Fighting_Fire_with_Data_Starter_Notebook.ipynb

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      "source": [
        "# Fighting Fire with Data - Starter Notebook\n",
        "\n",
        "This notebook should help you get started with the 'Fighting Fire with Data' hackathon on Zindi. In it we cover:\n",
        "- Loading the data\n",
        "- Simple EDA and an example of feature enginnering\n",
        "- Suggestions for validation split\n",
        "- Creating a simple model\n",
        "- Making a submission\n",
        "- Some tips for improving your score\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xKcJuq7bxof_"
      },
      "source": [
        "# Setup\n",
        "\n",
        "Make sure the files are available in the current directory. On Colab, upload them using the files tab on the left."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
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        },
        "id": "lwa4zIHBGh41",
        "outputId": "e0479e87-576f-4ada-c787-48698cf2e2e3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "SampleSubmission.csv  Test.csv\tTrain.csv  variable_definitions.csv\n"
          ]
        }
      ],
      "source": [
        "!ls *.csv # List files to check train, ss and test are present"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "id": "2lLzfdilxfsY"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from sklearn.linear_model import RidgeCV\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "from matplotlib import pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
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          "height": 1000
        },
        "id": "_xLOsPcFSwOz",
        "outputId": "f3329720-65f3-4b22-ecf4-49d861233104"
      },
      "outputs": [
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              "        document.querySelector('#id_e7b40c3f-c6d8-46e6-acdb-b00b854567d5 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('var_defs');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "         Variable                                        Description\n",
              "0              ID  The IDs take the form of [area ID]_yyyy-mm-dd....\n",
              "1            area                                            Area ID\n",
              "2            date          The date that the data is aggregated over\n",
              "3             lat                 Latitude of the center of the area\n",
              "4             lon                Longitude of the center of the area\n",
              "5       burn_area                       Percentage of the area burnt\n",
              "6     climate_aet  Actual evapotranspiration, derived using a one...\n",
              "7     climate_def  Climate water deficit, derived using a one-dim...\n",
              "8    climate_pdsi                      Palmer Drought Severity Index\n",
              "9     climate_pet  Reference evapotranspiration (ASCE Penman-Mont...\n",
              "10     climate_pr                         Precipitation accumulation\n",
              "11     climate_ro  Runoff, derived using a one-dimensional soil w...\n",
              "12   climate_soil  Soil moisture, derived using a one-dimensional...\n",
              "13   climate_srad               Downward surface shortwave radiation\n",
              "14    climate_swe  Snow water equivalent, derived using a one-dim...\n",
              "15   climate_tmmn                                Minimum temperature\n",
              "16   climate_tmmx                                Maximum temperature\n",
              "17    climate_vap                                     Vapor pressure\n",
              "18    climate_vpd                             Vapor pressure deficit\n",
              "19     climate_vs                                  Wind-speed at 10m\n",
              "20      elevation                                     Land elevation\n",
              "21    landcover_0  Water Bodies: at least 60% of area is covered ...\n",
              "22    landcover_1  Evergreen Needleleaf Vegetation: dominated by ...\n",
              "23    landcover_2  Evergreen Broadleaf Vegetation: dominated by e...\n",
              "24    landcover_3  Deciduous Needleleaf Vegetation: dominated by ...\n",
              "25    landcover_4  Deciduous Broadleaf Vegetation: dominated by d...\n",
              "26    landcover_5  Annual Broadleaf Vegetation: dominated by herb...\n",
              "27    landcover_6  Annual Grass Vegetation: dominated by herbaceo...\n",
              "28    landcover_7  Non-Vegetated Lands: at least 60% of area is n...\n",
              "29    landcover_8  Urban and Built-up Lands: at least 30% impervi...\n",
              "30  precipitation         Merged microwave/IR precipitation estimate"
            ]
          },
          "execution_count": 76,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# The training data.\n",
        "var_defs = pd.read_csv('variable_definitions.csv')\n",
        "var_defs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "uaQM7lJHxRoe",
        "outputId": "c1992756-e707-443b-b85b-0289d670ec34"
      },
      "outputs": [
        {
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              "<p>5 rows × 29 columns</p>\n",
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              "\n",
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            ],
            "text/plain": [
              "             ID        lat        lon  burn_area  climate_aet  climate_def  \\\n",
              "0  0_2001-01-01 -15.858835  29.237029        0.0         1195            0   \n",
              "1  1_2001-01-01 -15.858835  29.487029        0.0         1196            0   \n",
              "2  2_2001-01-01 -15.858835  29.737029        0.0         1190            0   \n",
              "3  3_2001-01-01 -15.858835  29.987029        0.0         1144            0   \n",
              "4  4_2001-01-01 -15.858835  30.237029        0.0         1187            0   \n",
              "\n",
              "   climate_pdsi  climate_pet  climate_pr  climate_ro  ...  landcover_0  \\\n",
              "0           263         1195         206          10  ...          0.0   \n",
              "1           232         1196         201          10  ...          0.0   \n",
              "2           314         1190         192          10  ...          0.0   \n",
              "3           321         1144         186          66  ...          0.0   \n",
              "4           413         1187         186           9  ...          0.0   \n",
              "\n",
              "   landcover_1  landcover_2  landcover_3  landcover_4  landcover_5  \\\n",
              "0          0.0     0.018654          0.0     0.714446     0.012174   \n",
              "1          0.0     0.000000          0.0     0.654783     0.000095   \n",
              "2          0.0     0.000000          0.0     0.516421     0.000000   \n",
              "3          0.0     0.000000          0.0     0.299000     0.163902   \n",
              "4          0.0     0.000000          0.0     0.277392     0.067742   \n",
              "\n",
              "   landcover_6  landcover_7  landcover_8  precipitation  \n",
              "0     0.244890     0.009836          0.0       0.256932  \n",
              "1     0.345121     0.000000          0.0       0.273093  \n",
              "2     0.483579     0.000000          0.0       0.285109  \n",
              "3     0.537098     0.000000          0.0       0.298418  \n",
              "4     0.654866     0.000000          0.0       0.315621  \n",
              "\n",
              "[5 rows x 29 columns]"
            ]
          },
          "execution_count": 77,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# The training data.\n",
        "train = pd.read_csv('Train.csv')\n",
        "train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 147
        },
        "id": "RHksNuWBxeDn",
        "outputId": "060fcee1-56ba-4de6-dec1-c50b935ac72c"
      },
      "outputs": [
        {
          "data": {
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              "type": "dataframe",
              "variable_name": "test"
            },
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>lat</th>\n",
              "      <th>lon</th>\n",
              "      <th>climate_aet</th>\n",
              "      <th>climate_def</th>\n",
              "      <th>climate_pdsi</th>\n",
              "      <th>climate_pet</th>\n",
              "      <th>climate_pr</th>\n",
              "      <th>climate_ro</th>\n",
              "      <th>climate_soil</th>\n",
              "      <th>...</th>\n",
              "      <th>landcover_0</th>\n",
              "      <th>landcover_1</th>\n",
              "      <th>landcover_2</th>\n",
              "      <th>landcover_3</th>\n",
              "      <th>landcover_4</th>\n",
              "      <th>landcover_5</th>\n",
              "      <th>landcover_6</th>\n",
              "      <th>landcover_7</th>\n",
              "      <th>landcover_8</th>\n",
              "      <th>precipitation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0_2014-01-01</td>\n",
              "      <td>-15.858835</td>\n",
              "      <td>29.237029</td>\n",
              "      <td>1146</td>\n",
              "      <td>74</td>\n",
              "      <td>-451</td>\n",
              "      <td>1220</td>\n",
              "      <td>120</td>\n",
              "      <td>6</td>\n",
              "      <td>212</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.416704</td>\n",
              "      <td>0.023724</td>\n",
              "      <td>0.530016</td>\n",
              "      <td>0.013524</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.404243</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1 rows × 28 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-36edb0f6-a591-4419-bafd-ab806ddc1c19 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-36edb0f6-a591-4419-bafd-ab806ddc1c19');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "             ID        lat        lon  climate_aet  climate_def  climate_pdsi  \\\n",
              "0  0_2014-01-01 -15.858835  29.237029         1146           74          -451   \n",
              "\n",
              "   climate_pet  climate_pr  climate_ro  climate_soil  ...  landcover_0  \\\n",
              "0         1220         120           6           212  ...          0.0   \n",
              "\n",
              "   landcover_1  landcover_2  landcover_3  landcover_4  landcover_5  \\\n",
              "0     0.000615     0.015418          0.0     0.416704     0.023724   \n",
              "\n",
              "   landcover_6  landcover_7  landcover_8  precipitation  \n",
              "0     0.530016     0.013524          0.0       0.404243  \n",
              "\n",
              "[1 rows x 28 columns]"
            ]
          },
          "execution_count": 78,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# The test set - notice there is no 'burn_area' column.\n",
        "test = pd.read_csv('Test.csv')\n",
        "test.head(1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tnl3iVg5xqfA"
      },
      "source": [
        "# EDA + Feature Engineering"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "1WCG4h0-S_v1",
        "outputId": "ce07db1b-78b5-48f7-fe5f-56a74699589d"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            },
            "text/html": [
              "\n",
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              "\n",
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              "    }\n",
              "\n",
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              "        text-align: right;\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>lat</th>\n",
              "      <th>lon</th>\n",
              "      <th>burn_area</th>\n",
              "      <th>climate_aet</th>\n",
              "      <th>climate_def</th>\n",
              "      <th>climate_pdsi</th>\n",
              "      <th>climate_pet</th>\n",
              "      <th>climate_pr</th>\n",
              "      <th>climate_ro</th>\n",
              "      <th>climate_soil</th>\n",
              "      <th>...</th>\n",
              "      <th>landcover_0</th>\n",
              "      <th>landcover_1</th>\n",
              "      <th>landcover_2</th>\n",
              "      <th>landcover_3</th>\n",
              "      <th>landcover_4</th>\n",
              "      <th>landcover_5</th>\n",
              "      <th>landcover_6</th>\n",
              "      <th>landcover_7</th>\n",
              "      <th>landcover_8</th>\n",
              "      <th>precipitation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>83148.000000</td>\n",
              "      <td>83148.000000</td>\n",
              "      <td>83148.000000</td>\n",
              "      <td>83148.000000</td>\n",
              "      <td>83148.000000</td>\n",
              "      <td>83148.000000</td>\n",
              "      <td>83148.000000</td>\n",
              "      <td>83148.000000</td>\n",
              "      <td>83148.000000</td>\n",
              "      <td>83148.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>83148.000000</td>\n",
              "      <td>83148.000000</td>\n",
              "      <td>83148.000000</td>\n",
              "      <td>8.314800e+04</td>\n",
              "      <td>83148.000000</td>\n",
              "      <td>83148.000000</td>\n",
              "      <td>83148.000000</td>\n",
              "      <td>83148.000000</td>\n",
              "      <td>83148.000000</td>\n",
              "      <td>83148.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-19.014557</td>\n",
              "      <td>29.860856</td>\n",
              "      <td>0.007632</td>\n",
              "      <td>484.491124</td>\n",
              "      <td>828.892721</td>\n",
              "      <td>-30.806237</td>\n",
              "      <td>1313.382992</td>\n",
              "      <td>54.576322</td>\n",
              "      <td>6.215134</td>\n",
              "      <td>305.477919</td>\n",
              "      <td>...</td>\n",
              "      <td>0.006158</td>\n",
              "      <td>0.000017</td>\n",
              "      <td>0.008583</td>\n",
              "      <td>4.066313e-08</td>\n",
              "      <td>0.160628</td>\n",
              "      <td>0.037453</td>\n",
              "      <td>0.785382</td>\n",
              "      <td>0.000354</td>\n",
              "      <td>0.001424</td>\n",
              "      <td>0.078551</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.588444</td>\n",
              "      <td>1.751090</td>\n",
              "      <td>0.029226</td>\n",
              "      <td>470.467480</td>\n",
              "      <td>546.934469</td>\n",
              "      <td>235.802930</td>\n",
              "      <td>315.209838</td>\n",
              "      <td>71.997561</td>\n",
              "      <td>21.480399</td>\n",
              "      <td>400.956123</td>\n",
              "      <td>...</td>\n",
              "      <td>0.053111</td>\n",
              "      <td>0.000141</td>\n",
              "      <td>0.059046</td>\n",
              "      <td>3.535121e-06</td>\n",
              "      <td>0.192957</td>\n",
              "      <td>0.106050</td>\n",
              "      <td>0.233953</td>\n",
              "      <td>0.001773</td>\n",
              "      <td>0.014233</td>\n",
              "      <td>0.104464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-22.358835</td>\n",
              "      <td>25.487029</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-563.000000</td>\n",
              "      <td>587.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000019</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-20.358835</td>\n",
              "      <td>28.487029</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>375.000000</td>\n",
              "      <td>-212.000000</td>\n",
              "      <td>1081.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.014993</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.655322</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.002017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-18.858835</td>\n",
              "      <td>29.987029</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>302.000000</td>\n",
              "      <td>858.000000</td>\n",
              "      <td>-108.000000</td>\n",
              "      <td>1290.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>149.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.076706</td>\n",
              "      <td>0.000139</td>\n",
              "      <td>0.880600</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.028503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>-17.858835</td>\n",
              "      <td>31.237029</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>931.000000</td>\n",
              "      <td>1211.000000</td>\n",
              "      <td>142.000000</td>\n",
              "      <td>1517.000000</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>329.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.246498</td>\n",
              "      <td>0.015166</td>\n",
              "      <td>0.971209</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.125500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>-15.858835</td>\n",
              "      <td>32.987029</td>\n",
              "      <td>0.843886</td>\n",
              "      <td>1713.000000</td>\n",
              "      <td>2614.000000</td>\n",
              "      <td>851.000000</td>\n",
              "      <td>2620.000000</td>\n",
              "      <td>501.000000</td>\n",
              "      <td>342.000000</td>\n",
              "      <td>3319.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.828131</td>\n",
              "      <td>0.004918</td>\n",
              "      <td>0.711603</td>\n",
              "      <td>3.073689e-04</td>\n",
              "      <td>0.981472</td>\n",
              "      <td>0.881323</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.016708</td>\n",
              "      <td>0.301823</td>\n",
              "      <td>0.801328</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 28 columns</p>\n",
              "</div>\n",
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              "\n",
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              "\n",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\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",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
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              "\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",
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              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-955a3824-5603-46b6-93c1-52f055250ca3 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-955a3824-5603-46b6-93c1-52f055250ca3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-cc35ee06-604f-427a-81a9-9198a060eaf7\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cc35ee06-604f-427a-81a9-9198a060eaf7')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-cc35ee06-604f-427a-81a9-9198a060eaf7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                lat           lon     burn_area   climate_aet   climate_def  \\\n",
              "count  83148.000000  83148.000000  83148.000000  83148.000000  83148.000000   \n",
              "mean     -19.014557     29.860856      0.007632    484.491124    828.892721   \n",
              "std        1.588444      1.751090      0.029226    470.467480    546.934469   \n",
              "min      -22.358835     25.487029      0.000000      0.000000      0.000000   \n",
              "25%      -20.358835     28.487029      0.000000     50.000000    375.000000   \n",
              "50%      -18.858835     29.987029      0.000000    302.000000    858.000000   \n",
              "75%      -17.858835     31.237029      0.000000    931.000000   1211.000000   \n",
              "max      -15.858835     32.987029      0.843886   1713.000000   2614.000000   \n",
              "\n",
              "       climate_pdsi   climate_pet    climate_pr    climate_ro  climate_soil  \\\n",
              "count  83148.000000  83148.000000  83148.000000  83148.000000  83148.000000   \n",
              "mean     -30.806237   1313.382992     54.576322      6.215134    305.477919   \n",
              "std      235.802930    315.209838     71.997561     21.480399    400.956123   \n",
              "min     -563.000000    587.000000      0.000000      0.000000      1.000000   \n",
              "25%     -212.000000   1081.000000      1.000000      0.000000     70.000000   \n",
              "50%     -108.000000   1290.000000     22.000000      1.000000    149.000000   \n",
              "75%      142.000000   1517.000000     87.000000      4.000000    329.000000   \n",
              "max      851.000000   2620.000000    501.000000    342.000000   3319.000000   \n",
              "\n",
              "       ...   landcover_0   landcover_1   landcover_2   landcover_3  \\\n",
              "count  ...  83148.000000  83148.000000  83148.000000  8.314800e+04   \n",
              "mean   ...      0.006158      0.000017      0.008583  4.066313e-08   \n",
              "std    ...      0.053111      0.000141      0.059046  3.535121e-06   \n",
              "min    ...      0.000000      0.000000      0.000000  0.000000e+00   \n",
              "25%    ...      0.000000      0.000000      0.000000  0.000000e+00   \n",
              "50%    ...      0.000000      0.000000      0.000000  0.000000e+00   \n",
              "75%    ...      0.000000      0.000000      0.000000  0.000000e+00   \n",
              "max    ...      0.828131      0.004918      0.711603  3.073689e-04   \n",
              "\n",
              "        landcover_4   landcover_5   landcover_6   landcover_7   landcover_8  \\\n",
              "count  83148.000000  83148.000000  83148.000000  83148.000000  83148.000000   \n",
              "mean       0.160628      0.037453      0.785382      0.000354      0.001424   \n",
              "std        0.192957      0.106050      0.233953      0.001773      0.014233   \n",
              "min        0.000000      0.000000      0.000019      0.000000      0.000000   \n",
              "25%        0.014993      0.000000      0.655322      0.000000      0.000000   \n",
              "50%        0.076706      0.000139      0.880600      0.000000      0.000000   \n",
              "75%        0.246498      0.015166      0.971209      0.000000      0.000000   \n",
              "max        0.981472      0.881323      1.000000      0.016708      0.301823   \n",
              "\n",
              "       precipitation  \n",
              "count   83148.000000  \n",
              "mean        0.078551  \n",
              "std         0.104464  \n",
              "min         0.000000  \n",
              "25%         0.002017  \n",
              "50%         0.028503  \n",
              "75%         0.125500  \n",
              "max         0.801328  \n",
              "\n",
              "[8 rows x 28 columns]"
            ]
          },
          "execution_count": 79,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Look at distribution of each variable\n",
        "train.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "id": "eF2NXmXbSOaW",
        "outputId": "6ac88e20-9ef9-4602-84ab-0f2bae1d276b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 80,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Look at correlation with target\n",
        "train.select_dtypes(include=['number']).corr()['burn_area'].sort_values().plot(kind='bar', figsize=(18, 6))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "DLVC6pU91PPE",
        "outputId": "1d8586b5-30e6-4047-91b3-456a2401af56"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7b05bfe367d0>"
            ]
          },
          "execution_count": 81,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Look at some scatter plots (only plotting for a subset of data to keep things fast)\n",
        "sample = train.sample(10000)\n",
        "plt.scatter(sample['climate_vap'], sample['burn_area'], alpha=0.3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "Fp-Wq1zrCets",
        "outputId": "fe3fed47-3f86-4f09-806b-8186e120ca4e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7b05c8a44dc0>"
            ]
          },
          "execution_count": 82,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Most fires occur when there's very little rain.\n",
        "plt.scatter(sample['climate_pr'], sample['burn_area'], alpha=0.3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xXIk62X9iONj"
      },
      "source": [
        "##  Adding date features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {
        "id": "pn-SKQxiHAon"
      },
      "outputs": [],
      "source": [
        "# Split the ID (eg 127_2017-01-03) to get the date string, which we convert to datetime to make life easier\n",
        "train['date'] = pd.to_datetime(train['ID'].apply(lambda x: x.split('_')[1]))\n",
        "test['date'] = pd.to_datetime(test['ID'].apply(lambda x: x.split('_')[1]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3tAU8Z9uV_AD",
        "outputId": "ee6c12a1-4a3a-45de-cc3d-1feaf2977b98"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Timestamp('2001-01-01 00:00:00')"
            ]
          },
          "execution_count": 84,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train['date'][1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "id": "eCeRSF9YUI_5",
        "outputId": "12f85321-5930-4bf7-9a82-a30093a32176"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-af178e23-4634-4de0-bb37-f25d89c0cad0\" 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>ID</th>\n",
              "      <th>lat</th>\n",
              "      <th>lon</th>\n",
              "      <th>burn_area</th>\n",
              "      <th>climate_aet</th>\n",
              "      <th>climate_def</th>\n",
              "      <th>climate_pdsi</th>\n",
              "      <th>climate_pet</th>\n",
              "      <th>climate_pr</th>\n",
              "      <th>climate_ro</th>\n",
              "      <th>...</th>\n",
              "      <th>landcover_1</th>\n",
              "      <th>landcover_2</th>\n",
              "      <th>landcover_3</th>\n",
              "      <th>landcover_4</th>\n",
              "      <th>landcover_5</th>\n",
              "      <th>landcover_6</th>\n",
              "      <th>landcover_7</th>\n",
              "      <th>landcover_8</th>\n",
              "      <th>precipitation</th>\n",
              "      <th>date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0_2001-01-01</td>\n",
              "      <td>-15.858835</td>\n",
              "      <td>29.237029</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1195</td>\n",
              "      <td>0</td>\n",
              "      <td>263</td>\n",
              "      <td>1195</td>\n",
              "      <td>206</td>\n",
              "      <td>10</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.018654</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.714446</td>\n",
              "      <td>0.012174</td>\n",
              "      <td>0.244890</td>\n",
              "      <td>0.009836</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.256932</td>\n",
              "      <td>2001-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1_2001-01-01</td>\n",
              "      <td>-15.858835</td>\n",
              "      <td>29.487029</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1196</td>\n",
              "      <td>0</td>\n",
              "      <td>232</td>\n",
              "      <td>1196</td>\n",
              "      <td>201</td>\n",
              "      <td>10</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.654783</td>\n",
              "      <td>0.000095</td>\n",
              "      <td>0.345121</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.273093</td>\n",
              "      <td>2001-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2_2001-01-01</td>\n",
              "      <td>-15.858835</td>\n",
              "      <td>29.737029</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1190</td>\n",
              "      <td>0</td>\n",
              "      <td>314</td>\n",
              "      <td>1190</td>\n",
              "      <td>192</td>\n",
              "      <td>10</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.516421</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.483579</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.285109</td>\n",
              "      <td>2001-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3_2001-01-01</td>\n",
              "      <td>-15.858835</td>\n",
              "      <td>29.987029</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1144</td>\n",
              "      <td>0</td>\n",
              "      <td>321</td>\n",
              "      <td>1144</td>\n",
              "      <td>186</td>\n",
              "      <td>66</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.299000</td>\n",
              "      <td>0.163902</td>\n",
              "      <td>0.537098</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.298418</td>\n",
              "      <td>2001-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4_2001-01-01</td>\n",
              "      <td>-15.858835</td>\n",
              "      <td>30.237029</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1187</td>\n",
              "      <td>0</td>\n",
              "      <td>413</td>\n",
              "      <td>1187</td>\n",
              "      <td>186</td>\n",
              "      <td>9</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.277392</td>\n",
              "      <td>0.067742</td>\n",
              "      <td>0.654866</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.315621</td>\n",
              "      <td>2001-01-01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 30 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-af178e23-4634-4de0-bb37-f25d89c0cad0')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-af178e23-4634-4de0-bb37-f25d89c0cad0 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-af178e23-4634-4de0-bb37-f25d89c0cad0');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-98f799a8-69c0-4c5a-bc38-c8c0fac3aa1d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-98f799a8-69c0-4c5a-bc38-c8c0fac3aa1d')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-98f799a8-69c0-4c5a-bc38-c8c0fac3aa1d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "             ID        lat        lon  burn_area  climate_aet  climate_def  \\\n",
              "0  0_2001-01-01 -15.858835  29.237029        0.0         1195            0   \n",
              "1  1_2001-01-01 -15.858835  29.487029        0.0         1196            0   \n",
              "2  2_2001-01-01 -15.858835  29.737029        0.0         1190            0   \n",
              "3  3_2001-01-01 -15.858835  29.987029        0.0         1144            0   \n",
              "4  4_2001-01-01 -15.858835  30.237029        0.0         1187            0   \n",
              "\n",
              "   climate_pdsi  climate_pet  climate_pr  climate_ro  ...  landcover_1  \\\n",
              "0           263         1195         206          10  ...          0.0   \n",
              "1           232         1196         201          10  ...          0.0   \n",
              "2           314         1190         192          10  ...          0.0   \n",
              "3           321         1144         186          66  ...          0.0   \n",
              "4           413         1187         186           9  ...          0.0   \n",
              "\n",
              "   landcover_2  landcover_3  landcover_4  landcover_5  landcover_6  \\\n",
              "0     0.018654          0.0     0.714446     0.012174     0.244890   \n",
              "1     0.000000          0.0     0.654783     0.000095     0.345121   \n",
              "2     0.000000          0.0     0.516421     0.000000     0.483579   \n",
              "3     0.000000          0.0     0.299000     0.163902     0.537098   \n",
              "4     0.000000          0.0     0.277392     0.067742     0.654866   \n",
              "\n",
              "   landcover_7  landcover_8  precipitation       date  \n",
              "0     0.009836          0.0       0.256932 2001-01-01  \n",
              "1     0.000000          0.0       0.273093 2001-01-01  \n",
              "2     0.000000          0.0       0.285109 2001-01-01  \n",
              "3     0.000000          0.0       0.298418 2001-01-01  \n",
              "4     0.000000          0.0       0.315621 2001-01-01  \n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "execution_count": 85,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {
        "id": "bag8HJpxCucw"
      },
      "outputs": [],
      "source": [
        "# Date variables\n",
        "train['month'] = train.date.dt.month\n",
        "train['year'] = train.date.dt.year"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 87,
      "metadata": {
        "id": "89bsb9HkUtrG"
      },
      "outputs": [],
      "source": [
        "# Group by month and calculate the mean burn_area\n",
        "monthly_mean_burn_area = train.groupby('month')['burn_area'].mean().reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "id": "vzlSxP_SUxxH",
        "outputId": "a6d3a9db-e5cd-4b7e-b8b8-0dc0e8d71440"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot the results\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.bar(monthly_mean_burn_area['month'], monthly_mean_burn_area['burn_area'], color='skyblue')\n",
        "plt.xlabel('Month')\n",
        "plt.ylabel('Mean Burn Area')\n",
        "plt.title('Mean Burn Area by Month')\n",
        "plt.xticks(monthly_mean_burn_area['month'])\n",
        "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "metadata": {
        "id": "Rp9KHVEKVN_m"
      },
      "outputs": [],
      "source": [
        "# Group by year and calculate the mean burn_area\n",
        "yearly_mean_burn_area = train.groupby('year')['burn_area'].mean().reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "id": "ispUqAsaVTRP",
        "outputId": "977087ee-8d65-4089-b5b9-21caa96ce7a9"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot the results\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.bar(yearly_mean_burn_area['year'], yearly_mean_burn_area['burn_area'], color='skyblue')\n",
        "plt.xlabel('Year')\n",
        "plt.ylabel('Mean Burn Area')\n",
        "plt.title('Mean Burn Area by Year')\n",
        "plt.xticks(yearly_mean_burn_area['year'])\n",
        "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 229
        },
        "id": "BCU3WP5fEAHq",
        "outputId": "dc2ba048-b506-4d74-862c-01f189ee4fa1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: xlabel='date'>"
            ]
          },
          "execution_count": 91,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1800x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Convert 'burn_area' to numeric, handling non-numeric values\n",
        "train['burn_area'] = pd.to_numeric(train['burn_area'], errors='coerce')\n",
        "\n",
        "# Now try plotting again\n",
        "ax = train.groupby('date')['burn_area'].mean().reset_index().plot(y='burn_area', x='date', figsize=(18, 5))\n",
        "train.groupby('date')['precipitation'].mean().reset_index().plot(y='precipitation', x='date', ax=ax)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mgzvz9iji1hb"
      },
      "source": [
        "## Adding more features - some ideas\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "*   Read the list of climate variables and what they mean. See if you can combine them in interesting ways - perhaps a 'hot_and_dry' metric...\n",
        "*   Fire depends on some processes that take a long time - for example, there may be more fuel if the previous growing season was a good one. Consider some lagged variables to give the model some inputs for what came before the current month.\n",
        "*   Make some categorical features - 'dominant_land_type' or 'is_peak_rainfall'.\n",
        "*   ...\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RDCOsUvCz3dA"
      },
      "source": [
        "# Data Split for Validation\n",
        "\n",
        "We don't want to just split randomly - this would give us artificially high scores. Instead, let's use the last 3 years of the dataset for validation to more closely match the test configuration."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SCk5CZKI0Kt4",
        "outputId": "99fa5f22-db74-4ec8-c076-c3373dbe56ea"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(63960, 32) (18655, 32)\n"
          ]
        }
      ],
      "source": [
        "train_all = train.copy().dropna()\n",
        "train = train_all.loc[train_all.date < '2011-01-01']\n",
        "valid = train_all.loc[train_all.date > '2011-01-01']\n",
        "print(train.shape, valid.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sIRFzOxTx3sE"
      },
      "source": [
        "# Simple Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {
        "id": "hMjgH87RzOqX"
      },
      "outputs": [],
      "source": [
        "# Define input and output columns - you can play with adding or removing inputs to the model\n",
        "in_cols = ['climate_aet', 'climate_def',\n",
        "       'climate_pdsi', 'climate_pet', 'climate_pr', 'climate_ro',\n",
        "       'climate_soil', 'climate_srad', 'climate_swe', 'climate_tmmn',\n",
        "       'climate_tmmx', 'climate_vap', 'climate_vpd', 'climate_vs', 'elevation',\n",
        "       'landcover_0', 'landcover_1', 'landcover_2', 'landcover_3',\n",
        "       'landcover_4', 'landcover_5', 'landcover_6', 'landcover_7',\n",
        "       'landcover_8', 'precipitation']\n",
        "target_col = 'burn_area'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l-8j9zDpzUpc",
        "outputId": "dbd40957-1257-4126-9200-1105a10993ad"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.027162202788683244"
            ]
          },
          "execution_count": 94,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Get our X and y training and validation sets ready\n",
        "X_train, y_train = train[in_cols], train[target_col]\n",
        "X_valid, y_valid = valid[in_cols], valid[target_col]\n",
        "\n",
        "# Create and fit the model\n",
        "model = RidgeCV()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions\n",
        "preds = model.predict(X_valid)\n",
        "\n",
        "# Score\n",
        "mean_squared_error(y_valid, preds)**0.5 # RMSE - should match Zindi score. Lower is better (Will try various models to see which one gives the best RMSE)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "metadata": {
        "id": "isI_-sfnzXED"
      },
      "outputs": [],
      "source": [
        "# Exercise. Try a RandomForestRegressor model. Use n_estimators=10 if the default takes too long to run, and experiment with the max_depth parameter."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Instantiate model with n_estimators=7\n",
        "model = RandomForestRegressor(n_estimators=5, random_state=42)  \n",
        "\n",
        "# Fit the model on the training data\n",
        "model.fit(train[in_cols].fillna(0), train[target_col])\n",
        "\n",
        "# Make predictions on the validation set\n",
        "preds = model.predict(valid[in_cols].fillna(0))\n",
        "\n",
        "# Evaluate the model\n",
        "rmse = mean_squared_error(valid[target_col], preds, squared=False)  \n",
        "print(\"RMSE:\", rmse)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "metadata": {
        "id": "xmK8kfNU7agI"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Eq7DM0URkkai"
      },
      "source": [
        "# Making A Submission\n",
        "\n",
        "Once you've got some features and a model you're happy with, it's time to submit!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "bb8DdgWskqQs",
        "outputId": "0aa2d1be-bc2a-4bce-fc5c-40236a6d87d1"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
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              "      let quickchartButtonEl =\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "             ID  burn_area\n",
              "0  0_2014-01-01          0\n",
              "1  1_2014-01-01          0\n",
              "2  2_2014-01-01          0\n",
              "3  3_2014-01-01          0\n",
              "4  4_2014-01-01          0"
            ]
          },
          "execution_count": 96,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Look at the sample submission file\n",
        "ss = pd.read_csv('SampleSubmission.csv')\n",
        "ss.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "id": "MlKjnZ8WkvaX",
        "outputId": "a7290790-979d-4985-cfea-a51346bf8eb1"
      },
      "outputs": [
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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-c5aa5075-cc08-4c19-a03a-202ba14dda74 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-c5aa5075-cc08-4c19-a03a-202ba14dda74');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-74227ade-7e65-4d50-af21-64eb0a6d59aa\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-74227ade-7e65-4d50-af21-64eb0a6d59aa')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-74227ade-7e65-4d50-af21-64eb0a6d59aa button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "             ID        lat        lon  climate_aet  climate_def  climate_pdsi  \\\n",
              "0  0_2014-01-01 -15.858835  29.237029         1146           74          -451   \n",
              "1  1_2014-01-01 -15.858835  29.487029         1118           98          -405   \n",
              "2  2_2014-01-01 -15.858835  29.737029         1067          137          -468   \n",
              "3  3_2014-01-01 -15.858835  29.987029          969          187          -533   \n",
              "4  4_2014-01-01 -15.858835  30.237029          927          270          -505   \n",
              "\n",
              "   climate_pet  climate_pr  climate_ro  climate_soil  ...  landcover_1  \\\n",
              "0         1220         120           6           212  ...     0.000615   \n",
              "1         1216         117           6           330  ...     0.000000   \n",
              "2         1204         111           6           194  ...     0.000000   \n",
              "3         1156         101           5            78  ...     0.000000   \n",
              "4         1197          96           5           139  ...     0.000000   \n",
              "\n",
              "   landcover_2  landcover_3  landcover_4  landcover_5  landcover_6  \\\n",
              "0     0.015418          0.0     0.416704     0.023724     0.530016   \n",
              "1     0.000000          0.0     0.406436     0.000446     0.593118   \n",
              "2     0.000000          0.0     0.254791     0.000000     0.745209   \n",
              "3     0.000000          0.0     0.219340     0.182153     0.598507   \n",
              "4     0.000000          0.0     0.220239     0.097748     0.682013   \n",
              "\n",
              "   landcover_7  landcover_8  precipitation       date  \n",
              "0     0.013524          0.0       0.404243 2014-01-01  \n",
              "1     0.000000          0.0       0.421489 2014-01-01  \n",
              "2     0.000000          0.0       0.419122 2014-01-01  \n",
              "3     0.000000          0.0       0.404596 2014-01-01  \n",
              "4     0.000000          0.0       0.371100 2014-01-01  \n",
              "\n",
              "[5 rows x 29 columns]"
            ]
          },
          "execution_count": 97,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# And the test data\n",
        "test.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "HItSe6MCkyPB",
        "outputId": "02b1ac1b-05bd-43c3-f3cf-13557c4a42f5"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"ss\",\n  \"rows\": 25584,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 25584,\n        \"samples\": [\n          \"66_2016-01-01\",\n          \"107_2014-02-01\",\n          \"359_2016-03-01\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"burn_area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.010277654938029053,\n        \"min\": 0.0,\n        \"max\": 0.07012742759147728,\n        \"num_unique_values\": 18804,\n        \"samples\": [\n          0.0005804035774887029,\n          0.01911346726278993,\n          0.03573951897345311\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "ss"
            },
            "text/html": [
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              "<style scoped>\n",
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              "    <tr style=\"text-align: right;\">\n",
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              "  </thead>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0_2014-01-01</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1_2014-01-01</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2_2014-01-01</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3_2014-01-01</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4_2014-01-01</td>\n",
              "      <td>0.0</td>\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-619c720a-84e1-4a86-bd8d-49cdc0c124de');\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",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
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              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
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              "    animation:\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ee3fa14a-2550-4a4d-afed-43a629c82f46 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "             ID  burn_area\n",
              "0  0_2014-01-01        0.0\n",
              "1  1_2014-01-01        0.0\n",
              "2  2_2014-01-01        0.0\n",
              "3  3_2014-01-01        0.0\n",
              "4  4_2014-01-01        0.0"
            ]
          },
          "execution_count": 98,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# So we need to predict the burn area for each row in test.\n",
        "\n",
        "# Add the same features to test as we did to train:\n",
        "test['month'] = test.date.dt.month\n",
        "test['year'] = test.date.dt.year\n",
        "\n",
        "# Get predictions\n",
        "preds = model.predict(test[in_cols].fillna(0)) # fillna(0) here could be improved by examining the missing data and filling more appropriately.\n",
        "\n",
        "# Add to submission dataframe\n",
        "ss['burn_area'] = preds\n",
        "\n",
        "# Depending on your model, you may have some predictions that don't make sense. Let's constrain our predictions to the range (0, 1)\n",
        "ss['burn_area'] = ss['burn_area'].clip(0, 1)\n",
        "\n",
        "# View\n",
        "ss.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "metadata": {
        "id": "13Ns-f5blIEs"
      },
      "outputs": [],
      "source": [
        "# Save ready for submission:\n",
        "ss.to_csv('starter_submission.csv', index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OeyjG8-4mK7Y"
      },
      "source": [
        "# Tips for improving your score\n",
        "\n",
        "- Once you've chosen a model, you should re-train it on all the data (including that which you previously held back for validation) to give it the best chance of succeeding\n",
        "- There is a timeseries component to this challenge. Think about how to use this to improve your accuracy.\n",
        "- Spend some time on feature engineering. Since no external data is allowed for this challenge, the winning solution will be the one that gets the most out of the data provided."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "yUuqtHNKnO3r"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Hello world, Lets help Zimbabwe to predict the burn area of the forest\n"
          ]
        }
      ],
      "source": [
        "# Just tring make sure everything work perfect\n",
        "print(\"Hello world, Lets help Zimbabwe to predict the burn area of the forest\")"
      ]
    }
  ],
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      "codemirror_mode": {
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      "file_extension": ".py",
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      "pygments_lexer": "ipython3",
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