basic_resources/intro_sklearn_practice.ipynb

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    "Tonight's exercise is going to be much more open than last weeks. You're goal is to build off of what's below, working with the forest-fires data set to try build the best model in terms of predicting the total area of burned fires (see the problem description and data set [here](https://archive.ics.uci.edu/ml/datasets/Forest+Fires)). I suggest trying to add in other features, engineer some of your own (using Pandas methods learned tonight), and even playing around with the hyperparameters of the model you fit. If you want other options, you might even try looking at other models that are availiable in `sklearn`. Good luck!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
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   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('data/forestfires.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
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   "source": [
    "from sklearn.ensemble import RandomForestRegressor # Import our model. \n",
    "random_forest = RandomForestRegressor(n_estimators=100) # Instantiate it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
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    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           n_estimators=100, n_jobs=1, oob_score=False, random_state=None,\n",
       "           verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = df[['X','Y', 'wind']]\n",
    "target = df['area']\n",
    "random_forest.fit(features, target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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