Logistic_Regression_Feat16.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Logistic Regression.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!unzip /content/EE559_Project-main_Final.zip"
      ],
      "metadata": {
        "id": "PxjyMdkEKG1c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "SLnLo2mgKHHp"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fires_train = pd.read_csv(\"/content/EE559_Project-main/datasets/algerian_fires_train.csv\")\n",
        "fires_test = pd.read_csv(\"/content/EE559_Project-main/datasets/algerian_fires_test.csv\")\n",
        "\n",
        "fires_trainx = fires_train.iloc[:,1:-1]\n",
        "fires_trainy = fires_train.iloc[:,-1]\n",
        "fires_testx = fires_test.iloc[:,1:-1]\n",
        "fires_testy = fires_test.iloc[:,-1]\n",
        "\n",
        "scalar = MinMaxScaler()\n",
        "\n",
        "fires = scalar.fit_transform(fires_trainx)\n",
        "firestst = scalar.fit_transform(fires_testx)"
      ],
      "metadata": {
        "id": "hFxRpmZN9ezY"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fires_train16 = pd.read_csv(\"/content/EE559_Project-main/datasets/firesfeatures.csv\")\n",
        "fires_test16 = pd.read_csv(\"/content/EE559_Project-main/datasets/fireststfeatures.csv\")\n",
        "\n",
        "\n",
        "scalar = MinMaxScaler()\n",
        "\n",
        "fires16 = scalar.fit_transform(fires_train16)\n",
        "firestst16 = scalar.fit_transform(fires_test16)"
      ],
      "metadata": {
        "id": "_E3UdT5NrqRw"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def metrics(true_labels, pred_labels, plot_title, work='test'):\n",
        "    cf_matrix = confusion_matrix(true_labels, pred_labels)\n",
        "    TP, TN, FP, FN = cf_matrix[1][1], cf_matrix[0][0], cf_matrix[0][1], cf_matrix[1][0]\n",
        "    Recall = TP/(TP+FN)\n",
        "    Precision = TP/(TP+FP)\n",
        "    F1_score = 2*Recall*Precision/(Recall+Precision)\n",
        "    Accuracy = (TP+TN)/true_labels.shape[0]\n",
        "    \n",
        "    if work == 'test':\n",
        "        ax = sns.heatmap(cf_matrix, annot=True, cmap='Blues')\n",
        "\n",
        "        ax.set_title(plot_title)\n",
        "        ax.set_xlabel('Predicted Values')\n",
        "        ax.set_ylabel('Actual Values ')\n",
        "\n",
        "        ax.xaxis.set_ticklabels(['0','1'])\n",
        "        ax.yaxis.set_ticklabels(['0','1'])\n",
        "\n",
        "        plt.savefig('/content/EE559_Project-main/cf_matrix_plots/'+plot_title+'.png')\n",
        "\n",
        "        return F1_score, Accuracy\n",
        "\n",
        "    else:\n",
        "        return F1_score, Accuracy, TP, TN, FP, FN"
      ],
      "metadata": {
        "id": "meS4gtUbM8ZK"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import statsmodels.api as sm\n",
        "import pandas as pd\n",
        "\n",
        "X = sm.add_constant(fires_train16)\n",
        "log_reg = sm.Logit(fires_trainy, X)\n",
        "log1 = log_reg.fit(method = \"cg\")\n",
        "print(log1.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l6zEdKb_Hkaf",
        "outputId": "bea0efdc-b2c3-43ef-a4da-919316f0f4bb"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: Maximum number of iterations has been exceeded.\n",
            "         Current function value: 0.522882\n",
            "         Iterations: 35\n",
            "         Function evaluations: 87\n",
            "         Gradient evaluations: 79\n",
            "                           Logit Regression Results                           \n",
            "==============================================================================\n",
            "Dep. Variable:                Classes   No. Observations:                  184\n",
            "Model:                          Logit   Df Residuals:                      167\n",
            "Method:                           MLE   Df Model:                           16\n",
            "Date:                Wed, 04 May 2022   Pseudo R-squ.:                  0.2096\n",
            "Time:                        01:30:01   Log-Likelihood:                -96.210\n",
            "converged:                      False   LL-Null:                       -121.73\n",
            "Covariance Type:            nonrobust   LLR p-value:                 1.566e-05\n",
            "==================================================================================\n",
            "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
            "----------------------------------------------------------------------------------\n",
            "const           -1.99e-05      3.456  -5.76e-06      1.000      -6.774       6.774\n",
            "Temperature     3.163e-07      0.082   3.85e-06      1.000      -0.161       0.161\n",
            "Ws                -0.0002      0.201     -0.001      0.999      -0.393       0.393\n",
            "RH                -0.0011      0.048     -0.023      0.981      -0.095       0.093\n",
            "Rain              -0.0002      0.216     -0.001      0.999      -0.424       0.424\n",
            "FFMC              -0.0002      0.035     -0.006      0.995      -0.070       0.069\n",
            "DMC                0.0023      0.019      0.123      0.902      -0.035       0.039\n",
            "ISI                0.0010      0.061      0.017      0.987      -0.119       0.121\n",
            "Temperature Ws     0.0028      0.004      0.669      0.504      -0.005       0.011\n",
            "Temperature RH    -0.0005      0.001     -0.447      0.655      -0.003       0.002\n",
            "Ws RH             -0.0023      0.002     -1.339      0.180      -0.006       0.001\n",
            "Ws FFMC            0.0009      0.002      0.521      0.602      -0.002       0.004\n",
            "RH FFMC            0.0003      0.000      0.711      0.477      -0.001       0.001\n",
            "Rain DMC          -0.0012      0.025     -0.046      0.964      -0.051       0.049\n",
            "Rain DC           -0.0018      0.007     -0.235      0.814      -0.016       0.013\n",
            "Rain ISI          -0.0002      0.091     -0.002      0.998      -0.179       0.179\n",
            "Rain BUI          -0.0010      0.024     -0.040      0.968      -0.048       0.046\n",
            "==================================================================================\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/base/model.py:512: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  \"Check mle_retvals\", ConvergenceWarning)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Taking all features\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "import statsmodels.api as sm\n",
        "from sklearn.metrics import confusion_matrix,roc_curve, auc,roc_auc_score\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "logreg = LogisticRegression()\n",
        "logreg.fit(fires_train16, fires_trainy)\n",
        "Y_pred = logreg.predict(fires_train16)\n",
        "val=logreg.score(fires_train16,fires_trainy)\n",
        "print(\"Accuracy for train:\",val)\n",
        "print(\"--------------------------\")\n",
        "conf_matrix = confusion_matrix(fires_trainy, Y_pred)\n",
        "sns.heatmap(conf_matrix, annot=True, square=True, cmap='Blues', fmt=\"d\", cbar=True)\n",
        "print(conf_matrix)\n",
        "plt.ylabel('Actual label');\n",
        "plt.xlabel('Predicted label');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "TvvsnstcRvPe",
        "outputId": "6574ff95-a741-41ac-9802-c43a0f275c0a"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy for train: 0.8967391304347826\n",
            "--------------------------\n",
            "[[ 58  11]\n",
            " [  8 107]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "F1_score, Accuracy = metrics(fires_trainy,Y_pred, \"Logistic_reg_train\")\n",
        "print(\"Train Logistic regression F1_score=\", F1_score, \"Train Logistic regression Accuracy=\", Accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "X26AvnFROeGy",
        "outputId": "7b93909e-7906-42b0-af91-d08fcdb68aa7"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Logistic regression F1_score= 0.9184549356223176 Train Logistic regression Accuracy= 0.8967391304347826\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAEWCAYAAABG030jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAd70lEQVR4nO3de5xVdb3G8c8zAwgICigiCioqXsjjLTHKNE2OtzC0zDAz85ijFqapR0lPmSc7aXVSE9NQUzJCUSnU1CzEa2HiDRX0SIACAlJyEQUV+J4/1prajDPDns2+rT3Pu9d6zd5r7b3Wd0/47N98100RgZmZZUddpQswM7O2cXCbmWWMg9vMLGMc3GZmGePgNjPLGAe3mVnGOLitTSRdL+k7BbxvO0krJdWXoq5qIulASa9Uug6rXfJx3LVN0lzgaxHxp/aw3Y0l6XvAzhHx5UrXYtYSj7gtkyR1qNB2Jcn/3VhF+R9gOyRpE0lXSXojna6StEnO8gskLUyXfU1SSNo5XXaLpMvSx1tKulfSMklvSXpMUp2kW4HtgHvS9sgFknZI19MhfW8vSTen21gq6XcbqPlgSfMlXShpEXBzuq1Rkv4m6R+SJkjqlfOer0h6LV32HUlzJQ1tZRtHABcBX0zrfj6d/7CkH0h6AngX2FHSKZJmSnpb0mxJpzetNef5XEnnS5ouabmk2yV1bsP/ZWbrcXC3TxcDQ4C9gb2A/YH/gn+G17nAUGBn4OBW1nMeMB/oDfQhCb2IiJOA14GjI6JbRPyomffeCnQFPgJsBVyZR91bA72A7YEG4CzgGOBTwDbAUuDa9HMMAn4OnAj0BTYHtm1t5RHxAPA/wO1p3XvlLD4p3WZ34DXgTWAYsBlwCnClpH1bWf3xwBHAAGBP4Kt5fF6zZjm426cTgf+OiDcjYglwKUkwQRIwN0fESxHxLvC9VtbzAUkobh8RH0TEY5HHThNJfYEjgTMiYmn63kfyqHsdcElEvBcRq4AzgIsjYn5EvJfWelw6qj8OuCciHo+I94HvAhuzQ+eW9HeyJq339xHxt0g8AjwIHNjK+38WEW9ExFvAPSRfmmYFcXC3T9uQjBobvZbOa1w2L2dZ7uOmfgzMAh5M2wWj8tx+f+CtiFia5+sbLYmI1TnPtwd+m7ZqlgEzgbUko//1Pkf6JfSPNm4v13q/B0lHSpqatoiWAUcBW7by/kU5j98Fum1ELdbOObjbpzdIQq/Rduk8gIVAv5xl/VtaSUS8HRHnRcSOwGeBcyUd2ri4le3PA3pJ6tHGupuucx5wZET0yJk6R8SCpp9DUhdgiwK28aH56f6Au4CfAH0iogdwH6D8P4pZ4Rzc7UNHSZ0bJ2A88F+SekvakqSN8Ov0tROAUyTtLqkr0OIx25KGSdpZkoDlJKPddenixcCOzb0vIhYC9wM/l9RTUkdJBxXwua4HfiBp+7Se3pKGp8vuBI6W9AlJnUjaKPkE62Jghw0cOdIJ2ARYAqyRdCRwWAH1mxXEwd0+3Aesypk6A9OA6cALwDPAZQARcT/wM2AKSRtkarqO95pZ70DgT8BK4C/AzyNiSrrshyRfDssknd/Me08i6ZG/TLKj75wCPtfVwN0krZq301o/ln6Ol0h2Xt5GMvpemW6nuc+R64705z8kPdPcCyLibeCbJF9yS4EvpXWYlYVPwLFWSdodeBHYJCLWVLqeQknqBiwDBkbEnErXY7YxPOK2D5F0bHqsd0/gCpKjMzIX2pKOltRV0qYk/egXgLmVrcps4zm4rTmnk7QV/kbStz6zHBuVdFF64kvT6f4CVzmcZKfrGyRtnREREZLub2E7FxXtw5iVkFslZmYZ4xG3mVnGVORCPfn4+sQZ/lPAPuSKo3ardAlWhbp3rtvoY+i77DMy78xZ9ezoih6z7xG3mVnGVO2I28ysrDJ0tV4Ht5kZQF12bs7k4DYzA1B2LjXj4DYzA7dKzMwyxyNuM7OM8YjbzCxjPOI2M8sYH1ViZpYxbpWYmWWMWyVmZhmToRF3dio1Mysl1eU/bWhV0i8lvSnpxZx5vST9UdKr6c+e6XxJ+pmkWZKmS9p3Q+t3cJuZAdTX5z9t2C3AEU3mjQImR8RAYHL6HOBIkht9DAQagOs2tHIHt5kZJD3ufKcNiIhHgbeazB4OjE0fjwWOyZn/q0hMBXpI6tva+h3cZmbQplaJpAZJ03Kmhjy20CciFqaPFwF90sfbAvNyXjc/ndci75w0M4M2HVUSEWOAMYVuKr33acE3i3Fwm5lBOY4qWSypb0QsTFshb6bzFwD9c17XL53XIrdKzMygqD3uFtwNnJw+PhmYlDP/K+nRJUOA5TktlWZ5xG1mBkU95V3SeOBgYEtJ84FLgMuBCZJOBV4Djk9ffh9wFDALeBc4ZUPrd3CbmUFRWyURcUILiw5t5rUBfKMt63dwm5mBT3k3M8ucDJ3y7uA2MwMHt5lZ5vh63GZmGeMet5lZxrhVYmaWMR5xm5llixzcZmbZ4uA2M8sY1Tm4zcwyxSNuM7OMcXCbmWWMg9vMLGuyk9sObjMz8IjbzCxz6up85qSZWaZ4xG1mljXZyW0Ht5kZeMRtZpY5Dm4zs4zxKe9mZhnjEbeZWcY4uM3MMsbBbWaWMQ5uM7OsyU5uO7jNzMCnvJuZZY5bJWZmWZOd3HZwV6PvH74zq9esY13AugiumDKHfptvwgn79KVDXR3rIrjtuYW8tnR1pUu1Mrn0uxfz+KMP07NXLyZMvAeAPz34AGOuG82cObMZO24Cgz6yR4WrzLYsjbiz09RpZ6567DV++NBsrpgyB4Bj9+jD72f+nR8+NJt7Zyzh2D36VLhCK6ejhx/DNdeNWW/eTjsP5EdXXsM+H92vQlXVFkl5T5XmEXdGBNClQ/I926VjHctXr6lsQVZW+350MG8sWLDevAE77lShampTNQRyvkoW3JJ2A4YD26azFgB3R8TMUm2zVgRw1ie3g4DH5izlibnLuHP6IkYesD2f+7c+SPCTh+dWukyzmpKla5WUpFUi6ULgNpJ2/1/TScB4SaNaeV+DpGmSps14cEIpSsuE/31kLpc/NIfRf36dT+3Yi5236MqBA3py5/RFXPzAq9w5fTFf/mjfSpdpVlPcKoFTgY9ExAe5MyX9FHgJuLy5N0XEGGAMwNcnzogS1Vb1GtsgK99by/ML32aHXl0Ysn0P7pi+GIBnFqzgxH0d3GbFVA2BnK9S7ZxcB2zTzPy+6TJrQad6sUnay+5UL3bfalPeWLGa5avWMHDLrgDs2ntTlqx8v5JlmtUcKf9pw+vStyS9JOlFSeMldZY0QNKTkmZJul1Sp0JrLdWI+xxgsqRXgXnpvO2AnYGRJdpmTei+SQdOH9IfgLo6mDZvBTMWv8O4NW/whT23pk7ig3XBuGcXVrhSK6eLLjyPp6f9lWXLlnHUvx9Mw5kj2Xzzzfnx5T9g6dK3OGfkGeyy626Mvv7GSpeaWcUacUvaFvgmMCgiVkmaAIwAjgKujIjbJF1P0pm4rqBtRJSmIyGpDtif9XdOPhURa/N5f3tulVjLrjhqt0qXYFWoe+eN37O464V/yDtzXrni8Ba3lwb3VGAvYAXwO+AaYBywdUSskfRx4HsRcXghtZbsqJKIWEdSvJlZ1WvLgFtSA9CQM2tMuo+OiFgg6SfA68Aq4EHgaWBZRDQexzuffw1q28zHcZuZAXVtGLTnHkjRlKSeJIdCDwCWAXcARxShxH9ycJuZ0bYR9wYMBeZExJJkvZoIHAD0kNQhHXX3I2kfF8SnvJuZUdTjuF8HhkjqquTFhwIzgCnAcelrTgYmFVqrg9vMjOIdDhgRTwJ3As8AL5Dk7BjgQuBcSbOALYCbCq3VrRIzM4p7I4WIuAS4pMns2SRH2m00B7eZGUXtcZecg9vMjGyd8u7gNjPDI24zs8zxiNvMLGMylNsObjMzaNuZk5Xm4DYzw60SM7PMyVBuO7jNzMAjbjOzzMlQbju4zczAOyfNzDLHrRIzs4zJUnBv8HJYkn4kaTNJHSVNlrRE0pfLUZyZWbkU8y7vpZbPdQwPi4gVwDBgLsmd2v+zlEWZmZVbEW+kUHL5tEoaX/MZ4I6IWF4NhZuZFVOWYi2f4L5X0sskdys+U1JvYHVpyzIzK6+aOqokIkZJ+hGwPCLWSnqX5A7GZmY1oy5DQ+58dk52Bb4OXJfO2gbYr5RFmZmVW63tnLwZeB/4RPp8AXBZySoyM6uALO2czCe4d4qIHwEfAETEu0DlKzczK6I65T9VWj47J9+X1AUIAEk7Ae+VtCozszKrqZ2TJLeYfwDoL2kccADw1VIWZWZWbspQIyGfo0r+KOkZYAhJi+TsiPh7ySszMyujDA24Nxzckg5KH76d/hwkiYh4tHRlmZmVVzXsdMxXPq2S3NPbOwP7A08Dny5JRWZmFZCh3M6rVXJ07nNJ/YGrSlaRmVkFZOkEnEIu6zof2L3YhZiZVVJNHVUi6RrSQwFJjvveG3imlEWZmZVbhgbceY24p+U8XgOMj4gnSlSPmVlF1FSrJCLGlqMQM7NKyk5stxLckl7gXy2S9RYBERF7lqwqM7Myq5XDAYeVrQozswrL0L7JloM7Il4rZyFmZpWUpaNK8rke9xBJT0laKel9SWslrShHcWZm5VLMy7pK6iHpTkkvS5op6eOSekn6o6RX0589C601n8u6jgZOAF4FugBfA64tdINmZtWoyJd1vRp4ICJ2A/YCZgKjgMkRMRCYnD4vrNZ8XhQRs4D6iFgbETcDRxS6QTOzalSsEbekzYGDgJsAIuL9iFhGcsvHxqP0xgLHFFprPsdxvyupE/Bceu/JheQZ+GZmWVHEDvcAYAlws6S9SK7tdDbQJyIWpq9ZBPQpdAMtBrCkwenDk9LXjQTeAfoDny90g2Zm1ai+TnlPkhokTcuZGnJW1QHYF7guIvYhyc312iIRETR/uHVeWhtxj5HUDbiN5GzJGcClhW7IzKyateU47ogYA4xpYfF8YH5EPJk+v5MkuBdL6hsRCyX1Bd4stNYWR9zpN8UwktPc75T0vKRRknYodGNmZtWqWHd5j4hFwDxJu6azDgVmAHcDJ6fzTgYmFVprqz3uiHiFZJR9adqrGQFMlrQoIg4odKNmZtWmyNcqOQsYl+4fnA2cQjJQniDpVOA14PhCV57XZV0l1QFbkTTTN2UjhvhmZtWomLkdEc8B+zWz6NBirL/V4JZ0IMkx3McAL5D0u78VEcuLsfHW/PSzg0q9CcugnoNHVroEq0Krnh290euoiWuVSJpHMpy/DfheRHiUbWY1q74Wghv4pK9XYmbtRYYuVeKLTJmZQY0Et5lZe1ITPW4zs/akJkbcTW4S/CER8c2SVGRmVgEZGnC3OuKe1soyM7Oa0iFDyd3azknfJNjM2o0M5faGe9ySegMXAoOAzo3zI+LTJazLzKysinzKe0nlc13tcSR3bxhAct2SucBTJazJzKzsinWRqXLIJ7i3iIibgA8i4pGI+A/Ao20zqylFvnVZSeVzOOAH6c+Fkj4DvAH0Kl1JZmblV18NiZynfIL7svQeaucB1wCbAd8qaVVmZmWWodzecHBHxL3pw+XAIaUtx8ysMlTMu06WWD5HldxMMyfipL1uM7OaUFMjbuDenMedgWNJ+txmZjWjpoI7Iu7KfS5pPPB4ySoyM6uAWr/I1ECS25iZmdWM+nwOjq4S+fS432b9HvcikjMpzcxqRpbOnMynVdK9HIWYmVVSlnrcG/zjQNLkfOaZmWVZlk55b+163J2BrsCWknrCPw9y3AzYtgy1mZmVTV2NHMd9OnAOsA3wNP8K7hXA6BLXZWZWVtUwks5Xa9fjvhq4WtJZEXFNGWsyMyu7DhlqcudzAMw6ST0an0jqKenrJazJzKzsstTjzie4T4uIZY1PImIpcFrpSjIzK786Ke+p0vI5AadekiIiACTVA51KW5aZWXlVQR7nLZ/gfgC4XdIv0uenp/PMzGpGhk6czCu4LwQagDPT538EbihZRWZmFVANLZB8bfBLJiLWRcT1EXFcRBwHzCC5oYKZWc2otR43kvYBTgCOB+YAE0tZlJlZuVU+jvPX2pmTu5CE9QnA34HbAUWE74JjZjWnCgbSeWttxP0y8BgwLCJmAUjyvSbNrCZl6XrcrfW4PwcsBKZIukHSoWTrrwkzs7zVtWGqtBZriIjfRcQIYDdgCsl1S7aSdJ2kw8pVoJlZORR756SkeknPSro3fT5A0pOSZkm6XVLB58Pkc1TJOxHxm4g4GugHPItvpGBmNUZS3lOezgZm5jy/ArgyInYGlgKnFlprm0b9EbE0IsZExKGFbtDMrBoVs1UiqR/wGeDG9LmATwN3pi8ZCxxTaK2F3HPSzKzmFHnn5FXABUDjHcS2AJZFxJr0+Xw24r4G1dBnNzOrOLVlkhokTcuZGv65HmkY8GZEPF2qWj3iNjMD6tsw4o6IMcCYFhYfAHxW0lFAZ5K7hl0N9JDUIR119wMWFFqrR9xmZhTvetwR8e2I6BcROwAjgIci4kSSo/OOS192MjCp0Fod3GZmgNrwvwJdCJwraRZJz/umQlfkVomZGaU55T0iHgYeTh/PBvYvxnod3GZm1M5d3s3M2o0MXarEwW1mBtm6kYKD28wMqMtObju4zcyAjTlapOwc3GZmZKvH7eO4q9ytY2/h2M9+hs8NH8aF55/Le++9V+mSrEDXX3Iir03+IdPuuKjZ5bvs0IeHx57Hsiev5JyTinMdt04dO3Dr5afw4qRLePRX57Nd314AfPpju/HEuAt4asJFPDHuAj41eJeibC/LynAcd9E4uKvY4sWL+c24XzF+wl1MnHQv69at5YH7fl/psqxAt94zleHfuLbF5UuXv8N5V9zBVb96qM3r3q5vL/5ww9kfmv/VYz7O0rdXscfwS7lm3BR+cPZwAP6xbCXHnfMLBh//P5z23Vv55WVfafM2a02d8p8qzcFd5dauXct7q1ezZs0aVq1eTe+ttqp0SVagJ575G28tf7fF5UuWruTpGa/zwZq1H1o24qjBPHbr+Uy9bRTXXDyCujzTY9jBezLunicBmPinZzl4/10BeP6V+SxcshyAGX9bSOdNOtKpY/vunGbpLu8O7irWp08fTv7qf3D40EMYevAn6d6tG5844JOVLsvKbNcBfTjusH055JSfMmTE5axdt44RRw3O673bbLU58xctBWDt2nWsWLmKLXpsut5rjh26N8+9PI/3P1jT3CrajbZcHbDSyv4VK+mUiLi5hWUNQAPA6J//glNPa2juZe3GiuXLmfLQZO57cDLdu3fnP889m3vvmcSwo4dXujQro0P235V9B23H47++AIAum3RkyVsrAbj9f09j+223oFPHevpv3Yupt40C4NrfPMytd0/d4Lp333FrLvvmcIZ9veUWTntRDSPpfFXib6NLgWaDO/dSiavXEOUsqhpNnfpntu3Xj169kh1Khw49jOeffdbB3c5I4tf3PMl3r7n7Q8u+eN4NQNLjvuG/T+Lw065eb/kbby6n39Y9WfDmMurr69isWxf+sewdALbdqge3/7SBr33nVubM/3vpP0iVy05sl6hVIml6C9MLQJ9SbLMWbd13G6Y//zyrVq0iInhy6l8YsNNOlS7LymzKX1/h2KF707tnNwB6btaV7fr2zOu9v3/kBU48+mMAfG7oPjzy1P8BsHm3Lky85gy+87NJ/OX52aUpPGsy1Csp1Yi7D3A4yQ0xcwn4c4m2WXP23HMv/v2wwxnxhWOpr+/AbrvvznFf+GKly7ICjf3hVznwowPZskc3Zj3wfb5//X107FAPwI13Pk6fLbrzxLgL6L5pZ9ZFMPLEg9nn8z/g5dmLuPTae7nnupHUSXywZi3funwCry9s+p/Xh93yuz/zy8u+wouTLmHpinc4aVTyx+4ZIw5ip/69+XbDkXy74UgAjj5zNEuWrizdL6DKZalVoojidyQk3QTcHBGPN7PsNxHxpQ2tw60Sa07PwSMrXYJVoVXPjt7o1H1q9vK8M2fwjptXNOVLMuKOiBZvO59PaJuZlV12Btw+5d3MDHytEjOzzMlQi9vBbWYGmeqUOLjNzCA5Xj4rHNxmZrhVYmaWORnKbQe3mRmQqeR2cJuZ4cMBzcwyxz1uM7OMcXCbmWWMWyVmZhnjEbeZWcZkKLcd3GZmQKaS28FtZka2bqTg4DYzI1MDbge3mRmQqeR2cJuZ4cMBzcwyJ0MtbuoqXYCZWTVQG6ZW1yP1lzRF0gxJL0k6O53fS9IfJb2a/uxZaK0ObjMzkhsp5DttwBrgvIgYBAwBviFpEDAKmBwRA4HJ6fOCOLjNzEhaJflOrYmIhRHxTPr4bWAmsC0wHBibvmwscEyhtTq4zcxoW6tEUoOkaTlTQ7PrlHYA9gGeBPpExMJ00SKgT6G1euekmRm06XDAiBgDjGl1dVI34C7gnIhYkdtiiYiQFIUV6hG3mRmQHA6Y7/82uC6pI0loj4uIiensxZL6psv7Am8WWquD28yM4vW4lQytbwJmRsRPcxbdDZycPj4ZmFRorW6VmJkBdcU7jvsA4CTgBUnPpfMuAi4HJkg6FXgNOL7QDTi4zcyAYp3zHhGPt7KyQ4uxDQe3mRnZOnPSwW1mRqauMeXgNjMDj7jNzDInj1PZq4aD28wMt0rMzDInQwNuB7eZGfhGCmZm2ZOd3HZwm5lBpnLbwW1mBlCXoSa3g9vMjGztnPTVAc3MMsYjbjMzsjXidnCbmeHDAc3MMscjbjOzjHFwm5lljFslZmYZ4xG3mVnGZCi3HdxmZkCmktvBbWZGtk55V0RUugbbAEkNETGm0nVYdfG/i/bLp7xnQ0OlC7Cq5H8X7ZSD28wsYxzcZmYZ4+DOBvcxrTn+d9FOeeekmVnGeMRtZpYxDm4zs4xxcFc5SUdIekXSLEmjKl2PVZ6kX0p6U9KLla7FKsPBXcUk1QPXAkcCg4ATJA2qbFVWBW4Bjqh0EVY5Du7qtj8wKyJmR8T7wG3A8ArXZBUWEY8Cb1W6DqscB3d12xaYl/N8fjrPzNoxB7eZWcY4uKvbAqB/zvN+6Twza8cc3NXtKWCgpAGSOgEjgLsrXJOZVZiDu4pFxBpgJPAHYCYwISJeqmxVVmmSxgN/AXaVNF/SqZWuycrLp7ybmWWMR9xmZhnj4DYzyxgHt5lZxji4zcwyxsFtZpYxDm5bj6S1kp6T9KKkOyR13Yh13SLpuPTxja1dIEvSwZI+UcA25krassm8myWd3mTeMZLuz6dWs2rn4LamVkXE3hGxB/A+cEbuQkkdCllpRHwtIma08pKDgTYHdwvGk5yslGtEOt8s8xzc1prHgJ3T0fBjku4GZkiql/RjSU9Jmt44ulVidHr98D8BWzWuSNLDkvZLHx8h6RlJz0uaLGkHki+Ib6Wj/QMl9ZZ0V7qNpyQdkL53C0kPSnpJ0o2Amql7MrCbpL7pezYFhgK/k/TddH0vShoj6UPvzx3FS9pP0sON60mvhf1XSc9KGp7O/0g677n09zGwCL97sxY5uK1Z6cj6SOCFdNa+wNkRsQtwKrA8IgYDg4HTJA0AjgV2Jbl2+FdoZgQtqTdwA/D5iNgL+EJEzAWuB65MR/uPAVenzwcDnwduTFdxCfB4RHwE+C2wXdNtRMRa4C7g+HTW0cDDEbECGB0Rg9O/KLoAw9rwa7kYeCgi9gcOAX6cfimcAVwdEXsD+5FcxdGsZAr6s9dqWhdJz6WPHwNuIgngv0bEnHT+YcCeOT3hzYGBwEHA+DQ435D0UDPrHwI82riuiGjputJDgUE5A+LNJHVLt/G59L2/l7S0hfePB35C8gUwArg1nX+IpAuArkAv4CXgnhbW0dRhwGclnZ8+70zyxfEX4GJJ/YCJEfFqnuszK4iD25palY4c/ykNz3dyZwFnRcQfmrzuqCLWUQcMiYjVzdSSjz8DfSXtRfLFM0JSZ+DnwH4RMU/S90jCt6k1/Ouv0dzlIvlL4ZUmr58p6UngM8B9kk6PiOa+tMyKwq0SK8QfgDMldQSQtEvaMngU+GLaA+9L0k5oaipwUNpaQVKvdP7bQPec1z0InNX4RFLjl8mjwJfSeUcCPZsrMJKL8NwOjAXuT78AGkP47+novaWjSOYCH00ff77J5z6rsS8uaZ/0547A7Ij4GTAJ2LOF9ZoVhYPbCnEjMAN4Jr1h7S9I/nr7LfBquuxXJC2E9UTEEqABmCjpeZJwhaRdcWzjzkngm8B+6c6+Gfzr6JZLSYL/JZKWyeut1Dke2Cv9SUQsI+mvv0gSwk+18L5LgaslTQPW5sz/PtARmJ5u//vp/OOBF9MW0x7pZzcrGV8d0MwsYzziNjPLGAe3mVnGOLjNzDLGwW1mljEObjOzjHFwm5lljIPbzCxj/h808t4knNF+1QAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Taking all features\n",
        "Y_predtst = logreg.predict(fires_test16)\n",
        "val=logreg.score(fires_test16,fires_testy)\n",
        "print(\"Accuracy for test:\",val)\n",
        "print(\"--------------------------\")\n",
        "conf_matrix = confusion_matrix(fires_testy, Y_predtst)\n",
        "sns.heatmap(conf_matrix, annot=True, square=True, cmap='Blues', fmt=\"d\", cbar=True)\n",
        "print(conf_matrix)\n",
        "plt.ylabel('Actual Test label');\n",
        "plt.xlabel('Predicted Test label');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "Y5AgZmpxSkVP",
        "outputId": "42e8f52b-76d0-42b3-b6b1-15f3e6dbf7cd"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy for test: 0.85\n",
            "--------------------------\n",
            "[[34  3]\n",
            " [ 6 17]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAATYAAAEGCAYAAADvxrkEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAYdUlEQVR4nO3debgddX3H8ffn3iRkIYFEkjTsAQI0pRA0sgilbAEEW5YHxWgVLDW4oKXVFqTIotaCC5aaqoRFgspagiBaIQISkC0JgUASMQjIbqCAIRggN/n2jzMXDvHmnDmTs8zM/bx85rlzZs785nvkyff5zfw2RQRmZmXS1ekAzMyazYnNzErHic3MSseJzcxKx4nNzEpnQKcDWJchu57o5toCeWnu9E6HYBkMHoDW5/pG/p2uXDB9ve7VCNfYzKx0cltjM7MCUD7rRk5sZpZdV3enI+iTE5uZZae2vTZriBObmWXnR1EzKx3X2MysdFxjM7PScY3NzErHraJmVjp+FDWz0vGjqJmVjmtsZlY6TmxmVjrdbjwws7LxOzYzKx0/ippZ6bjGZmal4xqbmZWOa2xmVjoeUmVmpeNHUTMrHT+KmlnpuMZmZqWT08SWz6jMrBi6utNvdUgaLOleSQ9IWiTprOT4eEn3SHpE0pWSBtUNqwk/zcz6Kyn9Vt/rwP4RsQswCThE0h7AOcC3ImI74CXg+HoFObGZWXbqSr/VERUrko8Dky2A/YH/SY7PBI6oV5YTm5ll10CNTdI0SfOqtml/Wpy6Jd0PLANmA78FXo6InuQrTwGb1QvLjQdmlpka6O4RETOAGXW+sxqYJGlj4FpgxyxxObGZWWaNJLZGRMTLkm4F9gQ2ljQgqbVtDjxd73o/ippZZupS6q1uWdLopKaGpCHAFGAJcCtwdPK1Y4Hr6pXlGpuZZdbkGts4YKakbiqVrqsi4gZJi4ErJH0FWABcVK8gJzYzy6yZiS0iFgK79nH8UWC3RspyYjOzzFr1jm19ObGZWXb5zGtObGaWnWtsZlY6XV357FjhxGZmmbnGZmblk8+85sRmZtm5xmZmpePEZmalk2aoVCc4sZlZZq6xmVnpOLGZWek4sZlZ6TixmVn55DOvObGZWXYeUmVmpeNHUTMrn3zmNSe2Vtpg0AB+cdFJDBo0gAHd3Vz7iwV85Xs/e/P8N//1aD56+J6M3utzHYzS1uX111/nYx/9MKveeIOe1auZctDBfOrEz3Y6rFxxja0fev2NHg6Z9l+8uvINBgzo4paL/5mbfrWYex98nHdO3JKNhw/tdIhWw6BBg7jw4pkMHTaMVatWcdxHPsTef7UPO+8yqdOh5UZeE1s+3/yVyKsr3wBg4IBuBgzoJiLo6hJfPekI/u28H3c4OqtFEkOHDQOgp6eHnp6eyuK/9iZVFkJOtbVTy2psknYEDuetVZufBq6PiCWtumcedXWJOy87mW23GM35V85h7kO/49NT9+Wntz3Icy8s73R4Vsfq1auZ+v6jeOKJJzhm6ofYeeddOh1SruR1rGhLamySTgauoPJq8d5kE3C5pFNqXDdN0jxJ83peWNSK0NpuzZpgjw+ezXYHn8bknbZir3duy1FTduU7V9zW6dAshe7ubq6adR033XIbDz24kKVLf9PpkHKlv9XYjgf+IiJWVR+UdC6wCDi7r4siYgYwA2DIridGi2LriD+sWMlt837DX0/enm22GM2i688AYOjggTx03RnsdPhZHY7QahkxYgTv3m137rzjdiZM2L7T4eRGf3vHtgbYtI/j45Jz/cImIzdkow2HADB4g4EcsPuOLFjyJOOnnMqOh53BjoedwR9fW+WkllMvvvgiy5dXXhe89tpr3H3XnWw9fpsOR5UvUvqtnVpVYzsJuFnSUuDJ5NiWwHbAiS26Z+782SYjuOBLH6G7q4uuLnHN7Pv439sf6nRYltILzy/jtFNPYc2a1axZExx08CH89b77dTqsXMlrjU0RrXnik9RFZfXm6saDuRGxOs31ZXsULbuX5k7vdAiWweAB69fFdoeTb0z97/Thcw5uWxZsWatoRKwB7m5V+WbWec2ssEnaArgUGAsEMCMizpN0JvBx4Pnkq6dGxM/6LqXCHXTNLLOu5nb36AE+FxH3SRoOzJc0Ozn3rYj4RtqCnNjMLLNm1tgi4lng2WT/FUlLeOtVVkM88sDMMmukH1t1P9Vkm1aj3K2BXYF7kkMnSloo6WJJI+vF5cRmZpk10t0jImZExOSqbUbfZWpD4BrgpIhYDnwX2BaYRKVG9816cflR1Mwya/ZEk5IGUklqP4qIWQAR8fuq8xcAN9SNq6lRmVm/0swOuqp0irsIWBIR51YdH1f1tSOBup1BXWMzs8ya3EF3L+AjwIOS7k+OnQpMlTSJSheQx4ET6hXkxGZmmTW5VfQO+p6Tt2aftb44sZlZZnkdUuXEZmaZ5TSvrTuxSfo2lWfaPkWEJ3836+eaPPKgaWrV2Oa1LQozK6TCPYpGxMzqz5KGRsQfWx+SmRVFTvNa/X5skvaUtBj4dfJ5F0nfaXlkZpZ7eZ0aPE0H3f8EDgb+DyAiHgD2aWVQZlYMhZ5BNyKeXCvjppos0szKrYiNB72elPQeIJJxXP8I9Ksl9Mysb4VrPKjyCeA8KvMiPQPcCHy6lUGZWTEUNrFFxAvAh9sQi5kVTE7zWqpW0W0k/UTS85KWSbpOktcgM7NCt4peBlxFZU3QTYGrgctbGZSZFUNeW0XTJLahEfGDiOhJth8Cg1sdmJnlX1eXUm/tVGus6Khk938lnQJcQWXs6DFkmEbEzMqnK6cv2Wo1Hsynksh6I6+e3C2AL7QqKDMrhpzmtZpjRce3MxAzK57CdvcAkLQTMJGqd2sRcWmrgjKzYsjpwIP6iU3SGcC+VBLbz4D3AndQWYrezPqxvA6pStMqejRwAPBcRHwM2AXYqKVRmVkhqIH/tVOaR9GVEbFGUo+kEcAyYIsWx2VmBZDTCluqxDZP0sbABVRaSlcAd7U0KjMrhMI2HkTEp5Ld70n6OTAiIha2NiwzK4Kc5rWaHXTfWetcRNzXmpDMrCiK2EH3mzXOBbB/k2Mxs4LJa6torQ66+7UzEDMrnpxW2FJ19zAz61OXlHqrR9IWkm6VtFjSIkn/mBwfJWm2pKXJ35F142rCbzOzfkoNbCn0AJ+LiInAHsCnJU0ETgFujogJwM3J55qc2Mwss2ZONBkRz/Y2SkbEK1TWVtkMOBzoXed4JnBEvbLSzKB7c5pjZtb/dCn91ghJWwO7AvcAYyPi2eTUc8DYetfX6u4xGBgKbJI80/aGNoJKFjWzfq6RVlFJ04BpVYdmRMSMPr63IXANcFJELK+u7UVESIp696rV3eME4CQq04HP563EthyYXq9gMyu/RkYeJEnsTxLZWuUNpJLUfhQRs5LDv5c0LiKelTSOyrDOmtb5KBoR5yVzsn0+IraJiPHJtktEOLGZWVMfRVXJkhcBSyLi3KpT1wPHJvvHAtfVjStF7M9JGp7c+DRJs2qNSjCz/qPJq1TtBXwE2F/S/cl2KHA2MEXSUuDA5HNNaQbBfzEirpa0d1Lo14HvArunidTMyquZ/XMj4o4aRR7QSFlpamyrk7+HUXnZ91NgUCM3MbNy6u5S6q2d0tTYnpZ0PjAFOEfSBrj/m5mR32mL0iSoDwA3AgdHxMvAKOBfWhqVmRVCYRdMjog/Umle3Ts51AMsbWVQZlYMzRwr2kxpF3OZDOwAfB8YCPyQSguGmfVjOX0STfWO7UgqQxt6x3A909v9o5UeufXc+l+y3Dj7Flfii+jMgyas1/V5fceWJrG9UT2MQdKwFsdkZgXRndPEts53bJK+muxelbSKbizp48AvqCzsYmb9XKsGwa+vWjW2Q4BTI+IbkqZQGSO6A3B6RMxuS3Rmlms5nRm8ZmLrrprVY36yAZUZLSPixVYHZ2b5VsR3bDvy1qwe1dOE9H7epoVxmVkBFLHGtjgidm1bJGZWODmtsKVqFTUz69OAnGa2WontvLZFYWaFlNO8VnNd0UvaGIeZFVARV4I3M6spp3nNic3Msitcq6ikb/P2bh5vExGfbUlEZlYY7Z5AMq1aNbZ5bYvCzAopp3mtZuPBzHWdMzMDUFNXPWieNPOxjQZOBiYCg3uPR8T+LYzLzAogrzW2NFOD/whYAowHzgIeB+a2MCYzK4i8zu6RJrG9IyIuAlZFxG0R8feAa2tm1ux1RZsmTXePVcnfZyUdBjxDZUEXM+vnunO6Xl2axPYVSRsBnwO+DYwA/qmlUZlZIRR25EFE3JDs/gHYr7XhmFmR5LXxIE2r6Pfpo6Nu8q7NzPqxZlbYJF0MvA9YFhE7JcfOBD4OPJ987dSI+Fm9stI8it5QtT+YyqpVzzQSsJmVU1dz+7FdAkwHLl3r+Lci4huNFJTmUfSa6s+SLgfuaOQmZlZOzayxRcQcSVs3o6wsbRoTgDHNuLmZFduALqXe1sOJkhZKujhZh6WuuolN0iuSlvduwE+ojEQws35OamTTNEnzqrZpKW7xXWBbYBLwLPDNNHGleRRt+arvZlZMjXT3iIgZwIxGyo+I3/fuS7qAt7/zX3dc9b4g6eY0x8ys/2mkxpatfI2r+ngk8FCa62rNxzYYGApsUrW+KFQ66G6WLUwzK5NmDjxIGib3pZJzngLOAPaVNIlKl7PHgRPSlFXrUfQE4CRgU95aXxQqK8JPzxK4mZVLM0ceRMTUPg5flKWsWvOxnQecJ+kzEfHtLIWbWbnldUhVmprkGkkb936QNFLSp1oYk5kVhBrY2ilNYvt4RLzc+yEiXqIyxMHM+rlWNx5klWZIVbckRUQASOoGBrU2LDMrgnbPs5ZWmsT2c+BKSecnn09IjplZP5fT6dhSJbaTgWnAJ5PPs4ELWhaRmRVGYRsPImJNRHwvIo6OiKOBxVQmnDSzfq7IU4MjaVdgKvAB4DFgViuDMrNiKNyjqKTtqSSzqcALwJWAIsKz6JoZUMzGg18DtwPvi4hHACR5rQMze1M+01rtmuRRVKYJuVXSBZIOIL+/w8w6oFtKvbXTOhNbRPw4Ij4I7AjcSmXc6BhJ35V0ULsCNLP8ymsH3TStoq9GxGUR8TfA5sACPNGkmQFq4H/t1FCjRkS8FBEzIuKAVgVkZsWR1xpbqu4eZmZ9afIqVU3jxGZmmeW0t4cTm5lll9chVU5sZpbZ+q2q1zpObGaWWbtbO9NyYjOzzHL6JOrE1k4rXlnON/79TB57dCmS+JfTvsRf/OWkTodlVe7+0X/yzENzGTx8Iw499TsA/Oric1i+7CkAVq18lYFDhvHeUzzBDbjGZsD0c8/h3XvuxZlnn8uqVat4/bWVnQ7J1rLN7gey/T7v4+4fnPvmsb3+/q3+6PfNupBBQ4Z1IrRcyus7trzOOlI6K1a8wsIF8zn0b48CYODAgWw4fESHo7K1jdluJwYNHd7nuYjgyQV3sNW79mlzVPnVJaXe2sk1tjZ57pmn2WjkSL725dP47dLfsP2OE/n0P5/MkCFDOx2apfT8bxcxePjGDB/j9cJ75bTC1v4am6SP1Tg3TdI8SfN+eMmF7Qyr5VavXs3Sh5fwt0cdw4wfXM3gwUO4fGamtWCtQ343/za2dG3tbfJaY+vEo+hZ6zqRjEOdHBGT/+64f2hnTC03esxYRo8Zy5/vtDMA++w/haUPL+lwVJbWmtWrefKBu9jqnU5s1fK6rmhLHkUlLVzXKWBsK+6Zd6PesQljxvwZT/zuMbbcajz3zbuHrcZv2+mwLKXnHr6fEWM3Z+jITTodSr7k9Fm0Ve/YxgIHAy+tdVzAnS26Z+595vNf4Kunn0JPzyrGbbo5//rFL3c6JFvLr77/NZY98iCvr1jOj794LH956IfZds+DeGL+HDca9KGZj5iSLgbeByyLiJ2SY6OoLEuwNfA48IFk0fbaZSXrIDeVpIuA70fEHX2cuywiPlSvjKdffqP5gVnLXHDv7zodgmVw5kET1iszzX30D6n/nb57m41q3kvSPsAK4NKqxPY14MWIOFvSKcDIiKg7H2RL3rFFxPF9JbXkXN2kZmYF0cSXbBExB3hxrcOHAzOT/ZnAEWnCcj82M8uskRl0q3s9JNu0FLcYGxHPJvvPkfIdvfuxmVlmjbxii4gZwIys94qIkJTq0dc1NjPLrA3dPX4vaRxA8ndZmouc2MwsM0mpt4yuB45N9o8FrktzkRObmWXWzMVcJF0O3AXsIOkpSccDZwNTJC0FDkw+1+V3bGaWWTP750bE1HWcanhVPCc2M8uun408MLN+wBNNmlnpeGpwMysdJzYzKx0/ippZ6bjGZmalk9O85sRmZushp5nNic3MMmv3WgZpObGZWWb5TGtObGa2PnKa2ZzYzCwzd/cws9LJ6Ss2JzYzyy6nec2JzcyyW48JJFvKic3MMstpXnNiM7PscprXnNjMbD3kNLM5sZlZZu7uYWal43dsZlY6XU5sZlY++cxsTmxmlpkfRc2sdHKa15zYzCw719jMrHQ8pMrMSqfZaU3S48ArwGqgJyImZynHic3MMmtRhW2/iHhhfQpwYjOzzPI68qCr0wGYWYEp/SZpmqR5Vdu0PkoM4CZJ89dxPhXX2Mwss0bqaxExA5hR52t7R8TTksYAsyX9OiLmNBqXa2xmllmXlHpLIyKeTv4uA64FdssUV5aLzMyg0niQdqtfloZJGt67DxwEPJQlLj+KmllejAWuTfrGDQAui4ifZynIic3MMmtmd4+IeBTYpRllObGZWWZ57e7hxGZmmeV0RJUTm5ll58RmZqXjR1EzKx3X2MysdHKa15zYzGw95DSzObGZWWZph0q1myKi0zH0O5KmJQOCrQD836t4PFa0MzJPx2Id4f9eBePEZmal48RmZqXjxNYZfl9TLP7vVTBuPDCz0nGNzcxKx4nNzErHia2NJB0i6WFJj0g6pdPxWG2SLpa0TFKm6amtc5zY2kRSN/DfwHuBicBUSRM7G5XVcQlwSKeDsMY5sbXPbsAjEfFoRLwBXAEc3uGYrIZk2bcXOx2HNc6JrX02A56s+vxUcszMmsyJzcxKx4mtfZ4Gtqj6vHlyzMyazImtfeYCEySNlzQI+CBwfYdjMislJ7Y2iYge4ETgRmAJcFVELOpsVFaLpMuBu4AdJD0l6fhOx2TpeEiVmZWOa2xmVjpObGZWOk5sZlY6TmxmVjpObGZWOk5sOSJptaT7JT0k6WpJQ9ejrEskHZ3sX1hrwL2kfSW9J8M9Hpe0yVrH7kl+wxOSnk/275e0dcoyJ0k6tEacN9S5/jhJ09P+huSaP/kdVmxObPmyMiImRcROwBvAJ6pPSsq0DmxE/ENELK7xlX2BhhPbOu61e0RMAk4Hrkx+z6SIeDxlEZOAPhObWVpObPl1O7BdUku5XdL1wGJJ3ZK+LmmupIWSTgBQxfRkvrdfAGN6C5L0S0mTk/1DJN0n6QFJNyc1qU8A/5TUrP5K0mhJ1yT3mCtpr+Tad0i6SdIiSReSch1wSdtK+rmk+clv2TE5/v6kdvqApDnJiIwvAccksRxTo8zdJN0laYGkOyXtUHV6i+Q3L5V0RtU1fyfp3qTs85OppKyMIsJbTjZgRfJ3AHAd8EkqtalXgfHJuWnAacn+BsA8YDxwFDAb6AY2BV4Gjk6+90tgMjCaygwjvWWNSv6eCXy+Ko7LgL2T/S2BJcn+fwGnJ/uHAQFsso7fchwwPdm/GZiQ7O8O3JLsPwhsluxvvPZ1fZS5L3BDsj8CGJDsHwhcU3X9s8A7gCHAQ8lv/3PgJ8DA5HvfAT6a7D++rt/hrZhbpkcba5khku5P9m8HLqLyiHhvRDyWHD8I2Ln3/RmwETAB2Ae4PCJWA89IuqWP8vcA5vSWFRHrmmvsQGCi9GaFbISkDZN7HJVc+1NJL9X7Qcl17wGuripvg+Tvr4BLJF0FzKpX1lo2AmZKmkAlwQ6sOjc7Iv4vuf8sYG+gB3gXMDeJYwiwrMF7WkE4seXLyqi8n3pT8o/w1epDwGci4sa1vtfM91JdwB4R8VofsWQp6+W1fxdARHxC0u5Uan/zJb2rgXK/DNwaEUcmj9O/rC567VtR+f9tZkR8oYF7WEH5HVvx3Ah8UtJAAEnbSxoGzKHybqpb0jhgvz6uvRvYR9L45NpRyfFXgOFV37sJ+EzvB0m9SWkO8KHk2HuBkfWCjYjlwGOS3p9cJ0m7JPvbRsQ9EXE68DyVaZ3WjmVdNuKtaZ+OW+vcFEmjJA0BjqBSM7wZOFrSmOTeoyRtleI+VkBObMVzIbAYuE+VRUbOp1LzvhZYmpy7lMqsFG8TEc9TeUc3S9IDwJXJqZ8AR/Y2HgCfBSYnjROLeat19iwqiXERlUfSJ1LG/GHg+OSei3hrSvSvS3ow+R13Ag8At1J5DK7ZeAB8DfgPSQv40yePe4FrgIVU3r3Ni0qr8GnATZIWUnkfOS5l/FYwnt3DzErHNTYzKx0nNjMrHSc2MysdJzYzKx0nNjMrHSc2MysdJzYzK53/Bx71xQ9wZkAgAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "F1_score, Accuracy = metrics(fires_testy,Y_predtst, \"Logistic_reg_test\")\n",
        "print(\"Test Logistic regression F1_score=\", F1_score, \"Test Logistic regression Accuracy=\", Accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "5kFQHLfPPM1C",
        "outputId": "11f4862e-8153-4723-b41e-7d659ad75cc3"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Logistic regression F1_score= 0.7906976744186046 Test Logistic regression Accuracy= 0.85\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        ">If our p value is less than 0.05 then we reject the null hypothesis \n",
        "And if you see we are getting a p value of 0.00, that means the 'ISI' is the best feature for the calculation so we will drop the others and predict logistic regression on just ISI Feature and See what it results."
      ],
      "metadata": {
        "id": "tBKFHj73QNsH"
      }
    }
  ]
}