ALDA_Project.ipynb

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  "metadata": {
    "colab": {
      "name": "ALDA_Project.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "aahTYoWxMZrg",
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        "P6Q9DYCQ9qtQ"
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    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.8"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1pz21ArYDFe0"
      },
      "source": [
        "# **WildFire Cause Prediction**\n",
        "\n",
        "Team :  P24\n",
        "\n",
        "1. Ganesh Thanu (gthanu)\n",
        "\n",
        "2. Arun Gaonkar (agaonka)\n",
        "\n",
        "3. Nikhil Patil (npatil)\n"
      ],
      "id": "1pz21ArYDFe0"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "93575a8b"
      },
      "source": [
        "#importing necessary libraries\n",
        "import sqlite3\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sb\n",
        "import numpy as np\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn import tree, preprocessing\n",
        "from sklearn.metrics import accuracy_score, plot_confusion_matrix, confusion_matrix, ConfusionMatrixDisplay,f1_score,precision_score,recall_score\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.model_selection import train_test_split\n",
        "import sklearn.ensemble as ske"
      ],
      "id": "93575a8b",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ALuYJmkemgrq",
        "outputId": "d11419b9-2ece-470c-d10a-bc4969884824"
      },
      "source": [
        "# Mounting Google Drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "id": "ALuYJmkemgrq",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jJq9DzDZjFcR",
        "outputId": "9e94e80c-f77e-45b5-cf40-05ba87499b52"
      },
      "source": [
        "cd '/content/drive/MyDrive/ALDA_Project'"
      ],
      "id": "jJq9DzDZjFcR",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/ALDA_Project\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Y_rtpddq9Zg"
      },
      "source": [
        "# reading from sqlite database \n",
        "from sqlalchemy import create_engine\n",
        "my_conn=create_engine(\"sqlite:////content/drive/MyDrive/ALDA_Project/FPA_FOD_20170508.sqlite\")"
      ],
      "id": "5Y_rtpddq9Zg",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vq3blnKvn_LT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        },
        "outputId": "76aad628-db25-46f1-b8b9-1a1405488ae3"
      },
      "source": [
        "# Reading the sql file \n",
        "df = pd.read_sql_query(\"SELECT FOD_ID, FIRE_YEAR, DISCOVERY_DATE, DISCOVERY_TIME, CONT_DATE, CONT_TIME, FIRE_SIZE, FIRE_SIZE_CLASS, LATITUDE, LONGITUDE, STATE, COUNTY, STAT_CAUSE_DESCR FROM Fires\", my_conn)\n",
        "df_original= df # a copy will be used later"
      ],
      "id": "Vq3blnKvn_LT",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-a5ea2350bf78>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Reading the sql file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_sql_query\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"SELECT FOD_ID, FIRE_YEAR, DISCOVERY_DATE, DISCOVERY_TIME, CONT_DATE, CONT_TIME, FIRE_SIZE, FIRE_SIZE_CLASS, LATITUDE, LONGITUDE, STATE, COUNTY, STAT_CAUSE_DESCR FROM Fires\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmy_conn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mdf_original\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;31m# a copy will be used later\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'my_conn' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "d4719597",
        "outputId": "32800cad-d59c-4d13-ad2e-c52ab85ea53f"
      },
      "source": [
        "#converting the data to normal datatime format\n",
        "df['START_DATE'] = pd.to_datetime(df['DISCOVERY_DATE'] - pd.Timestamp(0).to_julian_date(), unit='D')\n",
        "df['END_DATE'] = pd.to_datetime(df['CONT_DATE'] - pd.Timestamp(0).to_julian_date(), unit='D')\n",
        "df.drop(['DISCOVERY_DATE', 'CONT_DATE'], axis=1, inplace=True)\n",
        "df['MONTH'] = pd.DatetimeIndex(df['START_DATE']).month\n",
        "# Reordering the columns\n",
        "new_cols=['FOD_ID', 'FIRE_YEAR','MONTH','START_DATE','DISCOVERY_TIME', 'END_DATE','CONT_TIME', 'FIRE_SIZE', 'FIRE_SIZE_CLASS', 'LATITUDE', 'LONGITUDE', 'STATE', 'COUNTY', 'STAT_CAUSE_DESCR']\n",
        "df=df.reindex(columns=new_cols)\n",
        "print(df.shape)\n",
        "# First five rows\n",
        "print(df.head())"
      ],
      "id": "d4719597",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>FOD_ID</th>\n",
              "      <th>FIRE_YEAR</th>\n",
              "      <th>MONTH</th>\n",
              "      <th>START_DATE</th>\n",
              "      <th>DISCOVERY_TIME</th>\n",
              "      <th>END_DATE</th>\n",
              "      <th>CONT_TIME</th>\n",
              "      <th>FIRE_SIZE</th>\n",
              "      <th>FIRE_SIZE_CLASS</th>\n",
              "      <th>LATITUDE</th>\n",
              "      <th>LONGITUDE</th>\n",
              "      <th>STATE</th>\n",
              "      <th>COUNTY</th>\n",
              "      <th>STAT_CAUSE_DESCR</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2005</td>\n",
              "      <td>2</td>\n",
              "      <td>2005-02-02</td>\n",
              "      <td>1300</td>\n",
              "      <td>2005-02-02</td>\n",
              "      <td>1730</td>\n",
              "      <td>0.10</td>\n",
              "      <td>A</td>\n",
              "      <td>40.036944</td>\n",
              "      <td>-121.005833</td>\n",
              "      <td>CA</td>\n",
              "      <td>63</td>\n",
              "      <td>Miscellaneous</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2004</td>\n",
              "      <td>5</td>\n",
              "      <td>2004-05-12</td>\n",
              "      <td>0845</td>\n",
              "      <td>2004-05-12</td>\n",
              "      <td>1530</td>\n",
              "      <td>0.25</td>\n",
              "      <td>A</td>\n",
              "      <td>38.933056</td>\n",
              "      <td>-120.404444</td>\n",
              "      <td>CA</td>\n",
              "      <td>61</td>\n",
              "      <td>Lightning</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2004</td>\n",
              "      <td>5</td>\n",
              "      <td>2004-05-31</td>\n",
              "      <td>1921</td>\n",
              "      <td>2004-05-31</td>\n",
              "      <td>2024</td>\n",
              "      <td>0.10</td>\n",
              "      <td>A</td>\n",
              "      <td>38.984167</td>\n",
              "      <td>-120.735556</td>\n",
              "      <td>CA</td>\n",
              "      <td>17</td>\n",
              "      <td>Debris Burning</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2004</td>\n",
              "      <td>6</td>\n",
              "      <td>2004-06-28</td>\n",
              "      <td>1600</td>\n",
              "      <td>2004-07-03</td>\n",
              "      <td>1400</td>\n",
              "      <td>0.10</td>\n",
              "      <td>A</td>\n",
              "      <td>38.559167</td>\n",
              "      <td>-119.913333</td>\n",
              "      <td>CA</td>\n",
              "      <td>3</td>\n",
              "      <td>Lightning</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2004</td>\n",
              "      <td>6</td>\n",
              "      <td>2004-06-28</td>\n",
              "      <td>1600</td>\n",
              "      <td>2004-07-03</td>\n",
              "      <td>1200</td>\n",
              "      <td>0.10</td>\n",
              "      <td>A</td>\n",
              "      <td>38.559167</td>\n",
              "      <td>-119.933056</td>\n",
              "      <td>CA</td>\n",
              "      <td>3</td>\n",
              "      <td>Lightning</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   FOD_ID  FIRE_YEAR  MONTH  ... STATE COUNTY STAT_CAUSE_DESCR\n",
              "0       1       2005      2  ...    CA     63    Miscellaneous\n",
              "1       2       2004      5  ...    CA     61        Lightning\n",
              "2       3       2004      5  ...    CA     17   Debris Burning\n",
              "3       4       2004      6  ...    CA      3        Lightning\n",
              "4       5       2004      6  ...    CA      3        Lightning\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aahTYoWxMZrg"
      },
      "source": [
        "## EDA"
      ],
      "id": "aahTYoWxMZrg"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CPBUbL2gsl42",
        "outputId": "5bb34f25-a2c5-4377-f5ac-3718544fc75f"
      },
      "source": [
        "#check mising values and columns\n",
        "q = df.columns[df.isnull().any()].tolist() \n",
        "print(q)"
      ],
      "id": "CPBUbL2gsl42",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['DISCOVERY_TIME', 'END_DATE', 'CONT_TIME', 'COUNTY']"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "79dVv_NZvGTe",
        "outputId": "7fe76408-ca45-4f7b-8cf2-2299a3993c5c"
      },
      "source": [
        "# Number of Fires and different reasons\n",
        "df['STAT_CAUSE_DESCR'].value_counts().plot(kind='barh')\n",
        "plt.title('WildFire Reasons and Count')\n",
        "plt.show()"
      ],
      "id": "79dVv_NZvGTe",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mo1KgK-OZqKe",
        "outputId": "5122963f-5eb1-4d73-e436-7c6375beac23"
      },
      "source": [
        "# Wildfire cause and firecouny distribution\n",
        "causes = df['STAT_CAUSE_DESCR'].value_counts()\n",
        "fig = plt.figure(figsize =(10, 7))\n",
        "plt.pie(causes, autopct='%1.1f%%',labels=causes.index)\n",
        "plt.show()"
      ],
      "id": "Mo1KgK-OZqKe",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cnDbdeUbZxs7"
      },
      "source": [
        "Debris burning , Arson, Lightning are the major causes of wildfire accounting for more than 50% of total wildfires"
      ],
      "id": "cnDbdeUbZxs7"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oaU04JkmZ7ur",
        "outputId": "1e9a1bdd-89d3-435f-c3ff-1bca75cde1cc"
      },
      "source": [
        "#Yearwise firecount distribution\n",
        "df['FIRE_YEAR'].value_counts().plot(kind='barh',color='lightblue')\n",
        "plt.show()"
      ],
      "id": "oaU04JkmZ7ur",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WlEXM42yaBpm"
      },
      "source": [
        "There is no significant difference in year on year wildfire count"
      ],
      "id": "WlEXM42yaBpm"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EIUvNIb3aN16",
        "outputId": "ade3df03-9c14-4269-f538-a36e5615f70f"
      },
      "source": [
        "# monthwise firecount distribution\n",
        "months = df['MONTH'].value_counts()\n",
        "fig = plt.figure(figsize =(10, 7))\n",
        "plt.pie(months, autopct='%1.1f%%',labels=months.index)\n",
        "plt.show()"
      ],
      "id": "EIUvNIb3aN16",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kWCluJJXaSP8"
      },
      "source": [
        "There is significant difference between wildfire count from October-January and February-September. It might be because of weather, since weather is cold from October-January which is not conducive for wildfire. "
      ],
      "id": "kWCluJJXaSP8"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xKMI0Ej-vqKy",
        "outputId": "1658fc91-9b96-44dc-8ea3-b309fd01f6fc"
      },
      "source": [
        "# Plotting statewise firecount\n",
        "df['STATE'].value_counts().head(n=10).plot(kind='barh')\n",
        "plt.title('StateWise fire count')\n",
        "plt.show()"
      ],
      "id": "xKMI0Ej-vqKy",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eMoNXKDFalGf"
      },
      "source": [
        "df_CA = df[df['STATE']=='CA']\n",
        "df_GA = df[df['STATE']=='GA']\n",
        "df_TX = df[df['STATE']=='TX']"
      ],
      "id": "eMoNXKDFalGf",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9VqgTUzqaoIo",
        "outputId": "95872be7-f88a-417c-d4b2-056814ee4d9e"
      },
      "source": [
        "# causes of fire in CA\n",
        "df_CA['STAT_CAUSE_DESCR'].value_counts().plot(kind='barh',color='lightblue',title='causes of fires for CA')\n",
        "plt.show()"
      ],
      "id": "9VqgTUzqaoIo",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7YHbaOAHatTc",
        "outputId": "989f3566-74fa-4485-af11-2ce01b2ca225"
      },
      "source": [
        "# causes of fire in GA\n",
        "df_GA['STAT_CAUSE_DESCR'].value_counts().plot(kind='barh',color='lightblue',title='causes of fires for GA')\n",
        "plt.show()"
      ],
      "id": "7YHbaOAHatTc",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HpVSB+h/ddHECsKKkm4GvUi2PYnsesD9VpvZZVEu4r21lzJ2cQ6dtlWXSKcAF5SGYfy9J+xERseQGdUZ4SSvaXlCeCv0RcBtwHfAh2wcO7Oj6XzLCR0T0XLOM8D25BzgQDpC0H7AccAPwk/LgS3fLmxEREV0a1AHQ9veB7w/0OCIiYvjJd4FGREQtJQBGREQtJQBGREQtJQBGREQtJQBGREQtJQBGREQtDepfg4gXe/TphZw794GW6r534zX7eDQREUNbZoAREVFLtQuAkhaV5Lw3Svo/SaO7qd8m6biy/ULy3z4Y14K+aDciIjpXuwAIPGV7gu3xwH+A/+6qsu1ptj/VVR1JWUqOiBhi6hgAG10NjAWQtI2kqyXdIOlvkjYu5ZMknd/xQEmnSjqx5Dr8jqQJkv4uaZak30lapdQ7QNJ1kmZKOkfSy0v5eqW/2ZK+3n+nHBERUOMAWNIq7USV6BeqHH072N4S+DLwzRaaeTXwRtufoUrz9DnbmwOzga+UOufa3tr2FsDNwMdK+Q+AE2xvBjR9sqUxI/z8Rx7u2UlGRERTdQyAo0rewAeBNYCLS/nKwG9LLsPvA5u20NZvbS+StDIw2vblpfwXwJvK9nhJV0iaDezT0O52wBll+1fNOrB9ku02220rr7Jai6cYERHdqWMAfMr2BGBdQCy+B/g14LJyb/BdwMgW2nqihTqnAp8sM72jOrQ7eJMxRkQMc3UMgACUvIKfAj5bHmJZGbiv7N6/h23NBx6RtEMp+jDQPhtcCXhA0rJUM8B2VwF7le3G8oiI6Ae1fnrR9g2SZgF7A98BfiHpi8AFS9DcfsCJ5SGXO4CPlPIvAdcA88rPlUr5IcCvJX0O+EMrHYweuWx+wT0iopfIzircUNHW1uZp06YN9DAiIoYUSdNtt3Usr+0SaERE1FsCYERE1FICYERE1FICYERE1FICYERE1FICYERE1FICYERE1FKtfxF+qOlJRnhIVviIiK5kBhgREbU07GeAkl4FHAtsDTwK/As41PatfdTfMcCuwB+BfwBP2v5lX/QVERFLblgHQEkCfgf8wvZepWwLqjRIfRIAgSnAqrYXdTGuZWw/10f9R0REC4b7EuiOwELbJ7YX2J4J3CDpEknXl4zsewBIGifplpLt/VZJp0t6q6SrJN0maZtS70hJvyoZ3W+TdEApPw9YEZguaXKpd1jZN1XSsZKmAYdImijpcknTJV0kKTfsIiL60bCeAQLjgemdlD8NvMf2Y5LGAH8vwQtgQ+D9wEeB64APAtsDuwNfAN5d6m0OvAFYgSqgXmB7d0kLSr5BJB3Zod/lbLeV1EiXA3vYnidpMvCN0ueLSJpCNatkzFpjl+QaREREJ4Z7AGxGwDclvQl4HhhLtSwKcKft2QCS5gCX2HbJ6D6uoY0/2H4KeErSZcA2wO+76fes8nNjquB8cbVKywig08c7bZ8EnASw4fgtkrojIqKXDPcAOAfYs5PyfYDVgYm2F0q6i8WZ2p9pqPd8w/vnefH16hiMWglO7RnkBcyxvW0Lx0RERB8Y7vcALwWWL8uIAEjaHFgX+HcJfjuW9z21h6SRklYDJlEtl7ZqLrC6pG3LmJaVtOkSjCEiIpbQsJ4BlqXL9wDHlszrTwN3AUcCx5VlzWnALUvQ/CzgMmAM8DXb9/dgXM9K2rOMYWWqP4djqWasTSUjfERE70lG+CVQHm5ZYPu7/dlvMsJHRPRcMsJHREQ0GNZLoH3F9pEDPYaIiFg6mQFGREQtJQBGREQtJQBGREQtJQBGREQtJQBGREQt5SnQIaSnGeGbyS/TR0RkBhgRETVVmwAo6QhJcyTNkjRD0uuXsr1Jks7vpHx3SYcvTdsREdH3arEEWr50ejdgK9vPlByAy/VFX7bPA87rtmJERAyouswA1wQesv0MgO2HbN8v6S5J3yozwmmStirZ2f8h6UAAVY6RdGPJHj+5Y+OStpZ0g6QNJO0v6fhSfqqk4yT9TdId5QuwkfQyST8u2ecvlvTH9n0REdE/6hIA/wysLenWEnje3LDvnyWD+xXAqVT5A98AHFX2vxeYAGwBvBU4RtILT5FIeiNwIlV293900veaVBnldwO+3dDmOGAT4MNA07yAkqaU4Dxt/iMP9+ikIyKiuVoEQNsLgInAFGAecJak/cvu9uXK2cA1th+3PQ94RtJoquB1hu1Ftv8FXA5sXY55HVW29nfZ/meT7n9v+3nbN7E46/z2wG9L+YNUaZWajf0k222221ZeZbUlOPuIiOhMLe4BAtheBEwFppY8gPuVXY0Z3ztmg+/u+jxAlUl+S6BZPsDGNtWDIUdERB+qxQxQ0saSNmoomgDc3eLhVwCTJY2QtDrwJuDasu9R4J3AtyRN6sGQrgLeV+4FrkGVUT4iIvpRXWaAKwI/LEuazwG3Uy2H7tbCsb+jukc3EzDw/2w/KOm1ALb/JWk34E+SPtrieM4BdgJuAu4Brgfm9+B8IiJiKSUj/ACRtKLtBZJWo5pRblfuBzaVjPARET3XLCN8XWaAg9H5ZUa6HPC17oJfRET0rgTAAWJ70kCPISKizmrxEExERERHCYAREVFLCYAREVFLCYAREVFLCYAREVFLCYAREVFL+TWIIeTRpxdy7twH+ryf9268ZveVIiKGuNrNACW9StKZJeff9JKLb0pn2d1L/VMkbVK27yrJdDvWOVLSYX099oiI6D21mgFKEtV3e/7C9l6lbAtg92bH2P74UvS3jO3nlvT4iIjoO3WbAe4ILLR9YnuB7ZlUGR9WlHR2ydJ+egmWSJoq6SXfISfpiJJg90pg44byqZKOlTQNOETSREmXl9nmRe3JdEu9oyVdW9rZoY/PPSIiGtRqBgiMB6Y32bclsClVXr+rgO2AKzurKGkisBdVWqVlqLI5NLa7nO02SctSJdDdw/Y8SZOBbwDtWSOWsb2NpF2Br1BlnO/Y1xSqzBWMWWtsD041IiK6UrcA2JVrbd8LIGkGMI4mARDYAfid7SdL/fM67D+r/NyYKuheXCaUI6iS6LY7t/ycXvp7CdsnUWWdZ8PxWyR1R0REL6lbAJwD7NlkX2Pm9kUs3bV5ovwUMMf2tt30ubT9RURED9XtHuClwPJlWREASZtTzeh64q/AuyWNkrQS8K4m9eYCq0vatvS1rKRNl2DcERHRy2oVAF1l/30P8NbyaxBzgG8BPcrFZ/t6qmXOmcCfgOua1HuWasZ5tKSZwAzgjUt+BhER0VuSEX4ISUb4iIiea5YRvlYzwIiIiHYJgBERUUsJgBERUUsJgBERUUsJgBERUUsJgBERUUsJgBERUUsJgBERUUv5/skhpL8ywrdLZviIGM6G7QxQ0oJOyg6UtG83x+0v6fgm+77QYt9flfSS1EYRETF41GoG2JgIdwl9AfhmC/18eSn7iYiIPjZsZ4CdkXSkpMPK9taSZkmaIekYSTc2VF1L0oWSbpP0nVL/28CoUv90SeMk3SzpZElzJP1Z0qhS91RJe5btuyQdJel6SbMlvbaUry7p4nLsKZLuljSmf69IRER91SoAdvBz4BO2J1Dl42s0AZgMbAZMlrS27cOBp2xPsL1PqbcR8CPbmwKPAu9r0tdDtrcCTgAOK2VfAS4tx54NrNPZgZKmSJomadr8Rx5esjONiIiXqGUAlDQaWMn21aXo1x2qXGJ7vu2ngZuAdZs0daftGWW7aVZ3Os/8vj1wJoDtC4FHOjvQ9km222y3rbzKas1PKiIieqSWAbAFrWaH72m9ZH6PiBgkahkAbT8KPC7p9aVorxYPXShp2V4axlXABwAk7Qys0kvtRkREC4ZzAHy5pHsbXp/psP9jwMmSZgArAPNbaPMkYJak03thfEcBO5eHb95PlZX+8V5oNyIiWlDbjPCSVrS9oGwfDqxp+5B+7H95YJHt5yRtC5xQHshpKhnhIyJ6rllG+Drfj3qnpM9TXYO7gf37uf91gN9IehnwLHBAP/cfEVFrtQ2Ats8CzhrA/m8Dthyo/iMi6m443wOMiIhoKgEwIiJqKQEwIiJqKQEwIiJqKQEwIiJqKQEwIiJqqba/BjEU9XdG+J5KBvmIGEoGbAYoyZJOa3i/jKR5ks4v73cv39DSW/015uibKukl3woQERH1MZAzwCeA8ZJG2X4KeBtwX/tO2+cB5w3U4CIiYngb6HuAfwTeWbb3Bs5o3yFpf0nHl+33S7pR0kxJfy1lIyR9t5TPknRwKZ8o6XJJ0yVdJKnLdTlJJ5SEs3MkHdVQ3iyT+wqSfibpWkk3SNqjlI+U9PNS9wZJO3Y8j/L+fEmTyvhPLeOfLenTvXA9IyKiRQN9D/BM4Mtl2XNz4GfADp3U+zLwdtv3lWS2AFOokstOKF8ovWpJVfRDYA/b8yRNBr4BfLSLMRxh+z+SRgCXSNrc9qyy7yHbW0n6L6pM7h8HjqDK5P7RMpZrJf0FOBCw7c1KsPyzpNd00e8EYKzt8fBCkt6XkDSlnCtj1hrbRXMREdETAzoDLIFmHNXs749dVL0KOFXSAcCIUvZW4Ce2nytt/QfYGBgPXFzSHH0ReHU3w/iApOuBG4BNgU0a9nWWyX1n4PDS/lRgJNUXW28PnFbGcgvVF2x3FQDvANaX9ENJuwCPdVYpGeEjIvrGQM8AobrP911gEtDpv/C2DyzJa98JTJc0sUlbAubY3raVjiWtRzWz29r2I5JOpQpo7TrL5C7gfbbndmirWTfP8eL/aIws5/SIpC2At1PNHj9A1zPViIjoRQN9DxCqZc+jbM9uVkHSBravsf1lYB6wNnAx8AlJy5Q6qwJzgdVLfj0kLStp0y76fgXVwzjzJa0BvKOF8V4EHKwS8SS1Z3S4AtinlL2GalY4F7gLmCDpZZLWBrYpdcYAL7N9DtVMdasW+o6IiF4y4DNA2/cCx3VT7RhJG1HNvi4BZgI3Ui0xzpK0EDjZ9vHlVx2Ok7Qy1fkdC8xp0vdMSTcAtwD3UC21dudrpc1ZJZffncBuwI+BEyTNppr17W/7GUlXlTo3ATcD15d2xgI/L20AfL6FviMiopfUNiP8UJSM8BERPdcsI/xgWAKNiIjodwmAERFRSwmAERFRSwmAERFRSwmAERFRSwmAERFRSwmAERFRSwmAERFRSwP+TTDRusGeEb4VyRofEYNFv8wAJS2SNKPhtcSZ3iX9rTfH1sO+D5X08ib77irf79n+flJ7dvuIiBh8+msG+JTtCb3RkO039kY7S+hQqpRHTw7gGCIiohcM6D1ASbtIuqVkXT+ufcYk6UhJhzXUu1HSuLK9oPycJOmvki6QNFfSie1fLC1pgaRjSpb3v0jaRtJUSXdI2r3UGVHqXFcyyn+iod2pks4uYztdlU8BawGXSbqsh+f55obZ7w2SVirl/9PQ/1HdtRMREb2nvwLgqA5LoJMljQROBt4FTARetQTtbgMcTJXEdgPgvaV8Baqs7ZsCjwNfB94GvAf4aqnzMWC+7a2BrYEDSn5AgC2pZnubAOsD29k+Drgf2NH2jj0c52HAf5dZ8A7AU5J2BjYq5zABmCjpTT1sNyIillB/BcCnbE9oeJ0FvBa40/ZtrlJSnLYE7V5r+w7bi4AzqLKyAzwLXFi2ZwOX215YtseV8p2BfUtm92uokvFu1NDuvbafB2Y0HNOVztJqtJddBXyvzCJHlyz2O5fXDVQpkl7b0P8LJE2RNE3StPmPPNzCMCIiohWD9SnQTrOod6Jj0Gl/v9CL8zw9T8nsbvv59gS6VLkFD7Z9UWMDkiaxOBM8vDgbfFceBlYBHirvV23ftv1tSRcAuwJXSXp76f9btn/SVaO2TwJOAthw/BbJXRUR0UsG8h7gLcA4SRuU93s37LuLkiFd0lbAenRuG0nrlXt/k4Ere9D/RcBBkpYt/bxG0grdHPM4sFKTfVOBD5e2RgAfAi4r7zewPdv20cB1VLO9i4CPSlqx1Bkr6ZU9GH9ERCyF/poBjipLje0utH24pCnABZKeBK5gcXA5h2p5cg7V8uStTdq9Djge2JAq2PyuB2M6hWpp83pJAuYB7+7mmJOACyXd38l9wK9RZYSfSTW7u5DFy7qHStqRajY6B/hTyRb/OuDqqnsWUAXNf/fgHCIiYgkNmozwZd0LhIsAAAnhSURBVOnxMNu79UX94SAZ4SMiei4Z4SMiIhoMmodgbE+luo/WJ/UjIiIaZQYYERG1lAAYERG1lAAYERG1lAAYERG1lAAYERG1lAAYERG1NGh+DSK6NxwywtdFMt9HDH6ZAbZA0rslWdJrB3osERHROxIAW7M31Rdt791xR0N2iYiIGEISALtRsjVsT5VAd69SNknSFZLOA26StELJTD+zZK+fXOrtVDLAz5b0M0nLl/K7JB0l6fqyLzPLiIh+lgDYvT2oslfcCjwsaWIp3wo4xPZrgF2A+21vYXs8VcaIkcCpwGTbm1Hdbz2ood2HbG8FnECVMT4iIvpRAmD39gbOLNtnsngZ9Frbd5bt2cDbJB0taQfb84GNqTLet6dy+gXwpoZ2zy0/p9NFxvlkhI+I6Bu5f9UFSasCbwE2k2RgBFXW+QuAJ9rr2b61JO7dFfi6pEuAP3TTfHvW+S4zzicjfERE38gMsGt7Ar+yva7tcbbXBu4EdmisJGkt4EnbpwHHUC2PzqXKeL9hqfZh4PL+G3pERHQlM8Cu7Q0c3aHsHKp7ef9oKNsMOEbS88BC4CDbT0v6CPDb8qTodcCJ/TDmiIhowaDJCB/dS0b4iIieS0b4iIiIBgmAERFRSwmAERFRSwmAERFRSwmAERFRSwmAERFRSwmAERFRSwmAERFRS/kmmCEkGeEjoo7eu/GafdJuZoAREVFL/RYAJS2SNEPSnJI49rOSuuy/JJ49v8X2T5G0SYt1j5R0XxnPLZJO6G4sPSHpj5JG91Z7ERHR+/pzCfQp2xMAJL0S+DXwCuArS9uwpBG2P97Dw75v+7sl8P0VeDNwWYv9LWP7uWb7be/aw7FEREQ/G5AlUNv/BqYAn1RlhKRjJF0naZakTzRUf4WkCyTNlXRi+0xN0gJJ/ytpJrCtpKmS2kpbp0q6UdJsSZ/uZjjLASOBR0q7UyW1le0xku4q2/tLOk/SpcAl5f25ki6UdJuk77Q3KOmucuw4STdLOrnMfP8saVSps3U51xnl3G/slYsbEREtGbB7gLbvoEow+0rgY8B821sDWwMHSFqvVN0GOBjYBNgAeG8pXwG4xvYWtq9saHoCMNb2eNubAT9vMoRPS5oBPADcantGC8PeCtjT9psb+ppMlQ5psqS1OzlmI+BHtjcFHgXeV8p/DnyizIoXNeswGeEjIvrGYHkIZmdg3xKQrgFWowocANfavsP2IuAMYPtSvogqN19HdwDrS/qhpF2Ax5r0+f0SfF4JrCBprxbGebHt/zS8v8T2fNtPAzcB63ZyzJ0NwXU6VZLc0cBKtq8u5b9u1qHtk2y32W5beZXVWhhiRES0YsACoKT1qYLYvwEBB9ueUF7r2f5zqdoxYWH7+6dLUHzxTvsRYAtgKnAgcEpX47C9ELgQeFMpeo7F12Vkh+pPdHj/TMP2Ijq/p9pKnYiI6GcDEgAlrU6VHf14Vxl5LwIOkrRs2f8aSSuU6ttIWq/c+5sMXNlpo4vbHgO8zPY5wBepli27qi9gOxZneL8LmFi29+zpubXC9qPA45JeX4pamX1GREQv6s/ZyKiyxLks1SzrV8D3yr5TgHHA9SUgzQPeXfZdBxwPbEj1lObvuulnLPDzhl9r+HyTep+W9KEynlnAj0v5d4HfSJoCXNDy2fXcx4CTJT0PXA7M7+6A0SOX7bNfCI2IqBtVE7Dob5JWtL2gbB8OrGn7kK6OaWtr87Rp0/plfBERw4Wk6bbbOpbnftTAeaekz1P9GdwN7D+ww4mIqJcEwAFi+yzgrIEeR0REXQ2WX4OIiIjoV7kHOIRIehyYO9DjGMTGAA8N9CAGsVyf7uUadW2oXp91ba/esTBLoEPL3M5u5EZF0rRcn+ZyfbqXa9S14XZ9sgQaERG1lAAYERG1lAA4tJw00AMY5HJ9upbr071co64Nq+uTh2AiIqKWMgOMiIhaSgCMiIhaSgAcAiTtImmupNvL94YOW5LWlnSZpJskzZF0SClfVdLFkm4rP1cp5ZJ0XLk2syRt1dDWfqX+bZL2ayifKGl2Oea48gXsQ4qkEZJukHR+eb+epGvKOZ0lablSvnx5f3vZP66hjc+X8rmS3t5QPuQ/b5JGSzpb0i2Sbpa0bT5Di0n6dPn7daOkMySNrOVnyHZeg/gFjKBK1bQ+sBwwE9hkoMfVh+e7JrBV2V4JuBXYBPgOcHgpPxw4umzvCvyJKqfkG4BrSvmqVMmRVwVWKdurlH3Xlroqx75joM97Ca7TZ6gSKZ9f3v8G2KtsnwgcVLb/CzixbO8FnFW2NymfpeWB9cpnbMRw+bwBvwA+XraXA0bnM/TCtRkL3AmMavjs7F/Hz1BmgIPfNsDttu+w/SxwJrDHAI+pz9h+wPb1Zftx4Gaqv7B7UP2jRvnZni5rD+CXrvwdGC1pTeDtwMW2/+MqSfLFwC5l3yts/93V3+JfNrQ1JEh6NfBOSrLnMvt4C3B2qdLx+rRft7OBnUr9PYAzbT9j+07gdqrP2pD/vElamSrB9U8BbD/rKgdnPkOLLUOVom4Z4OXAA9TwM5QAOPiNBe5peH9vKRv2ylLLlsA1wBq2Hyi7HgTWKNvNrk9X5fd2Uj6UHAv8P+D58n414FHbz5X3jef0wnUo++eX+j29bkPJelQ5RX9elolPUZVgO58hwPZ9VHlP/0kV+OYD06nhZygBMAYlSSsC5wCH2n6scV/5X3ctf39H0m7Av21PH+ixDGLLAFsBJ9jeEniCasnzBTX/DK1CNSNbD1gLWAHYZUAHNUASAAe/+4C1G96/upQNW5KWpQp+p9s+txT/qyw9UX7+u5Q3uz5dlb+6k/KhYjtgd0l3US0tvQX4AdWyXft3+zae0wvXoexfGXiYnl+3oeRe4F7b15T3Z1MFxHyGKm8F7rQ9z/ZC4Fyqz1XtPkMJgIPfdcBG5Qmt5ahuQp83wGPqM+Xewk+Bm21/r2HXeUD7U3j7AX9oKN+3PMn3BmB+Wea6CNhZ0irlf7w7AxeVfY9JekPpa9+GtgY925+3/Wrb46g+C5fa3ge4DNizVOt4fdqv256lvkv5XuUJv/WAjage7BjynzfbDwL3SNq4FO0E3EQ+Q+3+CbxB0svL+NuvT/0+QwP9FE5e3b+onlK7lerJqiMGejx9fK7bUy1NzQJmlNeuVPccLgFuA/4CrFrqC/hRuTazgbaGtj5KdWP+duAjDeVtwI3lmOMp34g01F7AJBY/Bbo+1T8+twO/BZYv5SPL+9vL/vUbjj+iXIO5NDzFOBw+b8AEYFr5HP2e6inOfIYWj/8o4JZyDr+iepKzdp+hfBVaRETUUpZAIyKilhIAIyKilhIAIyKilhIAIyKilhIAIyKilhIAIyKilhIAIyKilv4/9ZGJi2iupKYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YKFM9FT0awfT",
        "outputId": "16af894d-4bb3-4be3-e8d7-da47566c00ef"
      },
      "source": [
        "# causes of fire in TX\n",
        "df_TX['STAT_CAUSE_DESCR'].value_counts().plot(kind='barh',color='lightblue',title='causes of fires for TX')\n",
        "plt.show()"
      ],
      "id": "YKFM9FT0awfT",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Azv0T2pytvx",
        "outputId": "aad80b09-5352-49fb-db74-50f4ebe543eb"
      },
      "source": [
        "# plotting firesize vs firecause\n",
        "df1 = df.groupby('STAT_CAUSE_DESCR').mean()\n",
        "plt.bar(df['STAT_CAUSE_DESCR'].unique(),df1['FIRE_SIZE'])\n",
        "plt.xticks(rotation=90)\n",
        "plt.title('Average Fire size (in acres) v/s Fire Cause')\n",
        "plt.show()"
      ],
      "id": "4Azv0T2pytvx",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iGkAOzcka7--"
      },
      "source": [
        "Causes of fire vary state by state, so location will be critical attribute in prediction."
      ],
      "id": "iGkAOzcka7--"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "69s3lmvoD5go"
      },
      "source": [
        "# Visualising the Dataset:\n"
      ],
      "id": "69s3lmvoD5go"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zjal5fZC0_2v"
      },
      "source": [
        "#encoding the labels\n",
        "le = preprocessing.LabelEncoder()\n",
        "df['STAT_CAUSE_DESCR'] = le.fit_transform(df['STAT_CAUSE_DESCR'])\n",
        "df['STATE'] = le.fit_transform(df['STATE'])\n",
        "df.rename(columns = {\"STAT_CAUSE_DESCR\":\"CAUSE\"}, inplace=\"True\")"
      ],
      "id": "Zjal5fZC0_2v",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PTbUc9_J1FI3",
        "outputId": "8e75a618-7054-48bd-cd6c-7e784dd686e7"
      },
      "source": [
        "print(df.head())"
      ],
      "id": "PTbUc9_J1FI3",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>FOD_ID</th>\n",
              "      <th>FIRE_YEAR</th>\n",
              "      <th>MONTH</th>\n",
              "      <th>START_DATE</th>\n",
              "      <th>DISCOVERY_TIME</th>\n",
              "      <th>END_DATE</th>\n",
              "      <th>CONT_TIME</th>\n",
              "      <th>FIRE_SIZE</th>\n",
              "      <th>FIRE_SIZE_CLASS</th>\n",
              "      <th>LATITUDE</th>\n",
              "      <th>LONGITUDE</th>\n",
              "      <th>STATE</th>\n",
              "      <th>COUNTY</th>\n",
              "      <th>CAUSE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2005</td>\n",
              "      <td>2</td>\n",
              "      <td>2005-02-02</td>\n",
              "      <td>1300</td>\n",
              "      <td>2005-02-02</td>\n",
              "      <td>1730</td>\n",
              "      <td>0.10</td>\n",
              "      <td>A</td>\n",
              "      <td>40.036944</td>\n",
              "      <td>-121.005833</td>\n",
              "      <td>4</td>\n",
              "      <td>63</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2004</td>\n",
              "      <td>5</td>\n",
              "      <td>2004-05-12</td>\n",
              "      <td>0845</td>\n",
              "      <td>2004-05-12</td>\n",
              "      <td>1530</td>\n",
              "      <td>0.25</td>\n",
              "      <td>A</td>\n",
              "      <td>38.933056</td>\n",
              "      <td>-120.404444</td>\n",
              "      <td>4</td>\n",
              "      <td>61</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2004</td>\n",
              "      <td>5</td>\n",
              "      <td>2004-05-31</td>\n",
              "      <td>1921</td>\n",
              "      <td>2004-05-31</td>\n",
              "      <td>2024</td>\n",
              "      <td>0.10</td>\n",
              "      <td>A</td>\n",
              "      <td>38.984167</td>\n",
              "      <td>-120.735556</td>\n",
              "      <td>4</td>\n",
              "      <td>17</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2004</td>\n",
              "      <td>6</td>\n",
              "      <td>2004-06-28</td>\n",
              "      <td>1600</td>\n",
              "      <td>2004-07-03</td>\n",
              "      <td>1400</td>\n",
              "      <td>0.10</td>\n",
              "      <td>A</td>\n",
              "      <td>38.559167</td>\n",
              "      <td>-119.913333</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2004</td>\n",
              "      <td>6</td>\n",
              "      <td>2004-06-28</td>\n",
              "      <td>1600</td>\n",
              "      <td>2004-07-03</td>\n",
              "      <td>1200</td>\n",
              "      <td>0.10</td>\n",
              "      <td>A</td>\n",
              "      <td>38.559167</td>\n",
              "      <td>-119.933056</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   FOD_ID  FIRE_YEAR  MONTH START_DATE  ...   LONGITUDE STATE COUNTY  CAUSE\n",
              "0       1       2005      2 2005-02-02  ... -121.005833     4     63      7\n",
              "1       2       2004      5 2004-05-12  ... -120.404444     4     61      6\n",
              "2       3       2004      5 2004-05-31  ... -120.735556     4     17      3\n",
              "3       4       2004      6 2004-06-28  ... -119.913333     4      3      6\n",
              "4       5       2004      6 2004-06-28  ... -119.933056     4      3      6\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kfcKRptX09TK"
      },
      "source": [
        "df['DAY_OF_WEEK'] = pd.DatetimeIndex(df['START_DATE']).weekday"
      ],
      "id": "kfcKRptX09TK",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zkc28mp-0IOs",
        "outputId": "b72bab6c-9d8c-4c75-f9e6-5ebf0121f9f7"
      },
      "source": [
        "#Corralation\n",
        "plt.figure(figsize=(12, 8))\n",
        "df_copy = df.drop('FOD_ID', axis=1)\n",
        "corr = df_copy.corr()\n",
        "sb.heatmap(corr, annot=True)\n",
        "plt.title('Correalation Matrix')\n",
        "plt.show()\n"
      ],
      "id": "Zkc28mp-0IOs",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xl1awiub2Ph6",
        "outputId": "397effab-3a01-4a90-a0e8-24711e91dc01"
      },
      "source": [
        "print(df.head())"
      ],
      "id": "Xl1awiub2Ph6",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>FOD_ID</th>\n",
              "      <th>FIRE_YEAR</th>\n",
              "      <th>MONTH</th>\n",
              "      <th>START_DATE</th>\n",
              "      <th>DISCOVERY_TIME</th>\n",
              "      <th>END_DATE</th>\n",
              "      <th>CONT_TIME</th>\n",
              "      <th>FIRE_SIZE</th>\n",
              "      <th>FIRE_SIZE_CLASS</th>\n",
              "      <th>LATITUDE</th>\n",
              "      <th>LONGITUDE</th>\n",
              "      <th>STATE</th>\n",
              "      <th>COUNTY</th>\n",
              "      <th>CAUSE</th>\n",
              "      <th>DAY_OF_WEEK</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2005</td>\n",
              "      <td>2</td>\n",
              "      <td>2005-02-02</td>\n",
              "      <td>1300</td>\n",
              "      <td>2005-02-02</td>\n",
              "      <td>1730</td>\n",
              "      <td>0.10</td>\n",
              "      <td>A</td>\n",
              "      <td>40.036944</td>\n",
              "      <td>-121.005833</td>\n",
              "      <td>4</td>\n",
              "      <td>63</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2004</td>\n",
              "      <td>5</td>\n",
              "      <td>2004-05-12</td>\n",
              "      <td>0845</td>\n",
              "      <td>2004-05-12</td>\n",
              "      <td>1530</td>\n",
              "      <td>0.25</td>\n",
              "      <td>A</td>\n",
              "      <td>38.933056</td>\n",
              "      <td>-120.404444</td>\n",
              "      <td>4</td>\n",
              "      <td>61</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2004</td>\n",
              "      <td>5</td>\n",
              "      <td>2004-05-31</td>\n",
              "      <td>1921</td>\n",
              "      <td>2004-05-31</td>\n",
              "      <td>2024</td>\n",
              "      <td>0.10</td>\n",
              "      <td>A</td>\n",
              "      <td>38.984167</td>\n",
              "      <td>-120.735556</td>\n",
              "      <td>4</td>\n",
              "      <td>17</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2004</td>\n",
              "      <td>6</td>\n",
              "      <td>2004-06-28</td>\n",
              "      <td>1600</td>\n",
              "      <td>2004-07-03</td>\n",
              "      <td>1400</td>\n",
              "      <td>0.10</td>\n",
              "      <td>A</td>\n",
              "      <td>38.559167</td>\n",
              "      <td>-119.913333</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2004</td>\n",
              "      <td>6</td>\n",
              "      <td>2004-06-28</td>\n",
              "      <td>1600</td>\n",
              "      <td>2004-07-03</td>\n",
              "      <td>1200</td>\n",
              "      <td>0.10</td>\n",
              "      <td>A</td>\n",
              "      <td>38.559167</td>\n",
              "      <td>-119.933056</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   FOD_ID  FIRE_YEAR  MONTH START_DATE  ... STATE COUNTY CAUSE  DAY_OF_WEEK\n",
              "0       1       2005      2 2005-02-02  ...     4     63     7            2\n",
              "1       2       2004      5 2004-05-12  ...     4     61     6            2\n",
              "2       3       2004      5 2004-05-31  ...     4     17     3            0\n",
              "3       4       2004      6 2004-06-28  ...     4      3     6            0\n",
              "4       5       2004      6 2004-06-28  ...     4      3     6            0\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZrvcBVMS2tew"
      },
      "source": [
        "xdf = df.drop(['FOD_ID','START_DATE','DISCOVERY_TIME','END_DATE','CONT_TIME','FIRE_SIZE_CLASS','COUNTY','CAUSE'], axis=1)\n",
        "ydf = df['CAUSE']\n",
        "\n",
        "X = df.drop(['FOD_ID','START_DATE','DISCOVERY_TIME','END_DATE','CONT_TIME','FIRE_SIZE_CLASS','COUNTY','CAUSE'], axis=1).values\n",
        "y = df['CAUSE'].values"
      ],
      "id": "ZrvcBVMS2tew",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ww2L5wT33_C7"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3, random_state=0) "
      ],
      "id": "ww2L5wT33_C7",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zYFRSV3x4Ggc"
      },
      "source": [
        "# RFC with 13 features in the cause\n",
        "model1 = ske.RandomForestClassifier(n_estimators=50)\n",
        "model1 = model1.fit(X_train, y_train)\n",
        "print(model1.score(X_test,y_test))"
      ],
      "id": "zYFRSV3x4Ggc",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fYEfa7SP6ZBr",
        "outputId": "64153075-7213-4a87-8f3f-f6698dc2ed93"
      },
      "source": [
        "# making 4 categories from 13 features\n",
        "def set_label(cat):\n",
        "    cause = 0\n",
        "    natural = ['Lightning']\n",
        "    accidental = ['Structure','Fireworks','Powerline','Railroad','Smoking','Children','Campfire','Equipment Use','Debris Burning']\n",
        "    malicious = ['Arson']\n",
        "    other = ['Missing/Undefined','Miscellaneous']\n",
        "    if cat in natural:\n",
        "        cause = 1\n",
        "    elif cat in accidental:\n",
        "        cause = 2\n",
        "    elif cat in malicious:\n",
        "        cause = 3\n",
        "    else:\n",
        "        cause = 4\n",
        "    return cause\n",
        "     \n",
        "\n",
        "df['LABEL'] = df_original['STAT_CAUSE_DESCR'].apply(lambda x: set_label(x)) # I created a copy of the original df earlier in the kernel\n",
        "df = df.drop('CAUSE',axis=1)\n",
        "print(df.head())"
      ],
      "id": "fYEfa7SP6ZBr",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>FOD_ID</th>\n",
              "      <th>FIRE_YEAR</th>\n",
              "      <th>MONTH</th>\n",
              "      <th>START_DATE</th>\n",
              "      <th>DISCOVERY_TIME</th>\n",
              "      <th>END_DATE</th>\n",
              "      <th>CONT_TIME</th>\n",
              "      <th>FIRE_SIZE</th>\n",
              "      <th>FIRE_SIZE_CLASS</th>\n",
              "      <th>LATITUDE</th>\n",
              "      <th>LONGITUDE</th>\n",
              "      <th>STATE</th>\n",
              "      <th>COUNTY</th>\n",
              "      <th>DAY_OF_WEEK</th>\n",
              "      <th>LABEL</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2005</td>\n",
              "      <td>2</td>\n",
              "      <td>2005-02-02</td>\n",
              "      <td>1300</td>\n",
              "      <td>2005-02-02</td>\n",
              "      <td>1730</td>\n",
              "      <td>0.10</td>\n",
              "      <td>A</td>\n",
              "      <td>40.036944</td>\n",
              "      <td>-121.005833</td>\n",
              "      <td>4</td>\n",
              "      <td>63</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2004</td>\n",
              "      <td>5</td>\n",
              "      <td>2004-05-12</td>\n",
              "      <td>0845</td>\n",
              "      <td>2004-05-12</td>\n",
              "      <td>1530</td>\n",
              "      <td>0.25</td>\n",
              "      <td>A</td>\n",
              "      <td>38.933056</td>\n",
              "      <td>-120.404444</td>\n",
              "      <td>4</td>\n",
              "      <td>61</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2004</td>\n",
              "      <td>5</td>\n",
              "      <td>2004-05-31</td>\n",
              "      <td>1921</td>\n",
              "      <td>2004-05-31</td>\n",
              "      <td>2024</td>\n",
              "      <td>0.10</td>\n",
              "      <td>A</td>\n",
              "      <td>38.984167</td>\n",
              "      <td>-120.735556</td>\n",
              "      <td>4</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2004</td>\n",
              "      <td>6</td>\n",
              "      <td>2004-06-28</td>\n",
              "      <td>1600</td>\n",
              "      <td>2004-07-03</td>\n",
              "      <td>1400</td>\n",
              "      <td>0.10</td>\n",
              "      <td>A</td>\n",
              "      <td>38.559167</td>\n",
              "      <td>-119.913333</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2004</td>\n",
              "      <td>6</td>\n",
              "      <td>2004-06-28</td>\n",
              "      <td>1600</td>\n",
              "      <td>2004-07-03</td>\n",
              "      <td>1200</td>\n",
              "      <td>0.10</td>\n",
              "      <td>A</td>\n",
              "      <td>38.559167</td>\n",
              "      <td>-119.933056</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   FOD_ID  FIRE_YEAR  MONTH START_DATE  ... STATE COUNTY DAY_OF_WEEK  LABEL\n",
              "0       1       2005      2 2005-02-02  ...     4     63           2      4\n",
              "1       2       2004      5 2004-05-12  ...     4     61           2      1\n",
              "2       3       2004      5 2004-05-31  ...     4     17           0      2\n",
              "3       4       2004      6 2004-06-28  ...     4      3           0      1\n",
              "4       5       2004      6 2004-06-28  ...     4      3           0      1\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rABwtISsFgZ_",
        "outputId": "d40aa432-ce93-463b-a080-f3fcaae266fe"
      },
      "source": [
        "w = df['LABEL'].value_counts()\n",
        "print(w)"
      ],
      "id": "rABwtISsFgZ_",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    830014\n",
              "4    490528\n",
              "3    281455\n",
              "1    278468\n",
              "Name: LABEL, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D-f8FGZt6tSV",
        "outputId": "3270804c-ac78-41ae-92fb-900070af8461"
      },
      "source": [
        "X = df.drop(['FOD_ID','START_DATE','DISCOVERY_TIME','END_DATE','CONT_TIME','FIRE_SIZE_CLASS','COUNTY','LABEL'], axis=1).values\n",
        "y = df['LABEL'].values\n",
        "print(set(y))"
      ],
      "id": "D-f8FGZt6tSV",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{1, 2, 3, 4}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gsXOqQ_FjIyL"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3, random_state=0)"
      ],
      "id": "gsXOqQ_FjIyL",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mpx6Gw11PHTa"
      },
      "source": [
        "# General Methods"
      ],
      "id": "mpx6Gw11PHTa"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9wsG4kEPAWZo"
      },
      "source": [
        "## Random Forest Classifier"
      ],
      "id": "9wsG4kEPAWZo"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M8bag2HMfNKm",
        "outputId": "93bbe564-093b-430a-b930-be61bd25527a"
      },
      "source": [
        "# Random Forest Classifier\n",
        "model2 = ske.RandomForestClassifier(n_estimators=50)\n",
        "y_pred = model2.fit(X_train, y_train).predict(X_test)\n",
        "#print(confusion_matrix(y_true=y_test,y_pred=y_pred))\n",
        "print(classification_report(y_test, y_pred))\n",
        "cm_rfc = confusion_matrix(y_test,y_pred,normalize='true')\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm_rfc,display_labels=['natural','accidental','malicious','other'])\n",
        "disp.plot()"
      ],
      "id": "M8bag2HMfNKm",
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[ 66578  11329    904   4505]\n",
            " [ 11203 199452  16986  21687]\n",
            " [  1567  36114  38876   7613]\n",
            " [  7038  46365   5222  88701]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.77      0.80      0.78     83316\n",
            "           2       0.68      0.80      0.74    249328\n",
            "           3       0.63      0.46      0.53     84170\n",
            "           4       0.72      0.60      0.66    147326\n",
            "\n",
            "    accuracy                           0.70    564140\n",
            "   macro avg       0.70      0.67      0.68    564140\n",
            "weighted avg       0.70      0.70      0.69    564140\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "zTGuZhvFhpCE",
        "outputId": "2f367c87-a588-4648-bccd-d868eef97c24"
      },
      "source": [
        ""
      ],
      "id": "zTGuZhvFhpCE",
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7facd88acad0>"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g78Ffg_sWcFw"
      },
      "source": [
        "## Adaboost Classifier"
      ],
      "id": "g78Ffg_sWcFw"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "1PT-3-KEWbGh",
        "outputId": "5956ee31-3f8e-45be-f1f3-868849530a85"
      },
      "source": [
        "#Adaboost classifier\n",
        "\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "\n",
        "clfab = AdaBoostClassifier(n_estimators=100, random_state=0)\n",
        "clfab.fit(X_train, y_train)\n",
        "y_pred_ab = clfab.predict(X_test)\n",
        "print(classification_report(y_test, y_pred_ab))\n",
        "\n",
        "cm_ab = confusion_matrix(y_test,y_pred_ab,normalize='true')\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm_ab,display_labels=['natural','accidental','malicious','other'])\n",
        "disp.plot()\n"
      ],
      "id": "1PT-3-KEWbGh",
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.59      0.67      0.63     83316\n",
            "           2       0.53      0.85      0.65    249328\n",
            "           3       0.47      0.10      0.17     84170\n",
            "           4       0.59      0.23      0.33    147326\n",
            "\n",
            "    accuracy                           0.55    564140\n",
            "   macro avg       0.55      0.46      0.45    564140\n",
            "weighted avg       0.55      0.55      0.49    564140\n",
            "\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7facd3bbe450>"
            ]
          },
          "execution_count": 49,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wSL9VT4SYWSy"
      },
      "source": [
        "## Decision Tree Classifier"
      ],
      "id": "wSL9VT4SYWSy"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "id": "MGYgnSVGYVR1",
        "outputId": "de7994c3-1202-4b7d-abc3-9fe66d921ef5"
      },
      "source": [
        "#Decision Tree Classifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "clfdt = DecisionTreeClassifier(random_state=0)\n",
        "clfdt.fit(X_train, y_train)\n",
        "y_pred_dt = clfdt.predict(X_test)\n",
        "print(classification_report(y_test, y_pred_dt))\n",
        "\n",
        "cm_dt = confusion_matrix(y_test,y_pred_dt,normalize='true')\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm_dt,display_labels=['natural','accidental','malicious','other'])\n",
        "disp.plot()\n"
      ],
      "id": "MGYgnSVGYVR1",
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.70      0.70      0.70     83316\n",
            "           2       0.66      0.64      0.65    249328\n",
            "           3       0.46      0.48      0.47     84170\n",
            "           4       0.59      0.60      0.59    147326\n",
            "\n",
            "    accuracy                           0.61    564140\n",
            "   macro avg       0.60      0.60      0.60    564140\n",
            "weighted avg       0.62      0.61      0.61    564140\n",
            "\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7facd53c0c90>"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P6Q9DYCQ9qtQ"
      },
      "source": [
        "## K Nearest Neighbor Classifier"
      ],
      "id": "P6Q9DYCQ9qtQ"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o2JhNxen9ufy",
        "outputId": "f08cf2e3-a8ce-4521-a2ab-5f43d396d535"
      },
      "source": [
        "# K Nearest Neighbor Classifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "knn = KNeighborsClassifier(n_neighbors = 5)\n",
        "knn.fit(X_train, y_train)\n",
        "y_pred_knn = knn.predict(X_test)\n",
        "\n",
        "print(classification_report(y_test, y_pred_knn))\n",
        "cm_knn = confusion_matrix(y_test,y_pred_knn,normalize='true')\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm_knn,display_labels=['natural','accidental','malicious','other'])\n",
        "disp.plot()"
      ],
      "id": "o2JhNxen9ufy",
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.67      0.77      0.72     83316\n",
            "           2       0.64      0.74      0.69    249328\n",
            "           3       0.49      0.37      0.42     84170\n",
            "           4       0.68      0.54      0.60    147326\n",
            "\n",
            "    accuracy                           0.64    564140\n",
            "   macro avg       0.62      0.61      0.61    564140\n",
            "weighted avg       0.63      0.64      0.63    564140\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "dSxdU85aTOcI",
        "outputId": "2d8d6c2f-e03e-4a7b-ace0-6cd47df29442"
      },
      "source": [
        ""
      ],
      "id": "dSxdU85aTOcI",
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7facd694a450>"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-xxMSL2ZNZ_4"
      },
      "source": [
        "# Bi-Directional LSTM"
      ],
      "id": "-xxMSL2ZNZ_4"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wftm6izlNb93"
      },
      "source": [
        "from sklearn.preprocessing import RobustScaler, MinMaxScaler\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Flatten\n",
        "from keras.layers import Dropout, Conv1D, MaxPooling1D\n",
        "import keras.utils\n",
        "from keras.utils.np_utils import to_categorical\n",
        "from keras.layers import LSTM"
      ],
      "id": "wftm6izlNb93",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k65eFsl7QEDo",
        "outputId": "5101ed2d-e57c-44be-d9f6-023a1efbfc0d"
      },
      "source": [
        "# redefining training and testing set\n",
        "xdf = df.drop(['FOD_ID','START_DATE','DISCOVERY_TIME','END_DATE','CONT_TIME','FIRE_SIZE_CLASS','COUNTY','LABEL'], axis=1)\n",
        "ydf = df['LABEL']\n",
        "\n",
        "print(\"Shape of x dataframe\",xdf.shape)\n",
        "print(\"Shape of y dataframe\",ydf.shape)"
      ],
      "id": "k65eFsl7QEDo",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1880465, 7)\n",
            "(1880465,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g5f-frNSRi0H",
        "outputId": "bd408f02-d21b-411c-ba98-ba36cefb8dd4"
      },
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(xdf,ydf,test_size=0.3,train_size=0.7)\n",
        "\n",
        "print(\"X_train, Y_train shape\",x_train.shape, y_train.shape)\n",
        "print(\"\\nX_test, Y_test shape\",x_test.shape, y_test.shape)"
      ],
      "id": "g5f-frNSRi0H",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train, Y_train shape (1316325, 7) (1316325,)\n",
            "\n",
            "X_test, Y_test shape (564140, 7) (564140,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cb7dyRKNN9Qs",
        "outputId": "456ae35d-51c9-42ca-c276-928a0e3fea2a"
      },
      "source": [
        "# Using Minmax scaler\n",
        "from sklearn.preprocessing import StandardScaler,MinMaxScaler\n",
        "\n",
        "scaler = MinMaxScaler()    # robust to outliers\n",
        "\n",
        "scaler_train = scaler.fit(x_train)\n",
        "scaler_val = scaler.fit(x_test)\n",
        "\n",
        "x_train.loc[:] = scaler_train.transform(x_train.to_numpy())\n",
        "x_test.loc[:] = scaler_train.transform(x_test.to_numpy())"
      ],
      "id": "cb7dyRKNN9Qs",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1734: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  isetter(loc, value[:, i].tolist())\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1734: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  isetter(loc, value[:, i].tolist())\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "4rAK85GyODDd"
      },
      "source": [
        "# converting fro 2D data 3D data\n",
        "from scipy.stats import mode\n",
        "\n",
        "def create_dataset(x, y, timesteps, step):\n",
        "  x_set = []\n",
        "  y_set = []\n",
        "  for i in range(0,len(x)-timesteps, step):\n",
        "    data = x.iloc[i: (i + timesteps)].values\n",
        "    labels = y.iloc[i: i + timesteps]\n",
        "    x_set.append(data)\n",
        "    y_set.append(mode(labels)[0][0])\n",
        "  \n",
        "  return np.array(x_set), np.array(y_set).reshape(-1,1)\n",
        "\n",
        "timesteps = 4\n",
        "steps = 1\n",
        "\n",
        "X_train, Y_train = create_dataset( x_train, y_train, timesteps, steps)        # making the dataset ready for training\n",
        "X_test , Y_test = create_dataset( x_test, y_test, timesteps, steps)           # making the dataset ready for validation"
      ],
      "id": "4rAK85GyODDd",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "j7GfT98EOD0_",
        "outputId": "5cd826df-e0fe-4e13-eae3-7621918ae050"
      },
      "source": [
        "from tensorflow.keras.utils import to_categorical\n",
        "\n",
        "print(\"Shape of x_train\",X_train.shape)          \n",
        "print(\"Shape of y_train\",Y_train.shape)          \n",
        "\n",
        "print(\"Shape of x_test\",X_test.shape)           \n",
        "print(\"shape of Y_test\",Y_test.shape)           \n",
        "print()\n",
        "\n",
        "Y_train = to_categorical(Y_train)\n",
        "Y_test = to_categorical(Y_test)\n",
        "print(\"Shpae of Y_train after categorical\",Y_train.shape)          \n",
        "print(\"Shape of Y_test after categorical\",Y_test.shape)           "
      ],
      "id": "j7GfT98EOD0_",
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(1316321, 4, 7)\n",
            "(1316321, 1)\n",
            "(564136, 4, 7)\n",
            "(564136, 1)\n",
            "\n",
            "(1316321, 5)\n",
            "(564136, 5)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "uXjnH3xBOFvc"
      },
      "source": [
        "# parameters model building\n",
        "batch_size = 128\n",
        "epochs = 10"
      ],
      "id": "uXjnH3xBOFvc",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "kzly43_IOHZ3"
      },
      "source": [
        "# Builfing Bi-LSTM model\n",
        "model = keras.Sequential()\n",
        "model.add(keras.layers.Bidirectional(LSTM( units = 128, input_shape = [X_train.shape[1],X_train.shape[2]])))\n",
        "model.add(Dropout(rate = 0.6))\n",
        "model.add(Dense(units=128,activation='relu'))\n",
        "model.add(Dense(units = 64,activation='relu'))\n",
        "model.add(Dense(units=5,activation='softmax'))\n",
        "\n",
        "model.compile(loss= 'categorical_crossentropy', optimizer = 'Adam', metrics = ['Accuracy'])\n"
      ],
      "id": "kzly43_IOHZ3",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0KilfYbMXRdl",
        "outputId": "3e77ac18-5980-4322-d797-4ef54023ae4a"
      },
      "source": [
        "# fitting the model\n",
        "history = model.fit(X_train,Y_train, epochs = 1, batch_size = batch_size, validation_data= (X_test,Y_test))"
      ],
      "id": "0KilfYbMXRdl",
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "10284/10284 [==============================] - 693s 67ms/step - loss: 0.8298 - Accuracy: 0.7249 - val_loss: 0.8151 - val_Accuracy: 0.7251\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X1qOvI5QYlih"
      },
      "source": [
        "# model prediction\n",
        "print(model.summary(line_length= 80))\n",
        "y_test_pred = model.predict(X_test)\n",
        "p = np.argmax(y_test_pred,axis=1)\n",
        "q = np.argmax(Y_test,axis=1)"
      ],
      "id": "X1qOvI5QYlih",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vmtq0BsZiEQi"
      },
      "source": [
        "from sklearn.metrics import precision_score, f1_score, classification_report,recall_score,accuracy_score\n",
        "prec = precision_score(q,p, average='weighted') # no warning\n",
        "print(\"\\nPrecision score :\",prec)\n",
        "f1 = f1_score(q,p, average='weighted')\n",
        "print(\"\\nF1_score \\t:\", f1)\n",
        "recall = recall_score(q,p,average= 'micro')\n",
        "print(\"\\nRecall score    :\",recall)\n",
        "accuracy = accuracy_score(q,p)\n",
        "print(\"\\nAccuracy \\t:\",accuracy)\n",
        "print()"
      ],
      "id": "Vmtq0BsZiEQi",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JTm-2qHsZHn5"
      },
      "source": [
        "# CNN"
      ],
      "id": "JTm-2qHsZHn5"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sFdDk-Q8UdgV"
      },
      "source": [
        "#2D to 3D conversion\n",
        "def create_dataset(x, y, timesteps, step):\n",
        "  x_set = []\n",
        "  y_set = []\n",
        "  for i in range(0,len(x)-timesteps, step):\n",
        "    data = x.iloc[i: (i + timesteps)].values\n",
        "    labels = y.iloc[i: i + timesteps]\n",
        "    x_set.append(data)\n",
        "    y_set.append(mode(labels)[0][0])\n",
        "  \n",
        "  return np.array(x_set), np.array(y_set).reshape(-1,1)"
      ],
      "id": "sFdDk-Q8UdgV",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TLKWThSOZMUA",
        "outputId": "c4e17d6f-fda5-42a0-b030-ad7bd9a9b5ae"
      },
      "source": [
        "timesteps = 40\n",
        "steps = 1\n",
        "X_train, Y_train = create_dataset( x_train, y_train, timesteps, steps)\n",
        "X_test , Y_test = create_dataset( x_test, y_test, timesteps, steps)\n",
        "\n",
        "print(\"Shape of x_train\",X_train.shape)          \n",
        "print(\"Shape of y_train\",Y_train.shape)          \n",
        "\n",
        "print(\"Shape of x_test\",X_test.shape)           \n",
        "print(\"shape of Y_test\",Y_test.shape)           \n",
        "print()\n",
        "\n",
        "Y_train = to_categorical(Y_train)\n",
        "Y_test = to_categorical(Y_test)\n",
        "print(\"Shpae of Y_train after categorical\",Y_train.shape)          \n",
        "print(\"Shape of Y_test after categorical\",Y_test.shape)  "
      ],
      "id": "TLKWThSOZMUA",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1316285, 40, 7)\n",
            "(1316285, 1)\n",
            "(564100, 40, 7)\n",
            "(564100, 1)\n",
            "(1316285, 5)\n",
            "(564100, 5)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YD5yIIPiZJ42",
        "outputId": "f747c98a-d50a-467f-ae65-9b6c517aae1f"
      },
      "source": [
        "# Building the model\n",
        "model = Sequential()\n",
        "model.add(Conv1D(filters=128, kernel_size=3, activation='relu', input_shape=(X_train.shape[1],X_train.shape[2])))\n",
        "model.add(MaxPooling1D(pool_size=2))\n",
        "model.add(Conv1D(filters=64, kernel_size=3, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(MaxPooling1D(pool_size=2))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(256, activation='relu'))\n",
        "model.add(Dense(5, activation='softmax'))\n",
        "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
      ],
      "id": "YD5yIIPiZJ42",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "________________________________________________________________________________\n",
            " Layer (type)                       Output Shape                    Param #     \n",
            "================================================================================\n",
            " conv1d (Conv1D)                    (None, 38, 128)                 2816        \n",
            "                                                                                \n",
            " max_pooling1d (MaxPooling1D)       (None, 19, 128)                 0           \n",
            "                                                                                \n",
            " conv1d_1 (Conv1D)                  (None, 17, 64)                  24640       \n",
            "                                                                                \n",
            " dropout (Dropout)                  (None, 17, 64)                  0           \n",
            "                                                                                \n",
            " max_pooling1d_1 (MaxPooling1D)     (None, 8, 64)                   0           \n",
            "                                                                                \n",
            " flatten (Flatten)                  (None, 512)                     0           \n",
            "                                                                                \n",
            " dense (Dense)                      (None, 256)                     131328      \n",
            "                                                                                \n",
            " dense_1 (Dense)                    (None, 5)                       1285        \n",
            "                                                                                \n",
            "================================================================================\n",
            "Total params: 160,069\n",
            "Trainable params: 160,069\n",
            "Non-trainable params: 0\n",
            "________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bFcQNrlzZa62",
        "outputId": "8240f794-e0ce-4fa4-f4f0-9e62cdf0156b"
      },
      "source": [
        "# fit network\n",
        "history = model.fit(X_train, Y_train, validation_data=(X_test,Y_test), epochs=3, batch_size=128, verbose=1)\n",
        "  "
      ],
      "id": "bFcQNrlzZa62",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "10284/10284 [==============================] - 329s 32ms/step - loss: 0.2859 - accuracy: 0.9267 - val_loss: 0.2888 - val_accuracy: 0.9238\n",
            "Epoch 2/3\n",
            "10284/10284 [==============================] - 327s 32ms/step - loss: 0.2785 - accuracy: 0.9268 - val_loss: 0.2859 - val_accuracy: 0.9238\n",
            "Epoch 3/3\n",
            "10284/10284 [==============================] - 328s 32ms/step - loss: 0.2760 - accuracy: 0.9268 - val_loss: 0.2863 - val_accuracy: 0.9238\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HmdUVNVoZetb"
      },
      "source": [
        "# model prediction\n",
        "print(model.summary(line_length= 80))\n",
        "y_test_pred = model.predict(X_test)\n",
        "p = np.argmax(y_test_pred,axis=1)\n",
        "q = np.argmax(Y_test,axis=1)"
      ],
      "id": "HmdUVNVoZetb",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w__3SLWetyNY",
        "outputId": "e04ce0b8-d787-4c91-fc0c-b76f86b2e158"
      },
      "source": [
        "from sklearn.metrics import precision_score, f1_score, classification_report,recall_score,accuracy_score\n",
        "prec = precision_score(q,p, average='weighted') # no warning\n",
        "print(\"\\nPrecision score :\",prec)\n",
        "f1 = f1_score(q,p, average='weighted')\n",
        "print(\"\\nF1_score \\t:\", f1)\n",
        "recall = recall_score(q,p,average= 'micro')\n",
        "print(\"\\nRecall score    :\",recall)\n",
        "accuracy = accuracy_score(q,p)\n",
        "print(\"\\nAccuracy \\t:\",accuracy)\n",
        "print()"
      ],
      "id": "w__3SLWetyNY",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1272: 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"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Precision score : 0.8533717876164396\n",
            "\n",
            "F1_score \t: 0.8871817313676837\n",
            "\n",
            "Recall score    : 0.923781244460202\n",
            "\n",
            "Accuracy \t: 0.923781244460202\n",
            "\n"
          ]
        }
      ]
    }
  ]
}