Supervised Learning - Linear Regresion.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import Series, DataFrame\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "boston = load_boston()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Boston House Prices dataset\n",
      "\n",
      "Notes\n",
      "------\n",
      "Data Set Characteristics:  \n",
      "\n",
      "    :Number of Instances: 506 \n",
      "\n",
      "    :Number of Attributes: 13 numeric/categorical predictive\n",
      "    \n",
      "    :Median Value (attribute 14) is usually the target\n",
      "\n",
      "    :Attribute Information (in order):\n",
      "        - CRIM     per capita crime rate by town\n",
      "        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\n",
      "        - INDUS    proportion of non-retail business acres per town\n",
      "        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
      "        - NOX      nitric oxides concentration (parts per 10 million)\n",
      "        - RM       average number of rooms per dwelling\n",
      "        - AGE      proportion of owner-occupied units built prior to 1940\n",
      "        - DIS      weighted distances to five Boston employment centres\n",
      "        - RAD      index of accessibility to radial highways\n",
      "        - TAX      full-value property-tax rate per $10,000\n",
      "        - PTRATIO  pupil-teacher ratio by town\n",
      "        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n",
      "        - LSTAT    % lower status of the population\n",
      "        - MEDV     Median value of owner-occupied homes in $1000's\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Creator: Harrison, D. and Rubinfeld, D.L.\n",
      "\n",
      "This is a copy of UCI ML housing dataset.\n",
      "http://archive.ics.uci.edu/ml/datasets/Housing\n",
      "\n",
      "\n",
      "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
      "\n",
      "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n",
      "prices and the demand for clean air', J. Environ. Economics & Management,\n",
      "vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n",
      "...', Wiley, 1980.   N.B. Various transformations are used in the table on\n",
      "pages 244-261 of the latter.\n",
      "\n",
      "The Boston house-price data has been used in many machine learning papers that address regression\n",
      "problems.   \n",
      "     \n",
      "**References**\n",
      "\n",
      "   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n",
      "   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n",
      "   - many more! (see http://archive.ics.uci.edu/ml/datasets/Housing)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print (boston.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10a81cf28>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAERCAYAAAB2CKBkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a876908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(boston.target,bins=50)\n",
    "\n",
    "plt.xlabel('Prices in $1000s')\n",
    "plt.ylabel('Number of houses')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10ab14828>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAERCAYAAAB2CKBkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJztvXt4FOXZ+P8BDWytCB6ofa1Wi8k+tlaqiWCNjRFpqYpW\nqoh5rUiob36CNNEWaxEPiCdQXimGStLGQxBLF8IhHjgUxDamoV/RUCtieUo80FqtBRug+mbDAvP7\n45nJnje7STa7m70/17VXdmdnnrlnMjv3PPexn2VZCIIgCNlL/1QLIAiCIKQWUQSCIAhZjigCQRCE\nLEcUgSAIQpYjikAQBCHLEUUgCIKQ5RyZzMGVUncAVwA5wC+AJqAWOAy8BUzTWkv8qiAIQgpJ2oxA\nKXURcL7WuhC4CBgGPArM1FpfCPQDrkzW/gVBEIT4SKZpaAywTSlVD7wAPA8UaK1fsb9fB3w7ifsX\nBEEQ4iCZpqGhwCnA5ZjZwAuYWYDDp8DgJO5fEARBiINkKoI9wF+01geBvyqlvMCXAr4fBOxN4v4F\nQRCEOEimIvgDcAswXyl1EnAUsEkpVay1bgAuBTZ1Nkhzc7M4kwVBELpAQUFBv87XSqIi0FqvUUpd\nqJTagvFF3Ay8D9QopQYAbwMr4hmroKAgWWImnebm5oyVP5NlB5E/1Yj8qaW5uTnudZMaPqq1/lmE\nxRclc5+CIAhCYkhCmSAIQpYjikAQBCHLEUUgCIKQ5YgiEARByHJEEQiCIGQ5oggEQRCyHFEEgiAI\nWY4oAkEQhCxHFIEgCEKWI4pAEAQhyxFFIAiCkOWIIhAEQchyRBEIgiBkOaIIBEEQshxRBIIgCFmO\nKAJBEIQsRxSBIAhCliOKQBAEIcsRRSAIgpDliCIQBEHIckQRCIIgZDmiCARBELIcUQSCIAhZjigC\nQRCELEcUgSAIQpYjikAQBCHLEUUgCIKQ5YgiEARByHJEEQiCIGQ5RyZ7B0qprcA+++O7wBygFjgM\nvAVM01pbyZZDEARBiExSFYFSygWgtR4VsOx5YKbW+hWlVBVwJVCfTDkEQRCE6CR7RvAN4Cil1G/t\nfd0J5GutX7G/XweMQRRB2uD1eqmt3QTA8OHHplia2ATKWlo6GpfLlZIxkjGu1+tlxYpXee21f6aN\nXM62Pt8BoB9gAf3w+Q7w6qs76d//CKqqpjBkyBAA2tvbqa5eE/e+QmUDun0Oa2rWs3nzDgoLFWVl\nl/bYeexzWJaVtJfb7f662+2+0X6f53a733G73R8EfH+x2+1eEmuM119/3cpkMkn+trY2q7j4SQsO\nWHDAys9fYLW1taVarIiEylpc/GSYrJ2d+3jGSJZsmSaXf9v9FtRE+GvGHDz4Aau1tdVqa2uz8vMX\nxL2vUNmKin5lFRVVd+scFhX9qmN7qLGKiqoTGiOTfruRsOWP616dbEUwwO12uwI+b3G73b6Az1e6\n3e6FscboI/+MjKCq6kX7R2PZr3arqurFVIsVkXhk7ezcJ+t4uztuOsrl3zbaX/+YJSVzEt5XpPWh\nvkfPIaxOaIxM+u1GIhFFkGzT0GRgODBNKXUSMAjYoJQq1lo3AJcCmzobpLm5OblSJplMkX/Xrl0R\nl6Wj/PHKGkv2ZB1vd8dNR7kibRuNf//73wnvK57xu3sOEx0DMue3223i1Rhdebnd7iPdbvcSt9v9\niv36pm0i+r3b7d7sdrufcLvd/WKN0Ue0ckbgn563W9CeIaYhI2v3TEPRx0iWbJkml3/bfbYpKPSv\nGTPcNBTfvkJl85uG9lmw2srNvcNqbW1N6FiNaajdfolpKNarn2Wld+Rmc3OzVVBQkGoxukxzczOZ\nJH+os7iwsDDFEkWnM8dnPOc+nZ3Fs2c/xamnnpo2cgU6i30+H6++uhOA/PxT2bp1V5izePPmzbz5\nZmvc+wqVzev1MnJkDTt33gpAcfES1q+/Lm6Zu+sszrTfbii2/P3iWjlejZGqVx/RyhlJJstuWSJ/\nsojX6Ryv/G1tbVZV1YtWVdWLQeOk2meVruc/XhKZEUhmsSAICVFbu4mGholADpBDQ8P1HU/yieL1\nernkkqVMnTqGqVPHcMklS/F6vT0qr9A5oggEQUgZsZRKaeloiouXAAeAAxQXP9uRXyD0LKIIBEFI\niN66QbtcLtavv46qqo1UVW1MyD8gJEbSaw0JgtC3cG7QtbUbASgt7foNurR0NB7PEhoargewlcp1\nQfuaMmVs94UWYiKKQBCEhOmpG3RPKhWh64giEAQhjGSF1UZCnvpTjygCQRCCcCJ5jBMXPJ7E4veF\nzEOcxYIgBNGT4aFCZiCKQBCEDrxeLw0N24C1gMTzZwtiGhIEAQg0CU23lywGJlBcvCIokkfoe8iM\nQBAEINwkBDdQUrIoyD/g9Xqprl5DdfUayQDuQ8iMQBCEqBQXnxWkBMSJ3DeRGYEgCEDnGcPJdCLL\nTCO1yIxAEAQgdcldMtNIPTIjEAShAye5a8qUsWE34mTVGJJw1dQjMwJByBK6my0s5SD6LjIjEIQs\nIJ3r/ku56dQjMwJByAL85pdDwCYaGo6jpmYd5eXfj3uMZNnyZaaRekQRCELW4AXqAHMjv//+BwDi\n7uUbbMvHtuVv7LEqpFJ4LnWIaUgQsoDS0tHk5s7BKAHjlN29+04qKvqnlZlISA2iCAQhC3C5XFRU\njIzwzZFxR+kky5YvOQSpR0xDgpAllJVdwsqV/m5g8Cxgagj5fD6qq9cA0SOKkmHLlxyC9EBmBIKQ\nJTg38srKteTm3guMB/pTVLSYurqP44ooipVn0BUkhyA9kBmBIGQRLpeL8vJxlJVd0nHD9flOoKLi\ncpLhBBYyA5kRCEIWEvhkn5MzIGVyRPI7lJRcID6DXkZmBIKQBvRmj+BQSktH4/H4fQfGCdw7/QdC\n/Q4lJVcxbtwq8Rn0MqIIBCHFRHOY9hapTugKzCGorl6TtFwFITqiCAQhxURL1Box4ou9JoMkdGU3\n4iMQBCFtkLpDqSHpMwKl1BeAZmA0cBiotf++BUzTWlvJlkEQ0ploNvrt27enWLLeJ9VmqmwlqYpA\nKZUD/BL4DOgHzAdmaq1fUUpVAVcC9cmUQRDSHbn5BSNmqt6nU0WglDoeyNdab1RKzQTOAWZprd+O\nY/x5QBVwh/05X2v9iv1+HTAGUQSCkPY3v1RGNQnJJx4fwW+AM5RS38akIr4AVHe2kVKqFNittd5g\nL+pnvxw+BQYnJK0gCL1OOvcyEHqGfpYV20SvlHpNaz1CKbUQaNFaP6aUel1rfW4n2zUAlv06G/gr\ncI7WeoD9/ZXAt7XW5bHGaW5uFh+CIKSQFSteZe7cMpyoJjjAjBlPMH78eakUS4iDgoKCfp2vFZ+P\noJ9S6lxgHHCRUurseLbTWhc775VSvwOmAPOUUsVa6wbgUiCuoiIFBQXxrJaWNDc3Z6z8mSw7iPw9\nxWuv/TNs2amnntqpbOkif1fpC/LHSzymoZ8BjwCPaq3fARYBP+mCXBYwHZitlNqMUSYrujCOIAi9\nSHdCOqXEdGYQz4zgr8CdwGGl1Mla68JEd6K1HhXw8aJEtxcEIXV0Naqpvb1dSkxnCFEVgVJKAYuB\nE4CP7MX/pZRqAyZqrd/oBfkEQUgDuhLV9MILb9DQ4PctSLmI9CXWjGApcKvWujFwoVLqW8ATQExn\nsSAIPY+EcQrJIJaP4HOhSgBAa/0HQK4+QehlMi2Mc8yYr5KbOwuTKrRfykWkMbFmBM1KqUXArwkw\nDQE3AK8nWzBByEYCn/iHDz826DtTnO4awKTmNDSMp7Z2U1qaWrxeL7fd9iYtLbMByMtbQH19mcxg\n0pRYiuBGoAK4HzgJkwz2D2AtsDD5oglCdrF3715GjHiElpbzgIvJz3+Kpqb8jpunz3cAWAZMsrdY\njM93fIqkjU1t7Sa2br0Zxz+wc+cteDziH0hXoioCrfUB4H+VUguAoZhCcZ9orQ/2lnCCkC14vV5G\njqzpeIKGJWzdOjnkib8fRgk4iV03YJ7LBKF7RPURKKVOVEp5gE+ALRhz0CdKqReUUl/uLQEFIRuo\nrd3Ezp234jRxh+uBl4PWycnJCdtu8eJNLFxYn3a+gtLS0eTnL0LKSWcGsZzFy4EXgeO11qdorU8B\njgc8GL+BIGQcmZTgdPLJrwTdPEtLR1NUVItzc4VHaG5+iIqKsYwZsyStjsflcrFw4QiqqjZSVbVR\n8gfSnFiKYKjW+tlAU5DW+qDW+tfAsTG2E4S0JJ2jbkKzd/PyHuOZZ66IcPM8jCnqOw+4BTN72EBj\n41BqatYB6aPsBg4cyJQpY5kyZawogTQnlrP4PaXU7USOGnon2YIJQk8TrSVkOjgww7N3y8Ma09TW\nbqKx8YeYqCHn+WwpYDJ3Fy5cwMSJe6X5u5AwsWYEPwC+DDQC/we02e+/BJQmXTJByDJcLleHKai2\ndhPt7e1R1hwN/BN4CKMEjF9h585buOmmxwOUXY6t7OKq7ShkMbGihvYCP+pFWQQhqURrCdmbxMoM\ndkxXztN8fv6ioPDRYPn/m+OP/zGffBI8/u9+937S5RT6HrFqDX0euBu4BjgZY5z8EBOvdpfWel+v\nSCgIPUSqW0KG3uhDzTahpqutW6cyefJ8nn76VlwuV5D8Pt8BfL7vMnfuA+zefae9h2fZvftB8vIW\nsHPnLUDXlF1ncgp9j1g+gl9jQkaLMfNQ8PsIfgNcllzRBKHnSWVLyM58FD6fL2wbjyePjz5a2nEj\ndsxHY8YsobGxFNPWYxaQj4nh2MyUKV/mqKO6ruzS2ZciJIdYikBprceFLPs78KBSanukDQRB6A4W\npuDvDfbnZ4Djw27ENTXrbSXg5BzcgWkNbtqE1NfXsmHDlfIEL8RNLGfxbqXUBKVUxzpKqX5Kqf8G\n/pV80QShb9FZg5ecnAHAtcBG+zUBGBA2zubNO0KWvIxRAkYxNDZO6paDuDuNaITMJNaM4HpMN7Ia\npdR+e9kxmMihSVG3EgQhIp35KIwzeGmHM9vMCCaE2fkLCxUez2KMongZk/c5Gn/pieTKKfQ9YkUN\n/Q24XCmVg2lO0w/YrbUON2QKghAXsXwUwc5gH3/722ecfnpj2I24rOxSli17gqYmxxw0lmOOeZj9\n+ysAV49EQ6XSlyL0PvE0offhTygTBCGJOM7g2tpNHHlkTsTQTZfLxbXXnkxT01icWcD+/RXk599B\nv379+d73vhW0/t69e5k6tRqAqqopDBkypFeORcgcYoWPTsJ4r/pF+NrSWj+TNKkEoQ8TK0Y/uBT1\nRP74RxMxBCaax5Si7mf7CZwndi+wnK1bFwDQ3LyY+vrFbNgwCa/Xy2mnPc6+fbcDsG7dI7z//jRR\nBkIQsWYEFwPjMcXnIiGKQBASJFaMfqRS1A0N46mpWcfKla12UxqnH8FoBg9+hH37fgo8j6kUvwHj\nK7iBxsa1dhjoNlsJmJnDvn0/ZerU+fzmNzN698CFtCaWj2CSUuo44A9a6yd7USZByBjiycANXMfn\n80WN0Q8uRQ0mXmMtmzdrGhqmY270Tj+CHPbtK+eaax7m978/yO7dd9nbLME8vwlC/MQKHwW4Cak0\nKggRiaeaaeg6lZVbEtpHbu4WRowYhkno34YxAzm4OOKIHFsJBPYxmENR0ceUlo6mqmoKgwc/ghMK\nOnjwPKqqpnT9oIU+SUxFoLX+UGv9v70ljCBkEsEZuJELvIWu09Iyg6FDHyCw3HRJyQVAePz+Kac8\nTGNjBfX1+zCJ/NOBx4D9OPH9hYVnhMk1YcLRbNgwCZfLxZAhQ3j//WmUlMynpGS++AeEiMRyFhcA\ng7TWv7eTyn6CKTfxF+AhuyidIAgJ4WL3bjdHHlnBwYOXsnPnFMaNq+vwEwTG7w8fPpr6+uaALGKA\n27n66jnk5AyksPAMJk68iJUrgwvpLV48PchE5XK5KC4+q+O9IIQSUREopc4FVgLT7EW3AFcAc4FR\nmDpEEmQsZDWlpaNZuvQpGhtPBLDNMZPC1gmseArPAsdx8OBCIvkJAuP3m5ubI+73z39up6VlJh4P\nrFy5hPr6q/B4gpO/HL+Ez+ejru5ju4+BFJATIhPNNDQH2AEUKKVmAeXA+8BITG+CkUqpe3pFQkFI\na/pjnonGEunn5Dzll5TMB17AuNycEhFeYA2w1g4LDSdS57KWljsINEd5PE1BncD27t3LWWfdx9Sp\nB6moGE1j4xHAIaQ/gRCNaIqgCWMCehrYjJk53APUAqsxV/DiXpBPENIW0zGslMAaP5MnLwhzGLtc\nLqqqpuBybQMux0y078XMDsYAY6mr2xOxraSjSJzev+XluUD0p/ngENTLgDpMKQq5+QvRiaYIfgF8\nB2MeWgpM11rvAs7DPNYstD8LghCAx/PVsOghr9fL1KnVeL13YpTGIOAcAkNBQwvFeb1eVqx4lerq\nNQAdT/xlZZfGLAgXHILqRBG9DPgiri8IEMVHoLXeo5Q6BzgT+EBrvdv+6g3gCq31n+MZXCl1BFAD\nuDFZylOAdszM4jDwFjBNa2115yAEIRVEtv9fR0ND/w6bvz+B7KshW0fP5fRvUwYYu77xAzQBRPQJ\nxCI391UqKkaSk7NRCsgJEYmVUHYA+FPIsr8mOP7lwGGt9beUUsWYJqsAM7XWryilqoArgfoExxWE\nlOOYbSZPno/H81XgKowJxofPZ55tamrW0dBwPOY56ClgMuBl2LDN9O+vaWn5MRDcSSxSY5gRI+7t\nyDiO5fANVU55eY+xZcvPJGRUiElnCWXdQmv9HCYpDeA0oBUo0Fq/Yi9bB3w7mTIIQrIpLDyDYcOa\nMNVY/Db/vXv3snBhC8ZW7ziTlzN06KO8++6DtLTczNChP+XRR58LKjPR0LANk0DmNy+Z2kOdN6QP\n9Sm8+Wa5KAGhUzqtPtpdtNaHlFK1wDhM/+PvBHz9KTA42TIIQiA91Zh97969jBxZY9vk+2Fu+OYp\nvrFxElOnzmfnzun4cwAmMXToNHbvXoRzU9+9ex5VVfdy881XBIw33V7f9BzIy/slO3fGnw0sJaSF\nROlUESiljgV+AByHvxKppbW+L96daK1LlVInAlsIDnkYBHSamBYtnjpTyGT5M1l2CJe/vb2d8vLX\n2Lr1ZgBqahaxcOEIBg4cmNC47e3tXHvtCj744GHMTT38p/Tvf/87bNk3vnEML70UvKylZSR33/1L\nPJ5dAeMB3MCYMXcxY8al3HbbU2zdOhWA/Pwqhg8fkRH/m0yQMRaZLn+8xDMjqMPcrN/CGDohcmnq\nMJRSE4GTtdZzgDZMMPPrSqlirXUDpvN2p3FtBQUF8ewuLWlubs5Y+TNZdogsf3X1GlsJmJvt1q1T\nefPN+BuzO7OJhoZtfPDB+QHfXADMB/w2/2XLZvK979XS2GiSzIqKFlNXdzcjRy5g585b7O2eBcbz\nwQeL+OCD4rD9HXfccWj9f2zaNCnAQXxTRjh8++L1k0kkosTiUQQnaq27asdfAdQqpRowv7xbMNk0\nNUqpAcDb9jqCkNZ4vV5qataxcGGLbQoag0mzeQr4b8xlfDPwAkOHrufSS4t56qmN/OMfOzER10cC\nh3G5XGzZUmY7f0cC4ykuXkFh4Rl4PBdjqocaR++AAQ/g8fwUj8fF/PkLKC/Ppazs0oxQAkKGYVlW\nzJfb7X7G7XZ/o7P1kvV6/fXXrUwmk+XPZNktK7L8bW1tVnHxkxa0W9BuFRc/abW1tcUcx79NvQUH\nLLDsV7sFdRY8YC9vteAOC1ZYsMhedsCCJy1os6Ddqqp60bIsy2ptbbVKSuZYJSVzrNbW1oB97LNg\ntTV06P9YsD9kX/VxyZsu9MXrJ5Ow5Y/rPhvPjOAsYKtS6l/4wxgsrfWw5KknQUgOiTRmDzQDNTRM\nA14JW6ekpAU4Ao/Hi0mZcZrKLMZYQl2YJ/yNOHESXq+X731vGY2NJrfgH/9YxoYNk2y5NgE5+Hxj\nqagIlevIoLpEgtBTxKMIxhHuE5AEMKFH6alInniIJ6omuJPYGMyNfTyBppvi4md5+ulbAdi06Wfs\n3v2/BDp6zc3f2Y+PoqLFlJZOpKZmnV3/5zIAGhsXU1OzjvLy73fIZc7Hog4HsZOsJgjJIGoegVLq\nCvvtRcCFAa9i+yUIPUI8DV56m9A+AubG/ntgPLm591JZubYj9t/lcnHhhZH6N5myDvBz+/1hADZv\n1gSWl4Ab7GV+XC4XCxeOoLJyLbm592KUUH8pESEkhVgzgnMxXq5RRJ4B9Jmexb35NCqEEymTNh3N\nHyUlOyguzqG09J6wa6Sw8GusXLkYozAAahk+fAtvvnkIU2FlAI2N36W2dpPtGPZiWk8CFDFixOlh\n+xs4cCDl5eMoK7sk4PqUEhFCzxOrxMQs+29pr0mTAmI1Exf6JqGKHwh7EAgt1eCYgQKvi8BxcnJy\ngGsx5iCAEs44YzdvvtmKefoHWIzPdzwTJ47irrseY//+n9nLH2bVqiHcfLM34nUnCWJCskl6ZnG6\nkylPo32ZSDddp+5OTxOq+JcufQrob5eTDn4QiOVUDh2nqKiWoqKlNDZO7jiG887LY/ny7xPsN1iL\nx9NkKwF/17GmprXU1Ky3FQpi/hF6laxXBELqSSSSp7uEKn7TXWwskR4EYj2Jh48ziUcffY4vfWk+\nAFVVUzqqhfrxsnjxJk4//YsYB3QgB7n//t+ye/flwMV4PEt56KHwfsSCkAwSUgRKqcGYTOHtSZKn\n1+nNp1Ghb+AvDHcQ+C5O1ZSqqj91VAj96CNTOtrfyvIgsI3m5vk0N3s55pi5AaahWgYO/Jtdgwhg\nCQ0N43nhhWcpLCzs1WMTspN4ag39DyZ//mfAVuBTpdRKrfWdyRauN+jNp1EhMr3ppwlV/EVFHwP+\nMhCdPQj4ZY1UGM5pIWlmFkuWrMHfyhJMpZZDwCD277+FCRPm0b//ERw65KOubhZ+U9H1mOqjgtA7\nxDMjuBlTKvp64DlMmYhXgT6hCECccammN/004Yp/ki1DfA8CobLCDZSUzKewUIUlgG3erGlsnE7k\n3AIXo0adzZQpY6muXkNdXfB+cnO3cMUVl3fnUAUhbuLqR6C1/jcm+2Wt1vogsZqmCkKa4yh+xw8Q\n+jlRiovPithCsrAwko3f5BaY5DLjEI7UoP61125PuCKqIHSVeGYE25VSLwKnAxuVUsuB15IrlpBN\n9LafJp7w0URldblc1NdfxdSpfmexy+Vi5Ur/urm5P+eccw5QVARlZRM79hM+SykX86TQq8SjCH4I\nnA+8pbU+oJRaDPw2uWIJ2URv+Gmcm7/P56Ou7mMaG68DNnD33VMZPFjxzjvG5h/qnzBVR9ezefMO\nCgsVZWWXsn79ddTUrO1Y5qw3btyqDt/BRx+ZcZzj8vl8gJucnAERlU0086QkOwq9QmdV6dxu90C3\n232nXYV0iNvtnuV2uwfEW9Wuu68+UgEwI8lE2dva2qyqqhetqqoXraampo5lprKnUw10kQXV9vvw\niqIFBT+ySkrmWB999JF1wQXVAdvNtU444WbrvffeCxhvv5Wbe4c1YcIDYdVCnUqjoft3KogGyhqp\nomhTU1PE7TKFTLx+Aukj8vdY9dHHgd1AASYGLhd4EpiYRP0kCAnhPLlXVv6RlpZzgQGcffZm1q0b\nxuWXP0hz84XAGozj9njASfQK/wk0N19Ec/OR1NXdxqFDT+N39v6YPXvWkZf3Sw4e/DqwCniflpbZ\ntLSAvzBdE4EN7CM5w2tq1rByZWvMSKkXXniDhoYyJNlRSDbxOIsLtNZ3AAe01p9iQh/ykyuWIMSP\nE9JZUTGWlpYHgH3AxbzxxtGcdtovaG5+yF42FpPI9S7+iuqjMTfwA/arFtgDXMahQ9dE2NuRHDw4\nG3gHeAP4CSYkdAMwBPgFgQ3soxXP27xZBxW1i9WQXhCSTTyK4LDdTczhBJwyioKQQrxeL9XVa5g8\neUGESqGvAD+kvX0EphvqJPw37DOA+zA3/v5AO1AB3Aoci3GL5WCSxZ7GrySexSgOMJPkb2JyAxZi\nbv5XAl+095NDY+MkJk9egM/no6joKTqPKPIfU3X1GsaM+WpYJJKUnhCSQTymoceAl4AvKqUew8yp\nZ8feRBD8JMPhGZyEdjDGmgeAP2Fu4HX4LZoaYyrqB7wJ/C/wGXAvpgUHmCjpcUAJRkH8HKM4HgDO\nAwrt9RcQrQ+Bx/NVPJ6Lyc19iAkT5vGtb32NsjITERUYUVRc/CwlJVcFJdadcspctmwpo75ekh2F\n5NKpItBaP6OUeh24GPMruFxr/WbSJRP6BMnKGvbb3Q9hJqj+xvGmQvoEBg+exX/+8yGHD38PY8KZ\nT6C938nePfnkz/jgA4Bm4H7Mjf0WjPnoceAHmFYcTgmIH2FmHPMJrxkE/j4Ez2B8BnW0tNxHSwt8\n/PESysoiR0qF+hL+/vefUVR0L9u2hZe9FoSepFPTkFLqLGCW1voXmJnB40opqYZF8DQ+1Y1U0pXQ\nBi+RbOGh5zH+8+oFlgKXAzdz9NEVjBt3P6ed9jYwh337fsLhw18Dvoe5iS/D7xsAkyA/lg8+qMJU\nUHndXp6HucnfBwzAXPargen263nMLGIvfr+CYz6aD2wG5mLKUjdhZiHhxx9PIltLy0jxHQhJJx4f\nwROYKx2t9duYX8cTSZQpI0jHrlqZyN69eznrrPuYOvUgU6deyJgxixkzZkmn57Wk5AKGDr0L/012\nEJ9+Wkl9vYv33y/GXKZbMDOBQN/Bo0A9pkLKTMyMog5jAZ0JVGLMSNMxpqEK4BJgcsg4H9rLL8GY\npp7HmIlKgUJOOOE9zj//WczsID5KS0eTl7eAYJ/ExXFvLwhdJR5FcJTWep3zQWu9Efh88kTKDOJ5\n0hXCyycEOjy9Xi8jR9bYFTsvA+pobDze7g0Qfl69Xi8LF67m2msfpKDgAXbvjtQe8og4pOoHHGO/\n30TgEzvcbS/bgGnS9xKRLahXdMhsTEdvAefgKJI9e37F9u0fA22YWULnDl+Xy8WWLWV2a8q1wHiK\ni1eIg1hIOvE4i3crpaZiunb3w3jOPk6qVEKfIVbWcG3tJnbuvJXgqpvzIo7j9XoZM2ax3fT9dntp\nNcYE45RD/39vAAAgAElEQVRzngfcCCwHHsY8zQe2j3wEY/t3YaJ+7sc4fENZgzE3jbb3URYyzjO2\nrDn233pcroN4veMwzuUlwHXs3z8T4zi+BVhLScmOsC5noQwZMoRt2+6htnYTu3Y9y6xZPxT/gJB0\n4lEEkzEG1kcw89xXgP9JplCZQF/tYxAY4TN8eKQn7sRJpLrrsGH7+NKXwstC19ZuorHxi5incEdx\n3IQpiOu0hywHXuaEE/7Knj1zMJfqIOAe4D8YpTHIXvcG+/MBTjjhAfbscYrpzsY8wbswN/SbgJeB\nFoy56LC9nf/mfPzxv+WTT6oJVmgbge84ZwC4jMJCKyx6KlJElXO+mpubRQn0EFKqIzbxRA3twl9Q\nXbDpi30MQiN88vMX0dSUn7TjClWmeXmPsWXLTFwuV0B9nmOprd3E//3fp5ibMQQ2gzHOXOfy3M8p\np6zk+OMHsWeP41d4D/Pkv4bworm7GDbsU6ZNOxd4jgULnufvf1+EX1mYp31j/x+IMRVdj3E6m9lB\nbu7POXz4WD75JPTofAwePI99+8ox1Uafpq4uuCVmff1Vdn0i6ZedTKQveRxEqz3hdrvX2H/fd7vd\n74W83o23hkV3X32k3kdGUFX1YljdHadeTrKIVm8ntD7PMcfcb9fyOWBBjQX7LKiy6wa1259vt2sH\nLbdgZkgtoTYLfmWv227BLy141oLbLJhlDRx4k71d4PHvs+DOgFpDNfY4bRastgoKfmRVVq6y5Xqy\nY+yhQ++xKitXWa2trR3HVlm5OuzclpTMiXm+M+naiUS6yN/V6zpd5O8qPVVrqMz+ew3GoCoIPU40\ns1FoTP3+/bfjT9S6gZNOmsiHH56JcQ7XA29jwj4vwzhaZ2BmAduBP2CcwNdiIntGYmz5TwAKmER7\nO8BTwC+Bk20pVtvrREoWu4xJk7CbzbuA6+zvfNx999mUl38foOPYqqvXdOc0CUJy6UxTuN3uHfFq\nlWS8+ohWzgj8T+HmyTY/f0G3ql12Vl0z1jaRnpbhxY73X/nKT4OqgsK4gPVX2U/8gU/yt1jwYwuW\nWbDagp9GqDy6zx7LbPf5z98eVlHUbNtuFRX9qqOCaOA5Ky5+Mmgm4Bx3tPVClwWep0y6diKRLvJH\nOvfxXI/pIn9X6enqo28opW7AZN+0OQu11n9LmnYSUkKo32P48BFdtqN2xS4bvM2FDB78CPv2/RQg\nyN6em/tzWlruxuQAbAK+AvwRE51zESa4zYn7B/MkPw94H2Prh8jlsl7Gn3cAn312P7m5szoa0sN8\nhg3bza23BjeWCTxnJSXR7f6RfEp9zc+Ujsh57px4FME3MYVVQvlKrI2UUjmYufapmF/fA8BfMMlp\nhzGB19O01lYC8gpJJtBU09zc3OVxutKHODQ3Y9++ckpK5lNcfBYlJdNYsuRlNm/egYlf8GJi+K/B\nOG+dpr+Lga346wU5uDGx/JfZnz/GZADvxh8W+hKhcRFTp+aTk7M2oDFNePhn4Dmrrl4T9bgjmcGk\nX3bvIOc5NvFEDZ3WxbF/AOzWWk9USh0L/BlT/Wum1voVpVQVplxjfRfHF9IYE/ETvsyxlXclhK+u\nbrfdDB5MqOhCTOLXJPxP/xOADzCRQnfZyxYD/7I/O+uVAg9ifAs/Bk7E+BuC8wVyco6nvHwc5eXB\nskg4otCXiJpZrJT6klJqlVLqLaVUtVJqSIJj12ECuJ39+IB8rfUr9rJ1wLcTllhIe7xeL3V1HxNY\n5/+CC56mru7jmKUjgrOQ9zN48EI8nulMnTqGc8+tprHxaEyJiBeJfOl4MclkdwI/BW7DNI85DHw9\nwvpvYBzMc4FvYKqPXolx+m4EJpCTMyBoCye7efjwhRGPJVYmtSCkK7FKTDwN7MD8olyYGrxxo7X+\nTGv9qVJqEEYp3BWyv0+BwYmJK2QCJvnrh5iY+zXAPHbteoXGxuNwavVHKsnh2HKrqjZSUrKIfftu\nxzETvfPOT4CjMEldn2Bq/cwDivArnPX4ZweDMKWlN2H6C3wXkyDm1PF5BlPL5yDGagnmEv9fjCvM\nxwknPExJyQUd8vkb4PQPyIgOLyTnHENV1UaJVxcyglimoZO01jMBlFIvYUw7CaGUOgXzSPa41vo3\nSqlHAr4ehCnf2CndsVWnA5ksf1dk37VrV8CnVuB2u8zzfEw45v8A/dm1a1fH+O3t7bzwwhsAXHHF\n2bzzzlERRj4Sf9G35zFF5X6HeZ64EdhPeO6jc4k7IZ7zMLOD6+3ln8OvCGqB0zCzAtiz51+MGvUE\njz9eyMCBA/F4/kBDw2kYV5fja/Afc+C5GjHiiwBs3749wnHETyZfOyDyZwzRwoncbvfWkM9/ijcU\nyV7/RLfb/Re32z0qYNnzbre72H5f7Xa7r+lsnD4SwpWRdFV2f7heeBIVzLBgWVAIX2jyWFHRr6zz\nz59vwf0BCWBOMpczzuwIoZ932EllzjYzLfibva2TdHajHTbaZoejhspXHxYu6oSB5uU9YjkN6+GB\nhMMREyWTrx3LEvlTTU+Fj/brpo6ZiXlUu0cp5fgKbgEq7daXbwMrurkPIQ1xzCOTJy/A4wl9Qj+P\ngoJNrF8/L6j4XGCkjakzNA9zuZgkLdNOsj9+s867AWN6AQ+mTpAX03JyNCapzIOJLKrniCP+H4cO\nVdnbLMZfgbRzggvk5QDlDB06jbvvHktZmZh/hMwmliI4Uyn1XsDnkwI+W1rrYbEG1lrfgvklh3JR\nYiIKmYjL5eLpp2/l9dfn09LidA57FhjPpElEuHF6MRFAYOz+geRgbuZzMNE/A/FH+HwPowCcdpE5\n9vuNGOvjJPv9AA4depjg3IIfAf/G+BAA7uPUU3PYtetS+/MzFBUdoqTkWqZOrSa4G5mLUaNOo6zs\nElECQsYTy1nsBkYFvFTAe+mWIXSKy+XitdduIjd3Fk59/aKiZZSVXRK0XknJBQweXIm50Y5h0KBH\nOe20jzHlpS+0l/8K48T9FqbQ3MXAPkwryUjtIgPxYXIGghk27BgGDfoQE8C2DvgSJ500hLlzV1BQ\nMJ1rrvmI5cuvZNy4VXg80wiMgoJnWL68QhoSCX2CqDMCrfX7vSiH0EOkW3y7qa8/y5apkdLSiWEy\neTxNARFCXv7zn1P4z3+cHABT29/0FliE6QE8HuPYfRPjfD5kf55kb/OIvf4BYCZDhnzE3r0L7LGM\nk/iEEx5k2rSRTJ/+ffyzhAP88Y/Ps2fPe+zcOZ/mZnjjjQXs3DnFXud4jMnqEKYngiuuRDlBSHfi\nySwWMoTeLLebiMJJLKtzE8EJYk5tfx8mdNRZPgmTBwAmImgiJpLIg8kq3sDnPvdbZs0qJidnJNOn\nP4dJNnseWE9x8clEdoNpdu70K6WdO/MwpqYvYExIl2OinzKDdHswENKTeFpVChlCb7XP7Ol+zePG\nFTBw4C2YJPPPIqzhw4SJhnIepproAcylvAM4AziCc87ZxAUXDOKoo462K4Rei1Ey7wFVrFw5ndmz\ntxNs7qll2LB9zlECSzHhqDMwPgmTA2EykdeT7glj0ldbiBdRBELC9KTC8Xq9FBUtpr39McxNdztw\nByYn4ABHHnk35sl/OoMHP4Jz03a5ZgN/A3IxN+bpmPyEe4BN/OlP3+Cllx6momIsy5b9i6KipZgZ\ngBP58wr799+Jf8axlmuu+Zjm5pl2ZvB6gnsZO43vV9vrbouaMOb1eqmuXkN19ZqU3nilr7YQL6II\n+hCZWN6gtnYTLS1Oxc8cTHmIIo4++iGGDbuLgwfvAu4kL28ZO3bcSGXlWgoKpuP15mDaSO7BmG7m\nYxzSXkw9xBsxT/AbaGo6kXHjjuWaa7bZ66zBzDLAmJVMf4GLL85nyJAhrF9/HSUlOyJI+3VMVvNo\n/vnPL0Y0tchTuJCRxJtwkKpXH0nq6DW60gMgGtFk72p990hE6h5lEr3Ck7sqK1dZRUW/CulDENov\n4A4L6ix/1zCz7umnz7EuuODxgG0X2V3PIh9D6DGasdrspLQ5Fqy2Hn3UE3aue7LLW3evnZ78P3UF\n+e2mlp7uRyBkEL1Vbvfqq4/jv/5rvl2auesO6dLS0Sxd6m9Wb3INrou47h/+8DaNjY4jF4xJaB1O\nSQhDPscc8xb792/BVBc1677zzk945511AdveyKxZ9Rx1VOQa9U5S3KRJ81i+/OsBMi3HmKHgzjsf\nxOudDrg6HPPBeIH1NDTsSImjVurwC/EiikAIIjDKZPjwYyN+749MGstHHy2hrCxstbhxuVxs2DCR\nRYueY9asTXz66SMYi+VizI30AAAXXPB0xO0HDnyB9naTAJaX9xhTppwKfI2f//w5u75RdI466vNB\nSjNShM23vvU1li/fbcu0lsCIJq93Jk7rSieMtLR0NB7PEhoaxmOUxiQ8HnOeUlGATurwC/EgPgKh\ng1D7dnn5a2H27WQ4IF0uFz/5yTXMnj0K02NgPvAdjP3fJHtt2/YR+flfJjSp66GHvtNR6XPLljKe\nf/5Tpk//Ph98UA08TGBE0Omnbyea/ySabd+Uob4SE53UeQE55ym8pGQRfqUhjlohvZEZgdBBaM2f\nrVun9niyVLS4dq/Xy6pVn+DvG3AfppG807z+UpYv/zHDhn2ed999HjiSoqJD3HzzlbhcLrxeL5Mn\nL6Chwck1yMEklc0DzqSoyOL5529myZI1bN6sKSw8I+axBz7hP/rofXa7ykMEJqUFts80isWYhlwu\nF8XFZ+Hx9NhpE4SkIjMCISYNDduCZgXdiUzau3cvZ511H1OnHmTq1AuDImoWLXqOpqa/22teDBwd\ntv3WrWN4990Hyct7j8pKiw0bJnUogUsuWYrH89WQLVyUlBxBVVVOx7p1dXvweKZTUTGWMWOWdBrR\n43K5qKgY2TEeXAXMo6BgOjt23EhVVWNYGKnX68XnO0Be3oIunSdB6HXi9Sqn6tVHPPcZQXikTI0F\n+yNG1CQamRRcxvmAHYWzr2OMoUPvCfnuY+uII26zS1mvtuAx+++LHds5+CN12uxtjfz5+QuC5Kus\nDC+LXVm5OuTY91mw2srNnWHNnbvEKimZYz36qMeOVtpnnxMjZ6QonOCS2vut3NzbrQkTHrAqK1cl\nHLHTlWunJ6PGuksmXfuR6CPyx3WfTfmNvrNXH/lnpD3ODaSycpU1YcIDATX7uxcC6RA5THR1x00r\n/Ltl1le+8lBA/f/7AxRFjVVZuSrK2G0WrLZKSuZYL7/8ctBNsaRkTth6BQU/6rhhtra2hiirGnvf\nNdYFFyy0z0vs0NBwWWIrjlgkeu2E9nXo7XDRUDLl2o9GH5E/rvusmIaEIEdpRcXlbN36GaYDV/cj\nXJws24aGbWHf5eZuiWouGTp0A++9dxtOFrCpROrP8v3DH96O0ie4P8XF/6aqagq33fam7fy9kLPO\nug+fz4vpwLofE6Y6lubm+R0mKo+nKaTnwA32vm+gqelk+vc/IsGjD6yblHyHsWQSC11FFIEQdgNp\naZlhl3Aw9u3Bg+cF9e6Nl0AF4/FMCyoRkZf3GK+9djsulyvM7zB06INceOHJMcdevvzrHTfwSH2C\nlyz5HVu3nobJIl5KS8tsVq68k2OO+RCTiZzoDbqNQ4d8AXb//eTm3ovP54vhQ/FFHCldSlAIQgfx\nTh1S9eoj07MO0smG61BZuco2Bb1omzPaLVhufw63ycdLuJlkmVVQ8COrsnJ12LG3trZaEyY8YA0d\nWmGbY/Zbgwc77SD3BbwPzPKNbLIK90c4bS6NOejkk6+L6CuI7CPZZ8EvrYEDf9Zhpho27KdWbu7c\niCaYtrY2q7Jyle1bWGb7FvyZva2trXGbb7puGkpNJnEofe23m2mIjyCNCJQ/3Wy4jkzBZRtqrNNP\nf9AKLd3QPUXgOHEjH7ffNl8fcoPeZ5WUzLGqql60WltbbRt/bN+F3xcQvJ5xNDsyLLX8fYzNDX/u\n3GdDtl9hwSpbES4LkSvc6ewo9tD/b2tra5DiT6QEhTiLU0sfkV98BOlGOtpwa2s30dhYSqBd/Oab\n8ygurqO7oY9+M8mLBFbyDDxur9fLiBGP2Lb50LQWE48/ZcpYhgwZwtNP30px8ScYi2awacbr9bJw\n4WqGD1+IxzMd4+NYislOBmjGNJbZAGhMO22nUN0E6ur+n9mj3WKzuHgfphjdd8jNfYN4qKlZR0OD\ns49DNDRcj8fT1HHuamrW8/LLWxM7iQniZBJPmTJWykkIcSMJZUIYRx11FOvXX8Hs2U9w6qmnxlWj\nJlKimMvlor7+KtzuWezefWXE7Uz10fPsT6MJTNgqKlqMz3cC1dVrOsZcv/46amrWsHBhCzt3zqai\nAurqngL609j4BfxlprHHWcv553/Aa6/5OHjwMoxi2IJpXgMmU/nXDBv2hQ6Z/PtZy+bNOzj77K/y\nxBP32HJezOmnv02/frqjF3Nx8bOUlFzFyJE19v6xj2M8Pt+BoGZBpufyE5jqqAQloglCyoh36pCq\nVx+ZnlmWlX423M5kivfcxzJ5GVOIUwnU7CMv75Eo3++zoM46+uiJ1ty5z1pFRdUxxoxkqgk3u5SU\nzAkJ+wxfZ+DAm6zW1tYox7TfCgwBdbnutOBflskRuKPDtxDJ5ON8Hyls1glxjfX/70vXfibSR+QX\n01C6ESm6JdXT956QqXOTlwtTvdM0gCkvz+3YhzEf1WFaQFYBV/Lpp0/y5JMf0th4XYwxHbz4awA5\nMwpj0jr55Lk8/fStnYZ9zp59IR5PU1AUj/+YXiEwwsjrvQczoxhES8u95OTkRD1fFRUj7e5ooeQA\nl1FcfFbK//+CAMiMINlksvzxyh7LARptxhHo1PQ7gkOfnEP7EUTKAvYnfTkRRjDDghXWySffbbW1\ntVmtra1BEUhHHHFPhzxFRb+KOPPwH1O0J/r4jzNSJFI8s8FMvnYsS+RPNdKPQOhV/KWXrwe85ObO\nxecbGRTjH1gTHwiym3s8S7j66jMiFGn7f8Cl9vtnMM5e/yxm8uQFtmM4B+MPmIcpWjcLcPHBB1d0\nFM17//1pTJ1qms7//OdTqa838vh8J1BRcTmRis2ZYzoak4RWastRCwwhUqG5aLX/neU+nw84npyc\nRukNIKQVogh6mWjVN1NJd2WK5sRdudJfgz+wgqnJNHZMSV4aGo7jC1/YRlHR7o4GNXl5j7Fz508w\n5iSACeTkNAbtM7jCpws4ExMt5JhjvB0ZzaWlo/nNb2Z0bD9lytiOiqXGLBX5mG64YR51dSdgymEf\nBHaSn/8ZpaU5YQ15otX+l54AQtoT79QhVa8+Mj2zLCt98wiiyZTouY9kIqqsXB0W1x4tv6CoqLpj\nfX/iVXTHeqjZpaioOiCBa5919NGzo57rcGdw5P34nb2xcyGSQV+69jORPiJ/XPfZlN/oO3v1kX+G\nZVk928+2p4glU08ogtzcO8Junv6bcOTELId4kqNC13E+R/I5RK5YalmBhepC9+Nfr/f/d33p2s9E\n+oj8cd1nJWpI6DFCawbl5T1GS8sdhEb+OPkFQ4euiTlePMlRoes4n4uLz4o5trHXd4xCtCge/zGF\n1w3y+XxSM0joE4gi6EW609QlE2QKDUUtL88lWgVTj6eJ3bvnExjumZf3WNz77qxwW2npaPLzF0U8\nLq/Xy7JlHxLY9vKYYx6JWFjPOabKSiuo0UxR0WLq6j4Oa20pCJlI0p3FSqnzgLla61FKqVxM2MVh\n4C1gmtbaSrYM6UKsyJKeJBHnbzJlmjhxFCtXOtFEkbJoA/MLfEH5BbHYu3cvI0fW2GUpTNRRaP6D\ny+Vi4cIRvPlm+HHV1m6iqakM03rS7Hv/fjceT1NUZ295+fcpK/N2nKdo0UbddQoH/u+GDz+2W2MJ\nQtzEa0Pqysvtdt/udrvfdLvdm+3Pz7vd7gvt91Vut3tcZ2P0ETtdr5GoQzqWHb4nGqOEFl0LXzex\nLOu2tjYrN3dGXPb6aPJHa5Lj5CnEQzL8PaHnL7TDWqYhv93Ukk4+ghZMk9d+9ud8rfUr9vt1wLeT\nvP+sI5HCdoH9AnrCvBFp385Tdqidv6sZzTU162lp+WaXZQQzSyoqqsUx85gchY+oq/s47uNPhpkv\n9Pxt3To15UUJhewgqYpAa70KE3zt0C/g/aeYEpBCiuhMabS3tyfVGdqVSpmbN+/ANLcPbmSTyE3Y\n5XKxYcNEJkyYh0lCOx6YTGPj5KDjD/VDBH4G0q5ciCB0ld52Fh8OeD8I2NvL++/z9NSTqtfrpbz8\ntYRmC73hDC8sVMAyYDymhPQsZsz4WpeS4EaNOhu4Hfg+oU7t0NnSmDGLGTNmSdD5AHq05HPo+cvP\nr0p5MIGQHfSzrOT6apVSpwG/0Vqfr5R6HnhUa92glKoGNmmt62Jt39zcnDXO5J6ivb2dF14wNfSv\nuOJsBg4cGHW98vLX2Lp1KgD5+VUsXDiCgQMHsmLFq8ydW4Y/S/cAM2Y8wfjx50UcK9F9d5X29nam\nTdvMG2/kAnD22S08/nhhl/aT2PHXY/oTJHY+uiJTMs+fkF0UFBT063yt3isx4dzMpwM1SqkBwNvA\ning2LigoSJZcSae5ubnL8nen9ENhYWFc6zU15QdEDN3UsY/XXvtn2LqnnnpqXMcS7767yh//mB9w\nXn7UIXPo+dq+fXuYvKHrJHL8ocR7PhLFOX/duXbSAZE/tTQ3N8e/crxe5VS9+ojnPmFSXY6ira3N\nys9fkHBUT6L7iKetYrwZxqHnq6mpqdN1Oh8vUvmK3uklka3XfrrQR+SP6z6b8ht9Z68+8s9ImHQo\nR9HU1NTl/red3bzjvSl3tp6zn2uuuc8yfYZftJzG9jNmPB40VqLnNFr5iu72A453nGy99tOFPiJ/\nXPdZqT4qRGXgwIEdCVJOxAx0bqZyHK2BZaZDo2qCI5aiJ2TFWs+/n2uAj4Dv2VuZNpHdJVLV0J5I\nGOvs3AhCbyMlJtKUdCpHkWi+QSK5DN0hWhcxuJ7c3LlcccXZQeunwzntrXMjCIkgiiBNSWVbS+fp\nf8WKVzucqz1984r3ptzVm3dFxciwiJtUtwr1ev39EQQhnRDTUBqTioYmoaaLP/5xCVdffVzc29bW\nbsLnO0BRUW1Hk5nwGkPx1ziKtZ6/i9h4TAG5Gzr2V1Z2Hdu3b484XjznNDS6COhW8x7/eZ0WJmvo\nuRGE3kYUQYaSrE5nkWzyV1+9huLiWMXjwhVIUdFTVFauJScnh5KSqyLKGu9NOVbnL6MkNuHzHQ+Y\n/XW3cF7osSxd+hTQn8bGUqBrdv3g83o9sJaSkh08/fSt4h8QUo4oggyktx2OOTkDOn16D1UgjY2T\nue460/s3mbImY9YUfiwnEphM1v1Ko07/gxxRAkJaID6CDCSZDsdoNvmu1AVKpqyd9SNIN9LBUS0I\n0RBFIAQR6FCdMeOJuJ/ee/NG19NVU0MJPZaioo+DqpV25dhS7agWhJjEm3CQqlcfSeroUbpayz9R\nutKPIDRRKhmyxpsY1p1zn6xkskSQaz+19BH5JaGsr9Jbnc4SJZK9PhWyOo70Xbt2ceaZZ3Zpf8lI\nJhOEdEUUQYaSitDSrtLTsvrDRsOjmCKFv4oZRhBiI4pA6DUSCXmNtW6sWUa8pSsEQfAjikDoFRIJ\neY1n3UyaEQlCuiNRQ0KvkEgYaXdCTiVMUxASR2YEQp8i0Gy0a9cuZs36ofgHBKETZEYg9AqJPKl3\n96neMRuNH3+eKAFBiAOZEQi9QiJhpOkaHisIfRVRBEKvkYiDV5zBgtB7iGlIEAQhyxFFIAiCkOWI\nIhAEQchyRBEIgiBkOaIIBEEQshxRBIIgCFmOKAJBEIQsRxSBIAhCliOKQBAEIcsRRSAIgpDl9HqJ\nCaVUf2ARMBxoB/5Ha/1Ob8shCIIgGFIxIxgHDNBaFwIzgEdTIIMgCIJgkwpFcAGwHkBr/Spwbgpk\nEARBEGxSoQiOAfYHfD5km4sEQRCEFJCKG/B+YFCgDFrrwymQQxAEQQD6WZbVqztUSl0FXKG1nqyU\n+iZwt9Y6auH55ubm3hVQEAShj1BQUNAvnvVSoQj64Y8aApistf5rrwohCIIgdNDrikAQBEFIL8RJ\nKwiCkOWIIhAEQchyRBEIgiBkOaIIBEEQspxerzUUD0qpI4AawA1YwBSt9fbUSpU4SqkvAM3A6EyL\njFJKbQX22R/f1VrfmEp5EkUpdQdwBZAD/EJrvTjFIsWNUmoSUGp//BzwDeBErfX+qBulCXZy6BOY\n3+5hoExrrVMrVfwopQZg5M8FfECF1vrPqZUqPpRS5wFztdajlFK5QC3mf/AWME1rHTUyKF1nBJcD\nh7XW3wLuAh5MsTwJo5TKAX4JfJZqWRJFKeUC0FqPsl+ZpgQuAs6361ldBAxLqUAJorVe7Jx74HWg\nPBOUgM0Y4PP2b/c+Mu+3Wwb8n33tlAFPpVieuFBK3Y55eB5oL5oPzNRaXwj0A66MtX1aKgKt9XPA\nTfbH04DW1EnTZeYBVcBHqRakC3wDOEop9Vul1Cb7SSOTGANsU0rVAy8Az6dYni6hlDoXOFNr/USq\nZUmANmCwnS80GDiQYnkS5Wv4a6H9FfiSUuqY1IoUFy3AVZibPkC+1voV+/064NuxNk5LRQCgtT6k\nlKoFKoGlKRYnIZRSpcBurfUGe1Fc2X1pxGfAPK31d4EpwK8zrB7UUKAAGI8tf2rF6TIzgXtTLUSC\nNAEuYAdmRrwwteIkzBsYiwR25YOhwOdTKlEcaK1XAQcDFgXecz7FKOWopPWPW2tdirE11iilPpdi\ncRJhMvAdpdTvgLOBxUqpE1MsUyL8FfvmqbXeCXwC/FdKJUqMPcAGrfVB+6nOq5Q6IdVCJYJSagjg\n1lo3pFqWBLkdaNJaK/zX/oAUy5QITwH7lVKNmJL5fwX+nVqRukRg/bZBwN5YK6elIlBKTbSdfWCm\nmocJPrC0RmtdrLW+yLbxvgHcoLX+ONVyJcBk7D4RSqmTMBVjM8nE9QfgEuiQ//MYZZZJXAhsSrUQ\nXQ5Op/wAAAQ2SURBVODz+KsLt2Kc9UekTpyEGQm8rLUuAlYAH2mt21MsU1f4k1Kq2H5/KfBKrJXT\nMmoI8w+oVUo1YC6kWzL0n5GpPAk8rZRyLp7JmVQhVmu9Ril1oVJqC+Zh5+ZYERNpihvIxM598zDX\nTiPmt3uH1rotxTIlggaWKaVmAl6MwziTcK7z6RhLygDgbcw9NSpSa0gQBCHLSUvTkCAIgtB7iCIQ\nBEHIckQRCIIgZDmiCARBELIcUQSCIAhZjigCQRCELCdd8wiELEYpdRrwLjBGa/1SwPL3gQu11n/r\n5vjvY2qxJC1jVCn1ZWAD8B9glNb602TtSxC6i8wIhHTFh0mIOTpgWU8lvVgkv/7TRUCz1nqEKAEh\n3ZEZgZCufIh5on4UfyVaoKPM9Cy7hAd2ccLfAb8HnsNk5J6FKeH8e0xt/2OB72utd9jDPKyUyseU\nMCnTWr9t14OqBk7BlDS5Q2u9SSl1L/BNe/lCrXV1gCxu4Ff2+J8BFRgldj9wtFJqkdb65oD1g8YC\nXg7dXmv9ui3Lk/Z6BzElhX9rb/9lYDjwBUyZ9ouB84A/a61LlFInY2pFHWUfR4XW+tV4T7yQfciM\nQEhnbgO+q5SKWUIX84TvPOWfhamDr4ARwKl2bfnfAP9fwDbbtdb5mHr5tfayx4CntNbnYuq3/zJg\nRjJAa31moBKweRZYoLX+BvBjTCr/X4B7gOcClUAAgWOFbW+XBVgIvGQvHw88ZTc6AjgTUxPnekyR\ntLnA14F8pdRw4IfAC1rrEZgicN/q5PwJWY4oAiFt0Vr/B1PrJdREFIt/aq3/bNcW+gB/4ba/YZ66\nHZ6w97EWON2uOf9t4D6l1J+AtZgZ8+kYJRP2RG3LdLrWut4e61VMpUqFUUqRzE8dY3Wy/SjMjACt\n9Xv2NufZ22+0az/9DVMUbYfW+hDwD2AI8BJwm1Lq18CXgF/Eee6ELEUUgZDWaK03AhsxHZccQm38\nOQHvQxuhHCQyh0I++zC/h1Fa63O01ucAFwDb7O+9EcboT/jNvh+m2mYsf4YzVrTtj4zwnbPckdUh\n7Pi01psxDVZ+C1yLac4jCFERRSBkAtMxXcdOsj/vAYYppQYqpY4DihIcrx/wAwCl1PeBv9gVMl8G\nptnLzwT+jLGzR3Qs2+0j37HHcBqZnIjpEdupM7qT7V8GbrSXD8Mopc1xjNtPKTUHmKi1fgYoB/I7\nk0XIbkQRCOlKxxN1gInoSPvzdmANsB1Yjr/WukX0J/HA7yzg67YJ6BZgkr28HPimUurPGJ/CD+yI\nn1jjXg9UKKXexHTTu0prfTAOWWJt78M4nS+2l68GbrR7WoSOG7oPC3gcuNo+vlWYLm2CEBUpQy0I\ngpDlyIxAEAQhyxFFIAiCkOWIIhAEQchyRBEIgiBkOaIIBEEQshxRBIIgCFmOKAJBEIQsRxSBIAhC\nlvP/AyRwyEvyK+avAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ab18e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(boston.data[:,5],boston.target)\n",
    "\n",
    "plt.ylabel('Prices in $1000s')\n",
    "plt.xlabel('Number of rooms')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  6.32000000e-03,   1.80000000e+01,   2.31000000e+00, ...,\n",
       "          1.53000000e+01,   3.96900000e+02,   4.98000000e+00],\n",
       "       [  2.73100000e-02,   0.00000000e+00,   7.07000000e+00, ...,\n",
       "          1.78000000e+01,   3.96900000e+02,   9.14000000e+00],\n",
       "       [  2.72900000e-02,   0.00000000e+00,   7.07000000e+00, ...,\n",
       "          1.78000000e+01,   3.92830000e+02,   4.03000000e+00],\n",
       "       ..., \n",
       "       [  6.07600000e-02,   0.00000000e+00,   1.19300000e+01, ...,\n",
       "          2.10000000e+01,   3.96900000e+02,   5.64000000e+00],\n",
       "       [  1.09590000e-01,   0.00000000e+00,   1.19300000e+01, ...,\n",
       "          2.10000000e+01,   3.93450000e+02,   6.48000000e+00],\n",
       "       [  4.74100000e-02,   0.00000000e+00,   1.19300000e+01, ...,\n",
       "          2.10000000e+01,   3.96900000e+02,   7.88000000e+00]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM  ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD  TAX  PTRATIO  \\\n",
       "0  0.00632  18   2.31     0  0.538  6.575  65.2  4.0900    1  296     15.3   \n",
       "1  0.02731   0   7.07     0  0.469  6.421  78.9  4.9671    2  242     17.8   \n",
       "2  0.02729   0   7.07     0  0.469  7.185  61.1  4.9671    2  242     17.8   \n",
       "3  0.03237   0   2.18     0  0.458  6.998  45.8  6.0622    3  222     18.7   \n",
       "4  0.06905   0   2.18     0  0.458  7.147  54.2  6.0622    3  222     18.7   \n",
       "\n",
       "        B  LSTAT  \n",
       "0  396.90   4.98  \n",
       "1  396.90   9.14  \n",
       "2  392.83   4.03  \n",
       "3  394.63   2.94  \n",
       "4  396.90   5.33  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_df = DataFrame(boston.data)\n",
    "\n",
    "boston_df.columns = boston.feature_names\n",
    "\n",
    "boston_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "boston_df['Price'] = boston.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM  ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD  TAX  PTRATIO  \\\n",
       "0  0.00632  18   2.31     0  0.538  6.575  65.2  4.0900    1  296     15.3   \n",
       "1  0.02731   0   7.07     0  0.469  6.421  78.9  4.9671    2  242     17.8   \n",
       "2  0.02729   0   7.07     0  0.469  7.185  61.1  4.9671    2  242     17.8   \n",
       "3  0.03237   0   2.18     0  0.458  6.998  45.8  6.0622    3  222     18.7   \n",
       "4  0.06905   0   2.18     0  0.458  7.147  54.2  6.0622    3  222     18.7   \n",
       "\n",
       "        B  LSTAT  Price  \n",
       "0  396.90   4.98   24.0  \n",
       "1  396.90   9.14   21.6  \n",
       "2  392.83   4.03   34.7  \n",
       "3  394.63   2.94   33.4  \n",
       "4  396.90   5.33   36.2  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10ad15908>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWEAAAFhCAYAAACh/xvXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ad15ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('RM','Price',data=boston_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506,)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = boston_df.RM\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 1)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.vstack(boston_df.RM)\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6.575],\n",
       "       [ 6.421],\n",
       "       [ 7.185],\n",
       "       [ 6.998],\n",
       "       [ 7.147],\n",
       "       [ 6.43 ],\n",
       "       [ 6.012],\n",
       "       [ 6.172],\n",
       "       [ 5.631],\n",
       "       [ 6.004],\n",
       "       [ 6.377],\n",
       "       [ 6.009],\n",
       "       [ 5.889],\n",
       "       [ 5.949],\n",
       "       [ 6.096],\n",
       "       [ 5.834],\n",
       "       [ 5.935],\n",
       "       [ 5.99 ],\n",
       "       [ 5.456],\n",
       "       [ 5.727],\n",
       "       [ 5.57 ],\n",
       "       [ 5.965],\n",
       "       [ 6.142],\n",
       "       [ 5.813],\n",
       "       [ 5.924],\n",
       "       [ 5.599],\n",
       "       [ 5.813],\n",
       "       [ 6.047],\n",
       "       [ 6.495],\n",
       "       [ 6.674],\n",
       "       [ 5.713],\n",
       "       [ 6.072],\n",
       "       [ 5.95 ],\n",
       "       [ 5.701],\n",
       "       [ 6.096],\n",
       "       [ 5.933],\n",
       "       [ 5.841],\n",
       "       [ 5.85 ],\n",
       "       [ 5.966],\n",
       "       [ 6.595],\n",
       "       [ 7.024],\n",
       "       [ 6.77 ],\n",
       "       [ 6.169],\n",
       "       [ 6.211],\n",
       "       [ 6.069],\n",
       "       [ 5.682],\n",
       "       [ 5.786],\n",
       "       [ 6.03 ],\n",
       "       [ 5.399],\n",
       "       [ 5.602],\n",
       "       [ 5.963],\n",
       "       [ 6.115],\n",
       "       [ 6.511],\n",
       "       [ 5.998],\n",
       "       [ 5.888],\n",
       "       [ 7.249],\n",
       "       [ 6.383],\n",
       "       [ 6.816],\n",
       "       [ 6.145],\n",
       "       [ 5.927],\n",
       "       [ 5.741],\n",
       "       [ 5.966],\n",
       "       [ 6.456],\n",
       "       [ 6.762],\n",
       "       [ 7.104],\n",
       "       [ 6.29 ],\n",
       "       [ 5.787],\n",
       "       [ 5.878],\n",
       "       [ 5.594],\n",
       "       [ 5.885],\n",
       "       [ 6.417],\n",
       "       [ 5.961],\n",
       "       [ 6.065],\n",
       "       [ 6.245],\n",
       "       [ 6.273],\n",
       "       [ 6.286],\n",
       "       [ 6.279],\n",
       "       [ 6.14 ],\n",
       "       [ 6.232],\n",
       "       [ 5.874],\n",
       "       [ 6.727],\n",
       "       [ 6.619],\n",
       "       [ 6.302],\n",
       "       [ 6.167],\n",
       "       [ 6.389],\n",
       "       [ 6.63 ],\n",
       "       [ 6.015],\n",
       "       [ 6.121],\n",
       "       [ 7.007],\n",
       "       [ 7.079],\n",
       "       [ 6.417],\n",
       "       [ 6.405],\n",
       "       [ 6.442],\n",
       "       [ 6.211],\n",
       "       [ 6.249],\n",
       "       [ 6.625],\n",
       "       [ 6.163],\n",
       "       [ 8.069],\n",
       "       [ 7.82 ],\n",
       "       [ 7.416],\n",
       "       [ 6.727],\n",
       "       [ 6.781],\n",
       "       [ 6.405],\n",
       "       [ 6.137],\n",
       "       [ 6.167],\n",
       "       [ 5.851],\n",
       "       [ 5.836],\n",
       "       [ 6.127],\n",
       "       [ 6.474],\n",
       "       [ 6.229],\n",
       "       [ 6.195],\n",
       "       [ 6.715],\n",
       "       [ 5.913],\n",
       "       [ 6.092],\n",
       "       [ 6.254],\n",
       "       [ 5.928],\n",
       "       [ 6.176],\n",
       "       [ 6.021],\n",
       "       [ 5.872],\n",
       "       [ 5.731],\n",
       "       [ 5.87 ],\n",
       "       [ 6.004],\n",
       "       [ 5.961],\n",
       "       [ 5.856],\n",
       "       [ 5.879],\n",
       "       [ 5.986],\n",
       "       [ 5.613],\n",
       "       [ 5.693],\n",
       "       [ 6.431],\n",
       "       [ 5.637],\n",
       "       [ 6.458],\n",
       "       [ 6.326],\n",
       "       [ 6.372],\n",
       "       [ 5.822],\n",
       "       [ 5.757],\n",
       "       [ 6.335],\n",
       "       [ 5.942],\n",
       "       [ 6.454],\n",
       "       [ 5.857],\n",
       "       [ 6.151],\n",
       "       [ 6.174],\n",
       "       [ 5.019],\n",
       "       [ 5.403],\n",
       "       [ 5.468],\n",
       "       [ 4.903],\n",
       "       [ 6.13 ],\n",
       "       [ 5.628],\n",
       "       [ 4.926],\n",
       "       [ 5.186],\n",
       "       [ 5.597],\n",
       "       [ 6.122],\n",
       "       [ 5.404],\n",
       "       [ 5.012],\n",
       "       [ 5.709],\n",
       "       [ 6.129],\n",
       "       [ 6.152],\n",
       "       [ 5.272],\n",
       "       [ 6.943],\n",
       "       [ 6.066],\n",
       "       [ 6.51 ],\n",
       "       [ 6.25 ],\n",
       "       [ 7.489],\n",
       "       [ 7.802],\n",
       "       [ 8.375],\n",
       "       [ 5.854],\n",
       "       [ 6.101],\n",
       "       [ 7.929],\n",
       "       [ 5.877],\n",
       "       [ 6.319],\n",
       "       [ 6.402],\n",
       "       [ 5.875],\n",
       "       [ 5.88 ],\n",
       "       [ 5.572],\n",
       "       [ 6.416],\n",
       "       [ 5.859],\n",
       "       [ 6.546],\n",
       "       [ 6.02 ],\n",
       "       [ 6.315],\n",
       "       [ 6.86 ],\n",
       "       [ 6.98 ],\n",
       "       [ 7.765],\n",
       "       [ 6.144],\n",
       "       [ 7.155],\n",
       "       [ 6.563],\n",
       "       [ 5.604],\n",
       "       [ 6.153],\n",
       "       [ 7.831],\n",
       "       [ 6.782],\n",
       "       [ 6.556],\n",
       "       [ 7.185],\n",
       "       [ 6.951],\n",
       "       [ 6.739],\n",
       "       [ 7.178],\n",
       "       [ 6.8  ],\n",
       "       [ 6.604],\n",
       "       [ 7.875],\n",
       "       [ 7.287],\n",
       "       [ 7.107],\n",
       "       [ 7.274],\n",
       "       [ 6.975],\n",
       "       [ 7.135],\n",
       "       [ 6.162],\n",
       "       [ 7.61 ],\n",
       "       [ 7.853],\n",
       "       [ 8.034],\n",
       "       [ 5.891],\n",
       "       [ 6.326],\n",
       "       [ 5.783],\n",
       "       [ 6.064],\n",
       "       [ 5.344],\n",
       "       [ 5.96 ],\n",
       "       [ 5.404],\n",
       "       [ 5.807],\n",
       "       [ 6.375],\n",
       "       [ 5.412],\n",
       "       [ 6.182],\n",
       "       [ 5.888],\n",
       "       [ 6.642],\n",
       "       [ 5.951],\n",
       "       [ 6.373],\n",
       "       [ 6.951],\n",
       "       [ 6.164],\n",
       "       [ 6.879],\n",
       "       [ 6.618],\n",
       "       [ 8.266],\n",
       "       [ 8.725],\n",
       "       [ 8.04 ],\n",
       "       [ 7.163],\n",
       "       [ 7.686],\n",
       "       [ 6.552],\n",
       "       [ 5.981],\n",
       "       [ 7.412],\n",
       "       [ 8.337],\n",
       "       [ 8.247],\n",
       "       [ 6.726],\n",
       "       [ 6.086],\n",
       "       [ 6.631],\n",
       "       [ 7.358],\n",
       "       [ 6.481],\n",
       "       [ 6.606],\n",
       "       [ 6.897],\n",
       "       [ 6.095],\n",
       "       [ 6.358],\n",
       "       [ 6.393],\n",
       "       [ 5.593],\n",
       "       [ 5.605],\n",
       "       [ 6.108],\n",
       "       [ 6.226],\n",
       "       [ 6.433],\n",
       "       [ 6.718],\n",
       "       [ 6.487],\n",
       "       [ 6.438],\n",
       "       [ 6.957],\n",
       "       [ 8.259],\n",
       "       [ 6.108],\n",
       "       [ 5.876],\n",
       "       [ 7.454],\n",
       "       [ 8.704],\n",
       "       [ 7.333],\n",
       "       [ 6.842],\n",
       "       [ 7.203],\n",
       "       [ 7.52 ],\n",
       "       [ 8.398],\n",
       "       [ 7.327],\n",
       "       [ 7.206],\n",
       "       [ 5.56 ],\n",
       "       [ 7.014],\n",
       "       [ 8.297],\n",
       "       [ 7.47 ],\n",
       "       [ 5.92 ],\n",
       "       [ 5.856],\n",
       "       [ 6.24 ],\n",
       "       [ 6.538],\n",
       "       [ 7.691],\n",
       "       [ 6.758],\n",
       "       [ 6.854],\n",
       "       [ 7.267],\n",
       "       [ 6.826],\n",
       "       [ 6.482],\n",
       "       [ 6.812],\n",
       "       [ 7.82 ],\n",
       "       [ 6.968],\n",
       "       [ 7.645],\n",
       "       [ 7.923],\n",
       "       [ 7.088],\n",
       "       [ 6.453],\n",
       "       [ 6.23 ],\n",
       "       [ 6.209],\n",
       "       [ 6.315],\n",
       "       [ 6.565],\n",
       "       [ 6.861],\n",
       "       [ 7.148],\n",
       "       [ 6.63 ],\n",
       "       [ 6.127],\n",
       "       [ 6.009],\n",
       "       [ 6.678],\n",
       "       [ 6.549],\n",
       "       [ 5.79 ],\n",
       "       [ 6.345],\n",
       "       [ 7.041],\n",
       "       [ 6.871],\n",
       "       [ 6.59 ],\n",
       "       [ 6.495],\n",
       "       [ 6.982],\n",
       "       [ 7.236],\n",
       "       [ 6.616],\n",
       "       [ 7.42 ],\n",
       "       [ 6.849],\n",
       "       [ 6.635],\n",
       "       [ 5.972],\n",
       "       [ 4.973],\n",
       "       [ 6.122],\n",
       "       [ 6.023],\n",
       "       [ 6.266],\n",
       "       [ 6.567],\n",
       "       [ 5.705],\n",
       "       [ 5.914],\n",
       "       [ 5.782],\n",
       "       [ 6.382],\n",
       "       [ 6.113],\n",
       "       [ 6.426],\n",
       "       [ 6.376],\n",
       "       [ 6.041],\n",
       "       [ 5.708],\n",
       "       [ 6.415],\n",
       "       [ 6.431],\n",
       "       [ 6.312],\n",
       "       [ 6.083],\n",
       "       [ 5.868],\n",
       "       [ 6.333],\n",
       "       [ 6.144],\n",
       "       [ 5.706],\n",
       "       [ 6.031],\n",
       "       [ 6.316],\n",
       "       [ 6.31 ],\n",
       "       [ 6.037],\n",
       "       [ 5.869],\n",
       "       [ 5.895],\n",
       "       [ 6.059],\n",
       "       [ 5.985],\n",
       "       [ 5.968],\n",
       "       [ 7.241],\n",
       "       [ 6.54 ],\n",
       "       [ 6.696],\n",
       "       [ 6.874],\n",
       "       [ 6.014],\n",
       "       [ 5.898],\n",
       "       [ 6.516],\n",
       "       [ 6.635],\n",
       "       [ 6.939],\n",
       "       [ 6.49 ],\n",
       "       [ 6.579],\n",
       "       [ 5.884],\n",
       "       [ 6.728],\n",
       "       [ 5.663],\n",
       "       [ 5.936],\n",
       "       [ 6.212],\n",
       "       [ 6.395],\n",
       "       [ 6.127],\n",
       "       [ 6.112],\n",
       "       [ 6.398],\n",
       "       [ 6.251],\n",
       "       [ 5.362],\n",
       "       [ 5.803],\n",
       "       [ 8.78 ],\n",
       "       [ 3.561],\n",
       "       [ 4.963],\n",
       "       [ 3.863],\n",
       "       [ 4.97 ],\n",
       "       [ 6.683],\n",
       "       [ 7.016],\n",
       "       [ 6.216],\n",
       "       [ 5.875],\n",
       "       [ 4.906],\n",
       "       [ 4.138],\n",
       "       [ 7.313],\n",
       "       [ 6.649],\n",
       "       [ 6.794],\n",
       "       [ 6.38 ],\n",
       "       [ 6.223],\n",
       "       [ 6.968],\n",
       "       [ 6.545],\n",
       "       [ 5.536],\n",
       "       [ 5.52 ],\n",
       "       [ 4.368],\n",
       "       [ 5.277],\n",
       "       [ 4.652],\n",
       "       [ 5.   ],\n",
       "       [ 4.88 ],\n",
       "       [ 5.39 ],\n",
       "       [ 5.713],\n",
       "       [ 6.051],\n",
       "       [ 5.036],\n",
       "       [ 6.193],\n",
       "       [ 5.887],\n",
       "       [ 6.471],\n",
       "       [ 6.405],\n",
       "       [ 5.747],\n",
       "       [ 5.453],\n",
       "       [ 5.852],\n",
       "       [ 5.987],\n",
       "       [ 6.343],\n",
       "       [ 6.404],\n",
       "       [ 5.349],\n",
       "       [ 5.531],\n",
       "       [ 5.683],\n",
       "       [ 4.138],\n",
       "       [ 5.608],\n",
       "       [ 5.617],\n",
       "       [ 6.852],\n",
       "       [ 5.757],\n",
       "       [ 6.657],\n",
       "       [ 4.628],\n",
       "       [ 5.155],\n",
       "       [ 4.519],\n",
       "       [ 6.434],\n",
       "       [ 6.782],\n",
       "       [ 5.304],\n",
       "       [ 5.957],\n",
       "       [ 6.824],\n",
       "       [ 6.411],\n",
       "       [ 6.006],\n",
       "       [ 5.648],\n",
       "       [ 6.103],\n",
       "       [ 5.565],\n",
       "       [ 5.896],\n",
       "       [ 5.837],\n",
       "       [ 6.202],\n",
       "       [ 6.193],\n",
       "       [ 6.38 ],\n",
       "       [ 6.348],\n",
       "       [ 6.833],\n",
       "       [ 6.425],\n",
       "       [ 6.436],\n",
       "       [ 6.208],\n",
       "       [ 6.629],\n",
       "       [ 6.461],\n",
       "       [ 6.152],\n",
       "       [ 5.935],\n",
       "       [ 5.627],\n",
       "       [ 5.818],\n",
       "       [ 6.406],\n",
       "       [ 6.219],\n",
       "       [ 6.485],\n",
       "       [ 5.854],\n",
       "       [ 6.459],\n",
       "       [ 6.341],\n",
       "       [ 6.251],\n",
       "       [ 6.185],\n",
       "       [ 6.417],\n",
       "       [ 6.749],\n",
       "       [ 6.655],\n",
       "       [ 6.297],\n",
       "       [ 7.393],\n",
       "       [ 6.728],\n",
       "       [ 6.525],\n",
       "       [ 5.976],\n",
       "       [ 5.936],\n",
       "       [ 6.301],\n",
       "       [ 6.081],\n",
       "       [ 6.701],\n",
       "       [ 6.376],\n",
       "       [ 6.317],\n",
       "       [ 6.513],\n",
       "       [ 6.209],\n",
       "       [ 5.759],\n",
       "       [ 5.952],\n",
       "       [ 6.003],\n",
       "       [ 5.926],\n",
       "       [ 5.713],\n",
       "       [ 6.167],\n",
       "       [ 6.229],\n",
       "       [ 6.437],\n",
       "       [ 6.98 ],\n",
       "       [ 5.427],\n",
       "       [ 6.162],\n",
       "       [ 6.484],\n",
       "       [ 5.304],\n",
       "       [ 6.185],\n",
       "       [ 6.229],\n",
       "       [ 6.242],\n",
       "       [ 6.75 ],\n",
       "       [ 7.061],\n",
       "       [ 5.762],\n",
       "       [ 5.871],\n",
       "       [ 6.312],\n",
       "       [ 6.114],\n",
       "       [ 5.905],\n",
       "       [ 5.454],\n",
       "       [ 5.414],\n",
       "       [ 5.093],\n",
       "       [ 5.983],\n",
       "       [ 5.983],\n",
       "       [ 5.707],\n",
       "       [ 5.926],\n",
       "       [ 5.67 ],\n",
       "       [ 5.39 ],\n",
       "       [ 5.794],\n",
       "       [ 6.019],\n",
       "       [ 5.569],\n",
       "       [ 6.027],\n",
       "       [ 6.593],\n",
       "       [ 6.12 ],\n",
       "       [ 6.976],\n",
       "       [ 6.794],\n",
       "       [ 6.03 ]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Y = boston_df.Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[array([ 6.575]), 1],\n",
       "       [array([ 6.421]), 1],\n",
       "       [array([ 7.185]), 1],\n",
       "       ..., \n",
       "       [array([ 6.976]), 1],\n",
       "       [array([ 6.794]), 1],\n",
       "       [array([ 6.03]), 1]], dtype=object)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# [X 1]\n",
    "X = np.array([[value,1] for value in X])\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "m, b = np.linalg.lstsq(X,Y)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10b9154e0>]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAECCAYAAADq7fyyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10abdc048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(boston_df.RM,boston_df.Price,'o')\n",
    "\n",
    "x = boston_df.RM\n",
    "\n",
    "plt.plot(x,m*x + b,'r',label='Best Fit Line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The room mean square error was 6.60\n"
     ]
    }
   ],
   "source": [
    "result = np.linalg.lstsq(X,Y)\n",
    "\n",
    "error_total = result[1]\n",
    "\n",
    "rmse = np.sqrt(error_total/len(X))\n",
    "\n",
    "print ('The room mean square error was %.2f' %rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sklearn\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lreg = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_multi = boston_df.drop('Price',1)\n",
    "\n",
    "Y_target = boston_df.Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lreg.fit(X_multi,Y_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The estimated intercept coefficient is 36.49 \n",
      "The number for coefficients used was 13 \n"
     ]
    }
   ],
   "source": [
    "print ('The estimated intercept coefficient is %.2f ' % lreg.intercept_)\n",
    "\n",
    "print ('The number for coefficients used was %d ' %len(lreg.coef_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Features</th>\n",
       "      <th>Coefficient Estimate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CRIM</td>\n",
       "      <td>-0.107171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ZN</td>\n",
       "      <td>0.046395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>INDUS</td>\n",
       "      <td>0.020860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHAS</td>\n",
       "      <td>2.688561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NOX</td>\n",
       "      <td>-17.795759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RM</td>\n",
       "      <td>3.804752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AGE</td>\n",
       "      <td>0.000751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DIS</td>\n",
       "      <td>-1.475759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>RAD</td>\n",
       "      <td>0.305655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>TAX</td>\n",
       "      <td>-0.012329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PTRATIO</td>\n",
       "      <td>-0.953464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>B</td>\n",
       "      <td>0.009393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>LSTAT</td>\n",
       "      <td>-0.525467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Price</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Features  Coefficient Estimate\n",
       "0      CRIM             -0.107171\n",
       "1        ZN              0.046395\n",
       "2     INDUS              0.020860\n",
       "3      CHAS              2.688561\n",
       "4       NOX            -17.795759\n",
       "5        RM              3.804752\n",
       "6       AGE              0.000751\n",
       "7       DIS             -1.475759\n",
       "8       RAD              0.305655\n",
       "9       TAX             -0.012329\n",
       "10  PTRATIO             -0.953464\n",
       "11        B              0.009393\n",
       "12    LSTAT             -0.525467\n",
       "13    Price                   NaN"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeff_df = DataFrame(boston_df.columns)\n",
    "coeff_df.columns = ['Features']\n",
    "\n",
    "coeff_df['Coefficient Estimate'] = pd.Series(lreg.coef_)\n",
    "\n",
    "coeff_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Training and Validation Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train,X_test,Y_train,Y_test = sklearn.cross_validation.train_test_split(X,boston_df.Price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(379, 2) (127, 2) (379,) (127,)\n"
     ]
    }
   ],
   "source": [
    "print (X_train.shape,X_test.shape,Y_train.shape,Y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[array([ 6.348]) 1]\n",
      " [array([ 6.715]) 1]\n",
      " [array([ 4.652]) 1]\n",
      " [array([ 6.495]) 1]\n",
      " [array([ 6.943]) 1]\n",
      " [array([ 6.174]) 1]\n",
      " [array([ 6.121]) 1]\n",
      " [array([ 6.29]) 1]\n",
      " [array([ 5.456]) 1]\n",
      " [array([ 6.229]) 1]\n",
      " [array([ 6.069]) 1]\n",
      " [array([ 6.437]) 1]\n",
      " [array([ 6.434]) 1]\n",
      " [array([ 6.49]) 1]\n",
      " [array([ 6.092]) 1]\n",
      " [array([ 8.034]) 1]\n",
      " [array([ 7.313]) 1]\n",
      " [array([ 6.513]) 1]\n",
      " [array([ 5.871]) 1]\n",
      " [array([ 6.041]) 1]\n",
      " [array([ 5.841]) 1]\n",
      " [array([ 6.482]) 1]\n",
      " [array([ 6.326]) 1]\n",
      " [array([ 6.63]) 1]\n",
      " [array([ 6.538]) 1]\n",
      " [array([ 6.229]) 1]\n",
      " [array([ 5.593]) 1]\n",
      " [array([ 5.961]) 1]\n",
      " [array([ 6.345]) 1]\n",
      " [array([ 6.172]) 1]\n",
      " [array([ 5.713]) 1]\n",
      " [array([ 6.461]) 1]\n",
      " [array([ 8.375]) 1]\n",
      " [array([ 6.484]) 1]\n",
      " [array([ 6.826]) 1]\n",
      " [array([ 5.762]) 1]\n",
      " [array([ 5.888]) 1]\n",
      " [array([ 6.009]) 1]\n",
      " [array([ 6.415]) 1]\n",
      " [array([ 6.297]) 1]\n",
      " [array([ 6.358]) 1]\n",
      " [array([ 6.169]) 1]\n",
      " [array([ 6.86]) 1]\n",
      " [array([ 7.104]) 1]\n",
      " [array([ 5.277]) 1]\n",
      " [array([ 6.833]) 1]\n",
      " [array([ 5.807]) 1]\n",
      " [array([ 7.163]) 1]\n",
      " [array([ 6.727]) 1]\n",
      " [array([ 5.731]) 1]\n",
      " [array([ 6.03]) 1]\n",
      " [array([ 7.024]) 1]\n",
      " [array([ 4.97]) 1]\n",
      " [array([ 5.854]) 1]\n",
      " [array([ 6.487]) 1]\n",
      " [array([ 6.211]) 1]\n",
      " [array([ 7.923]) 1]\n",
      " [array([ 6.405]) 1]\n",
      " [array([ 7.287]) 1]\n",
      " [array([ 6.377]) 1]\n",
      " [array([ 4.138]) 1]\n",
      " [array([ 6.373]) 1]\n",
      " [array([ 5.693]) 1]\n",
      " [array([ 5.99]) 1]\n",
      " [array([ 6.968]) 1]\n",
      " [array([ 5.782]) 1]\n",
      " [array([ 6.701]) 1]\n",
      " [array([ 6.51]) 1]\n",
      " [array([ 6.411]) 1]\n",
      " [array([ 7.007]) 1]\n",
      " [array([ 5.741]) 1]\n",
      " [array([ 6.728]) 1]\n",
      " [array([ 6.471]) 1]\n",
      " [array([ 6.655]) 1]\n",
      " [array([ 6.649]) 1]\n",
      " [array([ 6.375]) 1]\n",
      " [array([ 6.202]) 1]\n",
      " [array([ 6.382]) 1]\n",
      " [array([ 6.842]) 1]\n",
      " [array([ 5.869]) 1]\n",
      " [array([ 6.023]) 1]\n",
      " [array([ 6.312]) 1]\n",
      " [array([ 5.602]) 1]\n",
      " [array([ 6.393]) 1]\n",
      " [array([ 6.103]) 1]\n",
      " [array([ 6.453]) 1]\n",
      " [array([ 6.319]) 1]\n",
      " [array([ 5.682]) 1]\n",
      " [array([ 7.274]) 1]\n",
      " [array([ 6.433]) 1]\n",
      " [array([ 6.631]) 1]\n",
      " [array([ 5.706]) 1]\n",
      " [array([ 6.674]) 1]\n",
      " [array([ 5.627]) 1]\n",
      " [array([ 5.404]) 1]\n",
      " [array([ 5.683]) 1]\n",
      " [array([ 7.47]) 1]\n",
      " [array([ 6.021]) 1]\n",
      " [array([ 4.138]) 1]\n",
      " [array([ 5.454]) 1]\n",
      " [array([ 5.822]) 1]\n",
      " [array([ 6.127]) 1]\n",
      " [array([ 6.968]) 1]\n",
      " [array([ 6.77]) 1]\n",
      " [array([ 6.273]) 1]\n",
      " [array([ 5.362]) 1]\n",
      " [array([ 6.24]) 1]\n",
      " [array([ 6.335]) 1]\n",
      " [array([ 6.816]) 1]\n",
      " [array([ 7.875]) 1]\n",
      " [array([ 6.619]) 1]\n",
      " [array([ 5.404]) 1]\n",
      " [array([ 6.794]) 1]\n",
      " [array([ 8.266]) 1]\n",
      " [array([ 6.316]) 1]\n",
      " [array([ 6.004]) 1]\n",
      " [array([ 6.426]) 1]\n",
      " [array([ 6.167]) 1]\n",
      " [array([ 6.812]) 1]\n",
      " [array([ 5.877]) 1]\n",
      " [array([ 5.927]) 1]\n",
      " [array([ 5.757]) 1]\n",
      " [array([ 5.783]) 1]\n",
      " [array([ 6.718]) 1]\n",
      " [array([ 6.176]) 1]\n",
      " [array([ 6.162]) 1]\n",
      " [array([ 6.031]) 1]\n",
      " [array([ 6.137]) 1]\n",
      " [array([ 7.185]) 1]\n",
      " [array([ 8.337]) 1]\n",
      " [array([ 6.75]) 1]\n",
      " [array([ 5.637]) 1]\n",
      " [array([ 6.431]) 1]\n",
      " [array([ 7.148]) 1]\n",
      " [array([ 6.782]) 1]\n",
      " [array([ 5.52]) 1]\n",
      " [array([ 6.431]) 1]\n",
      " [array([ 6.852]) 1]\n",
      " [array([ 5.935]) 1]\n",
      " [array([ 6.38]) 1]\n",
      " [array([ 5.56]) 1]\n",
      " [array([ 5.986]) 1]\n",
      " [array([ 6.333]) 1]\n",
      " [array([ 6.122]) 1]\n",
      " [array([ 7.416]) 1]\n",
      " [array([ 8.247]) 1]\n",
      " [array([ 4.88]) 1]\n",
      " [array([ 6.762]) 1]\n",
      " [array([ 7.645]) 1]\n",
      " [array([ 6.383]) 1]\n",
      " [array([ 6.341]) 1]\n",
      " [array([ 6.549]) 1]\n",
      " [array([ 7.135]) 1]\n",
      " [array([ 5.403]) 1]\n",
      " [array([ 7.489]) 1]\n",
      " [array([ 6.951]) 1]\n",
      " [array([ 5.966]) 1]\n",
      " [array([ 5.885]) 1]\n",
      " [array([ 6.027]) 1]\n",
      " [array([ 6.037]) 1]\n",
      " [array([ 5.631]) 1]\n",
      " [array([ 6.635]) 1]\n",
      " [array([ 6.251]) 1]\n",
      " [array([ 5.57]) 1]\n",
      " [array([ 5.926]) 1]\n",
      " [array([ 6.726]) 1]\n",
      " [array([ 6.006]) 1]\n",
      " [array([ 5.875]) 1]\n",
      " [array([ 6.101]) 1]\n",
      " [array([ 6.417]) 1]\n",
      " [array([ 6.629]) 1]\n",
      " [array([ 7.088]) 1]\n",
      " [array([ 7.206]) 1]\n",
      " [array([ 6.279]) 1]\n",
      " [array([ 6.957]) 1]\n",
      " [array([ 6.317]) 1]\n",
      " [array([ 5.951]) 1]\n",
      " [array([ 5.794]) 1]\n",
      " [array([ 6.083]) 1]\n",
      " [array([ 6.31]) 1]\n",
      " [array([ 5.663]) 1]\n",
      " [array([ 6.458]) 1]\n",
      " [array([ 6.193]) 1]\n",
      " [array([ 6.209]) 1]\n",
      " [array([ 6.127]) 1]\n",
      " [array([ 7.454]) 1]\n",
      " [array([ 7.185]) 1]\n",
      " [array([ 7.358]) 1]\n",
      " [array([ 6.019]) 1]\n",
      " [array([ 8.297]) 1]\n",
      " [array([ 6.185]) 1]\n",
      " [array([ 6.897]) 1]\n",
      " [array([ 5.]) 1]\n",
      " [array([ 6.579]) 1]\n",
      " [array([ 5.412]) 1]\n",
      " [array([ 8.725]) 1]\n",
      " [array([ 6.59]) 1]\n",
      " [array([ 7.61]) 1]\n",
      " [array([ 5.936]) 1]\n",
      " [array([ 5.884]) 1]\n",
      " [array([ 6.03]) 1]\n",
      " [array([ 5.872]) 1]\n",
      " [array([ 6.683]) 1]\n",
      " [array([ 5.875]) 1]\n",
      " [array([ 5.608]) 1]\n",
      " [array([ 4.926]) 1]\n",
      " [array([ 6.249]) 1]\n",
      " [array([ 6.162]) 1]\n",
      " [array([ 6.376]) 1]\n",
      " [array([ 7.853]) 1]\n",
      " [array([ 7.412]) 1]\n",
      " [array([ 5.304]) 1]\n",
      " [array([ 6.153]) 1]\n",
      " [array([ 6.14]) 1]\n",
      " [array([ 6.051]) 1]\n",
      " [array([ 5.036]) 1]\n",
      " [array([ 6.794]) 1]\n",
      " [array([ 5.708]) 1]\n",
      " [array([ 5.713]) 1]\n",
      " [array([ 5.531]) 1]\n",
      " [array([ 7.802]) 1]\n",
      " [array([ 6.315]) 1]\n",
      " [array([ 6.372]) 1]\n",
      " [array([ 6.226]) 1]\n",
      " [array([ 6.824]) 1]\n",
      " [array([ 5.787]) 1]\n",
      " [array([ 6.728]) 1]\n",
      " [array([ 6.749]) 1]\n",
      " [array([ 6.13]) 1]\n",
      " [array([ 5.803]) 1]\n",
      " [array([ 5.952]) 1]\n",
      " [array([ 6.982]) 1]\n",
      " [array([ 6.849]) 1]\n",
      " [array([ 5.834]) 1]\n",
      " [array([ 6.112]) 1]\n",
      " [array([ 5.976]) 1]\n",
      " [array([ 6.193]) 1]\n",
      " [array([ 3.561]) 1]\n",
      " [array([ 5.427]) 1]\n",
      " [array([ 5.594]) 1]\n",
      " [array([ 5.569]) 1]\n",
      " [array([ 7.014]) 1]\n",
      " [array([ 6.115]) 1]\n",
      " [array([ 6.642]) 1]\n",
      " [array([ 6.546]) 1]\n",
      " [array([ 6.064]) 1]\n",
      " [array([ 5.648]) 1]\n",
      " [array([ 6.406]) 1]\n",
      " [array([ 5.701]) 1]\n",
      " [array([ 5.713]) 1]\n",
      " [array([ 5.39]) 1]\n",
      " [array([ 6.43]) 1]\n",
      " [array([ 6.12]) 1]\n",
      " [array([ 5.935]) 1]\n",
      " [array([ 7.831]) 1]\n",
      " [array([ 6.405]) 1]\n",
      " [array([ 5.965]) 1]\n",
      " [array([ 5.604]) 1]\n",
      " [array([ 5.349]) 1]\n",
      " [array([ 6.376]) 1]\n",
      " [array([ 6.545]) 1]\n",
      " [array([ 5.933]) 1]\n",
      " [array([ 6.066]) 1]\n",
      " [array([ 6.144]) 1]\n",
      " [array([ 7.236]) 1]\n",
      " [array([ 5.856]) 1]\n",
      " [array([ 8.398]) 1]\n",
      " [array([ 5.868]) 1]\n",
      " [array([ 7.061]) 1]\n",
      " [array([ 6.108]) 1]\n",
      " [array([ 6.343]) 1]\n",
      " [array([ 5.599]) 1]\n",
      " [array([ 5.836]) 1]\n",
      " [array([ 6.854]) 1]\n",
      " [array([ 6.144]) 1]\n",
      " [array([ 8.704]) 1]\n",
      " [array([ 6.575]) 1]\n",
      " [array([ 5.757]) 1]\n",
      " [array([ 6.014]) 1]\n",
      " [array([ 5.852]) 1]\n",
      " [array([ 6.185]) 1]\n",
      " [array([ 5.889]) 1]\n",
      " [array([ 5.854]) 1]\n",
      " [array([ 5.786]) 1]\n",
      " [array([ 7.178]) 1]\n",
      " [array([ 6.398]) 1]\n",
      " [array([ 5.759]) 1]\n",
      " [array([ 5.896]) 1]\n",
      " [array([ 6.251]) 1]\n",
      " [array([ 6.086]) 1]\n",
      " [array([ 7.327]) 1]\n",
      " [array([ 6.127]) 1]\n",
      " [array([ 6.223]) 1]\n",
      " [array([ 6.242]) 1]\n",
      " [array([ 6.195]) 1]\n",
      " [array([ 5.928]) 1]\n",
      " [array([ 6.63]) 1]\n",
      " [array([ 6.302]) 1]\n",
      " [array([ 5.093]) 1]\n",
      " [array([ 6.552]) 1]\n",
      " [array([ 7.147]) 1]\n",
      " [array([ 6.456]) 1]\n",
      " [array([ 5.983]) 1]\n",
      " [array([ 4.628]) 1]\n",
      " [array([ 6.8]) 1]\n",
      " [array([ 6.516]) 1]\n",
      " [array([ 6.326]) 1]\n",
      " [array([ 6.417]) 1]\n",
      " [array([ 5.905]) 1]\n",
      " [array([ 5.012]) 1]\n",
      " [array([ 6.481]) 1]\n",
      " [array([ 6.459]) 1]\n",
      " [array([ 6.442]) 1]\n",
      " [array([ 6.98]) 1]\n",
      " [array([ 6.245]) 1]\n",
      " [array([ 5.87]) 1]\n",
      " [array([ 7.079]) 1]\n",
      " [array([ 5.453]) 1]\n",
      " [array([ 6.951]) 1]\n",
      " [array([ 6.495]) 1]\n",
      " [array([ 5.186]) 1]\n",
      " [array([ 6.425]) 1]\n",
      " [array([ 5.985]) 1]\n",
      " [array([ 6.618]) 1]\n",
      " [array([ 6.114]) 1]\n",
      " [array([ 6.54]) 1]\n",
      " [array([ 5.414]) 1]\n",
      " [array([ 6.525]) 1]\n",
      " [array([ 5.942]) 1]\n",
      " [array([ 6.003]) 1]\n",
      " [array([ 6.266]) 1]\n",
      " [array([ 6.616]) 1]\n",
      " [array([ 6.595]) 1]\n",
      " [array([ 5.972]) 1]\n",
      " [array([ 6.152]) 1]\n",
      " [array([ 6.727]) 1]\n",
      " [array([ 5.837]) 1]\n",
      " [array([ 5.891]) 1]\n",
      " [array([ 6.98]) 1]\n",
      " [array([ 7.686]) 1]\n",
      " [array([ 6.212]) 1]\n",
      " [array([ 5.565]) 1]\n",
      " [array([ 6.417]) 1]\n",
      " [array([ 3.863]) 1]\n",
      " [array([ 6.782]) 1]\n",
      " [array([ 4.903]) 1]\n",
      " [array([ 6.229]) 1]\n",
      " [array([ 6.606]) 1]\n",
      " [array([ 6.015]) 1]\n",
      " [array([ 5.914]) 1]\n",
      " [array([ 6.436]) 1]\n",
      " [array([ 6.113]) 1]\n",
      " [array([ 6.108]) 1]\n",
      " [array([ 5.95]) 1]\n",
      " [array([ 5.96]) 1]\n",
      " [array([ 7.52]) 1]\n",
      " [array([ 7.016]) 1]\n",
      " [array([ 5.39]) 1]\n",
      " [array([ 5.859]) 1]\n",
      " [array([ 6.151]) 1]\n",
      " [array([ 5.876]) 1]\n",
      " [array([ 7.249]) 1]\n",
      " [array([ 6.939]) 1]\n",
      " [array([ 6.047]) 1]\n",
      " [array([ 6.657]) 1]\n",
      " [array([ 5.304]) 1]\n",
      " [array([ 5.987]) 1]\n",
      " [array([ 6.129]) 1]\n",
      " [array([ 6.167]) 1]\n",
      " [array([ 5.968]) 1]\n",
      " [array([ 6.421]) 1]\n",
      " [array([ 5.019]) 1]\n",
      " [array([ 6.301]) 1]\n",
      " [array([ 5.613]) 1]\n",
      " [array([ 8.069]) 1]\n",
      " [array([ 7.203]) 1]\n",
      " [array([ 5.747]) 1]\n",
      " [array([ 6.593]) 1]\n",
      " [array([ 6.23]) 1]] [[array([ 5.79]) 1]\n",
      " [array([ 6.879]) 1]\n",
      " [array([ 6.163]) 1]\n",
      " [array([ 5.399]) 1]\n",
      " [array([ 4.519]) 1]\n",
      " [array([ 5.813]) 1]\n",
      " [array([ 5.874]) 1]\n",
      " [array([ 5.856]) 1]\n",
      " [array([ 6.208]) 1]\n",
      " [array([ 5.707]) 1]\n",
      " [array([ 6.511]) 1]\n",
      " [array([ 5.966]) 1]\n",
      " [array([ 6.096]) 1]\n",
      " [array([ 5.957]) 1]\n",
      " [array([ 6.219]) 1]\n",
      " [array([ 5.605]) 1]\n",
      " [array([ 4.973]) 1]\n",
      " [array([ 7.155]) 1]\n",
      " [array([ 7.241]) 1]\n",
      " [array([ 5.67]) 1]\n",
      " [array([ 7.42]) 1]\n",
      " [array([ 6.416]) 1]\n",
      " [array([ 5.272]) 1]\n",
      " [array([ 5.936]) 1]\n",
      " [array([ 6.25]) 1]\n",
      " [array([ 5.878]) 1]\n",
      " [array([ 4.906]) 1]\n",
      " [array([ 6.315]) 1]\n",
      " [array([ 6.474]) 1]\n",
      " [array([ 6.781]) 1]\n",
      " [array([ 5.628]) 1]\n",
      " [array([ 6.065]) 1]\n",
      " [array([ 5.344]) 1]\n",
      " [array([ 6.635]) 1]\n",
      " [array([ 6.874]) 1]\n",
      " [array([ 5.913]) 1]\n",
      " [array([ 5.468]) 1]\n",
      " [array([ 5.851]) 1]\n",
      " [array([ 6.096]) 1]\n",
      " [array([ 7.393]) 1]\n",
      " [array([ 5.155]) 1]\n",
      " [array([ 6.059]) 1]\n",
      " [array([ 8.04]) 1]\n",
      " [array([ 5.895]) 1]\n",
      " [array([ 6.556]) 1]\n",
      " [array([ 5.813]) 1]\n",
      " [array([ 6.152]) 1]\n",
      " [array([ 5.709]) 1]\n",
      " [array([ 5.88]) 1]\n",
      " [array([ 6.095]) 1]\n",
      " [array([ 6.438]) 1]\n",
      " [array([ 6.678]) 1]\n",
      " [array([ 6.38]) 1]\n",
      " [array([ 5.888]) 1]\n",
      " [array([ 6.454]) 1]\n",
      " [array([ 5.92]) 1]\n",
      " [array([ 6.122]) 1]\n",
      " [array([ 7.107]) 1]\n",
      " [array([ 6.976]) 1]\n",
      " [array([ 5.998]) 1]\n",
      " [array([ 5.961]) 1]\n",
      " [array([ 6.02]) 1]\n",
      " [array([ 6.232]) 1]\n",
      " [array([ 4.963]) 1]\n",
      " [array([ 5.617]) 1]\n",
      " [array([ 6.625]) 1]\n",
      " [array([ 6.565]) 1]\n",
      " [array([ 5.949]) 1]\n",
      " [array([ 6.975]) 1]\n",
      " [array([ 5.705]) 1]\n",
      " [array([ 6.563]) 1]\n",
      " [array([ 6.402]) 1]\n",
      " [array([ 6.211]) 1]\n",
      " [array([ 5.727]) 1]\n",
      " [array([ 6.998]) 1]\n",
      " [array([ 7.333]) 1]\n",
      " [array([ 5.981]) 1]\n",
      " [array([ 5.857]) 1]\n",
      " [array([ 7.765]) 1]\n",
      " [array([ 6.739]) 1]\n",
      " [array([ 8.259]) 1]\n",
      " [array([ 6.389]) 1]\n",
      " [array([ 6.145]) 1]\n",
      " [array([ 5.898]) 1]\n",
      " [array([ 5.879]) 1]\n",
      " [array([ 7.929]) 1]\n",
      " [array([ 6.312]) 1]\n",
      " [array([ 5.536]) 1]\n",
      " [array([ 6.696]) 1]\n",
      " [array([ 4.368]) 1]\n",
      " [array([ 6.164]) 1]\n",
      " [array([ 7.82]) 1]\n",
      " [array([ 6.167]) 1]\n",
      " [array([ 6.871]) 1]\n",
      " [array([ 6.395]) 1]\n",
      " [array([ 6.004]) 1]\n",
      " [array([ 6.604]) 1]\n",
      " [array([ 7.041]) 1]\n",
      " [array([ 6.216]) 1]\n",
      " [array([ 5.926]) 1]\n",
      " [array([ 6.072]) 1]\n",
      " [array([ 6.485]) 1]\n",
      " [array([ 5.597]) 1]\n",
      " [array([ 6.861]) 1]\n",
      " [array([ 6.286]) 1]\n",
      " [array([ 6.182]) 1]\n",
      " [array([ 7.691]) 1]\n",
      " [array([ 6.404]) 1]\n",
      " [array([ 6.405]) 1]\n",
      " [array([ 6.012]) 1]\n",
      " [array([ 6.254]) 1]\n",
      " [array([ 8.78]) 1]\n",
      " [array([ 5.887]) 1]\n",
      " [array([ 5.818]) 1]\n",
      " [array([ 6.142]) 1]\n",
      " [array([ 6.081]) 1]\n",
      " [array([ 7.82]) 1]\n",
      " [array([ 6.209]) 1]\n",
      " [array([ 6.009]) 1]\n",
      " [array([ 6.758]) 1]\n",
      " [array([ 5.572]) 1]\n",
      " [array([ 6.567]) 1]\n",
      " [array([ 5.983]) 1]\n",
      " [array([ 7.267]) 1]\n",
      " [array([ 5.85]) 1]\n",
      " [array([ 5.924]) 1]\n",
      " [array([ 5.963]) 1]] 430    14.5\n",
      "111    22.8\n",
      "386    10.5\n",
      "302    26.4\n",
      "157    41.3\n",
      "140    14.0\n",
      "87     22.2\n",
      "65     23.5\n",
      "18     20.2\n",
      "479    21.4\n",
      "44     21.2\n",
      "472    23.2\n",
      "415     7.2\n",
      "350    22.9\n",
      "113    18.7\n",
      "204    50.0\n",
      "375    15.0\n",
      "463    20.2\n",
      "484    20.6\n",
      "322    20.4\n",
      "36     20.0\n",
      "278    29.1\n",
      "131    19.6\n",
      "292    27.9\n",
      "272    24.4\n",
      "109    19.4\n",
      "244    17.6\n",
      "71     21.7\n",
      "298    22.5\n",
      "7      27.1\n",
      "       ... \n",
      "316    17.8\n",
      "433    14.3\n",
      "319    21.0\n",
      "246    24.3\n",
      "32     13.2\n",
      "210    21.7\n",
      "261    43.1\n",
      "370    50.0\n",
      "389    11.5\n",
      "174    22.6\n",
      "139    17.8\n",
      "255    20.9\n",
      "55     35.4\n",
      "349    26.6\n",
      "27     14.8\n",
      "411    17.2\n",
      "417    10.4\n",
      "400     5.6\n",
      "154    17.0\n",
      "104    20.1\n",
      "340    18.7\n",
      "1      21.6\n",
      "141    14.4\n",
      "458    14.9\n",
      "126    15.7\n",
      "97     38.7\n",
      "260    33.8\n",
      "397     8.5\n",
      "501    22.4\n",
      "286    20.1\n",
      "Name: Price, dtype: float64 297    20.3\n",
      "222    27.5\n",
      "96     21.4\n",
      "48     14.4\n",
      "414     7.0\n",
      "23     14.5\n",
      "79     20.3\n",
      "123    17.3\n",
      "434    11.7\n",
      "493    21.8\n",
      "52     25.0\n",
      "38     24.7\n",
      "14     18.2\n",
      "418     8.8\n",
      "442    18.4\n",
      "245    18.5\n",
      "310    16.1\n",
      "182    37.9\n",
      "341    32.7\n",
      "495    23.1\n",
      "306    33.4\n",
      "173    23.6\n",
      "156    13.1\n",
      "457    13.5\n",
      "160    27.0\n",
      "67     22.0\n",
      "373    13.8\n",
      "288    22.3\n",
      "108    19.8\n",
      "101    26.5\n",
      "       ... \n",
      "299    29.0\n",
      "371    50.0\n",
      "494    24.5\n",
      "31     14.5\n",
      "443    15.4\n",
      "149    15.4\n",
      "290    28.5\n",
      "75     21.4\n",
      "215    25.0\n",
      "273    35.2\n",
      "402    12.1\n",
      "396    12.5\n",
      "6      22.9\n",
      "114    18.5\n",
      "364    21.9\n",
      "394    12.7\n",
      "440    10.5\n",
      "22     15.2\n",
      "459    20.0\n",
      "280    45.4\n",
      "464    21.4\n",
      "294    21.7\n",
      "274    32.4\n",
      "172    23.1\n",
      "314    23.8\n",
      "492    20.1\n",
      "276    33.2\n",
      "37     21.0\n",
      "24     15.6\n",
      "50     19.7\n",
      "Name: Price, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print (X_train,X_test,Y_train,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lreg = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lreg.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred_train = lreg.predict(X_train)\n",
    "pred_test = lreg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Fit a model X_train, and calculate the MSE with Y_train: 47.57\n",
      " Fit a model X_train, and calculate the MSE with X_test and Y_test: 31.93\n"
     ]
    }
   ],
   "source": [
    "print (\" Fit a model X_train, and calculate the MSE with Y_train: %.2f\" % np.mean((Y_train-pred_train)**2))\n",
    "\n",
    "print (\" Fit a model X_train, and calculate the MSE with X_test and Y_test: %.2f\" % np.mean((Y_test-pred_test)**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Residual Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10bcd5588>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAEKCAYAAAD3tSVSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nS/LGGONgluSNMcbB/j83B6XDGL0YzAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bc91898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train = plt.scatter(pred_train,(pred_train - Y_train),c='b',alpha=0.5)\n",
    "\n",
    "test = plt.scatter(pred_test,(pred_test - Y_test),c='r',alpha=0.5)\n",
    "\n",
    "plt.hlines(y=0,xmin=-10,xmax=40)\n",
    "\n",
    "plt.legend((train,test),('Training','Test'),loc='lower left')\n",
    "\n",
    "plt.title('Residual Plots')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}