NYC Modeling and advanced Engineering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import ipywidgets as widgets\n",
    "%matplotlib inline\n",
    "\n",
    "from mpl_toolkits.basemap import Basemap\n",
    "from matplotlib import cm\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(style=\"white\", palette=\"muted\", color_codes=True)\n",
    "import pandas as pd\n",
    "pd.options.mode.chained_assignment = None \n",
    "import numpy as np\n",
    "\n",
    "from haversine import haversine\n",
    "from scipy.spatial.distance import euclidean , cityblock\n",
    "from geopy.distance import great_circle\n",
    "from math import *\n",
    "\n",
    "from bokeh.io import output_notebook,show\n",
    "from bokeh.models import HoverTool\n",
    "from bokeh.plotting import figure\n",
    "from bokeh.palettes import Spectral4\n",
    "\n",
    "import folium \n",
    "from folium import plugins\n",
    "from folium.plugins import HeatMap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "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>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>...</th>\n",
       "      <th>dropoff_dayofyear</th>\n",
       "      <th>dropoff_dt</th>\n",
       "      <th>dropoff_week_hour</th>\n",
       "      <th>haversine_distance</th>\n",
       "      <th>log_haversine_distance</th>\n",
       "      <th>euclidean_distance</th>\n",
       "      <th>manhattan_distance</th>\n",
       "      <th>log_euclidean_distance</th>\n",
       "      <th>log_manhattan_distance</th>\n",
       "      <th>En_store_and_fwd_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>id2875421</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-14 17:24:55</td>\n",
       "      <td>2016-03-14 17:32:30</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.982155</td>\n",
       "      <td>40.767937</td>\n",
       "      <td>-73.964630</td>\n",
       "      <td>40.765602</td>\n",
       "      <td>...</td>\n",
       "      <td>74</td>\n",
       "      <td>6370139.0</td>\n",
       "      <td>17</td>\n",
       "      <td>1.498521</td>\n",
       "      <td>0.915699</td>\n",
       "      <td>0.017680</td>\n",
       "      <td>0.019859</td>\n",
       "      <td>0.017525</td>\n",
       "      <td>0.019665</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>id2377394</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-12 00:43:35</td>\n",
       "      <td>2016-06-12 00:54:38</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.980415</td>\n",
       "      <td>40.738564</td>\n",
       "      <td>-73.999481</td>\n",
       "      <td>40.731152</td>\n",
       "      <td>...</td>\n",
       "      <td>164</td>\n",
       "      <td>14086267.0</td>\n",
       "      <td>144</td>\n",
       "      <td>1.805507</td>\n",
       "      <td>1.031584</td>\n",
       "      <td>0.020456</td>\n",
       "      <td>0.026478</td>\n",
       "      <td>0.020249</td>\n",
       "      <td>0.026133</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>id3858529</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-01-19 11:35:24</td>\n",
       "      <td>2016-01-19 12:10:48</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.979027</td>\n",
       "      <td>40.763939</td>\n",
       "      <td>-74.005333</td>\n",
       "      <td>40.710087</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>1598837.0</td>\n",
       "      <td>36</td>\n",
       "      <td>6.385098</td>\n",
       "      <td>1.999464</td>\n",
       "      <td>0.059934</td>\n",
       "      <td>0.080158</td>\n",
       "      <td>0.058206</td>\n",
       "      <td>0.077108</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>id3504673</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-04-06 19:32:31</td>\n",
       "      <td>2016-04-06 19:39:40</td>\n",
       "      <td>1</td>\n",
       "      <td>-74.010040</td>\n",
       "      <td>40.719971</td>\n",
       "      <td>-74.012268</td>\n",
       "      <td>40.706718</td>\n",
       "      <td>...</td>\n",
       "      <td>97</td>\n",
       "      <td>8364969.0</td>\n",
       "      <td>67</td>\n",
       "      <td>1.485498</td>\n",
       "      <td>0.910473</td>\n",
       "      <td>0.013438</td>\n",
       "      <td>0.015480</td>\n",
       "      <td>0.013349</td>\n",
       "      <td>0.015361</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>id2181028</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-26 13:30:55</td>\n",
       "      <td>2016-03-26 13:38:10</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.973053</td>\n",
       "      <td>40.793209</td>\n",
       "      <td>-73.972923</td>\n",
       "      <td>40.782520</td>\n",
       "      <td>...</td>\n",
       "      <td>86</td>\n",
       "      <td>7392879.0</td>\n",
       "      <td>133</td>\n",
       "      <td>1.188588</td>\n",
       "      <td>0.783257</td>\n",
       "      <td>0.010690</td>\n",
       "      <td>0.010818</td>\n",
       "      <td>0.010633</td>\n",
       "      <td>0.010760</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0         id  vendor_id      pickup_datetime     dropoff_datetime  \\\n",
       "0           0  id2875421          2  2016-03-14 17:24:55  2016-03-14 17:32:30   \n",
       "1           1  id2377394          1  2016-06-12 00:43:35  2016-06-12 00:54:38   \n",
       "2           2  id3858529          2  2016-01-19 11:35:24  2016-01-19 12:10:48   \n",
       "3           3  id3504673          2  2016-04-06 19:32:31  2016-04-06 19:39:40   \n",
       "4           4  id2181028          2  2016-03-26 13:30:55  2016-03-26 13:38:10   \n",
       "\n",
       "   passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "0                1        -73.982155        40.767937         -73.964630   \n",
       "1                1        -73.980415        40.738564         -73.999481   \n",
       "2                1        -73.979027        40.763939         -74.005333   \n",
       "3                1        -74.010040        40.719971         -74.012268   \n",
       "4                1        -73.973053        40.793209         -73.972923   \n",
       "\n",
       "   dropoff_latitude          ...           dropoff_dayofyear  dropoff_dt  \\\n",
       "0         40.765602          ...                          74   6370139.0   \n",
       "1         40.731152          ...                         164  14086267.0   \n",
       "2         40.710087          ...                          19   1598837.0   \n",
       "3         40.706718          ...                          97   8364969.0   \n",
       "4         40.782520          ...                          86   7392879.0   \n",
       "\n",
       "  dropoff_week_hour  haversine_distance  log_haversine_distance  \\\n",
       "0                17            1.498521                0.915699   \n",
       "1               144            1.805507                1.031584   \n",
       "2                36            6.385098                1.999464   \n",
       "3                67            1.485498                0.910473   \n",
       "4               133            1.188588                0.783257   \n",
       "\n",
       "   euclidean_distance  manhattan_distance  log_euclidean_distance  \\\n",
       "0            0.017680            0.019859                0.017525   \n",
       "1            0.020456            0.026478                0.020249   \n",
       "2            0.059934            0.080158                0.058206   \n",
       "3            0.013438            0.015480                0.013349   \n",
       "4            0.010690            0.010818                0.010633   \n",
       "\n",
       "   log_manhattan_distance  En_store_and_fwd_flag  \n",
       "0                0.019665                      0  \n",
       "1                0.026133                      0  \n",
       "2                0.077108                      0  \n",
       "3                0.015361                      0  \n",
       "4                0.010760                      0  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('Dataset/Large NYC.csv')\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "31385d2b02d845b18379944b22f80490",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>ToggleButtons</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "ToggleButtons(button_style='info', description='Step:', options=('Data Gathering', 'Preprocessing', 'Features Engineering', 'Features Selection', 'Training', 'Evaluation', 'Deployment'), tooltips=['Description of slow', 'Description of regular', 'Description of fast'], value='Data Gathering')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "widgets.ToggleButtons(\n",
    "    options=['Data Gathering', 'Preprocessing', 'Features Engineering', 'Features Selection', 'Training', 'Evaluation', 'Deployment'],\n",
    "    description='Step:',\n",
    "    disabled=False,\n",
    "    button_style='info', # 'success', 'info', 'warning', 'danger' or ''\n",
    "    tooltips=['Description of slow', 'Description of regular', 'Description of fast'],\n",
    "     icons=['check'] * 0\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1458644 entries, 0 to 1458643\n",
      "Data columns (total 40 columns):\n",
      "Unnamed: 0                1458644 non-null int64\n",
      "id                        1458644 non-null object\n",
      "vendor_id                 1458644 non-null int64\n",
      "pickup_datetime           1458644 non-null object\n",
      "dropoff_datetime          1458644 non-null object\n",
      "passenger_count           1458644 non-null int64\n",
      "pickup_longitude          1458644 non-null float64\n",
      "pickup_latitude           1458644 non-null float64\n",
      "dropoff_longitude         1458644 non-null float64\n",
      "dropoff_latitude          1458644 non-null float64\n",
      "store_and_fwd_flag        1458644 non-null object\n",
      "trip_duration             1458644 non-null int64\n",
      "pickup_date               1458644 non-null object\n",
      "pickup_day                1458644 non-null int64\n",
      "pickup_month              1458644 non-null int64\n",
      "pickup_weekday            1458644 non-null int64\n",
      "pickup_weekofyear         1458644 non-null int64\n",
      "pickup_hour               1458644 non-null int64\n",
      "pickup_minute             1458644 non-null int64\n",
      "pickup_dayofyear          1458644 non-null int64\n",
      "pickup_dt                 1458644 non-null float64\n",
      "pickup_week_hour          1458644 non-null int64\n",
      "log_trip_duration         1458644 non-null float64\n",
      "dropoff_date              1458644 non-null object\n",
      "dropoff_day               1458644 non-null int64\n",
      "dropoff_month             1458644 non-null int64\n",
      "dropoff_weekday           1458644 non-null int64\n",
      "dropoff_weekofyear        1458644 non-null int64\n",
      "dropoff_hour              1458644 non-null int64\n",
      "dropoff_minute            1458644 non-null int64\n",
      "dropoff_dayofyear         1458644 non-null int64\n",
      "dropoff_dt                1458644 non-null float64\n",
      "dropoff_week_hour         1458644 non-null int64\n",
      "haversine_distance        1458644 non-null float64\n",
      "log_haversine_distance    1458644 non-null float64\n",
      "euclidean_distance        1458644 non-null float64\n",
      "manhattan_distance        1458644 non-null float64\n",
      "log_euclidean_distance    1458644 non-null float64\n",
      "log_manhattan_distance    1458644 non-null float64\n",
      "En_store_and_fwd_flag     1458644 non-null int64\n",
      "dtypes: float64(13), int64(21), object(6)\n",
      "memory usage: 445.1+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=15, min_samples_split=7,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "rf_model = RandomForestRegressor(n_estimators=10, min_samples_leaf=15, min_samples_split=7)\n",
    "\n",
    "target = data.log_trip_duration.values\n",
    "data = data.drop(['Unnamed: 0', 'id', 'trip_duration', 'log_trip_duration', 'pickup_datetime', 'dropoff_date',\n",
    "                 'store_and_fwd_flag', 'dropoff_datetime', 'pickup_date'], axis=1)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.30, random_state=42)\n",
    "\n",
    "rf_model.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances=rf_model.feature_importances_\n",
    "\n",
    "predictors = data.columns\n",
    "std = np.std([rf_model.feature_importances_ for tree in rf_model.estimators_],\n",
    "             axis=0)\n",
    "indices = np.argsort(importances)[::-1]\n",
    "sorted_important_features=[]\n",
    "for i in indices:\n",
    "    sorted_important_features.append(predictors[i])\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.title(\"Feature Importances By Random Forest\")\n",
    "plt.barh(range(len(indices)), importances[indices],\n",
    "       color=[\"green\",\"red\"], yerr=std[indices], align=\"center\")\n",
    "plt.yticks(range(len(indices)), sorted_important_features, rotation='horizontal');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "log_euclidean_distance 0.00830740304342\n",
      "euclidean_distance 0.00162348851616\n",
      "log_haversine_distance 0.0184231135218\n",
      "haversine_distance 0.0153588019538\n",
      "dropoff_latitude 0.0197159246423\n",
      "dropoff_hour 0.0377965428005\n",
      "pickup_hour 0.00235960180433\n",
      "dropoff_longitude 6.8628052917e-05\n",
      "pickup_longitude 0.00337525045542\n",
      "pickup_latitude 0.000765150130553\n",
      "dropoff_minute 0.0260041712691\n",
      "pickup_minute 0.0119407085827\n",
      "pickup_week_hour 0.000882019784876\n",
      "dropoff_week_hour 0.00354015344706\n",
      "vendor_id 0.00985064586969\n",
      "manhattan_distance 0.00244510655147\n",
      "log_manhattan_distance 7.56668068369e-05\n",
      "dropoff_dt 0.00249845919575\n",
      "pickup_dt 0.000845951096023\n",
      "pickup_weekday 0.0288584292021\n",
      "dropoff_weekday 0.0121485878579\n",
      "dropoff_day 0.000870701070068\n",
      "pickup_day 0.00511578161452\n",
      "passenger_count 0.00955073816503\n",
      "pickup_dayofyear 0.0379438004489\n",
      "dropoff_dayofyear 0.0756149520673\n",
      "dropoff_weekofyear 0.322973290357\n",
      "pickup_weekofyear 0.00789039480202\n",
      "dropoff_month 0.326942421175\n",
      "pickup_month 0.00621411571513\n",
      "En_store_and_fwd_flag 0.0\n"
     ]
    }
   ],
   "source": [
    "# Let's print the importance score for each attribute\n",
    "\n",
    "for i,e in zip(sorted_important_features, importances):\n",
    "    print(i, e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle \n",
    "from sklearn import linear_model \n",
    "import sklearn.metrics as sm \n",
    "\n",
    "# Create linear regressor object \n",
    "regressor = linear_model.LinearRegression() \n",
    " \n",
    "# Train the model using the training sets \n",
    "regressor.fit(X_train, y_train) \n",
    "\n",
    "# Predict the output \n",
    "y_test_pred = regressor.predict(X_test) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performance Analysis and Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear regressor performance:\n",
      "Mean absolute error = 0.35\n",
      "Mean squared error = 0.23\n",
      "Median absolute error = 0.27\n",
      "Explain variance score = 0.64\n",
      "R2 score = 0.64\n"
     ]
    }
   ],
   "source": [
    "# Compute performance metrics \n",
    "print(\"Linear regressor performance:\") \n",
    "print(\"Mean absolute error =\", round(sm.mean_absolute_error(y_test, y_test_pred), 2)) \n",
    "print(\"Mean squared error =\", round(sm.mean_squared_error(y_test, y_test_pred), 2))  \n",
    "print(\"Median absolute error =\", round(sm.median_absolute_error(y_test, y_test_pred), 2))  \n",
    "print(\"Explain variance score =\", round(sm.explained_variance_score(y_test, y_test_pred), 2)) \n",
    "print(\"R2 score =\", round(sm.r2_score(y_test, y_test_pred), 2)) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Advanced Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Recursive Feature Elemination\n",
    "2. Features importance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recursive Feature Elimination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False  True  True  True]\n",
      "[2 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "# load the iris datasets\n",
    "dataset = datasets.load_iris()\n",
    "# create a base classifier used to evaluate a subset of attributes\n",
    "model = LogisticRegression()\n",
    "# create the RFE model and select 3 attributes\n",
    "rfe = RFE(model, 3)\n",
    "rfe = rfe.fit(dataset.data, dataset.target)\n",
    "# summarize the selection of the attributes\n",
    "print(rfe.support_)\n",
    "print(rfe.ranking_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True  True  True  True  True False False False False False]\n",
      "[1 1 1 1 1 6 4 3 2 5]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import make_friedman1\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.svm import SVR\n",
    "\n",
    "X, y = make_friedman1(n_samples=50, n_features=10, random_state=0)\n",
    "estimator = SVR(kernel=\"linear\")\n",
    "selector = RFE(estimator, 5, step=1)\n",
    "selector = selector.fit(X, y)\n",
    "\n",
    "print(selector.support_ )\n",
    "print(selector.ranking_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.07943851  0.02466577  0.3149003   0.58099542]\n"
     ]
    }
   ],
   "source": [
    "# Feature Importance\n",
    "from sklearn import datasets\n",
    "from sklearn import metrics\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "# load the iris datasets\n",
    "dataset = datasets.load_iris()\n",
    "# fit an Extra Trees model to the data\n",
    "model = ExtraTreesClassifier()\n",
    "model.fit(dataset.data, dataset.target)\n",
    "# display the relative importance of each attribute\n",
    "print(model.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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