README.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "Houses prices is a small standard Regression problem, the data set was token from a kaggle competition, formatted in the AutoML format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_dir = 'sample_code_submission/'          \n",
    "problem_dir = 'ingestion_program/'  \n",
    "score_dir = 'scoring_program/'\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pylab as plt\n",
    "from sys import path; path.append(model_dir); path.append(problem_dir); path.append(score_dir); \n",
    "import seaborn as sns; sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 1: Exploratory data analysis\n",
    "\n",
    "We provide sample_data with the starting kit, but to prepare your submission, you must fetch the public_data from the challenge website and point to it.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'ls' n'est pas reconnu en tant que commande interne\n",
      "ou externe, un programme ex‚cutable ou un fichier de commandes.\n"
     ]
    }
   ],
   "source": [
    "datadir = 'sample_data/'             \n",
    "basename = 'houseprice'\n",
    "!ls $datadir*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of examples = 100\n",
      "Number of features = 18\n",
      "Number of variables to predict = 1\n"
     ]
    }
   ],
   "source": [
    "# We read data as Panda data frames\n",
    "feat_name = pd.read_csv(datadir+basename + '_feat.name', header=None)\n",
    "label_name = pd.read_csv(datadir+basename + '_label.name', header=None)\n",
    "\n",
    "\n",
    "X = pd.read_csv(datadir+basename + '_train.data', sep=' ', names = np.ravel(feat_name))\n",
    "Y = pd.read_csv(datadir+ basename + '_train.solution', sep=' ', names = np.ravel(label_name))\n",
    "\n",
    "\n",
    "\n",
    "X_valid = pd.read_csv(datadir+basename + '_valid.data', sep=' ', names = np.ravel(feat_name))\n",
    "Y_valid = pd.read_csv(datadir+ basename + '_valid.solution', sep=' ', names = np.ravel(label_name))\n",
    "X_test = pd.read_csv(datadir+basename + '_test.data', sep=' ', names = np.ravel(feat_name))\n",
    "Y_test = pd.read_csv(datadir+ basename + '_test.solution', sep=' ', names = np.ravel(label_name))\n",
    "\n",
    "\n",
    "\n",
    "X['waterfront'] = X['waterfront'].astype('category',ordered=True)\n",
    "X['view'] = X['view'].astype('category',ordered=True)\n",
    "X['condition'] = X['condition'].astype('category',ordered=True)\n",
    "X['grade'] = X['grade'].astype('category',ordered=False)\n",
    "\n",
    "\n",
    "X_valid['waterfront'] = X_valid['waterfront'].astype('category',ordered=True)\n",
    "X_valid['view'] = X_valid['view'].astype('category',ordered=True)\n",
    "X_valid['condition'] = X_valid['condition'].astype('category',ordered=True)\n",
    "X_valid['grade'] = X_valid['grade'].astype('category',ordered=False)\n",
    "\n",
    "\n",
    "X_test['waterfront'] = X_test['waterfront'].astype('category',ordered=True)\n",
    "X_test['view'] = X_test['view'].astype('category',ordered=True)\n",
    "X_test['condition'] = X_test['condition'].astype('category',ordered=True)\n",
    "X_test['grade'] = X_test['grade'].astype('category',ordered=False)\n",
    "\n",
    "[patnum, featnum] = X.shape\n",
    "[patnum2, classnum] = Y.shape\n",
    "assert(patnum==patnum2)\n",
    "print 'Number of examples = %d' % patnum\n",
    "print 'Number of features = %d' % featnum\n",
    "print 'Number of variables to predict = %d' % classnum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.270000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>2109.030000</td>\n",
       "      <td>14252.370000</td>\n",
       "      <td>1.530000</td>\n",
       "      <td>1842.120000</td>\n",
       "      <td>266.910000</td>\n",
       "      <td>1970.800000</td>\n",
       "      <td>120.080000</td>\n",
       "      <td>98086.130000</td>\n",
       "      <td>47.0</td>\n",
       "      <td>-121.930000</td>\n",
       "      <td>2015.720000</td>\n",
       "      <td>13273.380000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.802207</td>\n",
       "      <td>0.778499</td>\n",
       "      <td>965.116306</td>\n",
       "      <td>35109.687847</td>\n",
       "      <td>0.610597</td>\n",
       "      <td>825.823975</td>\n",
       "      <td>421.738899</td>\n",
       "      <td>32.413365</td>\n",
       "      <td>477.691284</td>\n",
       "      <td>53.207551</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.256432</td>\n",
       "      <td>624.246132</td>\n",
       "      <td>33179.528865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>590.000000</td>\n",
       "      <td>987.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>590.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1905.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98001.000000</td>\n",
       "      <td>47.0</td>\n",
       "      <td>-122.000000</td>\n",
       "      <td>1030.000000</td>\n",
       "      <td>1230.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1417.500000</td>\n",
       "      <td>4658.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1200.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1947.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98041.500000</td>\n",
       "      <td>47.0</td>\n",
       "      <td>-122.000000</td>\n",
       "      <td>1530.000000</td>\n",
       "      <td>5206.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1940.000000</td>\n",
       "      <td>7054.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1625.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1975.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98097.000000</td>\n",
       "      <td>47.0</td>\n",
       "      <td>-122.000000</td>\n",
       "      <td>1930.000000</td>\n",
       "      <td>7200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2567.500000</td>\n",
       "      <td>11995.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2295.000000</td>\n",
       "      <td>465.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98122.000000</td>\n",
       "      <td>47.0</td>\n",
       "      <td>-122.000000</td>\n",
       "      <td>2322.500000</td>\n",
       "      <td>9450.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6330.000000</td>\n",
       "      <td>308080.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4900.000000</td>\n",
       "      <td>1510.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>98199.000000</td>\n",
       "      <td>47.0</td>\n",
       "      <td>-121.000000</td>\n",
       "      <td>3890.000000</td>\n",
       "      <td>247856.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         bedrooms   bathrooms  sqft_living       sqft_lot      floors  \\\n",
       "count  100.000000  100.000000   100.000000     100.000000  100.000000   \n",
       "mean     3.270000    1.800000  2109.030000   14252.370000    1.530000   \n",
       "std      0.802207    0.778499   965.116306   35109.687847    0.610597   \n",
       "min      2.000000    0.000000   590.000000     987.000000    1.000000   \n",
       "25%      3.000000    1.000000  1417.500000    4658.250000    1.000000   \n",
       "50%      3.000000    2.000000  1940.000000    7054.000000    1.000000   \n",
       "75%      4.000000    2.000000  2567.500000   11995.500000    2.000000   \n",
       "max      5.000000    4.000000  6330.000000  308080.000000    3.000000   \n",
       "\n",
       "        sqft_above  sqft_basement     yr_built  yr_renovated       zipcode  \\\n",
       "count   100.000000     100.000000   100.000000    100.000000    100.000000   \n",
       "mean   1842.120000     266.910000  1970.800000    120.080000  98086.130000   \n",
       "std     825.823975     421.738899    32.413365    477.691284     53.207551   \n",
       "min     590.000000       0.000000  1905.000000      0.000000  98001.000000   \n",
       "25%    1200.000000       0.000000  1947.000000      0.000000  98041.500000   \n",
       "50%    1625.000000       0.000000  1975.500000      0.000000  98097.000000   \n",
       "75%    2295.000000     465.000000  2000.000000      0.000000  98122.000000   \n",
       "max    4900.000000    1510.000000  2014.000000   2013.000000  98199.000000   \n",
       "\n",
       "         lat        long  sqft_living15     sqft_lot15  \n",
       "count  100.0  100.000000     100.000000     100.000000  \n",
       "mean    47.0 -121.930000    2015.720000   13273.380000  \n",
       "std      0.0    0.256432     624.246132   33179.528865  \n",
       "min     47.0 -122.000000    1030.000000    1230.000000  \n",
       "25%     47.0 -122.000000    1530.000000    5206.750000  \n",
       "50%     47.0 -122.000000    1930.000000    7200.000000  \n",
       "75%     47.0 -122.000000    2322.500000    9450.000000  \n",
       "max     47.0 -121.000000    3890.000000  247856.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>699000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>285000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>359900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>847000</td>\n",
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      ],
      "text/plain": [
       "    price\n",
       "0  699000\n",
       "1  285000\n",
       "2  480000\n",
       "3  359900\n",
       "4  847000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Here we add the target values as a last column, this is convenient to use seaborn\n",
    "numerical_target = Y['price'] \n",
    "\n",
    "XY = X.assign(price=numerical_target.values) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first display the distribution of the target column (price) using a boxplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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09fUvTqaOG9dcchIol20BAIaeAb8U+zPPPJPzzz8/c+bMyQc/+MGsWrWq77lK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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe3b5d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var = 'bedrooms'\n",
    "f, ax = plt.subplots(figsize=(14, 6))\n",
    "fig = sns.boxplot(x=var, y='price', data=XY)\n",
    "fig.axis(ymin=0, ymax=1500000);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can also use a histogram for our target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 8.5, 0, 1750000)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vswDgyykrmylJWrFitc2VmC0az1NZ2Ux90h7UNfcutbuUiDtSXa70pLioen0Q/Wyf1gIA\nAADg0gjnAAAAgCEI5wAAAIAhCOcAAACAISxbrQUAAESXTl+LjlSXW9pnwN91nxJnosuyPjt9LVLS\nYMv6Ay6FcA4AAL4gM9OesOo5c1aSlJ5k4d06kwbbdrzA5xHOAQDAF1RWLrKl32hcdhLoT8w5BwAA\nAAzByDkAIGY8+ug8eTzNlvd7rs9zo8JWyswcbNsoOIAvr8fhvL6+Xu+9956++93v6sCBA7rpppsi\nWRcAAP3O42nW6ebTkivF2o6dTknS6fZ2a/v1ea3tD0Cf9SicP/fcc3r99df18ccf64477lBlZaUm\nTZqkGTNmRLo+AAD6lytFiVPvt7sKS/hfWGd3CQC+pB6F81/84heqqalRcXGxMjIy9OKLL+p73/se\n4RwAAMS0DRvW6ze/eaVX+waDwX6u5suJi+vdpYd33DFRbve0fq4G5/ToVYmLi1NCQkL4eWJiopx/\n+YoOAAAAQP/o0ch5fn6+li5dqra2Nr3++uvatGmTbr755kjXBgAAYDS3exqjyOhXPRo5f/DBB3X1\n1Vfr7//+7/XLX/5St912m8rLrb1jGAAAADDQ9WjkvK2tTYFAQD/72c906tQpbdy4UR0dHYqPZyVG\nAAAAoL/0aOR8zpw5+vjjjyVJLpdLwWBQDz74YEQLAwAAAGJNj8L5iRMnNHv2bElSSkqKZs+erT/9\n6U8RLQwAAACINT0K5w6HQ7///e/Dz48cOcKUFgADVmPjITU2HrK7DABADOpRwi4vL9f06dP1la98\nRZLU0tKi//iP/4hoYQBgl61bayRJubk32FwJACDW9Cic33LLLdq+fbvee+89xcfHKycn57x1zwFg\noGhsPKTDhxvDjwnoAAArXTKcr1y5UqWlpXrooYcu+PslS5ZEpCgAsMu5UfNzjwnnAAArXTKcX3/9\n9ZK6bkIEAADQExs2rFdd3Zu92tfjaZYklZXN/NL75ueP5YZAiHqXDOcTJkyQJL3yyit69tlnLSkI\nAOxUVFSsxYsXhh8DsFZiYqLdJQC26tGc8/b2dp08eVJf/epXI10PANgqN/cGDR+eG34M4MvjlvZA\n7/UonDc3N2vChAkaPHjweZ9ot23bFrHCAMAujJgDAOzSo3C+evVq7dy5U7/97W/ldDr1ta99TWPH\njo10bQBgC0bMAQB26VE4f/rpp+X3+1VcXKxgMKiXXnpJf/jDH/Twww9Huj4AAAAgZvQonB84cEC/\n+c1vws8nTJigiRMnRqwoAAAAIBbF9aTRlVdeqaNHj4afnz59Ony3UAAAAAD9o0cj552dnfr2t7+t\nvLw8xcfHq76+XllZWZo2retK7PXr1/eos46ODs2dO1fHjx9XXFycfvrTnyo+Pl5z586Vw+HQdddd\npwULFiguLk6rVq3Sjh07FB8fr4qKCo0cOVJHjx7tc1sAAADAVD0K5z/60Y/Oez59+vRedbZz5051\ndnZq48aN2rNnj1asWKGOjg6VlZVpzJgxqqys1LZt2zRkyBDV1dVp8+bNOnnypEpLS7VlyxYtWbKk\nT21vv/32XtUNAAAAWKFH4by/7hCanZ2tQCCgYDAor9er+Ph4vf322+E/v6CgQHv27FF2drbGjRsn\nh8OhIUOGKBAIyOPxqKGhoU9tCecAAAAwWY/CeX9JTk7W8ePHdeedd6qlpUVPP/209u/fL4fDIUly\nuVxqbW2V1+tVenp6eL9z20OhUJ/adicjI1nx8c7+PGQAGNCczq7pgllZqTZX0jPn6o0lTmdc1Lw+\nACwO5+vWrdO4ceM0Z84cnTx5Uvfdd586OjrCv/f5fEpLS1NKSop8Pt9521NTU8+bM96btt1paTnT\n10MEgJgSCAQlSU1N3Q+AmODTTz+V2tvlf2Gd3aVYw+fVp4HOqHl9gFhysQ/Nlg4hpKWlKTW1q5DL\nL79cnZ2dys3N1b59+yRJtbW1ysvL06hRo7R7924Fg0GdOHFCwWBQmZmZfW4LAAAAmMzSkfP7779f\nFRUVcrvd6ujo0OzZs3XDDTdo/vz5Wr58uXJyclRYWCin06m8vDxNnjxZwWBQlZWVkqTy8vI+tQUQ\nOzZsWK+6ujd7ta/P55UkuVwpvdo/P3+s3O5pvdoXkeVypajNGa/EqffbXYol/C+skyspye4yAHwJ\nloZzl8ulJ5988gvbq6urv7CttLRUpaWl523Lzs7uc1sA6I7f75fU+3AOAEBvWRrOAcAqbve0Xo9e\nl5XNlCStWLG6P0sCAKBbsXfZOgAAAGAowjkAAABgCKa1AMAA8Oij8+TxNFve77k+z00FslJm5mBV\nVi6yvF8AiCTCOQAMAB5Ps043n5ZcFq/M8Zeb+pxu91rbr6/d2v4AwCKEcwAYKFxJiv/+N+2uwhKd\nz79mdwkAEBHMOQcAAAAMQTgHAAAADEE4BwAAAAxBOAcAAAAMQTgHAAAADEE4BwAAAAxBOAcAAAAM\nwTrnAIDY4vPK/8I6a/v0/+WmSYkW3yTK55WSLO4TQJ8QzgEAMSMzc7At/XrO+Lr6tzooJyXZdswA\neodwDgCIGZWVi2zpt6xspiRpxYrVtvQPIHow5xwAAAAwBOEcAAAAMAThHAAAADAE4RwAAAAwBBeE\nwhgbNqxXXd2bvdrX5/NKklyulC+9b37+WLnd03rVLwAAQH9i5BwDgt/vl9/vt7sMAACAPmHkHMZw\nu6f1egSbZcoAAMBAwMg5AAAAYAjCOQAAAGAIwjkAAABgCMI5AAAAYAjCOQAAAGAIwjkAAABgCMuX\nUnzmmWf0xhtvqKOjQ1OnTlV+fr7mzp0rh8Oh6667TgsWLFBcXJxWrVqlHTt2KD4+XhUVFRo5cqSO\nHj3a57YAAACAqSxNq/v27dNbb72lF154QVVVVfroo4+0ZMkSlZWVacOGDQqFQtq2bZsaGhpUV1en\nzZs3a/ny5XrkkUckqc9tAQAAAJNZGs53796tYcOGadasWXrggQd02223qaGhQfn5+ZKkgoIC7d27\nV/X19Ro3bpwcDoeGDBmiQCAgj8fT57YAAACAySyd1tLS0qITJ07o6aef1rFjxzRz5kyFQiE5HA5J\nksvlUmtrq7xer9LT08P7ndve17bdychIVny8sz8PGRZxOrs+Z2ZlpdpcCQaCaHw/nas5ljidcVHz\nGkXjewqAPSwN5+np6crJyVFCQoJycnKUmJiojz76KPx7n8+ntLQ0paSkyOfznbc9NTX1vDnjvWnb\nnZaWM309RNgkEAhKkpqauv8QBnQnGt9P52qOJYFAMGpeo2h8TwGIrIt9WLd0qGX06NHatWuXQqGQ\nTp06pba2No0dO1b79u2TJNXW1iovL0+jRo3S7t27FQwGdeLECQWDQWVmZio3N7dPbQEAAACTWTpy\n/vWvf1379+/XpEmTFAqFVFlZqSuvvFLz58/X8uXLlZOTo8LCQjmdTuXl5Wny5MkKBoOqrKyUJJWX\nl/epLQAAAGAyy5dSfPDBB7+wrbq6+gvbSktLVVpaet627OzsPrcFAAAATBV7VxABAAAAhiKcAwAA\nAIawfFoLAADRaMOG9aqre7NX+3o8zZKksrKZvdo/P3+s3O5pvdoXQHQhnAMAEGGJiYl2lwAgShDO\nAQDoAbd7GqPXACKOOecAAACAIQjnAAAAgCGY1oJ+9eij88IXPlmprxdb9VZm5mBVVi6ytE8AADBw\nEc7RrzyeZnmam3R5krX9XvaX74ACvibL+vxzu2VdAQCAGEE4R7+7PEmqKBz4KxMsftVvdwkAAGCA\nYc45AAAAYAjCORBDGhsPqbHxkN1lAACAi2BaCxBDtm6tkSTl5t5gcyUAAOBCGDkHYkRj4yEdPtyo\nw4cbGT0HAMBQhHMgRpwbNf/8YwAAYA6mtQAwmh1r59u1br7E2vkAEOsI50CMKCoq1uLFC8OPo4XH\n06zTzU1yuqzrM+Ts+tnSbt26+ZIU8FnaHQDAQIRzIEbk5t6g4cNzw4+jidMlfeVeh91lRNyp6pDd\nJQAAbEY4B2JINI2Y48vx+bxSe7s6n3/N7lKs4WuXL2B3EQDQ/wjnQAyJthFzAABiDeEcAAYAlytF\nbU4p/vvftLsUS3Q+/5pcSSl2lwEA/Y6lFAEAAABDEM4BAAAAQxDOAQAAAEMQzgEAAABDEM4BAAAA\nQxDOAQAAAEMQzgEAAABDEM4BAAAAQ9hyE6Lm5mYVFRXp5z//ueLj4zV37lw5HA5dd911WrBggeLi\n4rRq1Srt2LFD8fHxqqio0MiRI3X06NE+twUQXXw+rwLt0qnqkN2lRFzAJ/kCXrvLAADYyPJw3tHR\nocrKSiUlJUmSlixZorKyMo0ZM0aVlZXatm2bhgwZorq6Om3evFknT55UaWmptmzZ0ue2t99+u9WH\nG3N8Pq/87dLiV/12lxJxf26XEkWQAgAA/cfycL506VJNmTJFa9askSQ1NDQoPz9fklRQUKA9e/Yo\nOztb48aNk8Ph0JAhQxQIBOTxePrclnAORB+XK0VnnW36yr0Ou0uJuFPVIW5JDwAxztJwvnXrVmVm\nZmr8+PHhcB4KheRwdP2n63K51NraKq/Xq/T09PB+57b3tW13MjKSFR/v7LfjjUVpaWk6qzZVFCba\nXUrELX7Vr4S0NGVlpdpdyoDmdMbWdDSnM65X76lYO09S788VAJjM0nC+ZcsWORwOvfnmm3r33XdV\nXl4uj8cT/r3P51NaWppSUlLk8/nO256amnrenPHetO1OS8uZvh5izAsEgnaXYKlAIKimpu4/+KH3\neE/1fL9Yw98/ANHsYoMLlg61PP/886qurlZVVZVGjBihpUuXqqCgQPv27ZMk1dbWKi8vT6NGjdLu\n3bsVDAZ14sQJBYNBZWZmKjc3t09tAQAAAJPZslrLZ5WXl2v+/Plavny5cnJyVFhYKKfTqby8PE2e\nPFnBYFCVlZX90hYAAAAwmW3hvKqqKvy4urr6C78vLS1VaWnpeduys7P73BYAAAAwVexdQQQAAAAY\ninAOAAAAGML2OecAgH7ia1fn869Z26e/o+tn4mXW9utrl1gTHsAARDjvgQ0b1us3v3mlV/sGg/Yu\nb/bZJSW/jDvumCi3e1o/VwMgUjIzB9vSr+dMc1f/VgflpBTbjhkAIolwDgADQGXlIlv6LSubKUla\nsWK1Lf0DwEBDOO8Bt3sao8gAAACIOC4IBQAAAAxBOAcAAAAMQTgHAAAADEE4BwAAAAxBOAcAAAAM\nQTgHAAAADEE4BwAAAAxBOAcAAAAMwU2IgCizYcN61dW92at9fT6vJMnl6t2t1vPzx3JDLgAAIohw\nDsQQv98vqffh3C4Bn3SqOmRZf8Gu06S4RMu6lNR1nEqytk8AgFkI5+h3f26XFr/qt7TPMx1dP5Mv\ns67PP7dLmS7r+jvH7Z7W69HrsrKZkqQVK1b3Z0kRlZk52PI+PWeaJUkZSRb3nWTP8QIAzEE4R7+y\nK1h0eLrClNNlXf+ZLoKUFSorF1neZzR+iAEADAyEc/QrO4KURJgCAAADA6u1AAAAAIYgnAMAAACG\nIJwDAAAAhmDOOWCDRx+dJ89fLmK10rk+z83Rt1Jm5mDbrkkAACBaEM4BG3g8zWpublJysrX9Op1d\nP9vamizt98wZS7sDACBqEc4BmyQnS/fcbXcV1vjFy3ZXAABAdGDOOQAAAGAIRs4BG/h8XrW3x86I\n8pkzUjDotbsMAACMx8g5AAAAYAhGzgEbuFwpiotri6k554MGpdhdBi5iw4b1qqt7s1f79nUFoPz8\nsXK7p/VqXwAYiAjngE3OnLF+WsvZs10/ExKs7ffMGWnQIGv7hDUSExPtLgEABhRLw3lHR4cqKip0\n/PhxnT17VjNnztS1116ruXPnyuFw6LrrrtOCBQsUFxenVatWaceOHYqPj1dFRYVGjhypo0eP9rkt\nYILMzMG29NvW1jXKOWiQtf0PGmTfMaN7bvc0Rq8BwBCWhvOXX35Z6enpeuKJJ9TS0qJ77rlHw4cP\nV1lZmcaMGaPKykpt27ZNQ4YMUV1dnTZv3qyTJ0+qtLRUW7Zs0ZIlS/rU9vbbb7fycIGLsutmPOem\nHqxYsdqW/gEAwKVZGs7vuOMOFRYWhp87nU41NDQoPz9fklRQUKA9e/YoOztb48aNk8Ph0JAhQxQI\nBOTxePrclnAOAAAAk1kazl0ulyTJ6/Xqxz/+scrKyrR06VI5HI7w71tbW+X1epWenn7efq2trQqF\nQn1q252MjGTFxzv77XhhHacCi5+wAAALY0lEQVSza8pSVlaqzZWYLZbO09q1a7Vr165e7XvuIsc5\nc2b1av/x48frX/7lX3q1LwAgtll+QejJkyc1a9Ysud1ufetb39ITTzwR/p3P51NaWppSUlLk8/nO\n256amnrenPHetO1OSwv3GI9WgUBQktTU1P2HsGjXHytrfP/79/Zq/2haWePMmbPh98WXde4ix97u\nf+bM2Zh4LwIAeu9iA2WWhvPTp09r+vTpqqys1NixYyVJubm52rdvn8aMGaPa2lrdfPPNGjp0qJ54\n4gnNmDFDH330kYLBoDIzM/vcFoh1sbSyBhc5AgCikSMUCoWs6mzRokX69a9/rZycnPC2hx9+WIsW\nLVJHR4dycnK0aNEiOZ1OrVy5UrW1tQoGg3rooYeUl5enDz/8UPPnz+9T20thpCt6caEjAACIJhcb\nObc0nJuOcB69COcAACCaXCycs/A3AAAAYAjCOQAAAGAIprV8BtNa7NUfq5D05i6U0bQCCQAAGBiM\nWK0FiJRYWoUEAAAMXIycfwYj5wAAALACF4QCAAAAhiOcAwAAAIYgnAMxpLHxkBobD9ldBgAAuAgu\nCAViyNatNZKk3NwbbK4EAABcCCPnQIxobDykw4cbdfhwI6PnAAAYinAOxIhzo+affwwAAMxBOAcA\nAAAMQTgHYkRRUfEFHwMAAHNwQSgQI3Jzb9Dw4bnhxwAAwDyEcyCGMGIOAIDZHKFQKGR3EaZoamq1\nuwQAAADEgKys1AtuZ845AAAAYAjCOQAAAGAIwjkAAABgCMI5AAAAYAjCOQAAAGAIwjkAAABgCMI5\nAAAAYAjCOQAAAGAIwjkAAABgCMI5AAAAYAjCOQAAAGAIwjkAAABgCMI5AAAAYIh4uwuIpGAwqIUL\nF+r3v/+9EhIStGjRIl199dV2lwUAAABc0IAeOX/99dd19uxZbdq0SXPmzNHjjz9ud0kAAADARQ3o\ncF5fX6/x48dLkv7pn/5Jhw4dsrkiAAAA4OIG9LQWr9erlJSU8HOn06nOzk7Fx1/4sLOyUq0qDQAA\nAPiCAT1ynpKSIp/PF34eDAYvGswBAAAAuw3ocD5q1CjV1tZKkt5++20NGzbM5ooAAACAi3OEQqGQ\n3UVEyrnVWt577z2FQiEtXrxY11xzjd1lAQAAABc0oMM5AAAAEE0G9LQWAAAAIJoQzgEAAABDEM4N\n1tzcrK997Ws6cuSI3aUY7Tvf+Y5KSkpUUlKihx56yO5yjPbMM89o8uTJKioq0ubNm+0ux0hbt24N\nv5+Ki4v1D//wD/r000/tLss4HR0dmjNnjqZMmSK3282/U5dw9uxZzZkzR8XFxZo+fbr++Mc/2l2S\ncQ4cOKCSkhJJ0tGjRzV16lS53W4tWLBAwWDQ5urM8dnzdM7ixYv1wgsv2FSRuT57rt5//31NnTpV\nU6ZM0cKFCxUIBGyu7tII54bq6OhQZWWlkpKS7C7FaH6/X5JUVVWlqqoqLVmyxOaKzLVv3z699dZb\neuGFF1RVVaWPPvrI7pKMVFRUFH4/XX/99Zo3b57S0tLsLss4O3fuVGdnpzZu3KhZs2ZpxYoVdpdk\nrJqaGiUnJ6umpkbz5s3TT3/6U7tLMsratWs1b9688L/nS5YsUVlZmTZs2KBQKKRt27bZXKEZPn+e\nPB6PfvCDH+iNN96wuTLzfP5cLV++XD/5yU+0ceNGtbe3G3/OCOeGWrp0qaZMmaIrrrjC7lKMdvjw\nYbW1tWn69OmaNm2a3n77bbtLMtbu3bs1bNgwzZo1Sw888IBuu+02u0sy2jvvvKP3339fkydPtrsU\nI2VnZysQCCgYDMrr9XIPiUt4//33VVBQIEnKycnhW4bPGTp0qFauXBl+3tDQoPz8fElSQUGB9u7d\na1dpRvn8efL5fCotLdW3v/1tG6sy0+fP1cqVK3XTTTfp7Nmzampq0uDBg22srnuEcwNt3bpVmZmZ\nGj9+vN2lGC8pKUkzZszQs88+q0ceeUT//u//rs7OTrvLMlJLS4sOHTqkJ598MnyuWKzp4p555hnN\nmjXL7jKMlZycrOPHj+vOO+/U/Pnzv/BVO/5qxIgR2r59u0KhkN5++22dOnXK+K/VrVRYWHjeh7tQ\nKCSHwyFJcrlcam1ttas0o3z+PF111VX6x3/8RxsrMtfnz5XT6dTx48c1ceJEtbS0KDs728bqukc4\nN9CWLVu0d+9elZSU6N1331V5ebmamprsLstI2dnZuvvuu+VwOJSdna309HTO1UWkp6dr3LhxSkhI\nUE5OjhITE+XxeOwuy0iffvqpPvjgA9188812l2KsdevWady4cXr11Vf10ksvae7cueGvkHG+7373\nu0pJSdG0adO0fft2XX/99XI6nXaXZay4uL9GE5/Px7Qy9Iu//du/1WuvvaapU6fq8ccft7ucSyKc\nG+j5559XdXW1qqqqNGLECC1dulRZWVl2l2WkF198MfyX7NSpU/J6vZyrixg9erR27dqlUCikU6dO\nqa2tTenp6XaXZaT9+/frlltusbsMo6WlpSk1NVWSdPnll6uzs5PR4It45513NHr0aFVVVekb3/iG\nrrrqKrtLMlpubq727dsnSaqtrVVeXp7NFSHaPfDAA+ELsV0u13kfAE3EJEFEtUmTJumhhx7S1KlT\n5XA4tHjxYua+XsTXv/517d+/X5MmTVIoFFJlZSWjdxfx4Ycf6sorr7S7DKPdf//9qqiokNvtVkdH\nh2bPnq3k5GS7yzLS1VdfrSeffFI///nPlZqaqscee8zukoxWXl6u+fPna/ny5crJyVFhYaHdJSHK\n/fCHP9TcuXN12WWXadCgQVq0aJHdJV0SdwgFAAAADGH2uD4AAAAQQwjnAAAAgCEI5wAAAIAhCOcA\nAACAIQjnAAAAgCEI5wCAfjd37lxt3brV7jIAIOoQzgEAAABDcLcWAEDYsmXL9OqrryojI0NZWVma\nMGGC1qxZo4yMDCUlJWnlypWqqKjQqVOn9PHHH2vs2LHhm+o8/vjj2rFjh6644goFAgHl5+dLkn75\ny1/queeeUzAY1PXXX68FCxYoMTHRzsMEAGMRzgEAkqQ33nhD9fX1euWVV9TW1qZ77rlHEyZM0Icf\nfqj//u//1pVXXqlXXnlFI0aM0M9+9jOdPXtWd911lxoaGnTs2DE1NjbqlVdeUWtrq+6++25J0h/+\n8AfV1NRo48aNSkxM1LJly/Tss8/qRz/6kc1HCwBmIpwDACRJe/fu1Z133qmEhAQlJCToG9/4hiRp\n8ODBuvLKKyVJEydO1MGDB7Vu3Tp98MEH+uSTT3TmzBnV1dXpm9/8pi677DJlZmaqoKBAkrRv3z4d\nPXpUxcXFkqSOjg7l5ubac4AAEAUI5wAASVJcXJyCweAXticlJYUfV1VV6dVXX1VxcbFuueUWvffe\newqFQnI4HAqFQuF28fFd/70EAgHdeeedmjdvniTJ5/MpEAhE+EgAIHpxQSgAQJJ0yy236LXXXtPZ\ns2fl9Xq1Y8cOnThx4rw2e/bs0eTJk3X33XfL7/fr8OHDCgaDGjt2rH7961/r7Nmz+vOf/6xdu3ZJ\nksaMGaP/+7//U3Nzs0KhkBYuXKjnnnvOjsMDgKjAyDkAQJJ022236a233tI999yjyy+/XFdcccUX\nLty87777tHDhQq1Zs0YpKSm68cYbdezYMX3ve9/TO++8o4kTJ+pv/uZvdM0110iShg8frn/7t3/T\nfffdp2AwqBEjRuiHP/yhHYcHAFHBEfrs95AAgJj11ltv6Y9//KPuuecedXR0aPLkyVq8eLGGDx9u\nd2kAEDMI5wAASdInn3yiOXPmqKmpSaFQSN/5znc0Y8YMu8sCgJhCOAcAAAAMwQWhAAAAgCEI5wAA\nAIAhCOcAAACAIQjnAAAAgCEI5wAAAIAhCOcAAACAIf4/+WXnkag88+QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe5eecc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var = 'grade'\n",
    "f, ax = plt.subplots(figsize=(12, 6))\n",
    "fig = sns.boxplot(x=var, y='price', data=XY)\n",
    "fig.axis(ymin=0, ymax=1750000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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V1eULX/jC/1l35plnHtNaAACAavGpUQAAgAKIKwAAgAKIKwAAgAKIKwAAgAL0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\nQAHEFQAAQAHEFQAAQAHEFQAAQAH+P/8Gaf6Rx6I4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xfe40898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var = 'bathrooms'\n",
    "f, ax = plt.subplots(figsize=(14, 6))\n",
    "fig = sns.boxplot(x=var, y=\"price\", data=XY)\n",
    "fig.axis(ymin=0, ymax=3500000);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  5.,  13.,  18.,  12.,   9.,   8.,   8.,   6.,   9.,   1.,   2.,\n",
       "          0.,   3.,   1.,   1.,   1.,   1.,   0.,   0.,   0.,   1.,   0.,\n",
       "          0.,   0.,   1.]),\n",
       " array([  110000.,   201000.,   292000.,   383000.,   474000.,   565000.,\n",
       "          656000.,   747000.,   838000.,   929000.,  1020000.,  1111000.,\n",
       "         1202000.,  1293000.,  1384000.,  1475000.,  1566000.,  1657000.,\n",
       "         1748000.,  1839000.,  1930000.,  2021000.,  2112000.,  2203000.,\n",
       "         2294000.,  2385000.]),\n",
       " <a list of 25 Patch objects>)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OTj3xxBNKT08PW6EAgOgJKSzuueceZWVl+V/HxcUFjXd2durgwYNyu936+te/rs2bN0+s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\nP9ANmpe6A3GKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8d5b2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(XY.price, bins = 25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Immediately, we notice that the distribution is skewed towards cheaper homes. To make the distribution more symmetric, we can use its logarithm:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  1.,   0.,   1.,   0.,   3.,   4.,   5.,   4.,   5.,  12.,   7.,\n",
       "          8.,   6.,   4.,   7.,  10.,   9.,   3.,   2.,   3.,   2.,   2.,\n",
       "          0.,   1.,   1.]),\n",
       " array([ 11.60823564,  11.73129461,  11.85435357,  11.97741253,\n",
       "         12.10047149,  12.22353045,  12.34658941,  12.46964838,\n",
       "         12.59270734,  12.7157663 ,  12.83882526,  12.96188422,\n",
       "         13.08494318,  13.20800214,  13.33106111,  13.45412007,\n",
       "         13.57717903,  13.70023799,  13.82329695,  13.94635591,\n",
       "         14.06941487,  14.19247384,  14.3155328 ,  14.43859176,\n",
       "         14.56165072,  14.68470968]),\n",
       " <a list of 25 Patch objects>)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vL09dXV3JXtawDhw4oE2bNqm5uVlTpkwZ8j4rTxsQaQZLe3H33Xfr4MGDKikpCXtmSyt7\nEWkGC3tx+vRpnT9/XvX19XrllVd07tw5NTQ0DDnG73sxmhn8uBe+iPdnn312+457+fJl9fT0KBAI\nJHlV4dra2tTa2qqWlhbNmDEj7P0WnjYg2gxW9mL16tX666+/JEnZ2dmaMGHoXdnCXkSbwcJe3H//\n/fr666/V0tKibdu26d5779XGjRuHHOP3vRjNDH7ci6TGu7KyUhcvXtSSJUvU3d2tkpISbdiwQY2N\njb76ziz93/9aNTQ03P75veM4+uCDDyT9d47CwkJNnDhRy5cv1+bNm/3xG+n/MZoZLOyFJL388suq\nrq6W4zj68ssvtWHDBkl29kKKPoOVvRiJpb0YiZ/3gofHA4BBvvixCQAgNsQbAAwi3gBgEPEGAIOI\nNwAYRLwBwCDiDQAG/QcQbF40mGdPqAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xfedef28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.log(XY.price), bins = 25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc3a0c50>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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t6JY/l+BgyfVTLUODtdj4dLrPNv50c1WWauQe+ZVXmwABm6a/plBF6tZ6oRQX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x100f12b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.stripplot(x='bedrooms', y='price',data=XY)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph indicates that the relation of price with bedrooms. Price goes up with number of bedrooms upto 6 and then comes down as no of bedrooms increase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x100fc7b8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5r8VYYVJfA0maeFvZyTmxlXkZkVilghEBNtR81GJsc+BTA0madElIbjGW4tHL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\nZY59MWAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10106198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.stripplot(x='grade', y='price',data=XY, size=5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Price of the house is dependent on grade of the house, higher the grade higher the price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10381668>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aYLMO4BRQiLqm80d3/vLOH/JGRMqLtI++kvvrM2AUlJWRzkCJwGZN5BQikcZw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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x102bf978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=XY\n",
    "df['age_of_renov'] = 100\n",
    "df.loc[df['yr_renovated'] != 0,'age_of_renov'] = 2015-df.loc[df['yr_renovated'] != 0,'yr_renovated']\n",
    "\n",
    "sns.regplot(x='age_of_renov',y='price',data=XY)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the relation between the price and age of renovation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x106edf28>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RR/Daa6/F7VwytSVj0zLGGFweCXaXOKFKxwpjOH+1F3X1rbjQHJpfMenVWLao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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x101f7f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x='sqft_living',y='price',data=XY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x106edf98>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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noZssBoIJJgaKEOdf1Ya8vB1oEVnplNFhI4rQ62pI9IgpWXtRQs91D91XEs0r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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107f41d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x='sqft_basement',y='price',data=XY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1090eda0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Z04rzsbjGGBNElMoIi4dhMDyJfyVTrMyYj2sXTa05jKmGgg0Zl2zfEiA6WDrc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\nhBBCFEfBhhBCiOIo2BBCCFHc/wcYlx14Kqu8qwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x101f7f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x='sqft_above',y='price',data=XY)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IIyIiIiIiIiIiUgEO5BEREREREREREakAB/KIiIiIiIiIiIhUgAN5REREREREREREKsCB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9snaGx34z9e/divH5KWYsSm6kLsP/bpB8F625Ca1DNfFr3fD7V8Vo3BsnD/NkiEDdtJW0RRwsrS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0BsnM/ebYpcRybS0WAMjI+M3XrwEA0V7j7pu7MR6YE11/rm0LPfXmLrtkTsuZb7XkMRuxGtWe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ak/dhvdHtxbadBzEwNAIAaGjtxjuHzmJLzYpg+BvdXuw+1Ix9ta7gdI2uHrz3cQtWLnSi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\niIhIBbiQR0REREREREREpAL/B0/BAT8qTaFuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x147580b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set()\n",
    "cols = ['price', 'sqft_living', 'grade', 'sqft_above', 'sqft_living15', 'bedrooms','bathrooms']\n",
    "sns.pairplot(XY[cols], size = 2.5,  palette='afmhot')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12ac5b70>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qk5+fz/Dhw8nLyyM8PByVSsXo0aMZM2YM9erVo2bNmo9tq0slaSGEqGjmNgFj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a54cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_matrix = XY.corr()\n",
    "\n",
    "sns.heatmap(corr_matrix,\n",
    "            xticklabels=corr_matrix.columns.values,\n",
    "            yticklabels=corr_matrix.columns.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building predictive models\n",
    "The initial classifier in your sandbox.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = X.values\n",
    "y=numerical_target \n",
    "\n",
    "X_test = X_test.values\n",
    "y_test=Y_test['price']\n",
    "\n",
    "X_valid = X_valid.values\n",
    "y_valid=Y_valid['price']\n",
    "\n",
    "from sklearn.base import BaseEstimator\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import datasets, linear_model\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "class Classifier(BaseEstimator):\n",
    "    def __init__(self):\n",
    "        self.clf = linear_model.LinearRegression()\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        self.clf.fit(X, y)\n",
    "\n",
    "    def predict(self, X):\n",
    "        return self.clf.predict(X)\n",
    "\n",
    "    def predict_proba(self, X):\n",
    "        return self.clf.predict_proba(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 1 R2 score metric =  0.663806315496\n",
      "Fold 2 R2 score metric =  0.517664725775\n",
      "Fold 3 R2 score metric =  0.133393254025\n",
      "Average score =  0.438288098432\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import StratifiedShuffleSplit\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix, mean_squared_error\n",
    "from sklearn import datasets, linear_model\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.model_selection import KFold # import KFold\n",
    "from numpy import zeros, mean\n",
    "\n",
    "kf = KFold(n_splits=3,random_state=1) # Define the split - into 2 folds \n",
    "i=0\n",
    "scores = zeros(3)\n",
    "\n",
    "    \n",
    "for idx_t, idx_v in kf.split(X):\n",
    "    Xtr = X[idx_t]\n",
    "    Ytr = y[idx_t]\n",
    "   \n",
    "    Xva = X[idx_v]\n",
    "    Yva = y[idx_v]\n",
    "    clf = Classifier()\n",
    "    clf.fit(Xtr, Ytr)\n",
    "    Y_predict = clf.predict(Xva)\n",
    "    scores[i] =r2_score(Yva,Y_predict )\n",
    "    print 'Fold', i+1, 'R2 score metric = ', scores[i]\n",
    "    i=i+1\n",
    "print 'Average score = ', mean(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.66380632  0.51766473  0.13339325]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import StratifiedShuffleSplit\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# This will perform 2-fold cross-validation\n",
    "# for 0.18 version or newer, use:\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "# for pre-0.18 versions of scikit, use:\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "\n",
    "\n",
    "scores = cross_val_score(linear_model.LinearRegression(), X, y, cv=3)\n",
    "print scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "outdir = 'sample_result_submission'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from model import model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model reloaded from: sample_code_submission/houseprice_model.pickle\n"
     ]
    }
   ],
   "source": [
    "M = model()\n",
    "trained_model_name = model_dir + basename\n",
    "M = M.load(trained_model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PREDICT: dim(X)= [100, 18]\n",
      "PREDICT: dim(y)= [100, 1]\n",
      "PREDICT: dim(X)= [50, 18]\n",
      "PREDICT: dim(y)= [50, 1]\n",
      "PREDICT: dim(X)= [50, 18]\n",
      "PREDICT: dim(y)= [50, 1]\n"
     ]
    }
   ],
   "source": [
    "if not(M.is_trained):\n",
    "    X_train = X\n",
    "    Y_train = numerical_target\n",
    "    M.fit(X_train, Y_train)  \n",
    "Y_hat_train = M.predict(X) # Optional, not really needed to test on taining examples\n",
    "Y_hat_valid = M.predict(X_valid)\n",
    "Y_hat_test = M.predict(X_test)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the trained model (will be ready to reload next time around) and save the prediction results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "M.save(trained_model_name)                 \n",
    "result_name = 'sample_result_submission/' + basename\n",
    "#import os\n",
    "#os.write(result_name + '_valid.predict', Y_hat_valid)\n",
    "#os.write(result_name + '_test.predict', Y_hat_test)\n",
    "#!ls $result_name*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 3: Making a submission\n",
    "\n",
    "## Unit testing\n",
    "\n",
    "It is <b><span style=\"color:red\">important that you test your submission files before submitting them</span></b>. All you have to do to make a submission is modify the file <code>model.py</code> in the <code>sample_code_submission/</code> directory, then run this test to make sure everything works fine. This is the actual program that will be run on the server to test your submission. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "outdir = 'sample_result_submission'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using input_dir: /Users/belrhalmia/Downloads/houseprice_starting_kit/sample_data\n",
      "Using output_dir: /Users/belrhalmia/Downloads/houseprice_starting_kit/sample_result_submission\n",
      "Using program_dir: /Users/belrhalmia/Downloads/houseprice_starting_kit/ingestion_program\n",
      "Using submission_dir: /Users/belrhalmia/Downloads/houseprice_starting_kit/sample_code_submission\n",
      "\n",
      "========== Ingestion program version 6 ==========\n",
      "\n",
      "************************************************\n",
      "******** Processing dataset Houseprice ********\n",
      "************************************************\n",
      "========= Reading and converting data ==========\n",
      "Info file NOT found : /Users/belrhalmia/Downloads/houseprice_starting_kit/sample_data/houseprice_public.info\n",
      "========= Reading /Users/belrhalmia/Downloads/houseprice_starting_kit/sample_data/houseprice_feat.type\n",
      "[+] Success in  0.00 sec\n",
      "========= Reading /Users/belrhalmia/Downloads/houseprice_starting_kit/sample_data/houseprice_train.data\n",
      "[+] Success in  0.00 sec\n",
      "========= Reading /Users/belrhalmia/Downloads/houseprice_starting_kit/sample_data/houseprice_train.solution\n",
      "[+] Success in  0.00 sec\n",
      "========= Reading /Users/belrhalmia/Downloads/houseprice_starting_kit/sample_data/houseprice_valid.data\n",
      "[+] Success in  0.00 sec\n",
      "========= Reading /Users/belrhalmia/Downloads/houseprice_starting_kit/sample_data/houseprice_test.data\n",
      "[+] Success in  0.00 sec\n",
      "DataManager : houseprice\n",
      "info:\n",
      "\ttask = regression\n",
      "\tvalid_num = 50\n",
      "\thas_categorical = 0\n",
      "\tfeat_type = Mixed\n",
      "\tformat = dense\n",
      "\tmetric = r2_metric\n",
      "\ttarget_type = Numerical\n",
      "\ttest_num = 50\n",
      "\tlabel_num = 0\n",
      "\ttarget_num = 1\n",
      "\ttrain_num = 100\n",
      "\thas_missing = 0\n",
      "\tusage = No Info File\n",
      "\tfeat_num = 18\n",
      "\ttime_budget = 600\n",
      "\tis_sparse = 0\n",
      "\tname = houseprice\n",
      "data:\n",
      "\tX_train = array(100, 18)\n",
      "\tY_train = array(100,)\n",
      "\tX_valid = array(50, 18)\n",
      "\tX_test = array(50, 18)\n",
      "feat_type:\tarray(18,)\n",
      "feat_idx:\tarray(18,)\n",
      "\n",
      "[+] Size of uploaded data  72.00 bytes\n",
      "[+] Cumulated time budget (all tasks so far)  600.00 sec\n",
      "[+] Time budget for this task 600.00 sec\n",
      "[+] Remaining time after reading data 600.00 sec\n",
      "======== Creating model ==========\n",
      "**********************************************************\n",
      "****** Attempting to reload model to avoid training ******\n",
      "**********************************************************\n",
      "Model reloaded from: /Users/belrhalmia/Downloads/houseprice_starting_kit/sample_code_submission/houseprice_model.pickle\n",
      "[+] Model reloaded, no need to train!\n",
      "PREDICT: dim(X)= [100, 18]\n",
      "PREDICT: dim(y)= [100, 1]\n",
      "PREDICT: dim(X)= [50, 18]\n",
      "PREDICT: dim(y)= [50, 1]\n",
      "PREDICT: dim(X)= [50, 18]\n",
      "PREDICT: dim(y)= [50, 1]\n",
      "[+] Prediction success, time spent so far  0.00 sec\n",
      "======== Saving results to: /Users/belrhalmia/Downloads/houseprice_starting_kit/sample_result_submission\n",
      "[+] Results saved, time spent so far  0.00 sec\n",
      "[+] End cycle, time left 600.00 sec\n",
      "[+] Done\n",
      "[+] Overall time spent  1.30 sec ::  Overall time budget 600.00 sec\n"
     ]
    }
   ],
   "source": [
    "!python $problem_dir/ingestion.py $datadir $outdir $problem_dir $model_dir"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Making submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using input_dir: /Users/belrhalmia/Downloads/houseprice_starting_kit/sample_data\n",
      "Using output_dir: /Users/belrhalmia/Downloads/houseprice_starting_kit/sample_result_submission\n",
      "Using program_dir: /Users/belrhalmia/Downloads/houseprice_starting_kit/ingestion_program\n",
      "Using submission_dir: /Users/belrhalmia/Downloads/houseprice_starting_kit/sample_code_submission\n",
      "\n",
      "========== Ingestion program version 6 ==========\n",
      "\n",
      "************************************************\n",
      "******** Processing dataset Houseprice ********\n",
      "************************************************\n",
      "========= Reading and converting data ==========\n",
      "Info file NOT found : /Users/belrhalmia/Downloads/houseprice_starting_kit/sample_data/houseprice_public.info\n",
      "========= Reading /Users/belrhalmia/Downloads/houseprice_starting_kit/sample_data/houseprice_feat.type\n",
      "[+] Success in  0.00 sec\n",
      "========= Reading /Users/belrhalmia/Downloads/houseprice_starting_kit/sample_data/houseprice_train.data\n",
      "[+] Success in  0.00 sec\n",
      "========= Reading /Users/belrhalmia/Downloads/houseprice_starting_kit/sample_data/houseprice_train.solution\n",
      "[+] Success in  0.00 sec\n",
      "========= Reading /Users/belrhalmia/Downloads/houseprice_starting_kit/sample_data/houseprice_valid.data\n",
      "[+] Success in  0.00 sec\n",
      "========= Reading /Users/belrhalmia/Downloads/houseprice_starting_kit/sample_data/houseprice_test.data\n",
      "[+] Success in  0.00 sec\n",
      "DataManager : houseprice\n",
      "info:\n",
      "\ttask = regression\n",
      "\tvalid_num = 50\n",
      "\thas_categorical = 0\n",
      "\tfeat_type = Mixed\n",
      "\tformat = dense\n",
      "\tmetric = r2_metric\n",
      "\ttarget_type = Numerical\n",
      "\ttest_num = 50\n",
      "\tlabel_num = 0\n",
      "\ttarget_num = 1\n",
      "\ttrain_num = 100\n",
      "\thas_missing = 0\n",
      "\tusage = No Info File\n",
      "\tfeat_num = 18\n",
      "\ttime_budget = 600\n",
      "\tis_sparse = 0\n",
      "\tname = houseprice\n",
      "data:\n",
      "\tX_train = array(100, 18)\n",
      "\tY_train = array(100,)\n",
      "\tX_valid = array(50, 18)\n",
      "\tX_test = array(50, 18)\n",
      "feat_type:\tarray(18,)\n",
      "feat_idx:\tarray(18,)\n",
      "\n",
      "[+] Size of uploaded data  72.00 bytes\n",
      "[+] Cumulated time budget (all tasks so far)  600.00 sec\n",
      "[+] Time budget for this task 600.00 sec\n",
      "[+] Remaining time after reading data 600.00 sec\n",
      "======== Creating model ==========\n",
      "**********************************************************\n",
      "****** Attempting to reload model to avoid training ******\n",
      "**********************************************************\n",
      "Model reloaded from: /Users/belrhalmia/Downloads/houseprice_starting_kit/sample_code_submission/houseprice_model.pickle\n",
      "[+] Model reloaded, no need to train!\n",
      "PREDICT: dim(X)= [100, 18]\n",
      "PREDICT: dim(y)= [100, 1]\n",
      "PREDICT: dim(X)= [50, 18]\n",
      "PREDICT: dim(y)= [50, 1]\n",
      "PREDICT: dim(X)= [50, 18]\n",
      "PREDICT: dim(y)= [50, 1]\n",
      "[+] Prediction success, time spent so far  0.00 sec\n",
      "======== Saving results to: /Users/belrhalmia/Downloads/houseprice_starting_kit/sample_result_submission\n",
      "[+] Results saved, time spent so far  0.01 sec\n",
      "[+] End cycle, time left 599.99 sec\n",
      "[+] Done\n",
      "[+] Overall time spent  1.31 sec ::  Overall time budget 600.00 sec\n"
     ]
    }
   ],
   "source": [
    "outdir = 'sample_result_submission'\n",
    "!python2 $problem_dir/ingestion.py $datadir $outdir $problem_dir $model_dir"
   ]
  }
 ],
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