Airbnb_Price Amenities Correlational Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Airbnb Data Analysis - Determining the relation of house prices and amenities using Random Forest Regressor**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\saj16\\PycharmProjects\\AirbnbDataAnalysisProject/airbnbData.csv\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ListingID</th>\n",
       "      <th>Title</th>\n",
       "      <th>UserID</th>\n",
       "      <th>baseurl</th>\n",
       "      <th>Price</th>\n",
       "      <th>AboutListing</th>\n",
       "      <th>HostName</th>\n",
       "      <th>MemberDate</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>...</th>\n",
       "      <th>RespTime</th>\n",
       "      <th>S_Accomodates</th>\n",
       "      <th>S_Bathrooms</th>\n",
       "      <th>S_BedType</th>\n",
       "      <th>S_Bedrooms</th>\n",
       "      <th>S_CheckIn</th>\n",
       "      <th>S_Checkout</th>\n",
       "      <th>S_NumBeds</th>\n",
       "      <th>S_PropType</th>\n",
       "      <th>ShortDesc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>281552</td>\n",
       "      <td>Harvard Sq Large 1BR overlooks park</td>\n",
       "      <td>1467518</td>\n",
       "      <td>https://www.airbnb.com/s/Cambridge--MA?page=1</td>\n",
       "      <td>175</td>\n",
       "      <td>\\n</td>\n",
       "      <td>Mary Catherine</td>\n",
       "      <td>December 2011</td>\n",
       "      <td>42.377119</td>\n",
       "      <td>-71.120112</td>\n",
       "      <td>...</td>\n",
       "      <td>within a few hours</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Not Found</td>\n",
       "      <td>1</td>\n",
       "      <td>Not Found</td>\n",
       "      <td>Not Found</td>\n",
       "      <td>2</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>\\n    Entire home/apt Ìâåá 24 reviews Ìâåá Har...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>182613</td>\n",
       "      <td>Luxury 2BR condo Charlestown Boston</td>\n",
       "      <td>875739</td>\n",
       "      <td>https://www.airbnb.com/s/Charlestown-MA?page=1</td>\n",
       "      <td>249</td>\n",
       "      <td>Entire large modern quiet city condo near ever...</td>\n",
       "      <td>Max</td>\n",
       "      <td>July 2011</td>\n",
       "      <td>42.377387</td>\n",
       "      <td>-71.060435</td>\n",
       "      <td>...</td>\n",
       "      <td>within an hour</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Not Found</td>\n",
       "      <td>2</td>\n",
       "      <td>Not Found</td>\n",
       "      <td>Not Found</td>\n",
       "      <td>3</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>\\n    Entire home/apt Ìâåá 17 reviews Ìâåá Cha...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1587540</td>\n",
       "      <td>Cozy House on Bunker Hill in Boston</td>\n",
       "      <td>2004732</td>\n",
       "      <td>https://www.airbnb.com/s/Charlestown-MA?page=1</td>\n",
       "      <td>225</td>\n",
       "      <td>\\n</td>\n",
       "      <td>Finola</td>\n",
       "      <td>March 2012</td>\n",
       "      <td>42.378898</td>\n",
       "      <td>-71.061182</td>\n",
       "      <td>...</td>\n",
       "      <td>within an hour</td>\n",
       "      <td>4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Not Found</td>\n",
       "      <td>3</td>\n",
       "      <td>12:00 PM (noon)</td>\n",
       "      <td>Not Found</td>\n",
       "      <td>3</td>\n",
       "      <td>House</td>\n",
       "      <td>\\n    Entire home/apt Ìâåá 5 reviews Ìâåá Char...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>469506</td>\n",
       "      <td>Luxury 1bd, Safe/Central Brookline</td>\n",
       "      <td>1766477</td>\n",
       "      <td>https://www.airbnb.com/s/Brookline--MA?page=1</td>\n",
       "      <td>140</td>\n",
       "      <td>\\n</td>\n",
       "      <td>Rupal</td>\n",
       "      <td>February 2012</td>\n",
       "      <td>42.338848</td>\n",
       "      <td>-71.135475</td>\n",
       "      <td>...</td>\n",
       "      <td>within an hour</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>1</td>\n",
       "      <td>4:00 PM</td>\n",
       "      <td>11:00 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>\\n    Entire home/apt Ìâåá 60 reviews Ìâåá Bro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3937268</td>\n",
       "      <td>Boston bedroom &amp; private bathroom</td>\n",
       "      <td>2530197</td>\n",
       "      <td>https://www.airbnb.com/s/Brookline--MA?page=1</td>\n",
       "      <td>99</td>\n",
       "      <td>I offer a private basement bedroom with a priv...</td>\n",
       "      <td>Natasha</td>\n",
       "      <td>June 2012</td>\n",
       "      <td>42.343206</td>\n",
       "      <td>-71.119955</td>\n",
       "      <td>...</td>\n",
       "      <td>within a few hours</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>1</td>\n",
       "      <td>2:00 PM</td>\n",
       "      <td>10:00 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>House</td>\n",
       "      <td>\\n    Private room Ìâåá 11 reviews Ìâåá Brookl...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ListingID                                Title   UserID  \\\n",
       "0     281552  Harvard Sq Large 1BR overlooks park  1467518   \n",
       "1     182613  Luxury 2BR condo Charlestown Boston   875739   \n",
       "2    1587540  Cozy House on Bunker Hill in Boston  2004732   \n",
       "3     469506   Luxury 1bd, Safe/Central Brookline  1766477   \n",
       "4    3937268    Boston bedroom & private bathroom  2530197   \n",
       "\n",
       "                                          baseurl  Price  \\\n",
       "0   https://www.airbnb.com/s/Cambridge--MA?page=1    175   \n",
       "1  https://www.airbnb.com/s/Charlestown-MA?page=1    249   \n",
       "2  https://www.airbnb.com/s/Charlestown-MA?page=1    225   \n",
       "3   https://www.airbnb.com/s/Brookline--MA?page=1    140   \n",
       "4   https://www.airbnb.com/s/Brookline--MA?page=1     99   \n",
       "\n",
       "                                        AboutListing        HostName  \\\n",
       "0                                           \\n        Mary Catherine   \n",
       "1  Entire large modern quiet city condo near ever...             Max   \n",
       "2                                           \\n                Finola   \n",
       "3                                           \\n                 Rupal   \n",
       "4  I offer a private basement bedroom with a priv...         Natasha   \n",
       "\n",
       "       MemberDate        Lat       Long  \\\n",
       "0   December 2011  42.377119 -71.120112   \n",
       "1       July 2011  42.377387 -71.060435   \n",
       "2      March 2012  42.378898 -71.061182   \n",
       "3   February 2012  42.338848 -71.135475   \n",
       "4       June 2012  42.343206 -71.119955   \n",
       "\n",
       "                         ...                                    RespTime  \\\n",
       "0                        ...                          within a few hours   \n",
       "1                        ...                              within an hour   \n",
       "2                        ...                              within an hour   \n",
       "3                        ...                              within an hour   \n",
       "4                        ...                          within a few hours   \n",
       "\n",
       "  S_Accomodates  S_Bathrooms  S_BedType  S_Bedrooms        S_CheckIn  \\\n",
       "0             3            1  Not Found           1        Not Found   \n",
       "1             8            1  Not Found           2        Not Found   \n",
       "2             4          1.5  Not Found           3  12:00 PM (noon)   \n",
       "3             2            1   Real Bed           1          4:00 PM   \n",
       "4             2            1   Real Bed           1          2:00 PM   \n",
       "\n",
       "   S_Checkout  S_NumBeds  S_PropType  \\\n",
       "0   Not Found          2   Apartment   \n",
       "1   Not Found          3   Apartment   \n",
       "2   Not Found          3       House   \n",
       "3    11:00 AM          1   Apartment   \n",
       "4    10:00 AM          1       House   \n",
       "\n",
       "                                           ShortDesc  \n",
       "0  \\n    Entire home/apt Ìâåá 24 reviews Ìâåá Har...  \n",
       "1  \\n    Entire home/apt Ìâåá 17 reviews Ìâåá Cha...  \n",
       "2  \\n    Entire home/apt Ìâåá 5 reviews Ìâåá Char...  \n",
       "3  \\n    Entire home/apt Ìâåá 60 reviews Ìâåá Bro...  \n",
       "4  \\n    Private room Ìâåá 11 reviews Ìâåá Brookl...  \n",
       "\n",
       "[5 rows x 65 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading the Dataset into pandas dataframe\n",
    "import pandas as pd\n",
    "import os\n",
    "mydir = os.getcwd()\n",
    "print(mydir + '/airbnbData.csv')\n",
    "housing = pd.read_csv(mydir + '/airbnbData.csv', encoding='ISO-8859-1')\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\saj16\\Anaconda3\\lib\\site-packages\\seaborn\\linearmodels.py:1290: UserWarning: The `corrplot` function has been deprecated in favor of `heatmap` and will be removed in a forthcoming release. Please update your code.\n",
      "  warnings.warn((\"The `corrplot` function has been deprecated in favor \"\n",
      "C:\\Users\\saj16\\Anaconda3\\lib\\site-packages\\seaborn\\linearmodels.py:1356: UserWarning: The `symmatplot` function has been deprecated in favor of `heatmap` and will be removed in a forthcoming release. Please update your code.\n",
      "  warnings.warn((\"The `symmatplot` function has been deprecated in favor \"\n"
     ]
    },
    {
     "data": {
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NGzemU6dO5oTN39+fiRMn8vjjj2dIvNJ/Jqs1WgaDgW3btvHll1/i7OzM9OnT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\nG6gf0tYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29449d6dac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation Plot\n",
    "import matplotlib.pyplot as plt # module for plotting \n",
    "import seaborn as sns\n",
    "from seaborn.linearmodels import corrplot\n",
    "%matplotlib inline \n",
    "\n",
    "sns.set(style=\"darkgrid\")\n",
    "\n",
    "f, ax = plt.subplots(figsize=(9, 9))\n",
    "corrplot(housing, annot=False, sig_stars=False, diag_names=False, ax=ax)\n",
    "plt.title('Correlation Matrix - All Variables')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figuring important attributes to predict the price of future housing on Airbnb using Random Forest. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cluster_dat = housing[[u'A_AC', u'A_Breakfast', u'A_CableTV', u'A_CarbonMonoxDetector', u'A_Doorman', \n",
    "                      u'A_Dryer', u'A_TV', u'A_Elevator', u'A_Essentials', u'A_Events', u'A_FamilyFriendly', \n",
    "                      u'A_FireExt', u'A_Fireplace', u'A_FirstAidKit', u'A_Gym', u'A_Heat', u'A_HotTub', u'A_Intercom', \n",
    "                      u'A_Internet', u'A_Kitchen', u'A_Parking', u'A_Pets', u'A_Pool', \n",
    "                      u'A_SafetyCard', u'A_Shampoo', u'A_SmokeDetector', u'A_Smoking', u'A_Washer', u'A_Wheelchair']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Jk7Bx48ZKH+gbE47WroaXlxfGjRuHN998EwqFAsCf3+W4u7tDLpfD3d0dcXFxAID8/HzE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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2944e577ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Applying PCA for Dimensionality reduction\n",
    "import numpy as np\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn import preprocessing\n",
    "pca = PCA()\n",
    "pcaResults = pca.fit(cluster_dat)\n",
    "\n",
    "plt.plot(npcaResultsp.cumsum(pcaResults.explained_variance_ratio_))\n",
    "plt.title('Cumalitive Proportion of Variance Explained - Principal Components')\n",
    "plt.xlabel('Number of Principal Components')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\saj16\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:662: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.preprocessing import Imputer\n",
    "from scipy.stats import randint\n",
    "\n",
    "#Create Parameter Grid\n",
    "tunedParameters = {'n_estimators': randint(low=1, high=21)}\n",
    "\n",
    "forest_reg = RandomForestRegressor(n_jobs = 1)\n",
    "clf = RandomizedSearchCV(forest_reg, param_distributions=tunedParameters, n_iter=10, cv=10)\n",
    "\n",
    "#Fit Random Forest\n",
    "Y = housing[u'Price'].astype(float)\n",
    "X = cluster_dat[[u'A_AC', u'A_Breakfast', u'A_CableTV', u'A_CarbonMonoxDetector', \n",
    "                   u'A_Doorman', u'A_Dryer', u'A_TV', u'A_Elevator', u'A_Essentials', u'A_Events', \n",
    "                   u'A_FamilyFriendly', u'A_FireExt', u'A_Fireplace', u'A_FirstAidKit', u'A_Gym', \n",
    "                   u'A_Heat', u'A_HotTub', u'A_Intercom', u'A_Internet', u'A_Kitchen', u'A_Parking', \n",
    "                   u'A_Pets', u'A_Pool', u'A_SafetyCard', u'A_Shampoo', u'A_SmokeDetector', u'A_Smoking', \n",
    "                   u'A_Washer', u'A_Wheelchair']]\n",
    "\n",
    "ImputeMissing = Imputer(strategy = 'most_frequent')\n",
    "Xt = pd.DataFrame(ImputeMissing.fit_transform(X))\n",
    "Xt.columns = X.columns\n",
    "clf.fit(Xt, Y)\n",
    "\n",
    "num_trees = []\n",
    "meanCVScore = []\n",
    "stdCVScore = []\n",
    "\n",
    "for n, mean, cv in clf.grid_scores_:\n",
    "    num_trees.append(n['n_estimators'])\n",
    "    meanCVScore.append(mean)\n",
    "    stdCVScore.append(np.std(cv) * 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\saj16\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:662: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[mean: -6.21534, std: 8.88483, params: {'n_estimators': 3},\n",
       " mean: -4.87263, std: 9.13634, params: {'n_estimators': 8},\n",
       " mean: -4.83718, std: 6.80300, params: {'n_estimators': 13},\n",
       " mean: -3.15971, std: 5.78795, params: {'n_estimators': 14},\n",
       " mean: -1.94033, std: 1.90026, params: {'n_estimators': 14},\n",
       " mean: -6.21977, std: 9.09012, params: {'n_estimators': 6},\n",
       " mean: -1.05542, std: 0.85853, params: {'n_estimators': 6},\n",
       " mean: -3.91062, std: 5.00818, params: {'n_estimators': 4},\n",
       " mean: -1.54010, std: 2.06283, params: {'n_estimators': 13},\n",
       " mean: -2.81867, std: 4.27715, params: {'n_estimators': 18}]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets print the grid_scores to determine the best model\n",
    "clf.grid_scores_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score='raise',\n",
       "       estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           n_estimators=10, n_jobs=1, oob_score=False, random_state=None,\n",
       "           verbose=0, warm_start=False),\n",
       "       fit_params={}, iid=True, n_jobs=1,\n",
       "       param_grid=[{'n_estimators': [7]}], pre_dispatch='2*n_jobs',\n",
       "       refit=True, scoring=None, verbose=0)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tunedParameters = [{'n_estimators':[7]}]\n",
    "clf2 = GridSearchCV(RandomForestRegressor(n_jobs = 1, criterion='mse'), \n",
    "                    param_grid = tunedParameters, cv=10)\n",
    "#Fit Model\n",
    "clf2.fit(Xt, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\saj16\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:2: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A_Essentials</th>\n",
       "      <td>0.341784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A_SmokeDetector</th>\n",
       "      <td>0.178341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A_FamilyFriendly</th>\n",
       "      <td>0.118997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A_AC</th>\n",
       "      <td>0.075010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A_TV</th>\n",
       "      <td>0.058831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A_Washer</th>\n",
       "      <td>0.056249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A_Pool</th>\n",
       "      <td>0.034518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A_Dryer</th>\n",
       "      <td>0.021259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A_Elevator</th>\n",
       "      <td>0.016411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A_FireExt</th>\n",
       "      <td>0.012924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A_CarbonMonoxDetector</th>\n",
       "      <td>0.010506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A_Internet</th>\n",
       "      <td>0.008542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A_Shampoo</th>\n",
       "      <td>0.008436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A_FirstAidKit</th>\n",
       "      <td>0.005871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A_CableTV</th>\n",
       "      <td>0.005196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A_HotTub</th>\n",
       "      <td>0.005063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A_Parking</th>\n",
       "      <td>0.004867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A_Fireplace</th>\n",
       "      <td>0.004808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A_Wheelchair</th>\n",
       "      <td>0.004782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A_Breakfast</th>\n",
       "      <td>0.004421</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       importance\n",
       "feature                          \n",
       "A_Essentials             0.341784\n",
       "A_SmokeDetector          0.178341\n",
       "A_FamilyFriendly         0.118997\n",
       "A_AC                     0.075010\n",
       "A_TV                     0.058831\n",
       "A_Washer                 0.056249\n",
       "A_Pool                   0.034518\n",
       "A_Dryer                  0.021259\n",
       "A_Elevator               0.016411\n",
       "A_FireExt                0.012924\n",
       "A_CarbonMonoxDetector    0.010506\n",
       "A_Internet               0.008542\n",
       "A_Shampoo                0.008436\n",
       "A_FirstAidKit            0.005871\n",
       "A_CableTV                0.005196\n",
       "A_HotTub                 0.005063\n",
       "A_Parking                0.004867\n",
       "A_Fireplace              0.004808\n",
       "A_Wheelchair             0.004782\n",
       "A_Breakfast              0.004421"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FeatImp = pd.DataFrame({'feature': list(Xt.columns), 'importance': list(clf2.best_estimator_.feature_importances_)})\n",
    "FeatImp = FeatImp.sort('importance', ascending = False)\n",
    "#Set Index To Field You want to Sort Bar Chart By\n",
    "FeatImp = FeatImp.set_index('feature')\n",
    "FeatImp.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Among the amenities in the house, the most important of all essentials, SmokeDetector and FamilyFriendly that were determinants of price of any new house. "
   ]
  }
 ],
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