forest_fire_ML.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [],
   "source": [
    "df_o = pd.read_csv('forestfires.csv')\n",
    "df = df_o.copy()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[1], line 2\u001B[0m\n\u001B[0;32m      1\u001B[0m \u001B[38;5;66;03m# reduce skew\u001B[39;00m\n\u001B[1;32m----> 2\u001B[0m area \u001B[38;5;241m=\u001B[39m \u001B[43mdf\u001B[49m[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124marea\u001B[39m\u001B[38;5;124m'\u001B[39m]\u001B[38;5;241m.\u001B[39mapply(\u001B[38;5;28;01mlambda\u001B[39;00m x: np\u001B[38;5;241m.\u001B[39mlog(x \u001B[38;5;241m+\u001B[39m np\u001B[38;5;241m.\u001B[39msqrt(x\u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39m\u001B[38;5;241m2\u001B[39m \u001B[38;5;241m+\u001B[39m \u001B[38;5;241m1\u001B[39m)))\n\u001B[0;32m      3\u001B[0m area\n",
      "\u001B[1;31mNameError\u001B[0m: name 'df' is not defined"
     ]
    }
   ],
   "source": [
    "# reduce skew\n",
    "area = df['area'].apply(lambda x: np.log(x + np.sqrt(x**2 + 1)))\n",
    "area"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [
    {
     "data": {
      "text/plain": "(array([508.,   6.,   1.,   0.,   0.,   0.,   1.,   0.,   0.,   1.]),\n array([   0.   ,  109.084,  218.168,  327.252,  436.336,  545.42 ,\n         654.504,  763.588,  872.672,  981.756, 1090.84 ]),\n <BarContainer object of 10 artists>)"
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.hist(df_o['area'])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "data": {
      "text/plain": "(array([270.,  50.,  46.,  62.,  32.,  33.,  15.,   6.,   1.,   2.]),\n array([0.        , 0.76878507, 1.53757014, 2.30635521, 3.07514028,\n        3.84392536, 4.61271043, 5.3814955 , 6.15028057, 6.91906564,\n        7.68785071]),\n <BarContainer object of 10 artists>)"
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(area)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [],
   "source": [
    "parameters = df.drop('area', axis=1)\n",
    "train_parameters, test_parameters, train_solutions, test_solutions = model_selection.train_test_split(parameters, area)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.04122621483486355\n"
     ]
    }
   ],
   "source": [
    "linreg = linear_model.LinearRegression()\n",
    "linreg.fit(train_parameters, train_solutions)\n",
    "print(linreg.score(train_parameters, train_solutions))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5348837209302325\n"
     ]
    }
   ],
   "source": [
    "train_solutions_log, train_parameters_log = train_solutions.copy().apply(lambda x: x > 0), train_parameters.copy().apply(lambda x: x > 0)\n",
    "\n",
    "logreg = linear_model.LogisticRegression()\n",
    "logreg.fit(train_parameters_log, train_solutions_log)\n",
    "print(logreg.score(train_parameters_log, train_solutions_log))"
   ],
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   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
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