",
            "Epoch 13/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 5301841428480.0000 - mae: 514783.7812\n",
            "Epoch 14/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 5479024033792.0000 - mae: 519142.9375\n",
            "Epoch 15/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 3507425378304.0000 - mae: 513605.4062\n",
            "Epoch 16/277\n",
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            "Epoch 17/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 3101893066752.0000 - mae: 474264.7812\n",
            "Epoch 18/277\n",
            "278/278 [==============================] - 0s 2ms/step - loss: 3443651772416.0000 - mae: 495608.1562\n",
            "Epoch 19/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 2644599111680.0000 - mae: 445832.2188\n",
            "Epoch 20/277\n",
            "278/278 [==============================] - 0s 2ms/step - loss: 2492311797760.0000 - mae: 425926.3125\n",
            "Epoch 21/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 2182035800064.0000 - mae: 428094.7188\n",
            "Epoch 22/277\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 3024651288576.0000 - mae: 462775.2812\n",
            "Epoch 23/277\n",
            "278/278 [==============================] - 1s 5ms/step - loss: 2526780588032.0000 - mae: 440673.3125\n",
            "Epoch 24/277\n",
            "278/278 [==============================] - 1s 4ms/step - loss: 2147118874624.0000 - mae: 428507.5938\n",
            "Epoch 25/277\n",
            "278/278 [==============================] - 1s 4ms/step - loss: 2014016307200.0000 - mae: 415668.0938\n",
            "Epoch 26/277\n",
            "278/278 [==============================] - 1s 4ms/step - loss: 1980298297344.0000 - mae: 397587.4375\n",
            "Epoch 27/277\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 1922117271552.0000 - mae: 389501.3750\n",
            "Epoch 28/277\n",
            "278/278 [==============================] - 1s 4ms/step - loss: 1910525919232.0000 - mae: 392515.9375\n",
            "Epoch 29/277\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 1482474520576.0000 - mae: 382011.2812\n",
            "Epoch 30/277\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 1449583050752.0000 - mae: 387508.5625\n",
            "Epoch 31/277\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 2262401024000.0000 - mae: 411908.7500\n",
            "Epoch 32/277\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 3735060480000.0000 - mae: 470750.5000\n",
            "Epoch 33/277\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 2882445246464.0000 - mae: 466682.7500\n",
            "Epoch 34/277\n",
            "278/278 [==============================] - 1s 4ms/step - loss: 1448343764992.0000 - mae: 390651.5625\n",
            "Epoch 35/277\n",
            "278/278 [==============================] - 1s 4ms/step - loss: 1429935620096.0000 - mae: 375902.1875\n",
            "Epoch 36/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 1685290876928.0000 - mae: 378230.7812\n",
            "Epoch 37/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 1951260082176.0000 - mae: 391992.3750\n",
            "Epoch 38/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 1237155446784.0000 - mae: 366505.0000\n",
            "Epoch 39/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 1264792502272.0000 - mae: 369782.8750\n",
            "Epoch 40/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 1457512906752.0000 - mae: 359733.8125\n",
            "Epoch 41/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 1122565488640.0000 - mae: 341974.4062\n",
            "Epoch 42/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 1280883949568.0000 - mae: 355851.8125\n",
            "Epoch 43/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 1141653766144.0000 - mae: 344813.1562\n",
            "Epoch 44/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 1304668536832.0000 - mae: 351112.1250\n",
            "Epoch 45/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 1675807162368.0000 - mae: 364406.7812\n",
            "Epoch 46/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 1104972349440.0000 - mae: 351486.4062\n",
            "Epoch 47/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 1220295655424.0000 - mae: 337243.6875\n",
            "Epoch 48/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 1004384092160.0000 - mae: 331513.3750\n",
            "Epoch 49/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 2043537522688.0000 - mae: 374270.9688\n",
            "Epoch 50/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 1521234608128.0000 - mae: 380766.5000\n",
            "Epoch 51/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 1402726645760.0000 - mae: 350988.3125\n",
            "Epoch 52/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 998318538752.0000 - mae: 344437.0312\n",
            "Epoch 53/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 858583072768.0000 - mae: 320449.5000\n",
            "Epoch 54/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 895679528960.0000 - mae: 309920.5625\n",
            "Epoch 55/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 1061537906688.0000 - mae: 322853.9062\n",
            "Epoch 56/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 1059017064448.0000 - mae: 340652.3750\n",
            "Epoch 57/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 3911274463232.0000 - mae: 479875.4688\n",
            "Epoch 58/277\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 1195238227968.0000 - mae: 327423.9062\n",
            "Epoch 59/277\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 903686520832.0000 - mae: 316282.4062\n",
            "Epoch 60/277\n",
            "278/278 [==============================] - 1s 4ms/step - loss: 710658424832.0000 - mae: 295106.1250\n",
            "Epoch 61/277\n",
            "278/278 [==============================] - 1s 4ms/step - loss: 773899288576.0000 - mae: 304427.0938\n",
            "Epoch 62/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 1199017033728.0000 - mae: 340574.0312\n",
            "Epoch 63/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 885571190784.0000 - mae: 304748.5625\n",
            "Epoch 64/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 919324065792.0000 - mae: 309189.0625\n",
            "Epoch 65/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 932237541376.0000 - mae: 310671.4688\n",
            "Epoch 66/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 961342341120.0000 - mae: 316184.9062\n",
            "Epoch 67/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 948521205760.0000 - mae: 303574.8125\n",
            "Epoch 68/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 1011876167680.0000 - mae: 303396.9375\n",
            "Epoch 69/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 1018031898624.0000 - mae: 301021.7500\n",
            "Epoch 70/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 766392401920.0000 - mae: 292337.3750\n",
            "Epoch 71/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 839363723264.0000 - mae: 284948.6562\n",
            "Epoch 72/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 761063604224.0000 - mae: 278970.3438\n",
            "Epoch 73/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 999045529600.0000 - mae: 298507.6875\n",
            "Epoch 74/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 941307330560.0000 - mae: 307065.2500\n",
            "Epoch 75/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 1345979940864.0000 - mae: 320328.1250\n",
            "Epoch 76/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 716644155392.0000 - mae: 286330.9375\n",
            "Epoch 77/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 1005084540928.0000 - mae: 296444.9688\n",
            "Epoch 78/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 809735159808.0000 - mae: 305485.3438\n",
            "Epoch 79/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 706699591680.0000 - mae: 284908.4375\n",
            "Epoch 80/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 982952312832.0000 - mae: 302727.9688\n",
            "Epoch 81/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 1532661989376.0000 - mae: 330413.5312\n",
            "Epoch 82/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 735884083200.0000 - mae: 282837.6875\n",
            "Epoch 83/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 990281334784.0000 - mae: 287628.2812\n",
            "Epoch 84/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 743713275904.0000 - mae: 272927.3125\n",
            "Epoch 85/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 945349853184.0000 - mae: 278941.6562\n",
            "Epoch 86/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 619612274688.0000 - mae: 261677.7344\n",
            "Epoch 87/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 2242437709824.0000 - mae: 370044.2500\n",
            "Epoch 88/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 1346269872128.0000 - mae: 350169.9375\n",
            "Epoch 89/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 870465732608.0000 - mae: 315727.7500\n",
            "Epoch 90/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 596671528960.0000 - mae: 288832.1250\n",
            "Epoch 91/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 866271297536.0000 - mae: 285540.8750\n",
            "Epoch 92/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 531358220288.0000 - mae: 243661.9375\n",
            "Epoch 93/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 742207193088.0000 - mae: 267987.1562\n",
            "Epoch 94/277\n",
            "278/278 [==============================] - 0s 2ms/step - loss: 805117493248.0000 - mae: 272024.8125\n",
            "Epoch 95/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 555888345088.0000 - mae: 260969.0625\n",
            "Epoch 96/277\n",
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            "Epoch 97/277\n",
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            "Epoch 98/277\n",
            "278/278 [==============================] - 0s 2ms/step - loss: 665319309312.0000 - mae: 261250.4531\n",
            "Epoch 99/277\n",
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            "Epoch 100/277\n",
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            "Epoch 101/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 753898815488.0000 - mae: 261680.6406\n",
            "Epoch 102/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 717806829568.0000 - mae: 271624.0000\n",
            "Epoch 103/277\n",
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            "Epoch 104/277\n",
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            "Epoch 105/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 2042893041664.0000 - mae: 346523.1562\n",
            "Epoch 106/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 612696195072.0000 - mae: 267857.7812\n",
            "Epoch 107/277\n",
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            "Epoch 108/277\n",
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            "Epoch 109/277\n",
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            "Epoch 110/277\n",
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            "Epoch 111/277\n",
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            "Epoch 112/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 486531268608.0000 - mae: 238371.7656\n",
            "Epoch 113/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 578150072320.0000 - mae: 253612.5312\n",
            "Epoch 114/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 1509465260032.0000 - mae: 292483.4688\n",
            "Epoch 115/277\n",
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            "Epoch 116/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 1068749029376.0000 - mae: 308240.6875\n",
            "Epoch 117/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 594141184000.0000 - mae: 254506.2656\n",
            "Epoch 118/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 603099430912.0000 - mae: 245129.4062\n",
            "Epoch 119/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 678373294080.0000 - mae: 254505.4062\n",
            "Epoch 120/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 508600188928.0000 - mae: 237755.7500\n",
            "Epoch 121/277\n",
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            "Epoch 122/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 697363988480.0000 - mae: 264741.1875\n",
            "Epoch 123/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 1612268961792.0000 - mae: 325953.5625\n",
            "Epoch 124/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 793586958336.0000 - mae: 267950.5625\n",
            "Epoch 125/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 636021374976.0000 - mae: 255951.3125\n",
            "Epoch 126/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 460144345088.0000 - mae: 237093.7656\n",
            "Epoch 127/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 474603552768.0000 - mae: 239041.5469\n",
            "Epoch 128/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 542085218304.0000 - mae: 242224.0312\n",
            "Epoch 129/277\n",
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            "Epoch 130/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 523880824832.0000 - mae: 242182.7812\n",
            "Epoch 131/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 429579599872.0000 - mae: 234902.6562\n",
            "Epoch 132/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 566088499200.0000 - mae: 237642.0625\n",
            "Epoch 133/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 346344456192.0000 - mae: 217735.2500\n",
            "Epoch 134/277\n",
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            "Epoch 135/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 683341053952.0000 - mae: 241600.0000\n",
            "Epoch 136/277\n",
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            "Epoch 137/277\n",
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            "Epoch 138/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 1038690353152.0000 - mae: 270162.9688\n",
            "Epoch 139/277\n",
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            "Epoch 140/277\n",
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            "Epoch 141/277\n",
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            "Epoch 142/277\n",
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            "Epoch 143/277\n",
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            "Epoch 144/277\n",
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            "Epoch 145/277\n",
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            "Epoch 146/277\n",
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            "Epoch 147/277\n",
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            "Epoch 148/277\n",
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            "Epoch 149/277\n",
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            "Epoch 150/277\n",
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            "Epoch 151/277\n",
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            "Epoch 152/277\n",
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            "Epoch 153/277\n",
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            "Epoch 154/277\n",
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            "Epoch 155/277\n",
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            "Epoch 156/277\n",
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            "Epoch 157/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 361492774912.0000 - mae: 193070.1406\n",
            "Epoch 158/277\n",
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            "Epoch 159/277\n",
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            "Epoch 160/277\n",
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            "Epoch 161/277\n",
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            "Epoch 162/277\n",
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            "Epoch 163/277\n",
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            "Epoch 164/277\n",
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            "Epoch 165/277\n",
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            "Epoch 166/277\n",
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            "Epoch 167/277\n",
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            "Epoch 168/277\n",
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            "Epoch 169/277\n",
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            "Epoch 170/277\n",
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            "Epoch 171/277\n",
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            "Epoch 172/277\n",
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            "Epoch 173/277\n",
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            "Epoch 174/277\n",
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            "Epoch 175/277\n",
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            "Epoch 176/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 573046194176.0000 - mae: 192278.6562\n",
            "Epoch 177/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 335661367296.0000 - mae: 175972.0469\n",
            "Epoch 178/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 319415549952.0000 - mae: 170849.5312\n",
            "Epoch 179/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 246054748160.0000 - mae: 157030.1875\n",
            "Epoch 180/277\n",
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            "Epoch 181/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 236005031936.0000 - mae: 153561.0625\n",
            "Epoch 182/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 321638498304.0000 - mae: 162692.7344\n",
            "Epoch 183/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 305466179584.0000 - mae: 158522.3281\n",
            "Epoch 184/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 1227553636352.0000 - mae: 240028.0781\n",
            "Epoch 185/277\n",
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            "Epoch 186/277\n",
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            "Epoch 187/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 979083788288.0000 - mae: 250836.6562\n",
            "Epoch 188/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 293040488448.0000 - mae: 166779.7344\n",
            "Epoch 189/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 235999166464.0000 - mae: 157563.6719\n",
            "Epoch 190/277\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 224599572480.0000 - mae: 153751.5625\n",
            "Epoch 191/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 279914577920.0000 - mae: 175579.5625\n",
            "Epoch 192/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 366745976832.0000 - mae: 166191.8281\n",
            "Epoch 193/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 486028967936.0000 - mae: 188976.6250\n",
            "Epoch 194/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 316622176256.0000 - mae: 169987.5312\n",
            "Epoch 195/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 306354520064.0000 - mae: 163360.7500\n",
            "Epoch 196/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 279532765184.0000 - mae: 160580.6875\n",
            "Epoch 197/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 342337224704.0000 - mae: 170822.6719\n",
            "Epoch 198/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 500940832768.0000 - mae: 196188.4688\n",
            "Epoch 199/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 315825750016.0000 - mae: 163558.2344\n",
            "Epoch 200/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 209869176832.0000 - mae: 146103.2500\n",
            "Epoch 201/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 310332293120.0000 - mae: 167490.1406\n",
            "Epoch 202/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 271499264000.0000 - mae: 155924.3594\n",
            "Epoch 203/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 495079555072.0000 - mae: 177624.3906\n",
            "Epoch 204/277\n",
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            "Epoch 205/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 283008139264.0000 - mae: 154771.3281\n",
            "Epoch 206/277\n",
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            "Epoch 207/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 451024224256.0000 - mae: 173358.7812\n",
            "Epoch 208/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 437421670400.0000 - mae: 179470.4844\n",
            "Epoch 209/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 248952619008.0000 - mae: 152358.3594\n",
            "Epoch 210/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 206864695296.0000 - mae: 145513.3281\n",
            "Epoch 211/277\n",
            "278/278 [==============================] - 1s 3ms/step - loss: 382569021440.0000 - mae: 169280.2812\n",
            "Epoch 212/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 352089243648.0000 - mae: 165711.6719\n",
            "Epoch 213/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 640684064768.0000 - mae: 198038.8125\n",
            "Epoch 214/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 428736675840.0000 - mae: 172458.5469\n",
            "Epoch 215/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 287570165760.0000 - mae: 162946.5000\n",
            "Epoch 216/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 212899971072.0000 - mae: 147001.1719\n",
            "Epoch 217/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 215826759680.0000 - mae: 147140.4688\n",
            "Epoch 218/277\n",
            "278/278 [==============================] - 2s 6ms/step - loss: 214705405952.0000 - mae: 140965.8906\n",
            "Epoch 219/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 312985714688.0000 - mae: 160888.0000\n",
            "Epoch 220/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 415401508864.0000 - mae: 180268.0156\n",
            "Epoch 221/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 445665280000.0000 - mae: 176970.1562\n",
            "Epoch 222/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 635830861824.0000 - mae: 190305.0938\n",
            "Epoch 223/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 177953652736.0000 - mae: 138817.0781\n",
            "Epoch 224/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 247342956544.0000 - mae: 153099.6250\n",
            "Epoch 225/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 232017477632.0000 - mae: 145004.4844\n",
            "Epoch 226/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 200875311104.0000 - mae: 142373.6875\n",
            "Epoch 227/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 205111934976.0000 - mae: 148122.2500\n",
            "Epoch 228/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 624745185280.0000 - mae: 186668.2344\n",
            "Epoch 229/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 268127469568.0000 - mae: 153104.1406\n",
            "Epoch 230/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 371882786816.0000 - mae: 162356.3906\n",
            "Epoch 231/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 748248825856.0000 - mae: 212551.9375\n",
            "Epoch 232/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 344537497600.0000 - mae: 166759.2344\n",
            "Epoch 233/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 256734347264.0000 - mae: 152390.4844\n",
            "Epoch 234/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 474110722048.0000 - mae: 173810.5469\n",
            "Epoch 235/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 1192476278784.0000 - mae: 233875.8594\n",
            "Epoch 236/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 286040031232.0000 - mae: 158513.7031\n",
            "Epoch 237/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 214009675776.0000 - mae: 143797.4062\n",
            "Epoch 238/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 183036674048.0000 - mae: 136984.0781\n",
            "Epoch 239/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 161015218176.0000 - mae: 129861.1250\n",
            "Epoch 240/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 180249821184.0000 - mae: 136638.7500\n",
            "Epoch 241/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 320213123072.0000 - mae: 158451.5938\n",
            "Epoch 242/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 574150541312.0000 - mae: 187759.4531\n",
            "Epoch 243/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 437328478208.0000 - mae: 152402.6562\n",
            "Epoch 244/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 200630140928.0000 - mae: 141095.7188\n",
            "Epoch 245/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 181405155328.0000 - mae: 136224.8281\n",
            "Epoch 246/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 162860072960.0000 - mae: 134872.6875\n",
            "Epoch 247/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 162741829632.0000 - mae: 127033.7734\n",
            "Epoch 248/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 601710395392.0000 - mae: 177768.0625\n",
            "Epoch 249/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 1152584384512.0000 - mae: 224394.9375\n",
            "Epoch 250/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 468607500288.0000 - mae: 180928.9062\n",
            "Epoch 251/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 313953026048.0000 - mae: 152957.2344\n",
            "Epoch 252/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 176643604480.0000 - mae: 137248.2500\n",
            "Epoch 253/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 186825408512.0000 - mae: 139236.9688\n",
            "Epoch 254/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 167869054976.0000 - mae: 134529.2500\n",
            "Epoch 255/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 257563607040.0000 - mae: 144403.5000\n",
            "Epoch 256/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 969781936128.0000 - mae: 211167.1250\n",
            "Epoch 257/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 281500745728.0000 - mae: 161082.5938\n",
            "Epoch 258/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 251010695168.0000 - mae: 151354.5625\n",
            "Epoch 259/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 209767628800.0000 - mae: 140045.2656\n",
            "Epoch 260/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 190473633792.0000 - mae: 139428.6250\n",
            "Epoch 261/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 208026861568.0000 - mae: 143604.6250\n",
            "Epoch 262/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 170465280000.0000 - mae: 136174.9375\n",
            "Epoch 263/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 597076541440.0000 - mae: 170458.9844\n",
            "Epoch 264/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 807776485376.0000 - mae: 208629.3594\n",
            "Epoch 265/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 484616470528.0000 - mae: 174178.4531\n",
            "Epoch 266/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 302076854272.0000 - mae: 160968.6875\n",
            "Epoch 267/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 252735832064.0000 - mae: 153381.6562\n",
            "Epoch 268/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 189976068096.0000 - mae: 136704.3125\n",
            "Epoch 269/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 152837046272.0000 - mae: 125190.4141\n",
            "Epoch 270/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 192018120704.0000 - mae: 136490.2969\n",
            "Epoch 271/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 278773628928.0000 - mae: 143490.4688\n",
            "Epoch 272/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 374709059584.0000 - mae: 149163.8125\n",
            "Epoch 273/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 177628741632.0000 - mae: 134600.7969\n",
            "Epoch 274/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 239191130112.0000 - mae: 135376.8125\n",
            "Epoch 275/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 372787019776.0000 - mae: 152602.1250\n",
            "Epoch 276/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 248901910528.0000 - mae: 141475.4219\n",
            "Epoch 277/277\n",
            "278/278 [==============================] - 1s 2ms/step - loss: 178605539328.0000 - mae: 135132.6719\n"
          ]
        }
      ],
      "source": [
        "history = model.fit(x_train, y_train, batch_size = 10, epochs = 277)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dDyRdKJLMgwY"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NwxXktmpMgwZ"
      },
      "outputs": [],
      "source": [
        "loss = pd.DataFrame(history.history)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "ZXTObi2sjbgs",
        "outputId": "94fcafad-3bc6-43a3-eb24-a9a489ab6a00"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             loss            mae\n",
              "0    5.357524e+12  607552.625000\n",
              "1    5.280454e+12  590462.875000\n",
              "2    5.308135e+12  569305.937500\n",
              "3    5.142682e+12  582279.937500\n",
              "4    5.113990e+12  572383.187500\n",
              "..            ...            ...\n",
              "272  1.776287e+11  134600.796875\n",
              "273  2.391911e+11  135376.812500\n",
              "274  3.727870e+11  152602.125000\n",
              "275  2.489019e+11  141475.421875\n",
              "276  1.786055e+11  135132.671875\n",
              "\n",
              "[277 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-84c050db-c62b-4445-9472-f85bb6221a36\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>loss</th>\n",
              "      <th>mae</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.357524e+12</td>\n",
              "      <td>607552.625000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.280454e+12</td>\n",
              "      <td>590462.875000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5.308135e+12</td>\n",
              "      <td>569305.937500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.142682e+12</td>\n",
              "      <td>582279.937500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.113990e+12</td>\n",
              "      <td>572383.187500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>272</th>\n",
              "      <td>1.776287e+11</td>\n",
              "      <td>134600.796875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>273</th>\n",
              "      <td>2.391911e+11</td>\n",
              "      <td>135376.812500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>274</th>\n",
              "      <td>3.727870e+11</td>\n",
              "      <td>152602.125000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>275</th>\n",
              "      <td>2.489019e+11</td>\n",
              "      <td>141475.421875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>276</th>\n",
              "      <td>1.786055e+11</td>\n",
              "      <td>135132.671875</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>277 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-84c050db-c62b-4445-9472-f85bb6221a36')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-84c050db-c62b-4445-9472-f85bb6221a36 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-84c050db-c62b-4445-9472-f85bb6221a36');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "VW3QtqXRMgwZ",
        "outputId": "4ab1cccd-c88f-42e1-8f5b-d66407d7313b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Loss vs Epochs')"
            ]
          },
          "metadata": {},
          "execution_count": 76
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.plot(loss['loss'],label = 'loss')\n",
        "plt.legend()\n",
        "plt.title('Loss vs Epochs')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pLuk69gmMgwZ"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "4DiWBFx0MgwZ",
        "outputId": "15071219-2bd9-4195-b802-8e0e73aff470"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'mae vs epochs')"
            ]
          },
          "metadata": {},
          "execution_count": 77
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.plot(loss['mae'],label = 'mae')\n",
        "plt.legend()\n",
        "plt.title('mae vs epochs')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "uDwTv7SNMgwZ",
        "outputId": "c4c76913-e5c5-47da-ca65-831f6628b771"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Original Data vs Predicted data for ANN')"
            ]
          },
          "metadata": {},
          "execution_count": 78
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "y_pred = model.predict(x_test)\n",
        "test = pd.DataFrame(y_test)\n",
        "test.reset_index(drop=True, inplace=True)\n",
        "plt.plot(test['fire'], c='r',label='Original Data')\n",
        "plt.plot(y_pred, color = 'blue', label = 'Predicted data')\n",
        "plt.legend()\n",
        "plt.title('Original Data vs Predicted data for ANN')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2weBmbnuMgwa",
        "outputId": "34d0ef67-2667-4bed-ded2-9e16e80f8877"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7984181517790703"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ],
      "source": [
        "## R2 Score\n",
        "r2_score(test['fire'].values, y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6FnZn1rgMgwa"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rEprhxc5Mgwa"
      },
      "source": [
        "# SARIMA MODEL"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hEwHqVtSMgwa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fc49d0fc-9dea-44bc-d505-e6128bb13dcd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ]
        }
      ],
      "source": [
        "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
        "from statsmodels.tsa.seasonal import seasonal_decompose"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CmmMt9RuMgwa"
      },
      "outputs": [],
      "source": [
        "from datetime import date, timedelta\n",
        "dt = pd.date_range(start='1/1/2001', end='7/3/2014')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "UUmFiHlEMgwb",
        "outputId": "e6db035e-bd61-4e4a-ff1d-78229c77b9c4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   fire       veg        tair        tmax          prec      wind  year  \\\n",
              "0   0.0  0.519537  281.850800  287.042877  0.000000e+00  2.924010  2001   \n",
              "1   0.0  0.522642  282.009521  286.676941  1.428358e-07  2.436872  2001   \n",
              "2   0.0  0.525748  280.715393  283.814606  1.779684e-05  1.660926  2001   \n",
              "3   0.0  0.528854  281.651886  283.489288  9.013197e-05  2.029600  2001   \n",
              "4   0.0  0.531959  280.482574  283.335846  1.318250e-04  3.849517  2001   \n",
              "\n",
              "   month    my_date  \n",
              "0      1 2001-01-01  \n",
              "1      1 2001-01-02  \n",
              "2      1 2001-01-03  \n",
              "3      1 2001-01-04  \n",
              "4      1 2001-01-05  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ddd6ea78-25da-4238-a298-8dd988ca9814\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fire</th>\n",
              "      <th>veg</th>\n",
              "      <th>tair</th>\n",
              "      <th>tmax</th>\n",
              "      <th>prec</th>\n",
              "      <th>wind</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>my_date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.519537</td>\n",
              "      <td>281.850800</td>\n",
              "      <td>287.042877</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>2.924010</td>\n",
              "      <td>2001</td>\n",
              "      <td>1</td>\n",
              "      <td>2001-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.522642</td>\n",
              "      <td>282.009521</td>\n",
              "      <td>286.676941</td>\n",
              "      <td>1.428358e-07</td>\n",
              "      <td>2.436872</td>\n",
              "      <td>2001</td>\n",
              "      <td>1</td>\n",
              "      <td>2001-01-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.525748</td>\n",
              "      <td>280.715393</td>\n",
              "      <td>283.814606</td>\n",
              "      <td>1.779684e-05</td>\n",
              "      <td>1.660926</td>\n",
              "      <td>2001</td>\n",
              "      <td>1</td>\n",
              "      <td>2001-01-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.528854</td>\n",
              "      <td>281.651886</td>\n",
              "      <td>283.489288</td>\n",
              "      <td>9.013197e-05</td>\n",
              "      <td>2.029600</td>\n",
              "      <td>2001</td>\n",
              "      <td>1</td>\n",
              "      <td>2001-01-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.531959</td>\n",
              "      <td>280.482574</td>\n",
              "      <td>283.335846</td>\n",
              "      <td>1.318250e-04</td>\n",
              "      <td>3.849517</td>\n",
              "      <td>2001</td>\n",
              "      <td>1</td>\n",
              "      <td>2001-01-05</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ddd6ea78-25da-4238-a298-8dd988ca9814')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ddd6ea78-25da-4238-a298-8dd988ca9814 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-ddd6ea78-25da-4238-a298-8dd988ca9814');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ],
      "source": [
        "df['my_date'] = dt\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GNpCaKSaMgwb"
      },
      "outputs": [],
      "source": [
        "df.index = df['my_date']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0mSS2kATMgwb"
      },
      "outputs": [],
      "source": [
        "fr = []\n",
        "for i in range(0,df.shape[0]):\n",
        "    res = df['fire'][i]+1e-9\n",
        "    fr.append(res)\n",
        "df['fire_scaled'] = fr"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "id": "oSzBSfDPMgwb",
        "outputId": "5c12af6f-6fac-4f05-ca83-9a712c25e7ab"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe6994d08d0>"
            ]
          },
          "metadata": {},
          "execution_count": 85
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "df['fire_scaled'].plot()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_JgZWr0UMgwc"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        },
        "id": "RNTsX_H9Mgwc",
        "outputId": "3af4af93-0bc9-44c1-86b7-1c9b577590ed"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 86
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "result_a = seasonal_decompose(df['fire_scaled'], model = 'multiplicative')\n",
        "result_a.plot()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4eK-D-zFMgwc"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yh_JtXNfMgwc",
        "outputId": "92a98681-0039-4030-d039-598a05c1de82"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/base/tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
            "  % freq, ValueWarning)\n"
          ]
        }
      ],
      "source": [
        "model = SARIMAX(df['fire_scaled'], order = (3,1,1), seasonal_order = (3,2,1,12))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_fit = model.fit()"
      ],
      "metadata": {
        "id": "EXmJ5zHbnPug"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pfVUbMjXMgwc"
      },
      "outputs": [],
      "source": [
        "residuals = model_fit.resid"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mOj9_rJhMgwd",
        "outputId": "68b8ea1a-adc2-4f5b-c965-9aa481c21374"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "my_date\n",
              "2001-01-01    1.000000e-09\n",
              "2001-01-02   -9.330250e-10\n",
              "2001-01-03    5.692836e-10\n",
              "2001-01-04   -2.499541e-10\n",
              "2001-01-05    3.239211e-10\n",
              "                  ...     \n",
              "2014-06-29    3.482950e+00\n",
              "2014-06-30   -8.345323e+00\n",
              "2014-07-01   -1.302949e+00\n",
              "2014-07-02    1.930504e+01\n",
              "2014-07-03    5.467132e+01\n",
              "Length: 4932, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ],
      "source": [
        "residuals"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "id": "af2zc__MMgwd",
        "outputId": "c5983ceb-76b4-42af-c1b9-cdfa84c89b9c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe69faf3cd0>"
            ]
          },
          "metadata": {},
          "execution_count": 91
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "residuals.plot()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "q9B7kEUKMgwd"
      },
      "outputs": [],
      "source": [
        "output = model_fit.forecast()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yJ-CdHxhMgwd",
        "outputId": "a5130c54-6339-45b9-e75d-9e002381fb03"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2014-07-04    8.938392\n",
              "Freq: D, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ],
      "source": [
        "output"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZWx9ySAlMgwd"
      },
      "outputs": [],
      "source": [
        "yhat = model_fit.predict()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2xDOdcSTMgwe",
        "outputId": "58dd8cb0-fe98-4f67-b816-aafa2e2465da"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2001-01-01    0.000000e+00\n",
              "2001-01-02    1.933025e-09\n",
              "2001-01-03    4.307164e-10\n",
              "2001-01-04    1.249954e-09\n",
              "2001-01-05    6.760789e-10\n",
              "                  ...     \n",
              "2014-06-29   -3.482950e+00\n",
              "2014-06-30    8.345323e+00\n",
              "2014-07-01    1.302949e+00\n",
              "2014-07-02   -1.930504e+01\n",
              "2014-07-03   -5.467132e+01\n",
              "Freq: D, Length: 4932, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ],
      "source": [
        "yhat"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "gDFuCDjbMgwe",
        "outputId": "1e7dac5f-714c-47a5-bb78-cc0eb0e5e277"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fe6a117df10>]"
            ]
          },
          "metadata": {},
          "execution_count": 96
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.plot(yhat, color = 'red')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CWIQ1GYqMgwe",
        "outputId": "ac6e38ed-0791-4f89-d552-0bb30f8afb54"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9999042977076221"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ],
      "source": [
        "## R2 Score\n",
        "r2_score(df['fire_scaled'].values, yhat)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Fj3FKMeWMgwf"
      },
      "outputs": [],
      "source": [
        "pred = pd.DataFrame(yhat)\n",
        "pred = pred.rename(columns={0: 'predicted_mean'})\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "hsov89eBk0rm",
        "outputId": "aad46f0c-21e3-4c13-f528-beb5ba17ffba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            predicted_mean\n",
              "2001-01-01    0.000000e+00\n",
              "2001-01-02    1.933025e-09\n",
              "2001-01-03    4.307164e-10\n",
              "2001-01-04    1.249954e-09\n",
              "2001-01-05    6.760789e-10\n",
              "...                    ...\n",
              "2014-06-29   -3.482950e+00\n",
              "2014-06-30    8.345323e+00\n",
              "2014-07-01    1.302949e+00\n",
              "2014-07-02   -1.930504e+01\n",
              "2014-07-03   -5.467132e+01\n",
              "\n",
              "[4932 rows x 1 columns]"
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              "      <th>2001-01-01</th>\n",
              "      <td>0.000000e+00</td>\n",
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              "      <td>1.249954e-09</td>\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e5Z0x4p7Mgwf",
        "outputId": "99e82f00-17b6-48db-efac-9bb0efd9fbe6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "As predicted by the model;\n",
            "The month 8 of the year 2001 is when the maximum value of fire was recorded. The value was 2725627.9851791654\n",
            "The month 7 of the year 2002 is when the maximum value of fire was recorded. The value was 763250.6163289691\n",
            "The month 7 of the year 2003 is when the maximum value of fire was recorded. The value was 490579.6718378853\n",
            "The month 6 of the year 2004 is when the maximum value of fire was recorded. The value was 2727171.3589626397\n",
            "The month 7 of the year 2005 is when the maximum value of fire was recorded. The value was 36142574.413049966\n",
            "The month 7 of the year 2006 is when the maximum value of fire was recorded. The value was 4687874.744677736\n",
            "The month 1 of the year 2007 is when the maximum value of fire was recorded. The value was 394.29912842165896\n",
            "The month 1 of the year 2008 is when the maximum value of fire was recorded. The value was 234.51239181772186\n",
            "The month 8 of the year 2009 is when the maximum value of fire was recorded. The value was 2929630.0077339415\n",
            "The month 7 of the year 2010 is when the maximum value of fire was recorded. The value was 9095516.645940145\n",
            "The month 6 of the year 2011 is when the maximum value of fire was recorded. The value was 5508849.2869936135\n",
            "The month 1 of the year 2012 is when the maximum value of fire was recorded. The value was 130.42757575583067\n",
            "The month 8 of the year 2013 is when the maximum value of fire was recorded. The value was 2924194.7415116243\n",
            "The month 1 of the year 2014 is when the maximum value of fire was recorded. The value was 46.41018518185386\n"
          ]
        }
      ],
      "source": [
        "# Predicted Worst month in every year\n",
        "years = range(2001, 2015)\n",
        "print('As predicted by the model;')\n",
        "for i in years:\n",
        "    year_df = pred[pred.index.year.isin([i])].copy()\n",
        "    max_value = year_df['predicted_mean'].max()\n",
        "    worst_month = year_df['predicted_mean'].idxmax().month\n",
        "    print('The month {} of the year {} is when the maximum value of fire was recorded. The value was {}'.format(\n",
        "        worst_month,i,max_value))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5rw32eFlMgwg"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9HStnKnUMgwg"
      },
      "source": [
        "### Future Prediction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PfSRv4l4Mgwg"
      },
      "outputs": [],
      "source": [
        "def predict(days):#days-Number of days ahead that you want to be predicted\n",
        "    result = model_fit.forecast(days)\n",
        "    for i in range(0,days-1):\n",
        "        if result[i]>0.00001:\n",
        "            text = 'Possible Fire'\n",
        "        else:\n",
        "            text = 'No Possible Fire'\n",
        "        date = result.index[i]\n",
        "        print(date.strftime('%Y-%m-%d'),': {}'.format(text))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "upnSiujSMgwh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3ae4a0d4-537b-45d1-cbfe-5fb1fb7ba623"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2014-07-04 : Possible Fire\n",
            "2014-07-05 : Possible Fire\n",
            "2014-07-06 : No Possible Fire\n",
            "2014-07-07 : No Possible Fire\n",
            "2014-07-08 : No Possible Fire\n",
            "2014-07-09 : Possible Fire\n",
            "2014-07-10 : Possible Fire\n",
            "2014-07-11 : Possible Fire\n",
            "2014-07-12 : Possible Fire\n"
          ]
        }
      ],
      "source": [
        "future_pred = predict(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SsqwUaViMgwh"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pc8N87w-Mgwh"
      },
      "source": [
        "### Worst year and month by the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FxJdBCPtMgwh"
      },
      "outputs": [],
      "source": [
        "idx_max = np.argmax(yhat)\n",
        "time = yhat.index[idx_max]\n",
        "month = time.month\n",
        "year = time.year"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Nh1e19bZMgwi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "009f6260-22ba-4964-c3be-6c70bcf22bd6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Worst Year as per the model: 2005\n",
            "Worst Month as per the model: 7\n"
          ]
        }
      ],
      "source": [
        "print('Worst Year as per the model: {}'.format(year))\n",
        "print('Worst Month as per the model: {}'.format(month))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wmSFMbO-Mgwi"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BHeaSnbuMgwi"
      },
      "source": [
        "## ConvLSTM Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TL4WXKvqMgwi"
      },
      "outputs": [],
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Conv1D, Flatten\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.layers.convolutional_recurrent import ConvLSTM2D"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NE3KBINCMgwi"
      },
      "outputs": [],
      "source": [
        "xtrain_reshaped = x_train.reshape(x_train.shape[0], x_train.shape[1], 1) "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.layers import Input, Dense, LSTM, MaxPooling1D, Conv1D\n",
        "from keras.models import Model\n",
        "from tensorflow.keras import layers\n",
        "from keras.layers import InputLayer, ConvLSTM1D\n",
        "from keras import layers\n",
        "from keras.models import Sequential\n",
        "from keras.layers.convolutional import Conv3D\n",
        "from keras.layers import Conv2DTranspose, ConvLSTM2D, BatchNormalization, TimeDistributed, Conv2D, Dense, Activation, LSTM\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import activations\n",
        "from tensorflow.keras.optimizers import SGD, Adam\n"
      ],
      "metadata": {
        "id": "YXllR0EpRzXw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "# Input shape: (samples, time, channels, rows, cols) see: https://keras.io/layers/recurrent/#convlstm2d\n",
        "model.add(ConvLSTM1D(\n",
        "        filters=32,\n",
        "        kernel_size=2 ,\n",
        "        input_shape=(None, 7,1),\n",
        "        padding='same',\n",
        "        return_sequences=True))\n",
        "model.add(layers.Activation(activations.relu))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(64, kernel_initializer='TruncatedNormal'))\n",
        "model.add(layers.Activation(activations.relu))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(Dense(32, kernel_initializer='TruncatedNormal'))\n",
        "model.add(layers.Activation(activations.relu))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(Dense(1))\n"
      ],
      "metadata": {
        "id": "rEfhUqXhYiUD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "outputId": "8aa581a6-084b-4d2d-e35e-7392e4194784"
      },
      "execution_count": null,
      "outputs": [
        {
          "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-141-4ddb286094b9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFlatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDense\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkernel_initializer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'TruncatedNormal'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mActivation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mactivations\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDropout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/training/tracking/base.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    627\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_self_setattr_tracking\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    628\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 629\u001b[0;31m       \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    630\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    631\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_self_setattr_tracking\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprevious_value\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m       \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/layers/core/dense.py\u001b[0m in \u001b[0;36mbuild\u001b[0;34m(self, input_shape)\u001b[0m\n\u001b[1;32m    137\u001b[0m     \u001b[0mlast_dim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdimension_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_shape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    138\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlast_dim\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 139\u001b[0;31m       raise ValueError('The last dimension of the inputs to a Dense layer '\n\u001b[0m\u001b[1;32m    140\u001b[0m                        \u001b[0;34m'should be defined. Found None. '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m                        f'Full input shape received: {input_shape}')\n",
            "\u001b[0;31mValueError\u001b[0m: The last dimension of the inputs to a Dense layer should be defined. Found None. Full input shape received: (None, None)"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr= 1e-1\n",
        "epochs = 50 #135\n",
        "bs = 2**8\n",
        "\n",
        "# compile & fit\n",
        "model.compile(optimizer=adam, loss = ['mse'], metrics=['mse'])\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "id": "jrgMbMireUBU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eb0b5ed4-4869-49d2-8ae5-13d0ea3ff2a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv_lstm1d_2 (ConvLSTM1D)  (None, None, 7, 32)       8576      \n",
            "                                                                 \n",
            " activation_4 (Activation)   (None, None, 7, 32)       0         \n",
            "                                                                 \n",
            " batch_normalization_2 (Batc  (None, None, 7, 32)      128       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " conv_lstm1d_3 (ConvLSTM1D)  (None, 7, 32)             16512     \n",
            "                                                                 \n",
            " activation_5 (Activation)   (None, 7, 32)             0         \n",
            "                                                                 \n",
            " batch_normalization_3 (Batc  (None, 7, 32)            128       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 7, 32)             0         \n",
            "                                                                 \n",
            " flatten_1 (Flatten)         (None, 224)               0         \n",
            "                                                                 \n",
            " dense_9 (Dense)             (None, 64)                14400     \n",
            "                                                                 \n",
            " activation_6 (Activation)   (None, 64)                0         \n",
            "                                                                 \n",
            " dropout_4 (Dropout)         (None, 64)                0         \n",
            "                                                                 \n",
            " dense_10 (Dense)            (None, 32)                2080      \n",
            "                                                                 \n",
            " activation_7 (Activation)   (None, 32)                0         \n",
            "                                                                 \n",
            " dropout_5 (Dropout)         (None, 32)                0         \n",
            "                                                                 \n",
            " dense_11 (Dense)            (None, 1)                 33        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 41,857\n",
            "Trainable params: 41,729\n",
            "Non-trainable params: 128\n",
            "_________________________________________________________________\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(SGD, self).__init__(name, **kwargs)\n",
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(Adam, self).__init__(name, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_layer = Input(shape=( 7, 1))\n",
        "conv1 = Conv1D(filters=32,\n",
        "kernel_size=2,\n",
        "strides=1,\n",
        "padding='same', \n",
        "activation='relu')(input_layer)\n",
        "pool1 = MaxPooling1D(pool_size=4)(conv1)\n",
        "lstm1 = LSTM(32)(pool1)\n",
        "output_layer = Dense(64, activation='relu')(lstm1)\n",
        "model = Model(inputs=input_layer, outputs=output_layer)"
      ],
      "metadata": {
        "id": "7ROR3BuvR2yY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "id": "aBikz_usR-Cl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "11903ccb-cb21-4791-f69e-67c5ce5d79f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_5 (InputLayer)        [(None, 7, 1)]            0         \n",
            "                                                                 \n",
            " conv1d_4 (Conv1D)           (None, 7, 32)             96        \n",
            "                                                                 \n",
            " max_pooling1d_4 (MaxPooling  (None, 1, 32)            0         \n",
            " 1D)                                                             \n",
            "                                                                 \n",
            " lstm_4 (LSTM)               (None, 32)                8320      \n",
            "                                                                 \n",
            " dense_15 (Dense)            (None, 64)                2112      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 10,528\n",
            "Trainable params: 10,528\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "keras.utils.plot_model(model , to_file='model.png' , show_shapes = True)"
      ],
      "metadata": {
        "id": "QJCBjBfZSBHa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "outputId": "dfea68d3-841b-4cf6-846c-6e45a0c82eb9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.Image object>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "early_stopping_monitor = EarlyStopping(patience=5000)\n",
        "\n",
        "\n",
        "\n",
        "history = model.fit(xtrain_reshaped, y_train, epochs=epochs, batch_size=bs, verbose=1, callbacks=[early_stopping_monitor])\n",
        "\n",
        "print(model.summary())"
      ],
      "metadata": {
        "id": "N1WHmlnKfFA9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "outputId": "8115ab48-c2d3-498a-ef1b-ed2b69570f30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-140-4dab95283529>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mhistory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxtrain_reshaped\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mearly_stopping_monitor\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m       \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36m_assert_compile_was_called\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   3057\u001b[0m     \u001b[0;31m# (i.e. whether the model is built and its inputs/outputs are set).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3058\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_compiled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3059\u001b[0;31m       raise RuntimeError('You must compile your model before '\n\u001b[0m\u001b[1;32m   3060\u001b[0m                          \u001b[0;34m'training/testing. '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3061\u001b[0m                          'Use `model.compile(optimizer, loss)`.')\n",
            "\u001b[0;31mRuntimeError\u001b[0m: You must compile your model before training/testing. Use `model.compile(optimizer, loss)`."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam', loss='categorical_crossentropy',metrics = 'mae')"
      ],
      "metadata": {
        "id": "XIUSlCppSK6w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "bETg0PwBwVZD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IxR0D8TSMgwj"
      },
      "outputs": [],
      "source": [
        "model = Sequential()\n",
        "model.add(Conv1D(32, 2, activation=\"relu\", input_shape=(7, 1)))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(64, activation=\"relu\"))\n",
        "model.add(Dense(1))\n",
        "model.compile(optimizer=keras.optimizers.Adam(learning_rate = 1e-1), loss = 'mean_squared_error',metrics = 'mae')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x0WqTFgfMgwj"
      },
      "outputs": [],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "keras.utils.plot_model(model , to_file='model.png' , show_shapes = True)"
      ],
      "metadata": {
        "id": "FS4v2-J6RHH1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(xtrain_reshaped, y_train, batch_size = 10, epochs = 277)"
      ],
      "metadata": {
        "id": "BcYhQm81fCRR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 936
        },
        "outputId": "96d73f6f-91c9-4e10-a0c4-5dba890b150f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/277\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-150-7fa1728bbf6c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhistory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxtrain_reshaped\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m277\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m       \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py\u001b[0m in \u001b[0;36mautograph_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1145\u001b[0m           \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint:disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1146\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"ag_error_metadata\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1147\u001b[0;31m               \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mag_error_metadata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1148\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1149\u001b[0m               \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: in user code:\n\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\", line 1021, in train_function  *\n        return step_function(self, iterator)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\", line 1010, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\", line 1000, in run_step  **\n        outputs = model.train_step(data)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\", line 860, in train_step\n        loss = self.compute_loss(x, y, y_pred, sample_weight)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\", line 919, in compute_loss\n        y, y_pred, sample_weight, regularization_losses=self.losses)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/compile_utils.py\", line 201, in __call__\n        loss_value = loss_obj(y_t, y_p, sample_weight=sw)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/losses.py\", line 141, in __call__\n        losses = call_fn(y_true, y_pred)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/losses.py\", line 245, in call  **\n        return ag_fn(y_true, y_pred, **self._fn_kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/losses.py\", line 1790, in categorical_crossentropy\n        y_true, y_pred, from_logits=from_logits, axis=axis)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/backend.py\", line 5083, in categorical_crossentropy\n        target.shape.assert_is_compatible_with(output.shape)\n\n    ValueError: Shapes (None, 1) and (None, 64) are incompatible\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zo05fnBkMgwj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 130
        },
        "outputId": "f41efd87-bc00-4860-dd75-c01d5e3b6574"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-149-fab4268a1296>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    history = model.fit((xtrain_reshaped, y_train, batch_size = bs, epochs = 50)\u001b[0m\n\u001b[0m                                                              ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
          ]
        }
      ],
      "source": [
        "history = model.fit((xtrain_reshaped, y_train, batch_size = bs, epochs = 50)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NeATU0mmMgwj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "83987f94-1b2d-45eb-c348-493d49f2c64f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            loss           mae\n",
              "0   5.897180e+12  482920.06250\n",
              "1   5.897179e+12  482920.09375\n",
              "2   5.897179e+12  482920.15625\n",
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              "12  5.897179e+12  482920.09375\n",
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              "14  5.897179e+12  482920.18750\n",
              "15  5.897179e+12  482920.09375\n",
              "16  5.897180e+12  482920.15625\n",
              "17  5.897179e+12  482920.15625\n",
              "18  5.897180e+12  482920.15625\n",
              "19  5.897179e+12  482920.18750\n",
              "20  5.897179e+12  482920.09375\n",
              "21  5.897179e+12  482920.09375\n",
              "22  5.897180e+12  482920.15625\n",
              "23  5.897179e+12  482920.15625\n",
              "24  5.897180e+12  482920.15625\n",
              "25  5.897180e+12  482920.09375\n",
              "26  5.897180e+12  482920.15625\n",
              "27  5.897179e+12  482920.15625\n",
              "28  5.897180e+12  482920.09375\n",
              "29  5.897179e+12  482920.15625\n",
              "30  5.897179e+12  482920.15625\n",
              "31  5.897179e+12  482920.15625\n",
              "32  5.897179e+12  482920.15625\n",
              "33  5.897180e+12  482920.09375\n",
              "34  5.897180e+12  482920.15625\n",
              "35  5.897179e+12  482920.15625\n",
              "36  5.897179e+12  482920.09375\n",
              "37  5.897179e+12  482920.09375\n",
              "38  5.897179e+12  482920.15625\n",
              "39  5.897180e+12  482920.15625\n",
              "40  5.897179e+12  482920.15625\n",
              "41  5.897179e+12  482920.09375\n",
              "42  5.897179e+12  482920.15625\n",
              "43  5.897180e+12  482920.09375\n",
              "44  5.897179e+12  482920.15625\n",
              "45  5.897179e+12  482920.09375\n",
              "46  5.897179e+12  482920.09375\n",
              "47  5.897180e+12  482920.09375\n",
              "48  5.897180e+12  482920.09375\n",
              "49  5.897179e+12  482920.09375"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b98022c3-d6a1-4a09-8293-297b2734af28\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>loss</th>\n",
              "      <th>mae</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.897180e+12</td>\n",
              "      <td>482920.06250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.897179e+12</td>\n",
              "      <td>482920.09375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5.897179e+12</td>\n",
              "      <td>482920.15625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.897180e+12</td>\n",
              "      <td>482920.15625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.897179e+12</td>\n",
              "      <td>482920.09375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5.897179e+12</td>\n",
              "      <td>482920.09375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5.897179e+12</td>\n",
              "      <td>482920.18750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>5.897179e+12</td>\n",
              "      <td>482920.09375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>5.897179e+12</td>\n",
              "      <td>482920.09375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>5.897179e+12</td>\n",
              "      <td>482920.09375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>5.897179e+12</td>\n",
              "      <td>482920.15625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>5.897180e+12</td>\n",
              "      <td>482920.09375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>5.897179e+12</td>\n",
              "      <td>482920.09375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>5.897179e+12</td>\n",
              "      <td>482920.15625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>5.897179e+12</td>\n",
              "      <td>482920.18750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>5.897179e+12</td>\n",
              "      <td>482920.09375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>5.897180e+12</td>\n",
              "      <td>482920.15625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>5.897179e+12</td>\n",
              "      <td>482920.15625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>5.897180e+12</td>\n",
              "      <td>482920.15625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>5.897179e+12</td>\n",
              "      <td>482920.18750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>5.897179e+12</td>\n",
              "      <td>482920.09375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>5.897179e+12</td>\n",
              "      <td>482920.09375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>5.897180e+12</td>\n",
              "      <td>482920.15625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>5.897179e+12</td>\n",
              "      <td>482920.15625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>5.897180e+12</td>\n",
              "      <td>482920.15625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>5.897180e+12</td>\n",
              "      <td>482920.09375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>5.897180e+12</td>\n",
              "      <td>482920.15625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>5.897179e+12</td>\n",
              "      <td>482920.15625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>5.897180e+12</td>\n",
              "      <td>482920.09375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>5.897179e+12</td>\n",
              "      <td>482920.15625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>5.897179e+12</td>\n",
              "      <td>482920.15625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>5.897179e+12</td>\n",
              "      <td>482920.15625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>5.897179e+12</td>\n",
              "      <td>482920.15625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>5.897180e+12</td>\n",
              "      <td>482920.09375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>5.897180e+12</td>\n",
              "      <td>482920.15625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>5.897179e+12</td>\n",
              "      <td>482920.15625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>5.897179e+12</td>\n",
              "      <td>482920.09375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>5.897179e+12</td>\n",
              "      <td>482920.09375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>5.897179e+12</td>\n",
              "      <td>482920.15625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>5.897180e+12</td>\n",
              "      <td>482920.15625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>5.897179e+12</td>\n",
              "      <td>482920.15625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>5.897179e+12</td>\n",
              "      <td>482920.09375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>5.897179e+12</td>\n",
              "      <td>482920.15625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>5.897180e+12</td>\n",
              "      <td>482920.09375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>5.897179e+12</td>\n",
              "      <td>482920.15625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>5.897179e+12</td>\n",
              "      <td>482920.09375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>5.897179e+12</td>\n",
              "      <td>482920.09375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>5.897180e+12</td>\n",
              "      <td>482920.09375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>5.897180e+12</td>\n",
              "      <td>482920.09375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>5.897179e+12</td>\n",
              "      <td>482920.09375</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b98022c3-d6a1-4a09-8293-297b2734af28')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b98022c3-d6a1-4a09-8293-297b2734af28 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-b98022c3-d6a1-4a09-8293-297b2734af28');\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",
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              "          element.appendChild(docLink);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ],
      "source": [
        "loss = pd.DataFrame(history.history)\n",
        "loss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ju84kBHHMgwk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "cd2462b3-dbd8-4ece-aebd-6e05986c0ecc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Loss vs Epochs')"
            ]
          },
          "metadata": {},
          "execution_count": 117
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.plot(loss['loss'],label = 'loss')\n",
        "plt.legend()\n",
        "plt.title('Loss vs Epochs')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nSVEs2MzMgwk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "c7976aee-d4bb-407e-92f3-efde4edddb97"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'mae vs epochs')"
            ]
          },
          "metadata": {},
          "execution_count": 118
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.plot(loss['mae'],label = 'mae')\n",
        "plt.legend()\n",
        "plt.title('mae vs epochs')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "He-9_BCfMgwl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "outputId": "8dd0ae27-56f3-475d-bd76-080ca4a9aae4"
      },
      "outputs": [
        {
          "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-120-cd4dd8054b30>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m## R2 score\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mr2_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36mr2_score\u001b[0;34m(y_true, y_pred, sample_weight, multioutput)\u001b[0m\n\u001b[1;32m    788\u001b[0m     \"\"\"\n\u001b[1;32m    789\u001b[0m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[0;32m--> 790\u001b[0;31m         \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmultioutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    791\u001b[0m     )\n\u001b[1;32m    792\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[0;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[1;32m    105\u001b[0m         raise ValueError(\n\u001b[1;32m    106\u001b[0m             \"y_true and y_pred have different number of output ({0}!={1})\".format(\n\u001b[0;32m--> 107\u001b[0;31m                 \u001b[0my_true\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    108\u001b[0m             )\n\u001b[1;32m    109\u001b[0m         )\n",
            "\u001b[0;31mValueError\u001b[0m: y_true and y_pred have different number of output (1!=64)"
          ]
        }
      ],
      "source": [
        "## R2 score\n",
        "\n",
        "r2_score(y_test, model.predict(x_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VwfRJLHTMgwl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "6f60821b-ae6f-4bf2-fa12-2dd5c9cf6dfa"
      },
      "outputs": [
        {
          "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-121-e59f48f1f04b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mr2_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36mr2_score\u001b[0;34m(y_true, y_pred, sample_weight, multioutput)\u001b[0m\n\u001b[1;32m    788\u001b[0m     \"\"\"\n\u001b[1;32m    789\u001b[0m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[0;32m--> 790\u001b[0;31m         \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmultioutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    791\u001b[0m     )\n\u001b[1;32m    792\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[0;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[1;32m     92\u001b[0m         \u001b[0mthe\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0margument\u001b[0m \u001b[0mpassed\u001b[0m \u001b[0mto\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m     \"\"\"\n\u001b[0;32m---> 94\u001b[0;31m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     95\u001b[0m     \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     96\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_consistent_length\u001b[0;34m(*arrays)\u001b[0m\n\u001b[1;32m    332\u001b[0m         raise ValueError(\n\u001b[1;32m    333\u001b[0m             \u001b[0;34m\"Found input variables with inconsistent numbers of samples: %r\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 334\u001b[0;31m             \u001b[0;34m%\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlengths\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    335\u001b[0m         )\n\u001b[1;32m    336\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Found input variables with inconsistent numbers of samples: [2774, 3945]"
          ]
        }
      ],
      "source": [
        "r2_score(y_train, y_train_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VvZ2ULc1Mgwl"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wEpo-FFxMgwl"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
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      "nbconvert_exporter": "python",
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    "colab": {
      "name": "Fire_Prediction_final.ipynb",
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      "collapsed_sections": [
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        "Pc8N87w-Mgwh"
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