boston_housing_old.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# datasets.load_boston"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "boston = datasets.load_boston()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'DESCR': \"Boston House Prices dataset\\n\\nNotes\\n------\\nData 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\\nThis is a copy of UCI ML housing dataset.\\nhttp://archive.ics.uci.edu/ml/datasets/Housing\\n\\n\\nThis dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\\n\\nThe Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\\nprices and the demand for clean air', J. Environ. Economics & Management,\\nvol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\\n...', Wiley, 1980.   N.B. Various transformations are used in the table on\\npages 244-261 of the latter.\\n\\nThe Boston house-price data has been used in many machine learning papers that address regression\\nproblems.   \\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",
       " 'data': 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]]),\n",
       " 'feature_names': array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n",
       "        'TAX', 'PTRATIO', 'B', 'LSTAT'], \n",
       "       dtype='|S7'),\n",
       " 'target': array([ 24. ,  21.6,  34.7,  33.4,  36.2,  28.7,  22.9,  27.1,  16.5,\n",
       "         18.9,  15. ,  18.9,  21.7,  20.4,  18.2,  19.9,  23.1,  17.5,\n",
       "         20.2,  18.2,  13.6,  19.6,  15.2,  14.5,  15.6,  13.9,  16.6,\n",
       "         14.8,  18.4,  21. ,  12.7,  14.5,  13.2,  13.1,  13.5,  18.9,\n",
       "         20. ,  21. ,  24.7,  30.8,  34.9,  26.6,  25.3,  24.7,  21.2,\n",
       "         19.3,  20. ,  16.6,  14.4,  19.4,  19.7,  20.5,  25. ,  23.4,\n",
       "         18.9,  35.4,  24.7,  31.6,  23.3,  19.6,  18.7,  16. ,  22.2,\n",
       "         25. ,  33. ,  23.5,  19.4,  22. ,  17.4,  20.9,  24.2,  21.7,\n",
       "         22.8,  23.4,  24.1,  21.4,  20. ,  20.8,  21.2,  20.3,  28. ,\n",
       "         23.9,  24.8,  22.9,  23.9,  26.6,  22.5,  22.2,  23.6,  28.7,\n",
       "         22.6,  22. ,  22.9,  25. ,  20.6,  28.4,  21.4,  38.7,  43.8,\n",
       "         33.2,  27.5,  26.5,  18.6,  19.3,  20.1,  19.5,  19.5,  20.4,\n",
       "         19.8,  19.4,  21.7,  22.8,  18.8,  18.7,  18.5,  18.3,  21.2,\n",
       "         19.2,  20.4,  19.3,  22. ,  20.3,  20.5,  17.3,  18.8,  21.4,\n",
       "         15.7,  16.2,  18. ,  14.3,  19.2,  19.6,  23. ,  18.4,  15.6,\n",
       "         18.1,  17.4,  17.1,  13.3,  17.8,  14. ,  14.4,  13.4,  15.6,\n",
       "         11.8,  13.8,  15.6,  14.6,  17.8,  15.4,  21.5,  19.6,  15.3,\n",
       "         19.4,  17. ,  15.6,  13.1,  41.3,  24.3,  23.3,  27. ,  50. ,\n",
       "         50. ,  50. ,  22.7,  25. ,  50. ,  23.8,  23.8,  22.3,  17.4,\n",
       "         19.1,  23.1,  23.6,  22.6,  29.4,  23.2,  24.6,  29.9,  37.2,\n",
       "         39.8,  36.2,  37.9,  32.5,  26.4,  29.6,  50. ,  32. ,  29.8,\n",
       "         34.9,  37. ,  30.5,  36.4,  31.1,  29.1,  50. ,  33.3,  30.3,\n",
       "         34.6,  34.9,  32.9,  24.1,  42.3,  48.5,  50. ,  22.6,  24.4,\n",
       "         22.5,  24.4,  20. ,  21.7,  19.3,  22.4,  28.1,  23.7,  25. ,\n",
       "         23.3,  28.7,  21.5,  23. ,  26.7,  21.7,  27.5,  30.1,  44.8,\n",
       "         50. ,  37.6,  31.6,  46.7,  31.5,  24.3,  31.7,  41.7,  48.3,\n",
       "         29. ,  24. ,  25.1,  31.5,  23.7,  23.3,  22. ,  20.1,  22.2,\n",
       "         23.7,  17.6,  18.5,  24.3,  20.5,  24.5,  26.2,  24.4,  24.8,\n",
       "         29.6,  42.8,  21.9,  20.9,  44. ,  50. ,  36. ,  30.1,  33.8,\n",
       "         43.1,  48.8,  31. ,  36.5,  22.8,  30.7,  50. ,  43.5,  20.7,\n",
       "         21.1,  25.2,  24.4,  35.2,  32.4,  32. ,  33.2,  33.1,  29.1,\n",
       "         35.1,  45.4,  35.4,  46. ,  50. ,  32.2,  22. ,  20.1,  23.2,\n",
       "         22.3,  24.8,  28.5,  37.3,  27.9,  23.9,  21.7,  28.6,  27.1,\n",
       "         20.3,  22.5,  29. ,  24.8,  22. ,  26.4,  33.1,  36.1,  28.4,\n",
       "         33.4,  28.2,  22.8,  20.3,  16.1,  22.1,  19.4,  21.6,  23.8,\n",
       "         16.2,  17.8,  19.8,  23.1,  21. ,  23.8,  23.1,  20.4,  18.5,\n",
       "         25. ,  24.6,  23. ,  22.2,  19.3,  22.6,  19.8,  17.1,  19.4,\n",
       "         22.2,  20.7,  21.1,  19.5,  18.5,  20.6,  19. ,  18.7,  32.7,\n",
       "         16.5,  23.9,  31.2,  17.5,  17.2,  23.1,  24.5,  26.6,  22.9,\n",
       "         24.1,  18.6,  30.1,  18.2,  20.6,  17.8,  21.7,  22.7,  22.6,\n",
       "         25. ,  19.9,  20.8,  16.8,  21.9,  27.5,  21.9,  23.1,  50. ,\n",
       "         50. ,  50. ,  50. ,  50. ,  13.8,  13.8,  15. ,  13.9,  13.3,\n",
       "         13.1,  10.2,  10.4,  10.9,  11.3,  12.3,   8.8,   7.2,  10.5,\n",
       "          7.4,  10.2,  11.5,  15.1,  23.2,   9.7,  13.8,  12.7,  13.1,\n",
       "         12.5,   8.5,   5. ,   6.3,   5.6,   7.2,  12.1,   8.3,   8.5,\n",
       "          5. ,  11.9,  27.9,  17.2,  27.5,  15. ,  17.2,  17.9,  16.3,\n",
       "          7. ,   7.2,   7.5,  10.4,   8.8,   8.4,  16.7,  14.2,  20.8,\n",
       "         13.4,  11.7,   8.3,  10.2,  10.9,  11. ,   9.5,  14.5,  14.1,\n",
       "         16.1,  14.3,  11.7,  13.4,   9.6,   8.7,   8.4,  12.8,  10.5,\n",
       "         17.1,  18.4,  15.4,  10.8,  11.8,  14.9,  12.6,  14.1,  13. ,\n",
       "         13.4,  15.2,  16.1,  17.8,  14.9,  14.1,  12.7,  13.5,  14.9,\n",
       "         20. ,  16.4,  17.7,  19.5,  20.2,  21.4,  19.9,  19. ,  19.1,\n",
       "         19.1,  20.1,  19.9,  19.6,  23.2,  29.8,  13.8,  13.3,  16.7,\n",
       "         12. ,  14.6,  21.4,  23. ,  23.7,  25. ,  21.8,  20.6,  21.2,\n",
       "         19.1,  20.6,  15.2,   7. ,   8.1,  13.6,  20.1,  21.8,  24.5,\n",
       "         23.1,  19.7,  18.3,  21.2,  17.5,  16.8,  22.4,  20.6,  23.9,\n",
       "         22. ,  11.9])}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['data', 'feature_names', 'DESCR', 'target']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Boston House Prices dataset\\n\\nNotes\\n------\\nData 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\\nThis is a copy of UCI ML housing dataset.\\nhttp://archive.ics.uci.edu/ml/datasets/Housing\\n\\n\\nThis dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\\n\\nThe Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\\nprices and the demand for clean air', J. Environ. Economics & Management,\\nvol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\\n...', Wiley, 1980.   N.B. Various transformations are used in the table on\\npages 244-261 of the latter.\\n\\nThe Boston house-price data has been used in many machine learning papers that address regression\\nproblems.   \\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\""
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston.DESCR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature names:['CRIM' 'ZN' 'INDUS' 'CHAS' 'NOX' 'RM' 'AGE' 'DIS' 'RAD' 'TAX' 'PTRATIO'\n",
      " 'B' 'LSTAT']\n",
      "target:[ 24.   21.6  34.7  33.4  36.2  28.7  22.9  27.1  16.5  18.9  15.   18.9\n",
      "  21.7  20.4  18.2  19.9  23.1  17.5  20.2  18.2  13.6  19.6  15.2  14.5\n",
      "  15.6  13.9  16.6  14.8  18.4  21.   12.7  14.5  13.2  13.1  13.5  18.9\n",
      "  20.   21.   24.7  30.8  34.9  26.6  25.3  24.7  21.2  19.3  20.   16.6\n",
      "  14.4  19.4  19.7  20.5  25.   23.4  18.9  35.4  24.7  31.6  23.3  19.6\n",
      "  18.7  16.   22.2  25.   33.   23.5  19.4  22.   17.4  20.9  24.2  21.7\n",
      "  22.8  23.4  24.1  21.4  20.   20.8  21.2  20.3  28.   23.9  24.8  22.9\n",
      "  23.9  26.6  22.5  22.2  23.6  28.7  22.6  22.   22.9  25.   20.6  28.4\n",
      "  21.4  38.7  43.8  33.2  27.5  26.5  18.6  19.3  20.1  19.5  19.5  20.4\n",
      "  19.8  19.4  21.7  22.8  18.8  18.7  18.5  18.3  21.2  19.2  20.4  19.3\n",
      "  22.   20.3  20.5  17.3  18.8  21.4  15.7  16.2  18.   14.3  19.2  19.6\n",
      "  23.   18.4  15.6  18.1  17.4  17.1  13.3  17.8  14.   14.4  13.4  15.6\n",
      "  11.8  13.8  15.6  14.6  17.8  15.4  21.5  19.6  15.3  19.4  17.   15.6\n",
      "  13.1  41.3  24.3  23.3  27.   50.   50.   50.   22.7  25.   50.   23.8\n",
      "  23.8  22.3  17.4  19.1  23.1  23.6  22.6  29.4  23.2  24.6  29.9  37.2\n",
      "  39.8  36.2  37.9  32.5  26.4  29.6  50.   32.   29.8  34.9  37.   30.5\n",
      "  36.4  31.1  29.1  50.   33.3  30.3  34.6  34.9  32.9  24.1  42.3  48.5\n",
      "  50.   22.6  24.4  22.5  24.4  20.   21.7  19.3  22.4  28.1  23.7  25.\n",
      "  23.3  28.7  21.5  23.   26.7  21.7  27.5  30.1  44.8  50.   37.6  31.6\n",
      "  46.7  31.5  24.3  31.7  41.7  48.3  29.   24.   25.1  31.5  23.7  23.3\n",
      "  22.   20.1  22.2  23.7  17.6  18.5  24.3  20.5  24.5  26.2  24.4  24.8\n",
      "  29.6  42.8  21.9  20.9  44.   50.   36.   30.1  33.8  43.1  48.8  31.\n",
      "  36.5  22.8  30.7  50.   43.5  20.7  21.1  25.2  24.4  35.2  32.4  32.\n",
      "  33.2  33.1  29.1  35.1  45.4  35.4  46.   50.   32.2  22.   20.1  23.2\n",
      "  22.3  24.8  28.5  37.3  27.9  23.9  21.7  28.6  27.1  20.3  22.5  29.\n",
      "  24.8  22.   26.4  33.1  36.1  28.4  33.4  28.2  22.8  20.3  16.1  22.1\n",
      "  19.4  21.6  23.8  16.2  17.8  19.8  23.1  21.   23.8  23.1  20.4  18.5\n",
      "  25.   24.6  23.   22.2  19.3  22.6  19.8  17.1  19.4  22.2  20.7  21.1\n",
      "  19.5  18.5  20.6  19.   18.7  32.7  16.5  23.9  31.2  17.5  17.2  23.1\n",
      "  24.5  26.6  22.9  24.1  18.6  30.1  18.2  20.6  17.8  21.7  22.7  22.6\n",
      "  25.   19.9  20.8  16.8  21.9  27.5  21.9  23.1  50.   50.   50.   50.\n",
      "  50.   13.8  13.8  15.   13.9  13.3  13.1  10.2  10.4  10.9  11.3  12.3\n",
      "   8.8   7.2  10.5   7.4  10.2  11.5  15.1  23.2   9.7  13.8  12.7  13.1\n",
      "  12.5   8.5   5.    6.3   5.6   7.2  12.1   8.3   8.5   5.   11.9  27.9\n",
      "  17.2  27.5  15.   17.2  17.9  16.3   7.    7.2   7.5  10.4   8.8   8.4\n",
      "  16.7  14.2  20.8  13.4  11.7   8.3  10.2  10.9  11.    9.5  14.5  14.1\n",
      "  16.1  14.3  11.7  13.4   9.6   8.7   8.4  12.8  10.5  17.1  18.4  15.4\n",
      "  10.8  11.8  14.9  12.6  14.1  13.   13.4  15.2  16.1  17.8  14.9  14.1\n",
      "  12.7  13.5  14.9  20.   16.4  17.7  19.5  20.2  21.4  19.9  19.   19.1\n",
      "  19.1  20.1  19.9  19.6  23.2  29.8  13.8  13.3  16.7  12.   14.6  21.4\n",
      "  23.   23.7  25.   21.8  20.6  21.2  19.1  20.6  15.2   7.    8.1  13.6\n",
      "  20.1  21.8  24.5  23.1  19.7  18.3  21.2  17.5  16.8  22.4  20.6  23.9\n",
      "  22.   11.9]\n"
     ]
    }
   ],
   "source": [
    "print \"feature names:{}\".format(boston.feature_names)\n",
    "print \"target:{target}\".format(target = boston.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the Boston dataset and examine its target (label) distribution.\n",
    "\n",
    "##  Load libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pylab as pl\n",
    "from sklearn import datasets\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn import cross_validation\n",
    "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error, make_scorer\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the Boston dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def load_data():\n",
    "    \"\"\"Load the boston data set.\"\"\"\n",
    "    boston = datasets.load_boston()\n",
    "    return boston"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'DESCR': \"Boston House Prices dataset\\n\\nNotes\\n------\\nData 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\\nThis is a copy of UCI ML housing dataset.\\nhttp://archive.ics.uci.edu/ml/datasets/Housing\\n\\n\\nThis dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\\n\\nThe Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\\nprices and the demand for clean air', J. Environ. Economics & Management,\\nvol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\\n...', Wiley, 1980.   N.B. Various transformations are used in the table on\\npages 244-261 of the latter.\\n\\nThe Boston house-price data has been used in many machine learning papers that address regression\\nproblems.   \\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",
       " 'data': 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]]),\n",
       " 'feature_names': array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n",
       "        'TAX', 'PTRATIO', 'B', 'LSTAT'], \n",
       "       dtype='|S7'),\n",
       " 'target': array([ 24. ,  21.6,  34.7,  33.4,  36.2,  28.7,  22.9,  27.1,  16.5,\n",
       "         18.9,  15. ,  18.9,  21.7,  20.4,  18.2,  19.9,  23.1,  17.5,\n",
       "         20.2,  18.2,  13.6,  19.6,  15.2,  14.5,  15.6,  13.9,  16.6,\n",
       "         14.8,  18.4,  21. ,  12.7,  14.5,  13.2,  13.1,  13.5,  18.9,\n",
       "         20. ,  21. ,  24.7,  30.8,  34.9,  26.6,  25.3,  24.7,  21.2,\n",
       "         19.3,  20. ,  16.6,  14.4,  19.4,  19.7,  20.5,  25. ,  23.4,\n",
       "         18.9,  35.4,  24.7,  31.6,  23.3,  19.6,  18.7,  16. ,  22.2,\n",
       "         25. ,  33. ,  23.5,  19.4,  22. ,  17.4,  20.9,  24.2,  21.7,\n",
       "         22.8,  23.4,  24.1,  21.4,  20. ,  20.8,  21.2,  20.3,  28. ,\n",
       "         23.9,  24.8,  22.9,  23.9,  26.6,  22.5,  22.2,  23.6,  28.7,\n",
       "         22.6,  22. ,  22.9,  25. ,  20.6,  28.4,  21.4,  38.7,  43.8,\n",
       "         33.2,  27.5,  26.5,  18.6,  19.3,  20.1,  19.5,  19.5,  20.4,\n",
       "         19.8,  19.4,  21.7,  22.8,  18.8,  18.7,  18.5,  18.3,  21.2,\n",
       "         19.2,  20.4,  19.3,  22. ,  20.3,  20.5,  17.3,  18.8,  21.4,\n",
       "         15.7,  16.2,  18. ,  14.3,  19.2,  19.6,  23. ,  18.4,  15.6,\n",
       "         18.1,  17.4,  17.1,  13.3,  17.8,  14. ,  14.4,  13.4,  15.6,\n",
       "         11.8,  13.8,  15.6,  14.6,  17.8,  15.4,  21.5,  19.6,  15.3,\n",
       "         19.4,  17. ,  15.6,  13.1,  41.3,  24.3,  23.3,  27. ,  50. ,\n",
       "         50. ,  50. ,  22.7,  25. ,  50. ,  23.8,  23.8,  22.3,  17.4,\n",
       "         19.1,  23.1,  23.6,  22.6,  29.4,  23.2,  24.6,  29.9,  37.2,\n",
       "         39.8,  36.2,  37.9,  32.5,  26.4,  29.6,  50. ,  32. ,  29.8,\n",
       "         34.9,  37. ,  30.5,  36.4,  31.1,  29.1,  50. ,  33.3,  30.3,\n",
       "         34.6,  34.9,  32.9,  24.1,  42.3,  48.5,  50. ,  22.6,  24.4,\n",
       "         22.5,  24.4,  20. ,  21.7,  19.3,  22.4,  28.1,  23.7,  25. ,\n",
       "         23.3,  28.7,  21.5,  23. ,  26.7,  21.7,  27.5,  30.1,  44.8,\n",
       "         50. ,  37.6,  31.6,  46.7,  31.5,  24.3,  31.7,  41.7,  48.3,\n",
       "         29. ,  24. ,  25.1,  31.5,  23.7,  23.3,  22. ,  20.1,  22.2,\n",
       "         23.7,  17.6,  18.5,  24.3,  20.5,  24.5,  26.2,  24.4,  24.8,\n",
       "         29.6,  42.8,  21.9,  20.9,  44. ,  50. ,  36. ,  30.1,  33.8,\n",
       "         43.1,  48.8,  31. ,  36.5,  22.8,  30.7,  50. ,  43.5,  20.7,\n",
       "         21.1,  25.2,  24.4,  35.2,  32.4,  32. ,  33.2,  33.1,  29.1,\n",
       "         35.1,  45.4,  35.4,  46. ,  50. ,  32.2,  22. ,  20.1,  23.2,\n",
       "         22.3,  24.8,  28.5,  37.3,  27.9,  23.9,  21.7,  28.6,  27.1,\n",
       "         20.3,  22.5,  29. ,  24.8,  22. ,  26.4,  33.1,  36.1,  28.4,\n",
       "         33.4,  28.2,  22.8,  20.3,  16.1,  22.1,  19.4,  21.6,  23.8,\n",
       "         16.2,  17.8,  19.8,  23.1,  21. ,  23.8,  23.1,  20.4,  18.5,\n",
       "         25. ,  24.6,  23. ,  22.2,  19.3,  22.6,  19.8,  17.1,  19.4,\n",
       "         22.2,  20.7,  21.1,  19.5,  18.5,  20.6,  19. ,  18.7,  32.7,\n",
       "         16.5,  23.9,  31.2,  17.5,  17.2,  23.1,  24.5,  26.6,  22.9,\n",
       "         24.1,  18.6,  30.1,  18.2,  20.6,  17.8,  21.7,  22.7,  22.6,\n",
       "         25. ,  19.9,  20.8,  16.8,  21.9,  27.5,  21.9,  23.1,  50. ,\n",
       "         50. ,  50. ,  50. ,  50. ,  13.8,  13.8,  15. ,  13.9,  13.3,\n",
       "         13.1,  10.2,  10.4,  10.9,  11.3,  12.3,   8.8,   7.2,  10.5,\n",
       "          7.4,  10.2,  11.5,  15.1,  23.2,   9.7,  13.8,  12.7,  13.1,\n",
       "         12.5,   8.5,   5. ,   6.3,   5.6,   7.2,  12.1,   8.3,   8.5,\n",
       "          5. ,  11.9,  27.9,  17.2,  27.5,  15. ,  17.2,  17.9,  16.3,\n",
       "          7. ,   7.2,   7.5,  10.4,   8.8,   8.4,  16.7,  14.2,  20.8,\n",
       "         13.4,  11.7,   8.3,  10.2,  10.9,  11. ,   9.5,  14.5,  14.1,\n",
       "         16.1,  14.3,  11.7,  13.4,   9.6,   8.7,   8.4,  12.8,  10.5,\n",
       "         17.1,  18.4,  15.4,  10.8,  11.8,  14.9,  12.6,  14.1,  13. ,\n",
       "         13.4,  15.2,  16.1,  17.8,  14.9,  14.1,  12.7,  13.5,  14.9,\n",
       "         20. ,  16.4,  17.7,  19.5,  20.2,  21.4,  19.9,  19. ,  19.1,\n",
       "         19.1,  20.1,  19.9,  19.6,  23.2,  29.8,  13.8,  13.3,  16.7,\n",
       "         12. ,  14.6,  21.4,  23. ,  23.7,  25. ,  21.8,  20.6,  21.2,\n",
       "         19.1,  20.6,  15.2,   7. ,   8.1,  13.6,  20.1,  21.8,  24.5,\n",
       "         23.1,  19.7,  18.3,  21.2,  17.5,  16.8,  22.4,  20.6,  23.9,\n",
       "         22. ,  11.9])}"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Analyze the Boston housing data. Evaluate and validate the performanance of a Decision Tree regressor on the housing data.Fine tune the model to make prediction on unseen data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate the Boston housing statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def explore_city_data(city_data):\n",
    "    \n",
    "    \n",
    "    #get the labels and features from the houssing data\n",
    "    housing_prices = city_data.target\n",
    "    housing_features = city_data.data\n",
    "     # Size of data\n",
    "    print \"No. of data points: {}\".format(housing_features.shape[0])\n",
    "\n",
    "    # Number of features\n",
    "    print \"No. of features: {}\".format(housing_features.shape[1])\n",
    "    \n",
    "    # Minimum housing price\n",
    "    print \"Minimum housing price: {}\".format(min(housing_prices))\n",
    "    \n",
    "    # Maximum housing price\n",
    "    print \"Maximum housing price: {}\".format(max(housing_prices))\n",
    "    \n",
    "    # Mean housing price\n",
    "    print \"Mean housing price: {}\".format(np.mean(housing_prices))\n",
    "    \n",
    "    # Calculate median?\n",
    "    print \"Median housing price: {}\".format(np.median(housing_prices))\n",
    "    \n",
    "    # Calculate standard deviation?\n",
    "    print \"Standard deviation of housing prices: {}\".format(np.std(housing_prices))\n",
    "\n",
    "    \n",
    "   \n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "city_data = load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of data points: 506\n",
      "No. of features: 13\n",
      "Minimum housing price: 5.0\n",
      "Maximum housing price: 50.0\n",
      "Mean housing price: 22.5328063241\n",
      "Median housing price: 21.2\n",
      "Standard deviation of housing prices: 9.18801154528\n"
     ]
    }
   ],
   "source": [
    "explore_city_data(city_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate and return the appropriate performance metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def performance_metric(label, prediction):\n",
    "    mse = mean_squared_error(label, prediction)\n",
    "    # http://scikit-learn.org/stable/modules/classes.html#sklearn-metrics-metrics\n",
    "    return mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function __main__.performance_metric>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performance_metric"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Randomly shuffle the sample set. Divide it into training and testing set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def split_data(city_data):\n",
    "    \"\"\"Randomly shuffle the sample set. Divide it into training and testing set.\"\"\"\n",
    "\n",
    "    # Get the features and labels from the Boston housing data\n",
    "    X, y = city_data.data, city_data.target\n",
    "\n",
    "    # Split up the dataset between training and testing\n",
    "    X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.45, random_state=42)\n",
    "\n",
    "    return X_train, y_train, X_test, y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate the performance of the model after a set of training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "def learning_curve(depth, X_train, y_train, X_test, y_test):\n",
    "    \"\"\"Calculate the performance of the model after a set of training data.\"\"\"\n",
    "\n",
    "    # We will vary the training set size so that we have 50 different sizes\n",
    "    sizes = np.linspace(1, len(X_train), 50)\n",
    "    train_err = np.zeros(len(sizes))\n",
    "    test_err = np.zeros(len(sizes))\n",
    "\n",
    "    print \"Decision Tree with Max Depth: \"\n",
    "    print depth\n",
    "\n",
    "    for i, s in enumerate(sizes):\n",
    "\n",
    "        # Create and fit the decision tree regressor model\n",
    "        regressor = DecisionTreeRegressor(max_depth=depth)\n",
    "        regressor.fit(X_train[:s], y_train[:s])\n",
    "\n",
    "        # Find the performance on the training and testing set\n",
    "        train_err[i] = performance_metric(y_train[:s], regressor.predict(X_train[:s]))\n",
    "        test_err[i] = performance_metric(y_test, regressor.predict(X_test))\n",
    "\n",
    "\n",
    "    # Plot learning curve graph\n",
    "    learning_curve_graph(sizes, train_err, test_err)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot training and test error as a function of the training size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def learning_curve_graph(sizes, train_err, test_err):\n",
    "    \"\"\"Plot training and test error as a function of the training size.\"\"\"\n",
    "\n",
    "    pl.figure()\n",
    "    pl.title('Decision Trees: Performance vs Training Size')\n",
    "    pl.plot(sizes, test_err, lw=2, label = 'test error')\n",
    "    pl.plot(sizes, train_err, lw=2, label = 'training error')\n",
    "    pl.legend()\n",
    "    pl.xlabel('Training Size')\n",
    "    pl.ylabel('Error')\n",
    "    pl.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate the performance of the model as model complexity increases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def model_complexity(X_train, y_train, X_test, y_test):\n",
    "    \"\"\"Calculate the performance of the model as model complexity increases.\"\"\"\n",
    "\n",
    "    print \"Model Complexity: \"\n",
    "\n",
    "    # We will vary the depth of decision trees from 2 to 25\n",
    "    max_depth = np.arange(1, 25)\n",
    "    train_err = np.zeros(len(max_depth))\n",
    "    test_err = np.zeros(len(max_depth))\n",
    "\n",
    "    for i, d in enumerate(max_depth):\n",
    "        # Setup a Decision Tree Regressor so that it learns a tree with depth d\n",
    "        regressor = DecisionTreeRegressor(max_depth=d)\n",
    "\n",
    "        # Fit the learner to the training data\n",
    "        regressor.fit(X_train, y_train)\n",
    "\n",
    "        # Find the performance on the training set\n",
    "        train_err[i] = performance_metric(y_train, regressor.predict(X_train))\n",
    "\n",
    "        # Find the performance on the testing set\n",
    "        test_err[i] = performance_metric(y_test, regressor.predict(X_test))\n",
    "\n",
    "    # Plot the model complexity graph\n",
    "    model_complexity_graph(max_depth, train_err, test_err)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Plot training and test error as a function of the depth of the decision tree learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def model_complexity_graph(max_depth, train_err, test_err):\n",
    "    \n",
    "\n",
    "    pl.figure()\n",
    "    pl.title('Decision Trees: Performance vs Max Depth')\n",
    "    pl.plot(max_depth, test_err, lw=2, label = 'test error')\n",
    "    pl.plot(max_depth, train_err, lw=2, label = 'training error')\n",
    "    pl.legend()\n",
    "    pl.xlabel('Max Depth')\n",
    "    pl.ylabel('Error')\n",
    "    pl.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find and tune the optimal model. Make a prediction on housing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fit_predict_model(city_data):\n",
    "    \"\"\"Find and tune the optimal model. Make a prediction on housing data.\"\"\"\n",
    "\n",
    "    # Get the features and labels from the Boston housing data\n",
    "    X, y = city_data.data, city_data.target\n",
    "\n",
    "    # Setup a Decision Tree Regressor\n",
    "    regressor = DecisionTreeRegressor()\n",
    "\n",
    "    # Setup parameters and scores for model optimization through Grid Search\n",
    "    parameters = {'max_depth':(1,2,3,4,5,6,7,8,9,10)}\n",
    "    scorer = make_scorer(mean_squared_error, greater_is_better=False)\n",
    "    gs = GridSearchCV(regressor, parameters, scoring=scorer)\n",
    "    gs.fit(X, y)\n",
    "\n",
    "    # Select the best settings for regressor\n",
    "    reg = gs.best_estimator_\n",
    "\n",
    "    # Fit the learner to the training data\n",
    "    print \"Final Model: \"\n",
    "    print reg.fit(X, y)\n",
    "    \n",
    "    # Use the model to predict the output of a particular sample\n",
    "    x = [11.95, 0.00, 18.100, 0, 0.6590, 5.6090, 90.00, 1.385, 24, 680.0, 20.20, 332.09, 12.13]\n",
    "    y = reg.predict(x)\n",
    "    print \"House: \" + str(x)\n",
    "    print \"Prediction: \" + str(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate and return the appropriate performance metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def main():\n",
    "    # load data\n",
    "    city_data = load_data()\n",
    "    #print city_data\n",
    "    \n",
    "    # Explore the data\n",
    "    explore_city_data(city_data)\n",
    "    \n",
    "    # Training/ Test dataset split\n",
    "    X_train, y_train, X_test, y_test = split_data(city_data)\n",
    "    \n",
    "    # learning Curve Graphs\n",
    "    max_depths = [1,2,3,4,5,6,7,8,9,10]\n",
    "    for max_depth in max_depths:\n",
    "        learning_curve(max_depth, X_train, y_train, X_test, y_test)\n",
    "    \n",
    "    ## Model Complexity Graph\n",
    "    model_complexity(X_train, y_train, X_test, y_test)\n",
    "    \n",
    "    ## Tune and predict Model\n",
    "    fit_predict_model(city_data)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of data points: 506\n",
      "No. of features: 13\n",
      "Minimum housing price: 5.0\n",
      "Maximum housing price: 50.0\n",
      "Mean housing price: 22.5328063241\n",
      "Median housing price: 21.2\n",
      "Standard deviation of housing prices: 9.18801154528\n",
      "Decision Tree with Max Depth: \n",
      "1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\Anaconda2\\lib\\site-packages\\ipykernel\\__main__.py:17: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "C:\\Users\\Administrator\\Anaconda2\\lib\\site-packages\\ipykernel\\__main__.py:20: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n"
     ]
    },
    {
     "data": {
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gBTBfRGJAKTAdWJ3LNrZvb2h/39jsei5pbkl2mp6vqqvLCyIfPSnk/BVy3sDy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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21e71160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree with Max Depth: \n",
      "2\n"
     ]
    },
    {
     "data": {
      "image/png": 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OV9UngFOBR4DngEUikgDKgTnAikK2sXVrQ+59Y2MrAC2tHV2mF6va2uqSyEdv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\nSTibZ7l9xNgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ce08470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree with Max Depth: \n",
      "3\n"
     ]
    },
    {
     "data": {
      "image/png": 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p5Bjr19fnHm+tbwagsSnVYXmxqqmpKol8dKeU81fKeQPLX7HrSzAs9MK9/nIx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\nF2mC80uY1gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2078fb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree with Max Depth: \n",
      "4\n"
     ]
    },
    {
     "data": {
      "image/png": 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iIqeo6ovAOcCzwBJgjojEgBLgUGBFLtuoqqrPvG5saAGgqbm9w/R8VVlZVhD5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\nxhiTEwsYxhhjcvL/ATsyGyhNNyQPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2053f198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree with Max Depth: \n",
      "5\n"
     ]
    },
    {
     "data": {
      "image/png": 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M4FlgKTBfRKJAITANWJXJMSoqqlue19Y0use65jbLs1V5eUlO5KMzuZy/XM4b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\nDGOMMRmxgGGMMSYjFjCMMcZk5P8DCqvGUdMj39sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21f057b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree with Max Depth: \n",
      "6\n"
     ]
    },
    {
     "data": {
      "image/png": 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Mq5IyxpjBEHQJ4+NARFU/CvwAuBG4DTcE6/FAWETOFJHxwBXAUcBpwE3+EK67FOtow7AS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\niIRIJj0aml0665raqW9qo66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      "text/plain": [
       "<matplotlib.figure.Figure at 0x21edeb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree with Max Depth: \n",
      "7\n"
     ]
    },
    {
     "data": {
      "image/png": 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XMD4DRFT1U8CPgJuA23BTsM4BwiJyhoiMAa4AjgFOA272p3DtVkGqDcNKGMYYMxiCDhjv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\nGGNMRixgGGOMycj/D3I8CQmUz1+uAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21ede860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree with Max Depth: \n",
      "8\n"
     ]
    },
    {
     "data": {
      "image/png": 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jWZGPrmRz/rI5b2D5y3T9CYbpPrg3UK4A7vQbtd8CHlTVpIgsAlbgqrquVtXm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\nY4y6hih1jVHqGmLUNUZpbI5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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2092c9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree with Max Depth: \n",
      "9\n"
     ]
    },
    {
     "data": {
      "image/png": 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rxvpmABqaWjstz1Xl5bG8yEd38jl/+Zw3sPzluv4Ew2w77g2UK4C7vUbtd4GH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\nZMUChjHGmKxYwDDGGJOV/wd7rp6d/nWIJgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x220c5908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree with Max Depth: \n",
      "10\n"
     ]
    },
    {
     "data": {
      "image/png": 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AMTADWJHLMSor69qfN9Q3A9DY3NZheb6qqCgNRT66E+b8hTlvYPnLd/0Jhrne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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fd8f828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Complexity: \n"
     ]
    },
    {
     "data": {
      "image/png": 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0vUMlBFsnrzt6P2S0XG1U2vXVRu19Y9ERHDk6nRpvM39+eT3N/iEz3JMQIgb0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\npY4AngSCwAat9dVRDa6ftSuLmcDrQGj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      "text/plain": [
       "<matplotlib.figure.Figure at 0x21e71198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Model: \n",
      "DecisionTreeRegressor(criterion='mse', max_depth=4, max_features=None,\n",
      "           max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2,\n",
      "           min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
      "           splitter='best')\n",
      "House: [11.95, 0.0, 18.1, 0, 0.659, 5.609, 90.0, 1.385, 24, 680.0, 20.2, 332.09, 12.13]\n",
      "Prediction: [ 21.62974359]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\Anaconda2\\lib\\site-packages\\sklearn\\utils\\validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}