DECISION_TREE_prediction_code.ipynb

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      "source": [
        "import pandas as pd\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.feature_selection import SelectKBest\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.ensemble import BaggingClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.feature_selection import SelectKBest, f_classif, f_regression\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.cluster import KMeans, DBSCAN\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.cluster import KMeans, DBSCAN\n",
        "from mlxtend.frequent_patterns import apriori, association_rules\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, mean_squared_error\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.cluster import KMeans\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.ensemble import GradientBoostingClassifier, AdaBoostClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from mlxtend.frequent_patterns import fpgrowth\n",
        "# Extract the longitude, latitude, and landslide_category columns"
      ],
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      "outputs": []
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          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv(\"dataset.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
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        "id": "LiOnPtGUKX0h",
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            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
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            "text/plain": [
              "(11033, 31)"
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          "execution_count": 3
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      "cell_type": "code",
      "source": [
        "df.head(5)"
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        {
          "output_type": "stream",
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          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
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          "data": {
            "text/plain": [
              "   source_name                                        source_link  event_id  \\\n",
              "0          AGU  https://blogs.agu.org/landslideblog/2008/10/14...       684   \n",
              "1    Oregonian  http://www.oregonlive.com/news/index.ssf/2009/...       956   \n",
              "2     CBS News  https://www.cbsnews.com/news/dozens-missing-af...       973   \n",
              "3      Reuters  https://in.reuters.com/article/idINIndia-41450...      1067   \n",
              "4  The Freeman  http://www.philstar.com/cebu-news/621414/lands...      2603   \n",
              "\n",
              "               event_date  event_time  \\\n",
              "0  08/01/2008 12:00:00 AM         NaN   \n",
              "1  01/02/2009 02:00:00 AM         NaN   \n",
              "2  01/19/2007 12:00:00 AM         NaN   \n",
              "3  07/31/2009 12:00:00 AM         NaN   \n",
              "4  10/16/2010 12:00:00 PM         NaN   \n",
              "\n",
              "                                         event_title  \\\n",
              "0      Sigou Village, Loufan County, Shanxi Province   \n",
              "1                                Lake Oswego, Oregon   \n",
              "2  San Ramon district, 195 miles northeast of the...   \n",
              "3                                   Dailekh district   \n",
              "4                    sitio Bakilid in barangay Lahug   \n",
              "\n",
              "                                   event_description  \\\n",
              "0  occurred early in morning, 11 villagers buried...   \n",
              "1  Hours of heavy rain are to blame for an overni...   \n",
              "2  (CBS/AP) At least 10 people died and as many a...   \n",
              "3  One person was killed in Dailekh district, pol...   \n",
              "4  Another landslide in sitio Bakilid in barangay...   \n",
              "\n",
              "                                location_description location_accuracy  \\\n",
              "0      Sigou Village, Loufan County, Shanxi Province           unknown   \n",
              "1                                Lake Oswego, Oregon               5km   \n",
              "2  San Ramon district, 195 miles northeast of the...              10km   \n",
              "3                                   Dailekh district           unknown   \n",
              "4                    sitio Bakilid in barangay Lahug               5km   \n",
              "\n",
              "  landslide_category  ... country_code admin_division_name  \\\n",
              "0          landslide  ...           CN             Shaanxi   \n",
              "1           mudslide  ...           US              Oregon   \n",
              "2          landslide  ...           PE               Junín   \n",
              "3          landslide  ...           NP         Mid Western   \n",
              "4          landslide  ...           PH     Central Visayas   \n",
              "\n",
              "  admin_division_population  gazeteer_closest_point  gazeteer_distance  \\\n",
              "0                       0.0                Jingyang           41.02145   \n",
              "1                   36619.0             Lake Oswego            0.60342   \n",
              "2                   14708.0               San Ramón            0.85548   \n",
              "3                   20908.0                 Dailekh            0.75395   \n",
              "4                  798634.0               Cebu City            2.02204   \n",
              "\n",
              "           submitted_date            created_date        last_edited_date  \\\n",
              "0  04/01/2014 12:00:00 AM  11/20/2017 03:17:00 PM  02/15/2018 03:51:00 PM   \n",
              "1  04/01/2014 12:00:00 AM  11/20/2017 03:17:00 PM  02/15/2018 03:51:00 PM   \n",
              "2  04/01/2014 12:00:00 AM  11/20/2017 03:17:00 PM  02/15/2018 03:51:00 PM   \n",
              "3  04/01/2014 12:00:00 AM  11/20/2017 03:17:00 PM  02/15/2018 03:51:00 PM   \n",
              "4  04/01/2014 12:00:00 AM  11/20/2017 03:17:00 PM  02/15/2018 03:51:00 PM   \n",
              "\n",
              "  longitude  latitude  \n",
              "0  107.4500   32.5625  \n",
              "1 -122.6630   45.4200  \n",
              "2  -75.3587  -11.1295  \n",
              "3   81.7080   28.8378  \n",
              "4  123.8978   10.3336  \n",
              "\n",
              "[5 rows x 31 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>source_name</th>\n",
              "      <th>source_link</th>\n",
              "      <th>event_id</th>\n",
              "      <th>event_date</th>\n",
              "      <th>event_time</th>\n",
              "      <th>event_title</th>\n",
              "      <th>event_description</th>\n",
              "      <th>location_description</th>\n",
              "      <th>location_accuracy</th>\n",
              "      <th>landslide_category</th>\n",
              "      <th>...</th>\n",
              "      <th>country_code</th>\n",
              "      <th>admin_division_name</th>\n",
              "      <th>admin_division_population</th>\n",
              "      <th>gazeteer_closest_point</th>\n",
              "      <th>gazeteer_distance</th>\n",
              "      <th>submitted_date</th>\n",
              "      <th>created_date</th>\n",
              "      <th>last_edited_date</th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AGU</td>\n",
              "      <td>https://blogs.agu.org/landslideblog/2008/10/14...</td>\n",
              "      <td>684</td>\n",
              "      <td>08/01/2008 12:00:00 AM</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Sigou Village, Loufan County, Shanxi Province</td>\n",
              "      <td>occurred early in morning, 11 villagers buried...</td>\n",
              "      <td>Sigou Village, Loufan County, Shanxi Province</td>\n",
              "      <td>unknown</td>\n",
              "      <td>landslide</td>\n",
              "      <td>...</td>\n",
              "      <td>CN</td>\n",
              "      <td>Shaanxi</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Jingyang</td>\n",
              "      <td>41.02145</td>\n",
              "      <td>04/01/2014 12:00:00 AM</td>\n",
              "      <td>11/20/2017 03:17:00 PM</td>\n",
              "      <td>02/15/2018 03:51:00 PM</td>\n",
              "      <td>107.4500</td>\n",
              "      <td>32.5625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Oregonian</td>\n",
              "      <td>http://www.oregonlive.com/news/index.ssf/2009/...</td>\n",
              "      <td>956</td>\n",
              "      <td>01/02/2009 02:00:00 AM</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Lake Oswego, Oregon</td>\n",
              "      <td>Hours of heavy rain are to blame for an overni...</td>\n",
              "      <td>Lake Oswego, Oregon</td>\n",
              "      <td>5km</td>\n",
              "      <td>mudslide</td>\n",
              "      <td>...</td>\n",
              "      <td>US</td>\n",
              "      <td>Oregon</td>\n",
              "      <td>36619.0</td>\n",
              "      <td>Lake Oswego</td>\n",
              "      <td>0.60342</td>\n",
              "      <td>04/01/2014 12:00:00 AM</td>\n",
              "      <td>11/20/2017 03:17:00 PM</td>\n",
              "      <td>02/15/2018 03:51:00 PM</td>\n",
              "      <td>-122.6630</td>\n",
              "      <td>45.4200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CBS News</td>\n",
              "      <td>https://www.cbsnews.com/news/dozens-missing-af...</td>\n",
              "      <td>973</td>\n",
              "      <td>01/19/2007 12:00:00 AM</td>\n",
              "      <td>NaN</td>\n",
              "      <td>San Ramon district, 195 miles northeast of the...</td>\n",
              "      <td>(CBS/AP) At least 10 people died and as many a...</td>\n",
              "      <td>San Ramon district, 195 miles northeast of the...</td>\n",
              "      <td>10km</td>\n",
              "      <td>landslide</td>\n",
              "      <td>...</td>\n",
              "      <td>PE</td>\n",
              "      <td>Junín</td>\n",
              "      <td>14708.0</td>\n",
              "      <td>San Ramón</td>\n",
              "      <td>0.85548</td>\n",
              "      <td>04/01/2014 12:00:00 AM</td>\n",
              "      <td>11/20/2017 03:17:00 PM</td>\n",
              "      <td>02/15/2018 03:51:00 PM</td>\n",
              "      <td>-75.3587</td>\n",
              "      <td>-11.1295</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Reuters</td>\n",
              "      <td>https://in.reuters.com/article/idINIndia-41450...</td>\n",
              "      <td>1067</td>\n",
              "      <td>07/31/2009 12:00:00 AM</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Dailekh district</td>\n",
              "      <td>One person was killed in Dailekh district, pol...</td>\n",
              "      <td>Dailekh district</td>\n",
              "      <td>unknown</td>\n",
              "      <td>landslide</td>\n",
              "      <td>...</td>\n",
              "      <td>NP</td>\n",
              "      <td>Mid Western</td>\n",
              "      <td>20908.0</td>\n",
              "      <td>Dailekh</td>\n",
              "      <td>0.75395</td>\n",
              "      <td>04/01/2014 12:00:00 AM</td>\n",
              "      <td>11/20/2017 03:17:00 PM</td>\n",
              "      <td>02/15/2018 03:51:00 PM</td>\n",
              "      <td>81.7080</td>\n",
              "      <td>28.8378</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>The Freeman</td>\n",
              "      <td>http://www.philstar.com/cebu-news/621414/lands...</td>\n",
              "      <td>2603</td>\n",
              "      <td>10/16/2010 12:00:00 PM</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sitio Bakilid in barangay Lahug</td>\n",
              "      <td>Another landslide in sitio Bakilid in barangay...</td>\n",
              "      <td>sitio Bakilid in barangay Lahug</td>\n",
              "      <td>5km</td>\n",
              "      <td>landslide</td>\n",
              "      <td>...</td>\n",
              "      <td>PH</td>\n",
              "      <td>Central Visayas</td>\n",
              "      <td>798634.0</td>\n",
              "      <td>Cebu City</td>\n",
              "      <td>2.02204</td>\n",
              "      <td>04/01/2014 12:00:00 AM</td>\n",
              "      <td>11/20/2017 03:17:00 PM</td>\n",
              "      <td>02/15/2018 03:51:00 PM</td>\n",
              "      <td>123.8978</td>\n",
              "      <td>10.3336</td>\n",
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              "        <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-224b0b7a-eb37-45e1-84e6-77af9bfef8f8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2S-9SVJpLYUn",
        "outputId": "d1a64c5a-dc77-46f7-abf8-98dacf333884"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PWz7QOSAgnXI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cc9885ef-abee-4ba5-91f7-0681515b5e38"
      },
      "source": [
        "# checking for null values\n",
        "\n",
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "source_name                      0\n",
              "source_link                    846\n",
              "event_id                         0\n",
              "event_date                       0\n",
              "event_time                   11033\n",
              "event_title                      0\n",
              "event_description              862\n",
              "location_description           102\n",
              "location_accuracy                2\n",
              "landslide_category               1\n",
              "landslide_trigger               23\n",
              "landslide_size                   9\n",
              "landslide_setting               69\n",
              "fatality_count                1385\n",
              "injury_count                  5674\n",
              "storm_name                   10456\n",
              "photo_link                    9537\n",
              "notes                        10716\n",
              "event_import_source           1563\n",
              "event_import_id               1562\n",
              "country_name                  1562\n",
              "country_code                  1564\n",
              "admin_division_name           1637\n",
              "admin_division_population     1562\n",
              "gazeteer_closest_point        1563\n",
              "gazeteer_distance             1562\n",
              "submitted_date                  10\n",
              "created_date                     1\n",
              "last_edited_date                 0\n",
              "longitude                        0\n",
              "latitude                         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cB1ftcu1nioj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "49567e20-95e0-4f66-81a6-f269f6548310"
      },
      "source": [
        "# change data type of 'event_date' Column\n",
        "\n",
        "df['event_date_cal'] = pd.to_datetime(df['event_date'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n",
            "<ipython-input-6-3b7b6fe75cc9>:3: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
            "  df['event_date_cal'] = pd.to_datetime(df['event_date'])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df['event_date_cal']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CifVsljDUVY5",
        "outputId": "4ab37777-7ecc-4c8c-cdbf-a15236d9c806"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       2008-08-01 00:00:00\n",
              "1       2009-01-02 02:00:00\n",
              "2       2007-01-19 00:00:00\n",
              "3       2009-07-31 00:00:00\n",
              "4       2010-10-16 12:00:00\n",
              "                ...        \n",
              "11028   2017-04-01 13:34:00\n",
              "11029   2017-03-25 17:32:00\n",
              "11030   2016-12-15 05:00:00\n",
              "11031   2017-04-29 19:03:00\n",
              "11032   2017-03-13 14:32:00\n",
              "Name: event_date_cal, Length: 11033, dtype: datetime64[ns]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wNCkJtlJG5vc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a87ce1aa-f6a8-4160-d464-4464086fdecc"
      },
      "source": [
        "# split date & time in to separate columns\n",
        "\n",
        "df['Date'] = pd.to_datetime(df['event_date_cal']).dt.date\n",
        "df['Time'] = pd.to_datetime(df['event_date_cal']).dt.time"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OoT0QwoyeyRA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f0198e39-3a35-45fa-d1d1-6cadab425d02"
      },
      "source": [
        "df.drop(['event_date','event_date_cal'],\n",
        "        axis=1,\n",
        "        inplace=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w3I66zWLda3d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "61cc65f6-20ae-41ae-ebda-fcefd2bfc204"
      },
      "source": [
        "# split year from date\n",
        "df['year'] = pd.to_datetime(df['Date']).dt.year"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# split year from date\n",
        "df['Month'] = pd.to_datetime(df['Date']).dt.month"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nk-bDYjvUdB2",
        "outputId": "27746b4a-51c3-4cef-9cab-bb16609afb6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# split year from date\n",
        "df['day'] = pd.to_datetime(df['Date']).dt.day"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WXyKgw7DUh4H",
        "outputId": "ba2711f1-edb4-4d7e-859d-88b9f5d96684"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "id": "jRswhEnzecDa",
        "outputId": "b7576026-abc8-4348-f718-b471f872a4e3"
      },
      "source": [
        "# varify the resuly\n",
        "\n",
        "df.head(2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  source_name                                        source_link  event_id  \\\n",
              "0         AGU  https://blogs.agu.org/landslideblog/2008/10/14...       684   \n",
              "1   Oregonian  http://www.oregonlive.com/news/index.ssf/2009/...       956   \n",
              "\n",
              "   event_time                                    event_title  \\\n",
              "0         NaN  Sigou Village, Loufan County, Shanxi Province   \n",
              "1         NaN                            Lake Oswego, Oregon   \n",
              "\n",
              "                                   event_description  \\\n",
              "0  occurred early in morning, 11 villagers buried...   \n",
              "1  Hours of heavy rain are to blame for an overni...   \n",
              "\n",
              "                            location_description location_accuracy  \\\n",
              "0  Sigou Village, Loufan County, Shanxi Province           unknown   \n",
              "1                            Lake Oswego, Oregon               5km   \n",
              "\n",
              "  landslide_category landslide_trigger  ...          submitted_date  \\\n",
              "0          landslide              rain  ...  04/01/2014 12:00:00 AM   \n",
              "1           mudslide          downpour  ...  04/01/2014 12:00:00 AM   \n",
              "\n",
              "             created_date        last_edited_date  longitude latitude  \\\n",
              "0  11/20/2017 03:17:00 PM  02/15/2018 03:51:00 PM    107.450  32.5625   \n",
              "1  11/20/2017 03:17:00 PM  02/15/2018 03:51:00 PM   -122.663  45.4200   \n",
              "\n",
              "         Date      Time  year  Month day  \n",
              "0  2008-08-01  00:00:00  2008      8   1  \n",
              "1  2009-01-02  02:00:00  2009      1   2  \n",
              "\n",
              "[2 rows x 35 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-23d8da8c-7498-40fd-9334-c2a9b40000c7\" 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>source_name</th>\n",
              "      <th>source_link</th>\n",
              "      <th>event_id</th>\n",
              "      <th>event_time</th>\n",
              "      <th>event_title</th>\n",
              "      <th>event_description</th>\n",
              "      <th>location_description</th>\n",
              "      <th>location_accuracy</th>\n",
              "      <th>landslide_category</th>\n",
              "      <th>landslide_trigger</th>\n",
              "      <th>...</th>\n",
              "      <th>submitted_date</th>\n",
              "      <th>created_date</th>\n",
              "      <th>last_edited_date</th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>Date</th>\n",
              "      <th>Time</th>\n",
              "      <th>year</th>\n",
              "      <th>Month</th>\n",
              "      <th>day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AGU</td>\n",
              "      <td>https://blogs.agu.org/landslideblog/2008/10/14...</td>\n",
              "      <td>684</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Sigou Village, Loufan County, Shanxi Province</td>\n",
              "      <td>occurred early in morning, 11 villagers buried...</td>\n",
              "      <td>Sigou Village, Loufan County, Shanxi Province</td>\n",
              "      <td>unknown</td>\n",
              "      <td>landslide</td>\n",
              "      <td>rain</td>\n",
              "      <td>...</td>\n",
              "      <td>04/01/2014 12:00:00 AM</td>\n",
              "      <td>11/20/2017 03:17:00 PM</td>\n",
              "      <td>02/15/2018 03:51:00 PM</td>\n",
              "      <td>107.450</td>\n",
              "      <td>32.5625</td>\n",
              "      <td>2008-08-01</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>2008</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Oregonian</td>\n",
              "      <td>http://www.oregonlive.com/news/index.ssf/2009/...</td>\n",
              "      <td>956</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Lake Oswego, Oregon</td>\n",
              "      <td>Hours of heavy rain are to blame for an overni...</td>\n",
              "      <td>Lake Oswego, Oregon</td>\n",
              "      <td>5km</td>\n",
              "      <td>mudslide</td>\n",
              "      <td>downpour</td>\n",
              "      <td>...</td>\n",
              "      <td>04/01/2014 12:00:00 AM</td>\n",
              "      <td>11/20/2017 03:17:00 PM</td>\n",
              "      <td>02/15/2018 03:51:00 PM</td>\n",
              "      <td>-122.663</td>\n",
              "      <td>45.4200</td>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>02:00:00</td>\n",
              "      <td>2009</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 35 columns</p>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-23d8da8c-7498-40fd-9334-c2a9b40000c7');\n",
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              "                                                    [key], {});\n",
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              "\n",
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              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "decision_tree_random_forest_features = ['longitude', 'latitude', 'landslide_size', 'admin_division_name', 'admin_division_population', 'year']\n",
        "\n",
        "X = df.drop('landslide_category', axis=1)\n",
        "y = df['landslide_category']\n",
        "\n",
        "X_dt_rf = X[decision_tree_random_forest_features]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C-riK206LTVy",
        "outputId": "aeb6bfb2-0428-49d0-d2ae-184286c92b78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a copy of the original data\n",
        "data_encoded = df.copy()\n",
        "# Initialize the LabelEncoder\n",
        "label_encoder = LabelEncoder()\n",
        "\n",
        "# Iterate over each column and label encode the features\n",
        "for column in df.columns:\n",
        "    # Check if the column contains categorical data\n",
        "    if df[column].dtype == 'object':\n",
        "        # Label encode the column\n",
        "        data_encoded[column] = label_encoder.fit_transform(df[column].astype(str))\n",
        "\n",
        "# Replace NaN values with the most frequent label in each column\n",
        "data_encoded = data_encoded.fillna(data_encoded.mode().iloc[0])\n",
        "\n",
        "# Use the encoded data for further analysis\n",
        "X_encoded = data_encoded.drop('landslide_category', axis=1)\n",
        "y_encoded = data_encoded['landslide_category']\n",
        "X_dt_rf_encoded = X_encoded[decision_tree_random_forest_features]\n",
        "print('Decision Tree and Random Forest features:', X_dt_rf_encoded.columns.tolist())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JgqfowzdMNFj",
        "outputId": "d516f91d-ef8a-4b28-9d28-73ffed676e6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Decision Tree and Random Forest features: ['longitude', 'latitude', 'landslide_size', 'admin_division_name', 'admin_division_population', 'year']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Decision Tree with regularization\n",
        "decision_tree_model = DecisionTreeClassifier(max_depth=9)   ##channging depth\n",
        "decision_tree_model.fit(X_dt_rf_encoded, y_encoded)\n",
        "\n",
        "y_dt_pred = decision_tree_model.predict(X_dt_rf_encoded)\n",
        "dt_accuracy = accuracy_score(y_encoded, y_dt_pred)\n",
        "dt_precision = precision_score(y_encoded, y_dt_pred, average='weighted')\n",
        "dt_recall = recall_score(y_encoded, y_dt_pred, average='weighted')\n",
        "dt_f1 = f1_score(y_encoded, y_dt_pred, average='weighted')\n",
        "\n",
        "\n",
        "print(\"Decision Tree Evaluation:\")\n",
        "print(\"Accuracy:\", dt_accuracy)\n",
        "print(\"Precision:\", dt_precision)\n",
        "print(\"Recall:\", dt_recall)\n",
        "print(\"F1-score:\", dt_f1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tob63Bm_L-gS",
        "outputId": "3d818130-ee3b-448f-bb7d-30b2878f0c9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Decision Tree Evaluation:\n",
            "Accuracy: 0.7678781836309254\n",
            "Precision: 0.7563986210028188\n",
            "Recall: 0.7678781836309254\n",
            "F1-score: 0.7472082105757618\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "# Load the dataset\n",
        "\n",
        "# Remove rows with NaN values in the landslide_category column\n",
        "df = df.dropna(subset=['landslide_category'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3ZTD-H5ZO_x8",
        "outputId": "2a0b674c-32e4-4aaa-eb05-ceb74ce3fa0b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ICuMMswS1QZ",
        "outputId": "5f133e2b-bd3f-4f8a-8a9a-a49ac91e05ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['source_name', 'source_link', 'event_id', 'event_time', 'event_title',\n",
              "       'event_description', 'location_description', 'location_accuracy',\n",
              "       'landslide_category', 'landslide_trigger', 'landslide_size',\n",
              "       'landslide_setting', 'fatality_count', 'injury_count', 'storm_name',\n",
              "       'photo_link', 'notes', 'event_import_source', 'event_import_id',\n",
              "       'country_name', 'country_code', 'admin_division_name',\n",
              "       'admin_division_population', 'gazeteer_closest_point',\n",
              "       'gazeteer_distance', 'submitted_date', 'created_date',\n",
              "       'last_edited_date', 'longitude', 'latitude', 'Date', 'Time', 'year',\n",
              "       'Month', 'day'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "id": "o4nq6SS9TNRG",
        "outputId": "c960f0ba-ca38-4fb7-8ba1-675572330e7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  source_name                                        source_link  event_id  \\\n",
              "0         AGU  https://blogs.agu.org/landslideblog/2008/10/14...       684   \n",
              "\n",
              "   event_time                                    event_title  \\\n",
              "0         NaN  Sigou Village, Loufan County, Shanxi Province   \n",
              "\n",
              "                                   event_description  \\\n",
              "0  occurred early in morning, 11 villagers buried...   \n",
              "\n",
              "                            location_description location_accuracy  \\\n",
              "0  Sigou Village, Loufan County, Shanxi Province           unknown   \n",
              "\n",
              "  landslide_category landslide_trigger  ...          submitted_date  \\\n",
              "0          landslide              rain  ...  04/01/2014 12:00:00 AM   \n",
              "\n",
              "             created_date        last_edited_date  longitude latitude  \\\n",
              "0  11/20/2017 03:17:00 PM  02/15/2018 03:51:00 PM     107.45  32.5625   \n",
              "\n",
              "         Date      Time  year  Month day  \n",
              "0  2008-08-01  00:00:00  2008      8   1  \n",
              "\n",
              "[1 rows x 35 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-82f8c64c-fcf5-4605-aa3a-1f91e9dae678\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>source_name</th>\n",
              "      <th>source_link</th>\n",
              "      <th>event_id</th>\n",
              "      <th>event_time</th>\n",
              "      <th>event_title</th>\n",
              "      <th>event_description</th>\n",
              "      <th>location_description</th>\n",
              "      <th>location_accuracy</th>\n",
              "      <th>landslide_category</th>\n",
              "      <th>landslide_trigger</th>\n",
              "      <th>...</th>\n",
              "      <th>submitted_date</th>\n",
              "      <th>created_date</th>\n",
              "      <th>last_edited_date</th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>Date</th>\n",
              "      <th>Time</th>\n",
              "      <th>year</th>\n",
              "      <th>Month</th>\n",
              "      <th>day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AGU</td>\n",
              "      <td>https://blogs.agu.org/landslideblog/2008/10/14...</td>\n",
              "      <td>684</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Sigou Village, Loufan County, Shanxi Province</td>\n",
              "      <td>occurred early in morning, 11 villagers buried...</td>\n",
              "      <td>Sigou Village, Loufan County, Shanxi Province</td>\n",
              "      <td>unknown</td>\n",
              "      <td>landslide</td>\n",
              "      <td>rain</td>\n",
              "      <td>...</td>\n",
              "      <td>04/01/2014 12:00:00 AM</td>\n",
              "      <td>11/20/2017 03:17:00 PM</td>\n",
              "      <td>02/15/2018 03:51:00 PM</td>\n",
              "      <td>107.45</td>\n",
              "      <td>32.5625</td>\n",
              "      <td>2008-08-01</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>2008</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>1 rows × 35 columns</p>\n",
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              "\n",
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              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "\n",
        "decision_tree_random_forest_features = ['longitude', 'latitude', 'year']\n",
        "\n",
        "X = df.drop('landslide_category', axis=1)\n",
        "y = df['landslide_category']\n",
        "\n",
        "X_dt_rf = X[decision_tree_random_forest_features]\n",
        "\n",
        "# Manual encoding of categories\n",
        "category_mapping = {\n",
        "    'landslide': 0,\n",
        "    'mudslide': 1,\n",
        "    'complex': 2,\n",
        "    'rock_fall': 3,\n",
        "    'debris_flow': 4,\n",
        "    'riverbank_collapse': 5,\n",
        "    'other': 6,\n",
        "    'unknown': 7,\n",
        "    'lahar': 8,\n",
        "    'snow_avalanche': 9,\n",
        "    'creep': 10,\n",
        "    'earth_flow': 11,\n",
        "    'translational_slide': 12,\n",
        "    'nan': 13,\n",
        "    'topple': 14\n",
        "}\n",
        "\n",
        "y_encoded = y.map(category_mapping)\n",
        "\n",
        "# Train the model\n",
        "decision_tree_model = DecisionTreeClassifier(max_depth=9)\n",
        "decision_tree_model.fit(X_dt_rf, y_encoded)\n",
        "\n",
        "# Function to predict landslide category\n",
        "def predict_landslide_category(longitude, latitude, year):\n",
        "    input_data = pd.DataFrame({'longitude': [longitude],\n",
        "                               'latitude': [latitude],\n",
        "                               'year': [year]})\n",
        "\n",
        "    landslide_category = decision_tree_model.predict(input_data)\n",
        "    predicted_category = list(category_mapping.keys())[list(category_mapping.values()).index(landslide_category[0])]\n",
        "    return predicted_category\n",
        "\n",
        "# Example usage\n",
        "longitude = 70.5812  # Replace with the actual longitude value\n",
        "latitude = 35.3191 # Replace with the actual latitude value\n",
        "year = 2024\n",
        "\n",
        "predicted_category = predict_landslide_category(longitude, latitude, year)\n",
        "print(\"Predicted Landslide Category:\", predicted_category)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CQbLgidDM_C1",
        "outputId": "bc23d086-3f73-4d06-aed8-4e0db7836cc5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Landslide Category: snow_avalanche\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "\n",
        "# Defining features and target\n",
        "decision_tree_random_forest_features = ['longitude', 'latitude', 'year', 'Month', 'day']\n",
        "\n",
        "\n",
        "\n",
        "X = df.drop(['landslide_category'], axis=1)\n",
        "y = df['landslide_category']\n",
        "\n",
        "X_dt_rf = X[decision_tree_random_forest_features]\n",
        "\n",
        "# Manual encoding of categories\n",
        "category_mapping = {\n",
        "    'landslide': 0,\n",
        "    'mudslide': 1,\n",
        "    'complex': 2,\n",
        "    'rock_fall': 3,\n",
        "    'debris_flow': 4,\n",
        "    'riverbank_collapse': 5,\n",
        "    'other': 6,\n",
        "    'unknown': 7,\n",
        "    'lahar': 8,\n",
        "    'snow_avalanche': 9,\n",
        "    'creep': 10,\n",
        "    'earth_flow': 11,\n",
        "    'translational_slide': 12,\n",
        "    'nan': 13,\n",
        "    'topple': 14\n",
        "}\n",
        "\n",
        "y_encoded = y.map(category_mapping)\n",
        "decision_tree_model = DecisionTreeClassifier(max_depth=9)\n",
        "decision_tree_model.fit(X_dt_rf, y_encoded)\n",
        "\n",
        "# Function to predict landslide category\n",
        "def predict_landslide_category(longitude, latitude, year,  Month, day):\n",
        "    input_data = pd.DataFrame({'longitude': [longitude],\n",
        "                               'latitude': [latitude],\n",
        "                               'year': [year],\n",
        "                               'Month': [Month],\n",
        "                               'day': [day]})\n",
        "\n",
        "\n",
        "    landslide_category = decision_tree_model.predict(input_data)\n",
        "    predicted_category = list(category_mapping.keys())[list(category_mapping.values()).index(landslide_category[0])]\n",
        "    return predicted_category\n",
        "\n",
        "\n",
        "\n",
        "longitude = 79.8500\n",
        "latitude = 50.3191\n",
        "year = 2015\n",
        "Month = 3\n",
        "day = 2\n",
        "predicted_category = predict_landslide_category(longitude, latitude, year, Month, day)\n",
        "print(\"Predicted Landslide Category:\", predicted_category)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p_VNtOxhVD8y",
        "outputId": "064527f0-c008-499b-b585-58141b2910a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Landslide Category: landslide\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    }
  ]
}