gistfile1.txt

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   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "<center>Birth Weight Predictor</center>\n",
      "<h4 align=\"right\">Authors: Ajay Lakshminarayanarao, Nargis Nigar, Nupur Dichwalkar, Rakesh Chada</h4>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Import relevant packages"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "import seaborn as sns\n",
      "from sklearn import linear_model\n",
      "from sklearn import neighbors\n",
      "from sklearn.ensemble import RandomForestRegressor\n",
      "from sklearn.tree import DecisionTreeRegressor\n",
      "from sklearn.ensemble import AdaBoostRegressor\n",
      "#import sklearn.metrics\n",
      "from sklearn.metrics import mean_absolute_error, mean_squared_error, explained_variance_score, r2_score\n",
      "from math import sqrt\n",
      "import os\n",
      "import sys\n",
      "import warnings\n",
      "from IPython.display import Image\n",
      "warnings.filterwarnings('ignore')\n",
      "\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Reading CSV and extracting only relevant columns"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "'''\n",
      "Data Citation: State Center for Health Statistics, 2009, \"North Carolina Vital Statistics -- Births 2008\", \n",
      "http://hdl.handle.net/1902.29/10446 UNF:5:aTuN+uNcon8IvlxC8H84YQ== \n",
      "Odum Institute for Research in Social Science [Distributor] V1 [Version]\n",
      "'''\n",
      "babiesDF = pd.read_csv('/home/rakesh/Desktop/Data Science/Data/2008_birth_data.csv')\n",
      "slicedDF = pd.DataFrame\n",
      "trainDF = pd.DataFrame\n",
      "testDF = pd.DataFrame\n",
      "evalDF = pd.DataFrame"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Obtain Filtered Dataset of Relevant Columns"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "colIndex = 0\n",
      "def binarizeColumn(x):\n",
      "    global colIndex\n",
      "    if x == colIndex:\n",
      "        return 1\n",
      "    else:\n",
      "        return 0\n",
      "\n",
      "def binarizeHisp(x):\n",
      "    if x == 'N':\n",
      "        return 0\n",
      "    else:\n",
      "        return 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### The following steps are executed as part of the below cell:\n",
      "- All post-birth features are eliminated.\n",
      "- Two columns - Weight (in pounds) and Weight (in ounces) are combined into a single column (WEIGHTLB) that represents the weight of the baby. This column is not considered while training the dataset.\n",
      "- Columns related to Race have values in the range of 0-9. These columns are split into several columns for each particular race and it would contain binary values (0 and 1) indicating if that person belongs to that race.\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "global colIndex\n",
      "global slicedDF\n",
      "requiredColumns = ['SEX', 'MARITAL','FAGE', 'GAINED', 'VISITS', 'MAGE', 'FEDUC', 'MEDUC', 'TOTALP', 'BDEAD', 'TERMS', 'LOUTCOME', 'WEEKS', 'BPOUND', 'BOUNCE', 'RACEMOM', 'RACEDAD', 'HISPMOM', 'HISPDAD', 'CIGNUM', 'DRINKNUM', 'ANEMIA', 'CARDIAC', 'ACLUNG', 'DIABETES', 'HERPES', 'HYDRAM', 'HEMOGLOB', 'HYPERCH', 'HYPERPR', 'ECLAMP',\n",
      "                             'CERVIX','PINFANT','PRETERM','RENAL','RHSEN','UTERINE']\n",
      "#requiredColumns = ['SEX', 'MARITAL','FAGE', 'MAGE', 'FEDUC', 'MEDUC', 'TOTALP', 'BDEAD', 'TERMS', 'LOUTCOME', 'WEEKS', 'BPOUND', 'BOUNCE', 'WEIGHT', 'RACEMOM', 'RACEDAD', 'HISPMOM', 'HISPDAD', 'CIGNUM', 'DRINKNUM', 'GAINED', 'ANEMIA', 'CARDIAC', 'ACLUNG', 'DIABETES', 'HERPES', 'HYDRAM', 'HEMOGLOB', 'HYPERCH', 'HYPERPR', 'ECLAMP',\n",
      "                             #'CERVIX','PINFANT','PRETERM','RENAL','RHSEN','UTERINE']\n",
      "#requiredColumns = ['SEX', 'MARITAL','FAGE', 'MAGE', 'FEDUC', 'MEDUC', 'TOTALP', 'BDEAD', 'TERMS', 'LOUTCOME', 'WEEKS', 'BPOUND', 'BOUNCE', 'WEIGHT', 'RACEMOM', 'RACEDAD', 'HISPMOM', 'HISPDAD', 'CIGNUM', 'DRINKNUM', 'GAINED', 'ANEMIA', 'CARDIAC', 'ACLUNG', 'DIABETES', 'HERPES', 'HEMOGLOB', 'HYPERCH', 'HYPERPR',\n",
      "                             #'PINFANT','PRETERM']\n",
      "slicedDF = babiesDF.ix[:,requiredColumns]\n",
      "\n",
      "print \"Initial number of rows present are:\", len(slicedDF)\n",
      "\n",
      "#Removing rows with missing weight details\n",
      "slicedDF = slicedDF[slicedDF['BPOUND']!=99]\n",
      "slicedDF = slicedDF[slicedDF['BOUNCE']!=99]\n",
      "len(slicedDF)\n",
      "\n",
      "#adding a new column for weight in pounds\n",
      "weightPound = slicedDF['BPOUND']\n",
      "weightOunces = slicedDF['BOUNCE']\n",
      "weight = weightPound.astype(np.float) + (0.0625 * weightOunces.astype(np.float))\n",
      "slicedDF['WEIGHTLB'] = weight\n",
      "raceColumns = ['OTHER_NON_WHITE', 'WHITE', 'BLACK', 'AMERICAN_INDIAN', 'CHINESE', 'JAPANESE', 'HAWAIIAN', 'FILIPINO', 'OTHER_ASIAN']\n",
      "for race in raceColumns:\n",
      "    slicedDF[race + '_MOM'] = slicedDF['RACEMOM'].map(binarizeColumn)\n",
      "    colIndex = colIndex + 1\n",
      "colIndex = 0\n",
      "for race in raceColumns:\n",
      "    slicedDF[race + '_DAD'] = slicedDF['RACEDAD'].map(binarizeColumn)\n",
      "    colIndex = colIndex + 1\n",
      "slicedDF['HISPMOM_BINARY'] = slicedDF['HISPMOM'].map(binarizeHisp)\n",
      "slicedDF['HISPDAD_BINARY'] = slicedDF['HISPDAD'].map(binarizeHisp)\n",
      "columnsToDrop = ['BPOUND','BOUNCE','RACEMOM','RACEDAD','HISPMOM','HISPDAD']\n",
      "for column in columnsToDrop:\n",
      "    slicedDF = slicedDF.drop(column, 1)\n",
      "    \n",
      "missing99Columns = ['MAGE','FEDUC','MEDUC','TOTALP','BDEAD','TERMS','WEEKS','CIGNUM','DRINKNUM','GAINED', 'VISITS']\n",
      "#missing99Columns = ['MAGE','FEDUC','MEDUC','TOTALP','BDEAD','TERMS','WEEKS','WEIGHT','CIGNUM','DRINKNUM']\n",
      "missing9Columns = ['ANEMIA','CARDIAC','ACLUNG','DIABETES','HERPES','HYDRAM','HEMOGLOB','HYPERCH','HYPERPR','ECLAMP'\n",
      "                   ,'CERVIX','PINFANT','PRETERM','RENAL','RHSEN','UTERINE','MARITAL']\n",
      "#missing9Columns = ['LOUTCOME','ANEMIA','CARDIAC','ACLUNG','DIABETES','HERPES','HEMOGLOB','HYPERCH','HYPERPR',\n",
      "                   #'PINFANT','PRETERM']\n",
      "missingValDF = slicedDF[slicedDF['FAGE']!=99]\n",
      "\n",
      "for col in missing99Columns:\n",
      "    missingValDF = missingValDF[missingValDF[col]!=99]\n",
      "    \n",
      "for col in missing9Columns:\n",
      "    missingValDF = missingValDF[missingValDF[col]!=9]\n",
      "    \n",
      "missing98Columns = ['CIGNUM','DRINKNUM']\n",
      "for col in missing98Columns:\n",
      "    missingValDF = missingValDF[missingValDF[col]!=98]\n",
      "    \n",
      "loutcome = missingValDF['LOUTCOME'][missingValDF['LOUTCOME'] == 9.0]\n",
      "loutcome[missingValDF['TOTALP'] == 1.0] = 1.0\n",
      "    \n",
      "print \"Number of rows after all the filtering are:\",len(missingValDF)\n",
      "print \"Number of features present are:\", len(missingValDF.columns)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Initial number of rows present are: 133422\n",
        "Number of rows after all the filtering are:"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 103401\n",
        "Number of features present are: 52\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "slicedDF.columns"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "Index([u'SEX', u'MARITAL', u'FAGE', u'GAINED', u'VISITS', u'MAGE', u'FEDUC', u'MEDUC', u'TOTALP', u'BDEAD', u'TERMS', u'LOUTCOME', u'WEEKS', u'CIGNUM', u'DRINKNUM', u'ANEMIA', u'CARDIAC', u'ACLUNG', u'DIABETES', u'HERPES', u'HYDRAM', u'HEMOGLOB', u'HYPERCH', u'HYPERPR', u'ECLAMP', u'CERVIX', u'PINFANT', u'PRETERM', u'RENAL', u'RHSEN', u'UTERINE', u'WEIGHTLB', u'OTHER_NON_WHITE_MOM', u'WHITE_MOM', u'BLACK_MOM', u'AMERICAN_INDIAN_MOM', u'CHINESE_MOM', u'JAPANESE_MOM', u'HAWAIIAN_MOM', u'FILIPINO_MOM', u'OTHER_ASIAN_MOM', u'OTHER_NON_WHITE_DAD', u'WHITE_DAD', u'BLACK_DAD', u'AMERICAN_INDIAN_DAD', u'CHINESE_DAD', u'JAPANESE_DAD', u'HAWAIIAN_DAD', u'FILIPINO_DAD', u'OTHER_ASIAN_DAD', u'HISPMOM_BINARY', u'HISPDAD_BINARY'], dtype='object')"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Split Relevant Dataset to Training Data and Test Data"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Randomly data is split into 85% training data and 15% test data."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_splitDF():\n",
      "        global missingValDF\n",
      "        global trainDF\n",
      "        global testDF\n",
      "        global evalDF\n",
      "        DF_temp = pd.DataFrame\n",
      "        rand_nos = np.random.rand(len(missingValDF)) < 0.85\n",
      "        trainDF = missingValDF[rand_nos]\n",
      "        testDF = missingValDF[~rand_nos]\n",
      "\n",
      "#         rand_nos = np.random.rand(len(DF_temp)) < 0.6\n",
      "#         testDF = DF_temp[rand_nos]\n",
      "#         evalDF = DF_temp[~rand_nos]\n",
      "\n",
      "        print 'Train(len) : {0} rows'.format(str(len(trainDF)))\n",
      "        print 'Test(len) : {0} rows'.format(str(len(testDF)))\n",
      "        #print 'Eval(len) : {0} rows'.format(str(len(evalDF)))\n",
      "\n",
      "get_splitDF()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Train(len) : 87974 rows\n",
        "Test(len) : 15427 rows\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trainWeights = trainDF['WEIGHTLB']\n",
      "testWeights = testDF['WEIGHTLB']\n",
      "#evalWeights = evalDF['WEIGHTLB']\n",
      "trainDF = trainDF.drop(['WEIGHTLB'], 1)\n",
      "testDF = testDF.drop(['WEIGHTLB'], 1)\n",
      "#evalDF = evalDF.drop(['WEIGHTLB','WEIGHT'], 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Input below Details of Sammi into the model. \n",
      "- Gender of Baby: Female\n",
      "- Father\u2019s Age: 27\n",
      "- Mother\u2019s Age: 27\n",
      "- Parity of Mother: 1\n",
      "- Gestation period: 41 Weeks\n",
      "- Weight Gained: 20 lbs.\n",
      "- Drinks per week: 0.25\n",
      "- Anaemic: True\n",
      "- Ethnicity of Father: WHITE\n",
      "- Ethnicity of Mother: WHITE\n",
      "- Marital Status: True\n",
      "- Education of Father: 12 years\n",
      "- Education of Mother: 16 years\n",
      "- Pre Natal Care: True\n",
      "- Number of Prenatal Visits: 11"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sammiRow = [2, 1, 27, 20, 11, 27, 16, 12, 1, 0, 0, 1, 40, 0, 0.25, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "lastIndex = testDF.index[len(testDF.index) - 1]\n",
      "testDF.loc[lastIndex + 1] = sammiRow\n",
      "#print testDF.loc[lastIndex + 1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "testDF.columns"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "Index([u'SEX', u'MARITAL', u'FAGE', u'GAINED', u'VISITS', u'MAGE', u'FEDUC', u'MEDUC', u'TOTALP', u'BDEAD', u'TERMS', u'LOUTCOME', u'WEEKS', u'CIGNUM', u'DRINKNUM', u'ANEMIA', u'CARDIAC', u'ACLUNG', u'DIABETES', u'HERPES', u'HYDRAM', u'HEMOGLOB', u'HYPERCH', u'HYPERPR', u'ECLAMP', u'CERVIX', u'PINFANT', u'PRETERM', u'RENAL', u'RHSEN', u'UTERINE', u'OTHER_NON_WHITE_MOM', u'WHITE_MOM', u'BLACK_MOM', u'AMERICAN_INDIAN_MOM', u'CHINESE_MOM', u'JAPANESE_MOM', u'HAWAIIAN_MOM', u'FILIPINO_MOM', u'OTHER_ASIAN_MOM', u'OTHER_NON_WHITE_DAD', u'WHITE_DAD', u'BLACK_DAD', u'AMERICAN_INDIAN_DAD', u'CHINESE_DAD', u'JAPANESE_DAD', u'HAWAIIAN_DAD', u'FILIPINO_DAD', u'OTHER_ASIAN_DAD', u'HISPMOM_BINARY', u'HISPDAD_BINARY'], dtype='object')"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Verify if sammi's data got added properly\n",
      "testDF.tail(5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>SEX</th>\n",
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        "      <th>FAGE</th>\n",
        "      <th>GAINED</th>\n",
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        "      <th>TOTALP</th>\n",
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        "        SEX  MARITAL  FAGE  GAINED  VISITS  MAGE  FEDUC  MEDUC  TOTALP  BDEAD  \\\n",
        "133179    2        1    29      25      12    32     16     17       2      0   \n",
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        "\n",
        "        TERMS  LOUTCOME  WEEKS  CIGNUM  DRINKNUM  ANEMIA  CARDIAC  ACLUNG  \\\n",
        "133179      0         1     42       0      0.00       0        0       0   \n",
        "133185      0         9     40       0      0.00       0        0       0   \n",
        "133189      1         2     38       0      0.00       0        0       0   \n",
        "133193      0         9     40       0      0.00       0        0       0   \n",
        "133194      0         1     40       0      0.25       1        0       0   \n",
        "\n",
        "        DIABETES  HERPES      \n",
        "133179         0       0 ...  \n",
        "133185         0       0 ...  \n",
        "133189         0       0 ...  \n",
        "133193         0       0 ...  \n",
        "133194         0       0 ...  \n",
        "\n",
        "[5 rows x 51 columns]"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Normalize the required columns to bring them to same scale as others. This is required for models such as k-nearest neighbours so that all features are given equal consideration for the prediction."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The method below normalizes the columns using <b>z-scores</b>."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def normalizeData(df, nonStandardColumns):\n",
      "   for col in nonStandardColumns:\n",
      "       xx = df[col]\n",
      "       mean_xx = np.mean(df[col])\n",
      "       std_xx = np.std(df[col])\n",
      "       df[col+'_NEW'] = (df[col]-mean_xx)/std_xx\n",
      "       df.drop(axis=1,labels=[col], inplace=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "columns = ['FAGE','MAGE','FEDUC','VISITS','MEDUC','TOTALP','BDEAD','TERMS','WEEKS','CIGNUM','DRINKNUM','GAINED']\n",
      "normalizeData(trainDF,columns)\n",
      "normalizeData(testDF,columns)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Initial data analysis "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trainDF.columns"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "Index([u'SEX', u'MARITAL', u'LOUTCOME', u'ANEMIA', u'CARDIAC', u'ACLUNG', u'DIABETES', u'HERPES', u'HYDRAM', u'HEMOGLOB', u'HYPERCH', u'HYPERPR', u'ECLAMP', u'CERVIX', u'PINFANT', u'PRETERM', u'RENAL', u'RHSEN', u'UTERINE', u'OTHER_NON_WHITE_MOM', u'WHITE_MOM', u'BLACK_MOM', u'AMERICAN_INDIAN_MOM', u'CHINESE_MOM', u'JAPANESE_MOM', u'HAWAIIAN_MOM', u'FILIPINO_MOM', u'OTHER_ASIAN_MOM', u'OTHER_NON_WHITE_DAD', u'WHITE_DAD', u'BLACK_DAD', u'AMERICAN_INDIAN_DAD', u'CHINESE_DAD', u'JAPANESE_DAD', u'HAWAIIAN_DAD', u'FILIPINO_DAD', u'OTHER_ASIAN_DAD', u'HISPMOM_BINARY', u'HISPDAD_BINARY', u'FAGE_NEW', u'MAGE_NEW', u'FEDUC_NEW', u'VISITS_NEW', u'MEDUC_NEW', u'TOTALP_NEW', u'BDEAD_NEW', u'TERMS_NEW', u'WEEKS_NEW', u'CIGNUM_NEW', u'DRINKNUM_NEW', u'GAINED_NEW'], dtype='object')"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "requiredColumns = [u'SEX', u'MARITAL', u'LOUTCOME', u'ANEMIA', u'WHITE_MOM', u'BLACK_MOM', u'WHITE_DAD', u'BLACK_DAD', u'HISPMOM_BINARY', u'HISPDAD_BINARY', u'FAGE_NEW', u'MAGE_NEW', u'FEDUC_NEW', u'VISITS_NEW', u'MEDUC_NEW', u'TOTALP_NEW', u'BDEAD_NEW', u'TERMS_NEW', u'WEEKS_NEW', u'CIGNUM_NEW', u'DRINKNUM_NEW', u'GAINED_NEW']\n",
      "corrTrainDF = trainDF.ix[:,requiredColumns]\n",
      "corrTrainDF['WEIGHT'] = trainWeights\n",
      "corrDf = corrTrainDF.corr()\n",
      "#replace all -ve values with +ve values\n",
      "corrDf=corrDf.where(corrDf>0, corrDf * -1)\n",
      "plt.imshow(corrDf, cmap='hot', interpolation='none')\n",
      "plt.colorbar()\n",
      "plt.xticks(range(len(corrDf)),corrDf.columns)\n",
      "locs, labels = plt.xticks()\n",
      "plt.setp(labels, rotation=40, ha='right')\n",
      "ticks = plt.yticks(range(len(corrDf)), corrDf.columns)\n",
      "plt.title('Correlation plot between different features in the data set')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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VxfIA/Bz4W67OnzQ6j0Qi0d1pXscvIgOwga8DDlLVJ3LbDsCiUrcDT6hq3RS0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\nx59ycrFUeM85x4pBXbFnNZaCL0ZMLhzMvOIcSwU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MnorZbwNgz1j+vK+q3zWzR0vOPTcd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3lx3/OjU0q3B1PqgTO4XjizfD6u8WQzuyL6WoaIZRCRW8zsYXHvm98D/zKzG8S9Xlpx9U0Dl+KH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\nmtneJtbIBagLdsj8j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HpWYAvopglwurt/UfZ6Cbr5I3IUOwE7u/sXKaVW8f8uaJDK8Gg4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\n2HJp/x8KHg0EYxwsNQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f35cf3d0950>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Effect of Smoking on Birth Weight"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "noCigList = missingValDF['WEIGHTLB'][missingValDF['CIGNUM']==0.0]\n",
      "sns.distplot(noCigList, bins= 15, color = 'blue', hist=False, label= 'Non-Smokers')\n",
      "#cigList = missingValDF['WEIGHTLB'][missingValDF['CIGNUM'].isin(frange(1.0,99.0,1.0))]\n",
      "cigList = missingValDF['WEIGHTLB'][missingValDF['CIGNUM'] != 0.0]\n",
      "sns.distplot(cigList, bins = 15, color = 'red', hist=False, label= 'Smokers')\n",
      "plt.ylabel('Probability')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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orpS6ruyUt4H3gHeUUgsBO9bwVP1/pBGihhxr8oDwittgXZAXF2fSqVNkCwSe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\neb9TpsDOnfCXv8Do0cF3zTvlGKNErcb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84o5Am1/+E2hQxphBbvZLOC1b5pZiKWbW2ZXA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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f35cf558590>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Effect of Drinking on Birth Weight"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "noDrinkList = missingValDF['WEIGHTLB'][missingValDF['DRINKNUM']==0.0]\n",
      "sns.distplot(noDrinkList, bins= 15, color = 'blue', hist=False, label= 'Non-Drinkers')\n",
      "#cigList = missingValDF['WEIGHTLB'][missingValDF['CIGNUM'].isin(frange(1.0,99.0,1.0))]\n",
      "drinkList = missingValDF['WEIGHTLB'][missingValDF['DRINKNUM'] != 0.0]\n",
      "sns.distplot(drinkList, bins = 15, color = 'red', hist=False, label= 'Drinkers')\n",
      "plt.ylabel('Probability')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ZB4CG4Y5GhEF54xqe4vgoKAPoXuJ2hQ2tSqnhQIrWOrVoCdYPgNQSu7wLjNBa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\n24QmTo8H+0aNPXsntpwc8PvxGU4yj7Z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       "text": [
        "<matplotlib.figure.Figure at 0x7f35cf46e050>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Birth Weight Distribution according to Gender"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "boyList = missingValDF['WEIGHTLB'][missingValDF['SEX']== 1.0]\n",
      "sns.distplot(boyList, bins= 15, color = 'blue', hist=False, label= 'Boys')\n",
      "#cigList = missingValDF['WEIGHTLB'][missingValDF['CIGNUM'].isin(frange(1.0,99.0,1.0))]\n",
      "girlList = missingValDF['WEIGHTLB'][missingValDF['SEX'] == 2.0]\n",
      "sns.distplot(girlList, bins = 15, color = 'red', hist=False, label= 'Girls')\n",
      "plt.ylabel('Probability')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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afxL+QGutgR2xC0mIFFRejmP3rnq3+Uik4naYf8gwABxbNldrlz2i2qaG1lmE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\nIiIiIiIiIiIiYnz5C6rFjyUWb4msAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f35cf74a9d0>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Birth Weight Distribution according to APGAR Score"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "babiesDF = babiesDF[babiesDF['APGAR1']!=99]\n",
      "ap = babiesDF['APGAR1']\n",
      "ap.hist()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7f35cf62a2d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f35cf7fe490>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Based on the the histogram plotted above, we observed that the most dominant APGAR scores are 8 and 9."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Probablity Distribution of APGAR Scores 8 and 9"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "babiesDF = babiesDF[babiesDF['APGAR1']!=99]\n",
      "weightPound_x = babiesDF['BPOUND']\n",
      "weightOunces_x = babiesDF['BOUNCE']\n",
      "weight_x= weightPound_x.astype(np.float) + (0.0625 * weightOunces_x.astype(np.float))\n",
      "babiesDF['WEIGHTLB'] = weight_x\n",
      "\n",
      "\n",
      "apList1 = babiesDF['WEIGHTLB'][babiesDF['APGAR1']== 9.0]\n",
      "sns.distplot(apList1, bins= 15, color = 'blue', hist=False, label= 'APGAR-9')\n",
      "#cigList = missingValDF['WEIGHTLB'][missingValDF['CIGNUM'].isin(frange(1.0,99.0,1.0))]\n",
      "apList2 = babiesDF['WEIGHTLB'][babiesDF['APGAR1']== 8.0]\n",
      "sns.distplot(apList2, bins = 15, color = 'red', hist=False, label= 'APGAR-8')\n",
      "plt.ylabel('Probability')\n",
      "plt.xlim((0,15))\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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tVl730YFy2pla61YhiEeIkCgaZO7iw/nhCnxNm+Fv0zbMUVVOaqqPWZ8UjysU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\nSUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f35cf45a190>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "missingValDF.columns"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "Index([u'SEX', u'MARITAL', u'FAGE', u'GAINED', u'VISITS', u'MAGE', u'FEDUC', u'MEDUC', u'TOTALP', u'BDEAD', u'TERMS', u'LOUTCOME', u'WEEKS', u'CIGNUM', u'DRINKNUM', u'ANEMIA', u'CARDIAC', u'ACLUNG', u'DIABETES', u'HERPES', u'HYDRAM', u'HEMOGLOB', u'HYPERCH', u'HYPERPR', u'ECLAMP', u'CERVIX', u'PINFANT', u'PRETERM', u'RENAL', u'RHSEN', u'UTERINE', u'WEIGHTLB', u'OTHER_NON_WHITE_MOM', u'WHITE_MOM', u'BLACK_MOM', u'AMERICAN_INDIAN_MOM', u'CHINESE_MOM', u'JAPANESE_MOM', u'HAWAIIAN_MOM', u'FILIPINO_MOM', u'OTHER_ASIAN_MOM', u'OTHER_NON_WHITE_DAD', u'WHITE_DAD', u'BLACK_DAD', u'AMERICAN_INDIAN_DAD', u'CHINESE_DAD', u'JAPANESE_DAD', u'HAWAIIAN_DAD', u'FILIPINO_DAD', u'OTHER_ASIAN_DAD', u'HISPMOM_BINARY', u'HISPDAD_BINARY'], dtype='object')"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "blackMomList = missingValDF['WEIGHTLB'][missingValDF['BLACK_MOM']==1.0]\n",
      "blackList = blackMomList[missingValDF['BLACK_DAD']==1.0]\n",
      "blackWhiteList = blackMomList[missingValDF['WHITE_DAD']==1.0]\n",
      "\n",
      "whiteMomList = missingValDF['WEIGHTLB'][missingValDF['WHITE_MOM']==1.0]\n",
      "whiteList = whiteMomList[missingValDF['WHITE_DAD']==1.0]\n",
      "whiteBlackList = whiteMomList[missingValDF['BLACK_DAD']==1.0]\n",
      "\n",
      "\n",
      "sns.distplot(blackList, bins= 15, color = 'blue', hist=False, label= 'White Mother & Father')\n",
      "sns.distplot(whiteList, bins = 15, color = 'black', hist=False, label= 'Black Mother & Father')\n",
      "sns.distplot(blackWhiteList, bins = 15, color = 'red', hist=False, label= 'Black Mother & White Father')\n",
      "sns.distplot(whiteBlackList, bins = 15, color = 'green', hist=False, label= 'White Mother & Black Father')\n",
      "#sns.distplot(blackDadList, bins= 15, color = 'green', hist=False, label= 'White Fathers')\n",
      "#sns.distplot(whiteDadList, bins = 15, color = 'black', hist=False, label= 'Black Fathers')\n",
      "plt.ylabel('Probability')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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XcmRRT7Rt245Fi5YAcPToYZ588kk+/vhLQNdGatasGZs2/c6KFf/BbC7FxcWV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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f35c6768110>"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "early = missingValDF['WEIGHTLB'][missingValDF['WEEKS']<33.0]\n",
      "late = missingValDF['WEIGHTLB'][missingValDF['WEEKS']>40.0]\n",
      "sns.distplot(early, bins= 15, color = 'blue', hist=False, label= 'Early Delivery (weeks < 33)')\n",
      "sns.distplot(late, bins = 15, color = 'black', hist=False, label= 'Late Delivery (weeks > 40)')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7f35c66cc210>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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HXrO5OMuUMV432LPnAFWq1Lb4+TPLET5zcJw4s8pcSWcT0BVACFETuCqlfPho\nmwcwXwiR69HjOkCYmTaK4pIe1++tO0LHJDi4KgAnT4bY5PyKfciwhy+l3CeEOPyoVJMIDBFC9AHu\nSylXCiE+B7YJIRKAY1LKNQCp2+Twa1AUu2etRU/SU6ZMWXx8fFTCd3Fma/hSyjGpngpJse1n4OdM\ntFEUl/Z4DL51b7oy0ev1BAVV5sSJ48THx+Pp6WmTOBTbUnfaKooVSBmOl5cXpUsH2CyGoKAqGAyG\n5D8+iutRCV9RclhSUhJnzoQTGCjQ6/U2iyM4uAoAoaGqrOOqVMJXlBx25co/REdHW23Rk/RUrqwS\nvqtTCV9RcpitL9iaBAUFodPp1B23LkwlfEXJYeHhpkVPbJvw/fxyExBQhpMnT6BpZu+FVJyQSviK\nksNMPXxbjcFPKTi4Kvfu3ePatau2DkWxAZXwFSWHSRmGh4cHAQFlbB0KlSsHA+oGLFelEr6i5CBN\n0wgPD6dcuUA8PDxsHY4aqePiVMJXlBx0/fo1oqL+tXn93sQ0Ukf18F2TSviKkoNsfYdtasWLlyB/\n/vyqh++iVMJXlBxkTxdsAXQ6HZUrV+HChfNERTnfbJBKxlTCV5QcZJol015KOvC4rHPq1CkbR6JY\nm0r4ipKDwsPD0Ov1lC1bztahJHs8UueEjSNRrE0lfEXJIZqmIWUYZcqUxcvLy9bhJDPNja/uuHU9\nKuErSg65efMm9+7ds6tyDhgvIHt4eBAaqnr4rsbsfPhCiClAXYzr0o6QUh5Ksa05MBHjQifhwJtA\nU2AZYOo+hEgph1s4bkWxe48v2NrHCB0TT09PAgMFYWFhJCUl4eam+n2uIsNPWgjRFAiUUjYABgDT\nU+0yF+gqpWwE5AbaYPzDsF1K2fzRP5XsFZdkL5OmpSUoqDLR0Q+5dOmirUNRrMjcn/YWwJ8A0vjb\nm18I4Zdi+/NSStOkHJFAAcuHqCiO6fEYfPtL+JUqVQbg1KlQG0eiWJO5hF8UuJXicSRQzPRASvkA\nQAhRDGgNrAd0QJAQYpUQYpcQopVlQ1YUxyBlODqdjsDA8rYO5SmVK5sSvrpw60rM1vBT0WEs2SQT\nQhQGVgODpZR3hRASGC+lXCaEKItxkfNyUsoEy4Ss2EJCAuzcqWf7dnf0eggMTKJzZwM+PraOzH5J\nGUbp0gH42OGbFBRkHJp5+rQai+9KzCX8axh7+SbFgeumB0KIPBh79R9LKbcASCmvYbxoi5TyvBDi\nBlACuJQF0lixAAAgAElEQVTRifz9c2c5eFtwxTgPHIBeveDs2SefnzTJm+nT4bXXsn9sR3g/sxNj\nZGQkt27don79+lZ7jVk5T6FCgvz58xMefsrqn4EjfObgOHFmhbmEvwmYAMwVQtQErkopH6bY/i0w\nRUq5yfSEEOJ1oLyUcsKj3n9hwOzk25GR9n+bt79/bpeL8+efPfjoIy+SkqBXLwNduiTg5aWxaZM7\n8+Z50r27jpMn4xg+PN6mceaU7Ma4b99hAAICAq3yGrMTZ6VKldm3bw8XL94gV65cORTZkxzhMwfH\niTOrMkz4Usp9QojDQog9GIdeDhFC9AHuA38BvYFAIcSbj5r8BiwBFgkhdgN6jKUeVc5xQD/95MHo\n0d4UKpTE3LmxNGqUmLytVq14OnVK4PXXffjySy/y5tXo08dgw2jti+mCrb3MoZOWoKDK7N27GynD\nqFHjeVuHo1iB2Rq+lHJMqqdSTrPnnU6zjtmOSLELa9e6Jyf7P/+MoUKFpKf2CQpKYvXqaF580Zcx\nY7yoUCGJevUS0zia6wkPPw3Yd8JPOVJHJXzXoO64UJ5y6pQbQ4d64+ursWxZ2snepFQpjR9+iEXT\nYPBgb6KirBioHTNNmhYYKGwcSfqCgtRIHVejEr7yhLt3oU8fH6KjdcyYEUvlyukne5OGDRMZMSKe\nq1fd+Oor+5kzxpbCw8MoVaq01Wrj2VGhQiV0Op0aqeNCVMJXkiUkwKBBPly65MaoUXF06JD5Sy8j\nR8ZTvnwiP/7owaFDrv1rdffuHW7ejLCbRU/S4+fnR+nSAZw6dRJN08w3UByea//PVJ7wxRde7Njh\nTuvWCXz4YdZG3Xh5wbffxqFpOsaN88aV84eUErDPO2xTCwoK5s6dO0RE3LB1KIoVqISvALB0qTuz\nZ3tSvnwi330XQ3bm06pXL5F27QwcOqRn7dqs3tPnPOxtlauMPK7jqykWXIFK+ApHjrjx/vve5Mmj\nsXBhDHnyZP9YY8fG4e6u8cUXXiS46GBcU8IvX95+L9iaqDl1XItK+C4uIkJH374+GAwwd24M5co9\nWy2mbFmNXr0MXLzoxooVrtnLDwuz/yGZJmpOHdeiEr4Lu3cPunf34cYNN8aOjaNFC8uMoR82LB53\nd41p0zxJdMFh+eHhYZQoUZLcuZ/hq5KVlC5dBl9fXzVSx0WohO+iHjyAHj18CQ3V07dvPO+8Y7m7\nZJ97TuPVVw2cOeN6tfz79+9x48Z1h+jdA+j1eipUqIiUYRgM6k5pZ6cSvguKiNDx8su+HD6sp2tX\nA//5Txw6nWXPMXx4PG5uGpMne5Jkfii/0wgLM12wrWTjSDIvKCgYg8HA2bNnbB2KksNUwncxBw+6\n0br14579jBmx2RqRY07ZshqvvJLA6dN6Nm3SW/4Edso0pULFio6U8I11/NOn1YVbZ6cSvoswGODb\nbz15+WVfIiJ0fPppHF9/HYc+B3Pxu+8ax/JPnuzlMuPyHWEOndTUSB3XoRK+C9ixQ0+rVr58/bUX\nhQpp/P57DMOHx1u8jJNahQpJtG9v4NgxPbt3u0Yv31TSsfe7bFN6nPDVSB1npxK+EwsPd+P1133o\n1s2XsDA3evaMZ9euhzRpYr2hM++8Y+zlz53rabVz2pKUYZQs+Rx+fo6zeEbBggUpWrSYGqnjAlTC\nd0KRkfDhh140a+bLli3uNGyYwObN0UyZEkfevNaNpVatJJ5/PpFNm/ScP5/DXyls7N69u0RE3HCo\nco5JpUpBXL16hXv37to6FCUHqYTvROLiYNYsDwIDYcECTwICNBYujGbFihiqVrXdUJm33opH03TM\nm+fcvXxHHKFjota4dQ1mB0kLIaYAdTEuXj5CSnkoxbbmwESMq2GFA29KKbWM2iiWp2mwYYM748d7\ncfGiG/nzw1dfxdK3rwEPD1tHB+3bJ1CiRBKLF3vw0UfW/5ZhLY44Qsck5Zw69es3tHE0Sk7JsIcv\nhGgKBEopGwADgOmpdpkLdJVSNgJyA20y0UaxoPBwN7p08aFvXx+uXNExaFA8Z8/CwIH2kewB3N2h\nf38D0dE6fv3VToLKAY44QsdEjdRxDeZKOi2APwGkcUao/EIIvxTbn5dSmhYojwQKZqKNYgEGA0yZ\n4knLlr7s3m2c0njnzod8+WUcBQrYOrqn9e4dj6+vxvz5nk47qZppHdvy5R1nhI5J+fICd3d3NVLH\nyZlL+EWBWykeRwLFTA+klA8AhBDFgNbAenNtlGd36pQbbdr4MmmSF/nzG+v0v/4aQ2Cg/Q52z5cP\nXnvNwJUrbmzY4JzTLYSFnaZUqdL4+Tle/8bLy4vy5QVhYadJcqVbo11MVi/a6jDW5ZMJIQoDq4HB\nUso7mWmjZN8ff7jTtq0vISF6evaMZ/fuh7Rp4xgzlA0YYJyrZcEC5yvr3Llzm8jImw5ZzjGpVCmI\nhw+juHz5kq1DUXKIua7WNYw9dpPiwHXTAyFEHoy9+o+llFsy0yY9/v6OMW7ZVnFqGkycCJ9+Cnny\nwOLF8PLLnkDaI1/s8f3094dmzWD7dndu386Nv799xplaZmI8ffooADVqVLPZa3rW89au/TwrVvzB\n1avnqV27qoWiepojfObgOHFmhbmEvwmYAMwVQtQErkopH6bY/i0wRUq5KQtt0hQZ+W/WIrcBf//c\nNotz0iRPpkzx4rnnkli6NJrAQI3IyLT3tWWc5vTq5c727T5MmRLPnDmedhunSWbfy/37DwPw3HNl\nbfKaLPGZly4dCMD+/Ydo2LClJcJ6ij3/bqbkKHFmVYYJX0q5TwhxWAixB+PQyyFCiD7AfeAvoDcQ\nKIR481GT36SUP6Ruk4Pxu4SFCz2YMsWLMmWSWLEimhIlHLdC1rZtAoULJ7F0qQdTptg6Gstx5CGZ\nJqax+GqkjvMye/VMSjkm1VMhKX72zmQbJZv27NEzerQXBQsae/aOnOwBPDygVy8Dkyd7sWQJdOhg\n64gsIzw8DJ1O55AjdEyKFStO3rz51EgdJ6butLVj//4Lw4YZ/6b+9FMsAQGOnexNevc24Oam8d13\nto7EcsLDw3juudL4+vraOpRs0+l0BAVV5sKF80RHR9s6HCUHqIRvx8aN8+LKFTfefTeeevUcYyRO\nZpQoodG6dQKHD8PRo47/K3j79m1u3YqkYkXHHaFjEhRUmaSkpOSF2BXn4vj/25zUwYNu/PabJ8HB\niYwcGW/rcCyuXz/jEM2ffnL8+XUe32HruPV7E9Mdt2pOHeekEr4d0jT4/HMvACZOjMPT8XPiU5o2\nTaRcOVi50p27Dj5BY1iY406pkNrjOXVUHd8ZqYRvh/76S8+BA+60aWNwqlJOSm5u8PbbEBurY+lS\nx74RyxlG6JhUrBgEqJE6zkolfDujacYlAXU6jU8+cb5STkr9+oGXl8aCBY690LlphE5goLB1KM/M\nz8+P0qUDOHXqJJqrrEvpQlTCtzMHDug5dkzPSy8lUKGCA2fBTChYEF5+OYHz593Yu9dxl0AMDz9N\n6dIBDj1CJ6WgoGBu377NzZs3bR2KYmEq4duZ2bON5Y233zbYOBLr6NXL+Dp/+80xyzqRkZHcvn3b\nKco5JqqO77xUwrcjFy7o2LjRnZo1E6lTxzlr96nVrZtIuXJJrF3rzr17to4m65xphI6JKeGrkTrO\nRyV8O7JkiQeapmPAgHh0zr38azKdDl5/3UBcnI7lyx2vl+/Ii56kR/XwnZdK+HYiMRGWLvUgd26N\ndu2cdIWQdLz6qgF3d80hyzqOvI5tegICyuLj46NG6jghlfDtxK5deq5dc6NTJwNOcu0v04oUMd55\ne/KknhMnHOtXMjz8NG5ubpQv7/gjdEz0ej0VKlREyjASnHV5MhflWP+7nNiSJcbe7WuvucbF2tR6\n9jS+bkda81bTNMLDTxMQUAZv7zTnEXRYQUHBxMfHc+7cWVuHoliQSvh2IDoaNm50JyAgidq1nXso\nZnqaN0+kWLEkli/3wFHm7YqMjOTu3btOVc4xUXV856QSvh3YutWd6GgdL79scJmLtam5u0P37gb+\n/VfH2rWOseatM16wNVFz6jgnlfDtgCnBdejg2vXSHj0ca0y+KyR81cN3Lma7UkKIKUBdjAuRj5BS\nHkqxzRuYC1SSUtZ+9FwzYBlg+k0JkVIOt3DcTiM2FjZtcqdUqSSqVHHNco5JQIBG48YJ7Nrlzrlz\nOsqVs+9b+51xhI5JoUKFKFKkqBqp42Qy7OELIZoCgVLKBsAAYHqqXf4L/J1G021SyuaP/qlkn4Ed\nO/REReno0CHBZcs5KZku3i5aZP+9/NOnQ9Hr9QQGlrd1KDmiUqUgrlz5hwcP7ts6FMVCzJV0WgB/\nAkjjigj5hRB+KbaPAdak0U6lrkzatMn4Jeull1xzdE5qL72UQL58GkuWeGCw47ckKSmJU6dCKV9e\nON0IHZPHa9yqOr6zMJfwiwK3UjyOBIqZHkgpH/J0cteAICHEKiHELiFEK4tE6oQ0DTZvdqdgwSRq\n1nTtco6Jtzd07WogMtKNLVvs9+LtpUsXefgwKjkpOiM1Usf5ZPV/lA5jQs/IGWC8lHKZEKIssE0I\nUU5KmeEVSX//3FkMxTYsGefRo3DjBvTuDUWLWvb1O/L7OWwY/PADLFvmwxtv2CCoVNKKcdeucwDU\nrVvLbt5rS8fRsGEdAC5ePGPRY9vL+2WOo8SZFeYS/jWMvXyT4sD1VPs88QdASnkN40VbpJTnhRA3\ngBLApYxOFBn5b2bitSl//9wWjXPpUk/Ai8aNY4iMtNwIHUvHmVPSi7NYMahe3Zf16904ceIhxYrZ\n7uJtejHu3Wu8dFW6dHm7eK9z4jMvVKgker2eQ4eOWOzYjv676ejMlXQ2AV0BhBA1gauPyjgpPVHS\nEUK8LoT47NHPhYHCwFXLhOtctmxxR6/XaN7ctYdjpqVnTwNJSbrkO5DtjWn0SuXKzlvS8fLyQogK\nnDoVSmKia8ze6uwyTPhSyn3AYSHEHmAqMEQI0UcI0QlACLEF2AhUFkKECCH6AauB54UQu4FVwGBz\n5RxXdOcOHDniRp06ieTNa+to7E/nzgZ8fTUWLfKwy9WwQkNPUqhQIQoXLmLrUHJU1arViY5+qKZY\ncBJma/hSyjGpngpJsS29C7IdnyUoV7BnjzuapqNpU9VzSkvu3NCxYwJLlniwZ4+exo3t5336998H\nXL58kSZNmqNz8rG01apVZ+nSRRw/fhQhKtg6HOUZqTttbWTnTuOSfo0bqy8/6bHXCdVCQ52/nGNS\ntWoNAI4fP2rjSBRLUAnfRnbvdsfPT6NGDTusV9iJOnUSESKRdevcuXPH1tE8Fhpq/JLrCgk/OLgK\nbm5uHD9+zNahKBagEr4NXLum49w5N+rXT8Tdfoea25xOZ+zlx8fr+OMP++nlmy7YOvMYfBNfX18q\nVKhISMgJdeHWCaiEbwOqnJN53bol4OFhXA1Ls5OpdUJCjuHp6ekyNW3ThduzZ8/YOhTlGamEbwO7\ndxu79Y0aqR6TOYUKabRtm8Dp03oOH7b9r2t8fDynToUSFFQZT09PW4djFdWqVQdUHd8Z2P5/kIvR\nNONyhgULJhEUpOr3mdGrl/Hi7S+/2D7BhoWdIj4+nmrVato6FKsxXbg9cULV8R2dSvhWdv68juvX\n3WjUKBE39e5nSpMmiQQEJPHnn+7cvWvbWI4dM/ZyTb1eV6Au3DoPlXKsbOdOVc7JKjc36NcvnthY\nnc0XRzElvWrVatg0Dmvy9fWlYsUgjh8/SlxcnK3DUZ6BSvhWtmuXumCbHT16GPDx0ViwwBNbDhY5\nfvwoXl5eVKzofIueZKRRo8bExsZy6FBay18ojkIlfCtKSjLeYVuyZBJlytjJkBMHkS+fcdrky5fd\n2LJFb5MY4uLiOH06lMqVg/HwsJ9hotbQuHEzAHbt2mHbQJRnohK+FYWGunH3ro5GjRLV6lbZ0L+/\n8eLt/Pm2uXh7+nQoBoPBpco5JvXrN8DNzY3du3faOhTlGaiEb0WqnPNsKldOon79BLZvd+fsWev/\nxTRdsK1e3XVG6JjkyZOX6tVrcOTIIaKiomwdjpJNKuFb0a5dxgu29jQRmKMZMMDYy//xR+v38k3j\n0F2xhw/Gsk5CQgIHDuy1dShKNqmEbyXx8bBvn57y5RMpWlTV77OrbdsEihVLYvFiD6sP0Txy5DC+\nvr4uc4dtao0aNQFg1y5V1nFUKuFbydGjeqKjdap3/4w8POCtt+J5+FDHTz9Zr5f/4MF9wsJOUaPG\n87i76ARItWvXxdPTU124dWAq4VuJqX6vxt8/uzfeMJAvn8a8eR5ER1vnnIcPH0LTNGrVqmOdE9oh\nX19f6tZtQEjIca5c+cfW4SjZYDbhCyGmCCH2CiH2CCFqpdrmLYRYKIQ4mNk2rmr3bj06nUbDhuqC\n7bPy84P+/eO5fdvNanPlm8af167tugkfoGPHTgCsWbPKxpEo2ZFhwhdCNAUCpZQNgAHA9FS7/Bf4\nO4ttXE50NBw6pKdq1STy57d1NM5h0KB4/Pw0pk715GHqVZZzwMGDBwB4/nnXTvgvvdQBNzc3Vq/+\n09ahKNlgroffAvgTQEoZBuQXQvil2D4GWJPFNi7nwAE98fE6Vc6xoAIFjLX8W7fccnxcflJSEocP\nH6JcuUAKFiyYo+eyd/7+/jRs2JjDhw9y9eoVW4ejZJG5hF8UuJXicSRQzPRASvkQSD0gOsM2rmj3\nbjX+PicMHhxPvnwaM2d65uiInfDwMP7994FL1+9T6tjxFQDWrFlp40iUrMrqcAMdkNUxhZlq4++f\nO4uHtY3sxLlvn3F0Sfv2vuTKlQNBpcGZ38/HbWHsWHjvPZg+PTczZ1owsBTCwo4D0KJFU7t+X60V\n2xtv9GD06FGsX7+asWPHZLm9Pb+HKTlKnFlhLuFfw9hjNykOXE+1T+pknpk2T4mM/NfcLjbn7587\ny3HeuweHD/tRt24i0dExVhlVkp04bcEScb72Gsye7cvs2W506xZt8TUG/P1zs3WrcRhixYrV7PZ9\nteZnrtP50LBhE3bt2s7BgycICCiT6bau9Ltpj8yVdDYBXQGEEDWBq4/KOCmlLulkpo3L2LvXHU1T\n4+9ziqcnfPllHElJOt5/3ztHZtLcv38vefLkddkbrtLSrdtrAPzxx1IbR6JkRYYJX0q5DzgshNgD\nTAWGCCH6CCE6AQghtgAbgcpCiBAhRL+02uTsS7Bvj+fPUQk/p7RokUinTgYOHdLz/feWHaZ5+fJl\nLl26SIMGDdHrbTNLpz1q374jPj4+LFu2BM1eFhtWzDJbw5dSpi7ShaTY1iqTbVzW7t16fH01atZU\nCT8nTZoUx+7dev7zHy+aN0+0WGln27ZtADRs2Ngix3MWfn65adu2HStW/MHhwwfVBW0Hoe60zUER\nETrCw/XUq5eIi6x3bTMFC2pMmRJLXJyOfv18ePDAMsd9nPCbWOaATqRbt+6AKus4EpXwc9Dj6RTU\ncExrePHFRIYNi+PCBTeGDPEh4Rnfdk3T2LZtGwUKFCAoqLJlgnQiTZu2wN+/MCtXLic+Pt7W4SiZ\noBJ+DjKNv2/SRJVzrGXMmHiaNk3gr7/cGT3ai2cpL1+6dJHLly9Tv34j3NSK809xd3enc+eu3Llz\nh61bt9g6HCUT1G9xDtE04/z3+fJpBAdbdqigkj53d/jxxxiqVEnkl188GTs2+0l/z55dgHE9VyVt\nprLOsmVLbByJkhkq4eeQ8+d1/POPG40aJaA6h9aVOzcsXhxDhQqJzJ3ryahRXhgMWT+OaTm/Ro2a\nWjhC51GlSjUqVKjIpk0buH//nq3DUcxQqSiHbN1qHADVsqUq59hC4cIaK1fGULVqIr/95knnzj5E\nRGR+WcTExER27NhKsWLF1Pj7DOh0Orp1605cXByrV6upFuydSvg5xJTwmzdXF2xtpWBBjZUro3n5\nZQMHDrjzwgu+HDyYuV/5Q4cOcuvWLTp06IBOrTifoS5dXkWn06myjgNQCT8HxMTA3r16KlVKpHhx\ndVOKLfn5wdy5sYwbF8vNmzo6dfJl6lRPsyN4Nm5cB0DHjh2tEKVjK1GiJI0aNWH//r2cP3/O1uEo\nGVAJPwfs368nJkZH8+aqnGMPdDoYOtTA77/HUKCAxsSJXrRv78uZM+n/+m/cuA5fX19atmxpxUgd\nV48evQBYvPhXG0eiZEQl/BxgKue0aKHKOfakSZNEdu58SJcuBo4c0dOypS/ff+9BUqpBVGfPnuHc\nubM0a9YSb29v2wTrYNq160jevPlYsuQ3Ep71Bgglx6iEnwO2bTNOp1C3rurh25v8+WH27Fh+/DEG\nPz+NceO86dTJh/PnH9fpN2wwlnPatHnJVmE6HB8fH7p06UZExA3+97/Ntg5HSYdK+Bb2zz86pNTT\nqFEiXl62jkZJT/v2CezYEU27dgb273enefNczJ1r7O2vX78GNzc3Xnihja3DdCg9e/YB4LfffrZx\nJEp6VMK3sG3b1OgcR+Hvr/Hjj7HMnRuDj4/Gp59607r1eQ4fPkiTJs1cfjnDrKpSpSrVqtVg8+a/\nuHbtqq3DUdKgEr6Fbd1qnE5B1e8dg04HnTolsHOnsbd/4sRPABQt+uZTtX3FvH793iQxMZEFC+bb\nOhQlDSrhW5DBADt3ulOmTBJlyqjhmI6kcGGN7767h6/vQnS6IixZ0pWXX/bhzBlbR+ZYXnmlKwUK\nFGDhwh+JiYmxdThKKirhW9CBA3qionSqnOOg1q1bRXT0XQYM6En79nDggDtVq8L333vkyEpazsjH\nx4devfpy584dVq5cbutwlFTMJnwhxBQhxF4hxB4hRK1U21oJIQ482v7po+eaCSEihRDbHv2bnlPB\n25sNG4z1+zZtVMJ3RD///CMAAwe+wY8/xvLDDzH4+cG4cd68/LIPp06p/lFm9O07AL1ez9y5s9Vq\nWHYmw99gIURTIFBK2QAYAKRO3tOAzkBDoLUQohLGRc23SymbP/o3PAfitjuaZkz4efNqNGyouoOO\n5sCB/Rw4sI+WLV+gTJmyAHTsmEBoKHTsaODvv91p3tyXt97y5uxZNdVCRkqWfI527ToSGhrC9u1b\nbR2OkoK5LksL4E8AKWUYkF8I4QcghCgL3JFSXpVSasB6wGVvSzxxwo0rV9x44YUEPCy7rKpiBdOn\nfwvAiBHvP/F84cLwww+xLF4cTZUqSfz5pweNGuWif39vNm7Uo9b9SNuIEaMA+Pbbr1Uv346YS/hF\ngVspHkc+es60LTLFtptAsUc/BwkhVgkhdgkh0lz31tmsX28s57Rtq8o5jiYk5ASbN/9FvXoNqFev\nfpr7tGyZyObN0fz0UwyVKiWxdq0Hb7zhS9WquRg1yosVK9yzNBuns6tSpRovvtiWv//en7yugGJ7\nGf6GCiHmAOuklKsfPd4F9JNSnhVCNADel1J2frRtAFAWmAU0lFIue/QtYBtQTkqZbibUHLwLoGkQ\nFAQXL8KtW5Arl60jUrKie/fuLF26lPXr19O2bVuz+2saHDkCv/wCixfDzZuPtwkBTZsa/zVpAs89\nl4OB27mDBw9Sp04dmjdvztatqrSTE3RZnMrV3cz2azzu0QMUB64/+vlqqm0lgatSymvAMgAp5Xkh\nxA2gBHApoxNFRv6bhbBtw98/d5pxhoS4ERaWiw4dDERHxxIdbYPgUkgvTntjD3GeO3eG33//neDg\nqjz/fMOn4kkvxlKl4JNPYPRo4+e/Z4+ePXvc2b9fz7x5OubNM+2XRNOmCXTsmECjRono9TnzOuzh\nvUwtIKAiLVq0YuvWLSxduoIWLV6wyzjT4ihxZpW5ks4moCuAEKImxoT+EEBKeQnII4QoLYRwB9oB\nm4QQrwshPnvUpjBQGOMfB6e1YoWxaN+5syrnOJqZM6ehaRrvvvtetua9d3eHGjWSGDrUwOLFMZw5\nE8WmTQ+ZMCGWNm0MPHig45dfPOnWzZc6dXIxa5YHDx/mwAuxU2PHfo6bmxuffvqRWujcDmSY8KWU\n+4DDQog9wFRgiBCijxCi06NdBgOLgZ3AEinlWWA18LwQYjewChicUTnH0SUlwcqV7uTOrdGypdO+\nTKd09eoVfv99MeXKBdKunWXmvXd3h+rVkxg82MDChbGEhUWxalU0vXvHc/u2jgkTvKlTJxe//vr0\nLJ3OqHLlYPr06c/Zs2eYP3+urcNxeXZxlUnTNM0Rvj6l9TVv/349HTv60qOHgWnTYm0U2ZMc5euo\nreP89NPRzJ07m2nTvkuezz01S8Z47x58/70n33/vSXS0jgYNEpgxI5bnnnv2S1i2fi8zcufOberV\nq0FiYhKnT5/C0zOPrUMyy57fz5QKF86TpRyu7iR5RosWGcs5XbpkY5VsxWZu3brFL78soESJknTp\n8qpVzpkvH3z0UTz79j2kTRsDe/e606pVLrZsyaHCvp0oUKAgY8d+zr//PqBfv34kucJXGzulEv4z\nePAAVq1yp1SpJBo1UjdbOZJ5874jJiaGIUOG4+npadVzFyum8fPPsUyebLzA37OnDz/84Nw3b/Tq\n1YdWrVqzefNm5s+fY+twXJZK+M9g+XIPYmJ09OplwE29kw7jwYP7zJ8/j0KFCvH662/YJAadDnr1\nMrBmTTSFCml8/LE3kyZ54tgDlNOn0+mYMmUWhQoV4osvPiMk5LitQ3JJKk1lk6bBr796oNdr9Oih\nyjmOZMGC+Tx4cJ+33hqCr6+vTWOpXj2JDRuiCQhIYsoUL77+2nmTfpEiRfjpp5+IjY2lT5/XuXXr\nlvlGikWphJ9N+/frCQnR06ZNAkWKOOn/UCd0//49Zs+eQe7ceejX701bhwNAqVIaK1cak/7kyc6d\n9Nu3b8+HH37MlSv/MGhQXwwG1VmyJpXws2nmTGPdd/BgNbbYkfzf/33N7du3GTbsXfLkyWvrcJIV\nL/5k0v/mG+teV7CmUaM+pG3b9uzevZMJEz61dTguRSX8bAgPd2PzZndq106kTh014sBRhIeHMX/+\nHLNqdGkAAA6/SURBVEqXDuDtt4faOpynFC+u8eef0ZQqlcT//Z8Xc+Y454VcNzc3Zs2aQ4UKFZk7\ndzZLlvxm65Bchkr42TB1qrH39c47qnfvKBITE/n44w9ISEjgiy/+g7e3t61DSlOJEhp//BFNkSJJ\njB3rzeLF5mY/cUx+frn5+edF5MmTlw8+eJeDBw/YOiSXoBJ+FoWEuLF8uQfBwYlqZkwHMm7cGHbt\n2kHr1m148UXzE6TZUkCAxrJlMeTPrzFypDdr1zpn0i9bNpC5c38iISGBN97ozvnz52wdktNTCT+L\nvvjCC4CxY+PUUEwHMXv2TObN+56KFSsxa9bcbM2ZY20VKyaxeHE0Pj7w9tve7NjhnDdntWjRiv/+\ndwq3b9+mR48u3Ew59ahicSplZcHKlbB9uztNmiTQvLm60creRUX9y7Bhb/PZZx9TpEhRFi36g7x5\n89k6rEyrWTOJhQtj0OmgTx8f9u93zqTfu3dfRo58nwsXztO5czsiIiJsHZLTUgk/k+7ehbffBi8v\njYkT42wdjpKBxMREfv99MU2b1mfp0kVUq1aDVavWU7Kk401O37hxInPnxhIfD6+95sO2bc6Z9D/6\naCxvvz0UKcPp3LkdV69esXVITkkl/EzQNBg92puICPjgg3iEUCNz7FFSUhJr1qyiRYuGDB36FhER\nN3j33fdZt24zZcsG2jq8bGvbNoEFC2JISoLevX2csqav0+mYMOErBg8expkzktatm3H48EFbh+V0\nVMLPhOnTPVm50oMGDdTIHHukaRobNqyjZcvGDBjQm/DwMLp378n+/Uf5+ONxVp8rJye0bp3I4sUx\neHjAm296s3Ch8w3Z1Ol0jB//JV999TW3b9+iU6eXmDdvtppszYJUwjdjyRJ3vvrKixIlkli+3Djf\nuWIfNE1j8+aNvPBCU/r06cGpUyfp0uVV9uw5yPTpsx2yhJORRo0SWb48mrx54f33vXnvPS+br65m\naTqdjoEDB7No0R/4+fnxySej6dbtZaQMt3VoTsFswhdCTBFC7BVC7BFC1Eq1rZUQ4sCj7Z9mpo2j\n0DT47jsPhg/3IV8+jYULYyha1Hw7JefFxcWxbNkSXnyxGT17vkpIyHFeeaULu3b9zezZP1CuXHlb\nh5hjatZMYtOmhwQHJ/LLL560aJHLKS/mtmjRiu3b9/Pii23ZtWsHTZrUZeTIoYSHh9k6NIeWYcIX\nQjQFAqWUDYABwPRUu0wDOgMNgdZCiEqZaGP3rl7V0bOnD+PHe1OkSBKrVkVTpYr6WmkrSUlJXLhw\nnjVrVjJ8+GCqVhUMGTKI/2/v3oOjqu4Ajn83IRgQzWPBBEhKAP3pxI4iU0rbAYOg1mKQKmo61teo\nUxjpTKv2Zduhg3bq0I7S4tgZ7SgCw8D4qFUL+Iqk+KAq6QhqIT8EVCCQQB5akIRsdvvHuZvcrJsH\nFnJv6O8zs5O7d/dufrmbe+65557zO5s3v0t5+Wyqqjby8MNLETk76FD7xZgxCdas+Zx5846ya1eE\nK64Yypw5bozIyaSgoIDly1ezbNkqzjpLWLlyOVOnfp3y8kt56KElfPDB+8RiNhbmWPTYIVlEFgIf\nq+pj3vOtwCRVPSQi44BlqjrVe+0XwCFgRHfbdPd7wjDjVSIB1dUZrFqVxerVWbS1RSgri7FkSQsj\nR7pMVgNlFpyBEufw4cOoqfmYPXs+Yc+ePd7P3ezdu5eGhoM0NzfR1NREU1Mjra2dPaMKCgq5+uoK\nbrrpFkpKxp7QGMO+L995J4MFC7Kprna1/MmTY1x1VYyyshhjxyYI25CDL7s/29vbWbv276xYsZSq\nqlc71g8ePJhx48ZzxhmFRKP5RKPDyc+Pkp8fJRqNdjyPRqPk5eX3+X5O2L/3pGOd8aq3FulCoNr3\n/IC37kPv5wHfa/XAeGB4mm1GAtuPJbDjIRaD+voIbW1uORZzy599FqGx0T12745QU5PBpk2ZHDzo\nakglJXHuuquFa66JHbfBVYcPH+bIkSMkEgnfI57y3D3i8a7rIbn+i+/1b5N8X27uUBobD6X5LHr9\n3f5tkp/X3t5Oa2trx6OtrY2MjIyORyQSIRKJdFnX+VoG8Xg7Bw7UU19fT13dfurq9nsF+x4O9zCj\nd05OLnl5eZSWnsvYseMpLT2XKVMuZMKEiWTYqDcAJk2Ks3bt52zadBqLFsXYsGEQb73lDuv8/DgX\nXBBHJE5hYZzCwgT5+QmysyE7u/NnVhYMHZogN8RDFDIzM5k1azazZs2mrq6OqqpKXn99AzU1W9mx\nYwfbtm3t0+ecdtrp3onAnRTy8vLJy8sjN9c93HIuRUUFtLTEyc4eQnZ2NpmZmd0eo+AqhJFI12PC\nfwwkn+fk5JCVFdwN92O9BdnT2aS71yIk90g/q6gYwmuv9e1PHDUqzrXXtnHllW2UlbUf15uzNTXb\nmDFjCkePWg+fpNzcXM4880xGjhxNUVExRUVfobi4mKKiYkaPLiYajZKZefK1TZ8IkQjMnAmTJh2h\ntjbCunWDePvtTKqrM6msHERlZe+fkZGR4LnnPh8QyQALCgqoqLiOiorrOta1tLTQ1NRIQ0MDDQ0H\naWxsoLGxgYaG9D+3bNkcSGrmcePGs3HjvwIb7d1bsVaLq8knjQL2ect7U14r8t5/tIdt0oqEYKx7\nbS088YR7mBOvubmZ5uZmNm+2mY/CIB6H8vKgozj57dy5g4KC4NJy93Zd/BJwNYCITAT2quphAFX9\nGDhdRMaIyCDgcuDFnrYxxhgTnF5r1iJyH3Ah0A7MByYCn6rq30RkKrDIe+tTqvpAum1U9b0TEbwx\nxhhjjDHGGGOMMcYYY4wxxgx0gXeHBPB6+TwKjMN1Ff2Jqr4RbFSdRGQxMBk3nuBHqrop4JDSEpHf\nA1Nw+/A+VX0m4JC6JSJDgPeBe1R1WdDxpCMi3wd+CsSABaq6NuCQvkBEhgHLgVzgFGChqr4UbFSd\nROQ84BngAVV9SESKgRW4HoL7gBtUNfABKt3EuRR3LLUB16tq4DOzpMbpW/9tYJ2q9tjzMizDFa8H\nDntpGm4FHgg4ng4DJTeQiFwEnOvFeRnwx4BD6s2vgQYCGpTXGxGJAgtweaLKgdnBRtStm4Ftqjod\n1x36T8GG00lEhgL347prJ7/ne4AHVfVC3Ij9WwIKr0M3cd4LPKKq03AF7J3BRNcpJU7/+mzgbtw4\nqB6FpcBfCdzlLR8EogHGkmo67gtHVbcBeV6tKmw2ANd6y58Cp4pIKK7gUonIOcA5wBpCcpWZxsXA\nK6p6WFX3q+rcoAPqRh2dx0s+XdOdBK0Vd7L014zLgOe85edx+zlo/jiT/4/zgae95bCUSen2J8Av\ngQdxVyI9CkWBr6ptqnrEe/pj3AkgLApxX3hSMjdQqKhqu2+A263AGlUNZe0Z+ANwR9BB9GIMMFRE\nnhWRDSIyPeiA0lHVJ4FiEdkOVBGCmmiS9z+ZOh/oqaqaLJhCcSyli9M70beLSCZwOyEok9LFKSIC\nlKrq091s1kW/T+chIrcCt6WsXqCqL4vIfGACMKu/4zoGgeUG6gsRmY27TL4k6FjSEZEbgQ2q+klY\nr0A8Gbga85VACbAedxIIFRG5HvhEVWd67bt/wd1vGgjC/P3jFfYrgEpVXR90PCmSZdD9wA/7ulG/\nF/iq+ijuBm0X3ongcuC7qtre33H1oKd8QqHi3bi5G7hMVcOa23UmME5ErsLlX2oVkd2q+mov2/W3\n/cBGVY0DO0XkPyIyXFUP9rZhP/sWLp0JqrpFRIpEJBLiq7tDInKKV1MdTR/anQO0FKhR1XuDDiQd\nERmFaxpd7Sr6jBSR9ap6UXfbhGLCPi+3/lygLAx37FO8BCwEHglzbiARycE1lUxX1eag4+mOqn4v\nuSwivwF2hbCwB/e9Py4ii3A1/WEhLOzB3ficDPxVRMbgOj+ErbCP0FmbfwV3c3klMAdYF1RQaXRc\ncXg9tFpVdWGA8XQnAkRUtRbomN5NRHb1VNhDSAp8XJtzFFjrnakALvW19QVGVTeKSLWIvEFnPqEw\nqsDtwyd9+/BGVd0dXEgDl6rWishTwD+9VX2+bO5nDwOPiUgV7nj+QaDR+IjIN3BNTGcAMRGZi+tB\n9ri3/BEQeJfcNHHOAzKBIyKSbMr5t6oGeux3sz+nqWqj95awneiNMcYYY4wxxhhjjDHGGGOMMcYY\nY4wxxhhjTlahHtpsTCoReRe4IznUXURuB+aq6vm+9yguR8sOoMm3eUxVLxGRm4EZqnqD9/5LcQmo\nhuD6Mh8Cfq6q1d7rH+EGtO30/Y4qXEbF84ArvNVluCR2CVxiuE3AvV4WWP/fUALUAG967x2GG75/\n95ffM8b0LiwDr4zpqxdwGRaTA2IuAYaJyAhVPSAiXwFy6Cxs043i7Rig4uWf+TPwHVXd7q2bBTwr\nImd5Sf3SDWhJAAlVXQws9raL404Mce/5tB7+jvrkqEgvZ8tWEVmlqlv6tBeM+RJCkS3TmGPwAl5i\nOK+g/Cqwms40uxcDL/fyGf4r25/hJovZnlyhqs8DJb4MrifacCCLL6a9Nea4sgLfDDRv4rLC5gFf\nA97FpQVOFvgzcCcF6FuTZSnuaqALVY2lrDrezZ8jRGS9iPwD+AA32YYV+OaEsiYdM6Co6lGvkJwO\nnI1LxvU6kJzubRou1/5twP0i4m/Dr1TV36Z8ZDuudg2AiDyNS5Y2HNck9ASusF8pIv4a/4T/8U85\n4GvSycLlw5nvn7bOmOPNCnwzEL2Iu0FaCsxT1SMisk9EyoF9qlovIgngzj5k4twCfBOvlq+qcwBE\nZCnuZiq49vrrUm7aHrf86KraJiJP4k5SVuCbE8aadMxA9AKuJl+oqh96617Ftcf75/vsSzPM74A7\nRcTfy6cIOB/orzZ8cCew9/rx95n/Q1bDNwOOqu70Jm5+w7e6Ejfp+K9861KbdMDNu5DwHqjqDu/K\nYLGI5AItuIrQElVddYyhpfbmSQATRWSrb90qXErgEb6rhMG4LqRhnTfXGGOMMcYYY4wxxhhjjDHG\nGGOMMcYYY4wxxhhjjDHGGGOMOTn9F+RfAKoeK/AqAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f35c6743bd0>"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(missingValDF[missingValDF['TOTALP'] == 1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "34153"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "early = missingValDF['WEIGHTLB'][missingValDF['TOTALP']==1.0]\n",
      "late = missingValDF['WEIGHTLB'][missingValDF['TOTALP']==2.0]\n",
      "sns.distplot(early, bins= 15, color = 'blue', hist=False, label= 'Early Delivery (weeks < 33)')\n",
      "sns.distplot(late, bins = 15, color = 'black', hist=False, label= 'Late Delivery (weeks > 40)')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7f35c684eb50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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GAzla66+UUk8Ci5VSJcAGrfW3AOXb1PB7EKJOcfXfBwTU7ICtS0yMUeBbtOjJ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\nxBQaGig5PcjTOT/6yItFi/wZMaKEzp0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       "text": [
        "<matplotlib.figure.Figure at 0x7f35c67739d0>"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<center><h2> A brief overview of models and evaluation framework</h2></center>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We have used the following machine learning models on our dataset.\n",
      "- Ordinary Least Squares Regression\n",
      "- Ridge Regression\n",
      "- K Nearest Neighbors\n",
      "- Decision Trees\n",
      "- Adaboost (with decision trees)\n",
      "- Random Forest Regression"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For the evaluation, we show the plots of error distribution and values for the following error metrics:\n",
      "- Root Mean Square Error\n",
      "- R2 score\n",
      "- Mean Absolute Error\n",
      "- Explained Variance Score"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def frange(start, end=None, inc=None):\n",
      "    \"A range function, that does accept float increments...\"\n",
      "\n",
      "    if end == None:\n",
      "        end = start + 0.0\n",
      "        start = 0.0\n",
      "\n",
      "    if inc == None:\n",
      "        inc = 1.0\n",
      "\n",
      "    L = []\n",
      "    while 1:\n",
      "        next = start + len(L) * inc\n",
      "        if inc > 0 and next >= end:\n",
      "            break\n",
      "        elif inc < 0 and next <= end:\n",
      "            break\n",
      "        L.append(next)\n",
      "        \n",
      "    return L"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dictny = {}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The following is our common evaluation function which is called for each machine learning model we built. It produces an error distribution plot with lines indicating mean and standard deviation. It also outputs the error metrics for Root Mean Square Error, Mean absolute error, r2 score and explained variance score."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def error_distribution(testDF, actualWeight, predictedweight, modelName, plotIndex):\n",
      "   #diff = 'WT_DIFF'\n",
      "   #plt.subplot(3, 1, plotIndex)\n",
      "   plt.xticks(frange(-7,7,0.5))\n",
      "   testDF['WT_DIFF'] = (actualWeight - predictedweight)\n",
      "   ax = sns.distplot(testDF['WT_DIFF'], bins=50, label='Error in Weight')\n",
      "   meanError = np.mean(testDF['WT_DIFF'])\n",
      "   #print \"Mean Error is\", meanError\n",
      "   stdError = np.std(testDF['WT_DIFF'])\n",
      "   line1_string = 'Mean'\n",
      "   line2_string = 'Std Dev'\n",
      "   #ax.set_ylim((0,1))\n",
      "   ax.set_xlim((-7,7))   \n",
      "   ax.set_title('Probablity Distribution of Error -' + modelName)\n",
      "   ax1 = ax.plot([meanError,meanError],[0,0.5])\n",
      "   pos1 = [0,0.45]\n",
      "   pos2 = [1,0.4]\n",
      "   pos3 = [-1,0.4]\n",
      "   ax.text(pos1[0], pos1[1], line1_string, size=9, color = 'b', ha=\"center\", va=\"center\")\n",
      "   ax2 = ax.plot([stdError,stdError],[0,0.5], color='r')\n",
      "   ax.text(pos2[0], pos2[1], line2_string, size=9, color = 'b', ha=\"center\", va=\"center\")\n",
      "   ax3 = ax.plot([-stdError,-stdError],[0,0.5], color='r')\n",
      "   ax.text(pos3[0], pos3[1], line2_string, size=9, color = 'b', ha=\"center\", va=\"center\")\n",
      "   ax.set_xlabel('Error in Weight')\n",
      "   ax.set_ylabel('Probability')\n",
      "   rms = sqrt(mean_squared_error(actualWeight, predictedweight))\n",
      "   print 'The root mean square error for', modelName, 'is', rms\n",
      "   r2score = r2_score(actualWeight, predictedweight)\n",
      "   print 'The r2 score for', modelName, 'is', r2score\n",
      "   evs = explained_variance_score(actualWeight, predictedweight)\n",
      "   mae = mean_absolute_error(actualWeight,predictedweight)\n",
      "   print 'The mean absolute error for', modelName, 'is', mae\n",
      "   print 'The explained variance score for', modelName, 'is', evs\n",
      "   print '\\n'\n",
      "   values = {}\n",
      "   values['RMS'] = rms\n",
      "   values['R2 Score'] = r2score\n",
      "   values['Explained Variance Score'] = evs\n",
      "   values['Mean Absolute Error'] = mae\n",
      "   dictny[modelName] = values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Baseline Model"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Our baseline model just considers the average of all the birth weights in the data set. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Evaluation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "testDFCopy = testDF.iloc[:-1]\n",
      "testDFCopy['BASELINE_WEIGHT'] = np.mean(testWeights)\n",
      "testDFCopy['ACTUAL_WEIGHT'] = testWeights\n",
      "error_distribution(testDFCopy, testDFCopy['ACTUAL_WEIGHT'], testDFCopy['BASELINE_WEIGHT'], 'Baseline Model', 1)\n",
      "#plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The root mean square error for Baseline Model is 1.3442847521\n",
        "The r2 score for Baseline Model is 0.0\n",
        "The mean absolute error for Baseline Model is 0.990359182478\n",
        "The explained variance score for Baseline Model is 2.22044604925e-16\n",
        "\n",
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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CdPfHGBiKj3+AokyCQuaWfxl4rTFmrjGmBacf4GvFNUtRpo7egREGhhJlEf9P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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f8124a776d0>"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Prediction on Sammi"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The prediction of baseline model on sammi is just the average of all weights in the dataset."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"Prediction for Sammi's child according to Baseline Model is\", np.mean(testWeights)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Prediction for Sammi's child according to Baseline Model is 7.28015974128\n"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Ordinary Least Squares Regression Model"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Converting our final dataframe into a n-dimensional list. This is required for passing our data to the interpolation model."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "allRowsList = []\n",
      "for idx,row in trainDF.iterrows():\n",
      "    currentRowList = row.values.tolist()\n",
      "    allRowsList.append(currentRowList)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Training the data using the OLS model."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf = linear_model.LinearRegression()\n",
      "clf.fit(allRowsList, trainWeights)\n",
      "coeffArray = clf.coef_\n",
      "intercept = clf.intercept_ "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"The co-efficients and the intercept determined by the OLS Regression are:\"\n",
      "print \"-----------------Co-efficients----------------------\"\n",
      "print coeffArray\n",
      "print \"-----------------Intercept--------------------------\"\n",
      "print intercept"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The co-efficients and the intercept determined by the OLS Regression are:\n",
        "-----------------Co-efficients----------------------\n",
        "[ -2.70213295e-01  -8.06135819e-02  -3.81818215e-02  -1.45214190e-02\n",
        "   5.32802658e-02  -7.42943312e-02   2.78418759e-01   2.43592750e-03\n",
        "  -3.19226572e-01   1.20769696e-01  -1.77036703e-01  -3.63541831e-01\n",
        "  -6.57437971e-01  -3.84169236e-01   8.78003018e-01  -4.57628975e-01\n",
        "  -1.01971452e-01   3.72928291e-02  -4.09604373e-01  -9.49511358e+08\n",
        "  -9.49511358e+08  -9.49511359e+08  -9.49511358e+08  -9.49511358e+08\n",
        "  -9.49511358e+08  -9.49511358e+08  -9.49511358e+08  -9.49511358e+08\n",
        "  -2.58124304e-01  -1.51715604e-01  -1.95195790e-01  -1.74724442e-01\n",
        "  -2.45004337e-01  -4.44261927e-01  -3.46932293e-01  -4.22393888e-01\n",
        "  -4.88366721e-01   2.75639711e-02  -1.46268366e-02  -1.10017616e-03\n",
        "   6.52680278e-02  -4.69657698e-03   2.89776260e-02   2.67399735e-02\n",
        "   5.99841599e-02  -2.26130197e-02  -5.97697100e-02   7.17852706e-01\n",
        "  -1.14649490e-01  -7.15271528e-03   1.45556824e-01]\n",
        "-----------------Intercept--------------------------\n",
        "949511366.345\n"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Testing the OLS Model on our test data set"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "olsPrediction = clf.predict(testDF)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Don't consider sammi's row for evaluation\n",
      "testDFCopy = testDF.iloc[:-1]\n",
      "testDFCopy['ACTUAL_WEIGHT'] = testWeights"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 34
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Evaluation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "testDFCopy['PREDICTED_WEIGHT_OLS'] = olsPrediction[:-1]\n",
      "error_distribution(testDFCopy, testDFCopy['ACTUAL_WEIGHT'], testDFCopy['PREDICTED_WEIGHT_OLS'], 'OLS Regression', 2)\n",
      "#plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The root mean square error for OLS Regression is 1.06872212291\n",
        "The r2 score for OLS Regression is 0.367956376917\n",
        "The mean absolute error for OLS Regression is 0.829991777917\n",
        "The explained variance score for OLS Regression is 0.368015375539\n",
        "\n",
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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832dgcICd3VGWNlYxPjrkxt4XOfReXhairbGSPftH6Rscm7PFaYzFTbYQ0KnB\n/3NT/l4Z/DeMkiMaHeS39yjjk3Eqyj3u29DFnY9uY2ys+KkYljW7l/5TW3tzSBrG7JAtBPSV4P/7\n5swaw5gDRqfcbb+0uY5ITR0jw8UPAQEsa67kyefhya37Of+MlcU2x1gEZAsB7cqyn6+qqwtgj2EU\nnP6hSQCaG+ZmEfh8iVSGWdpYwXMdffTs76Oy3GUoraurL7JlxkIl2zyAbGEeG6xslCzTDqBufsXZ\nR0eGqS6LMRXz+e39HSxvqWZ0ZJiLzlzHkiXmBIzZJ1sfwElBhs5X41I7J/5eHfwZRskR930ODE3S\nUFNBedn8WxBveYtLTLd/yCdSU0d1pKbIFhkLmWwtgFOBm3EtgeQafyKrp40CMkqO3oFxpmI+zfXz\nK/yToLEmTFnYo7tvpNimGIuAvDqBg1nBbbjYf89cGWcYs83uHvdibWmYX+GfBKGQR1tjNV37Rxgd\nnyq2OcYCJ59kcH+KS+P8NPCMiOwWkbcV3DLDKAC7Eg5gHo+zb292YaB9/cUfmmosbPIJgn4aeIWq\ntqvqUlw/wL8W1izDKAyJFsB8nmi1NHAAey0MZBSYfBzAHlV9PvFFVRW3jKNhlBRx32dXzwi11eF5\n2QGcoLWhyvoBjDkh2zyAxEifTSLyTeA2XOfvq4Etc2CbYcwqXb3DjE3EWLOkutimZCW5H2BsIlZs\nc4wFTLZRQJ/m4OgfD3hB0mebB2CUHFs7XaK1lvqKIluSm/bmCF37R+gdmCi2KcYCJtsooFdl2iYi\n7yiINYZRQErNAQD0DNgqYUbhyLkimIisAT4CtARFVbiO4F8V0C7DmHW2dg5SVRGiPjKTpbCLQ3ND\nFaGQx/7ByWKbYixg8ukJ+xHQB5wNPIZbIvK9hTTKMGabgaFxuvtGWLu0Fs8r7hrA+RAOebTUVzIw\nPMn4pPUDGIUhHwcwpapfAvaq6reBNwJ/V1izDGN22dzRD8Da9tJJrdDWWI0P7Nxno4GMwpCPA4iI\nyFogLiLH4ZaFtFy1RkmxqaMPgGPai78GcL60NbrRSjv2zo901cbCIx8HcAVuEZivAU8CvcCDhTTK\nMGab53b04QFrlpZWCwBgx97hIltiLFRy9oap6vWJzyLSBNSpan9BrTKMWWQqFkd3HmBFWy1VFeFi\nm5M3kaoyIpVhdnQP4/s28tqYffLJBXSKiPxSRDYCTwHfEZETCm+aYcwOO7qiTEzGOH5VQ7FNmTEt\n9RUMj008pXCoAAAgAElEQVTR1WutAGP2yXcU0M3A24F3AncAPy6kUYYxW8TjcZ7ULgDWtlXOiwXg\nZ0JLXTngQliGMdvkMyA6qqrJuf83isjbC2WQYcwW8XicsfFJHtnsFlnvHRihY2cPkZp6IrV1RbYu\nP5qDSWubOvp5VXFNMRYg2XIBhXBpH+4MXvi3AXHgQuCeuTHPMI4SL8T+6CStjdU0NTYQmyitIZWN\nNeWUhz02d1gLwJh9srUAsq1GEQO+OMu2GMasE4/7xOM+K9tKZ/hnMqGQx8q2CB1dg/i+XxKT2IzS\nIVsuoPmbL9cw8iQWdwH/FUtK0wEArF5aw/a9w0zFfMrLzAEYs0c+uYDqgI8DL8WFgB4CvqGqtlyR\nMe+JxX08D5a31jA+Xpp5ddYscXMXpmLxeb2OgVF65HM3/Q9QB3wPuBpoD8oMY17j+z5x36e1oYqK\n8tIZ/59KYvLaVCxeZEuMhUY+o4CWquq7kr7fKCJ3F8ogw5gtpmIu/JNIrVyqNNdV0FBbweSUOQBj\ndsk3F9D0/HkRqQUqC2eSYcwOk0GNub2ltB2A53nI6ibivuvQNozZIp8WwH8Dz4nIY8H3F+NWCzOM\nec3UlHtZJnLqlDInrG4CLAxkzC755AK6RkRuB87AdQL/naruLrhlhnEUDI1OEonHCYc8ysKl33Eq\n5gCMApDVAYiIB/xKVd8O7Jwbkwzj6Nm8s58X4RZWWQgcv7qJYWAyZiEgY/bI6gBU1ReRLSLyAeAB\nYCJp27ZcykXk68CZuOwrl6nqo0nbzsdNJosBm4EPqard3cas8FyHcwChBeIAaqvLGQuFmJqK4/s+\nIZsQZswC+bSN/xQX878Z+GPSX1ZE5DxgnaqeDXwQuDJF5CrgHap6Dm6Y6etmYLdhZOW5jn48vAX1\noiwLe/j4dO0vrXQWxvwlWy6gBuBfgGeAe4Gvq+pMZtJcAFwPoKqbRKRJRGpVNbG80YtVdTD43AM0\nz9h6w0jDgaFxuvaPUBb2WDivfygPhxifjLFtzwArWktnYRtj/pKtBfAdXOjmv4ETgc/MUHc7bvWw\nBD3AssSXxMtfRJYBrwFumqF+w0jLpmD934U2a7YsOJ/tXdEiW2IsFLL1AaxR1fcAiMjNuHUAjgaP\nlEzsIrIEuAH4G1tlzJgtNu10t1JZ2CNW4p2mvu8TjQ4yMDBAxAMPj627+iwxnDErZHMA0+EeVY2J\nyEzHn+3BtQISLAe6El9EpB5X6/9nVb09X6VtbTPL4z4T+ULqNvm5k9/SOUikqoyK8jBjfoy62ioA\n6mqrGB2uIBQqny4DMpRBTU1VHnIVeBPetP5scsll+evfzyObu3m+Z4rXTcUJhaCzdxRCU7S1Zo+a\nlsrvtRDl55Mt2chnItiRcivweeAqETkD6FTV5HXt/gPXr3DrTJT29OTf/G1rq8tbfiayJj//5BM1\n5QNDE3T1DnPKmgZiUzH8uE90aIy62iqiQ2MMD08QCsWorB6b3jddmSsfyyk3PDyBH/YhBNGhsaxy\nh5flpz8UClNTWw9AKBTCB57atI/TTyjP+/rkwuRnT34+2ZKLbA7gbBHZlXzcpO++qq7OplhVHxSR\nx0TkftxQz0tF5BJgAPgD8BfAOhH5ULDLT1XVkswZR0Q0OshtD29lX9TVxsMhn9GJKULewuoHCAdh\nn47uYU63lbmNoySbAzjq20tVL08p2pD0uQrDmEWqIzX073WNzFXLmhZkjDwxr6Gj2xaJN46ebAvC\n7JhDOwxjVtjbN0JFWYimuoWZrzAUgoqyEB37zAEYR8/Cah8bi5rhsSmGRidZ2hxZUBPAUmmuL6c/\nOsGBofFim2KUOOYAjAVDz4DLVFLq+f9z0VJXAcDznQNFtsQodcwBGAuGngOuRry0ufTTP2ejpd45\ngC27zQEYR4c5AGNB4Ps+PQMTVJaHF2z8P0FzXTkhz1oAxtFjDsBYEPRFJxgZj7G0uXpBjv5Jpiwc\nYkVrhB17o0xMxoptjlHCmAMwFgRbOt3EmKULPP6f4JhltcTiPjv2Wl4g48gxB2AsCLYGDmChdwAn\nOLa9FoCtFgYyjgJzAEbJ4/s+WzujVJSFaKytKLY5c8LadpcOeqt1BBtHgTkAo+TZd2CUA8OTtDVW\nLPj4f4LG2gpa6qvY2jmA75d2xlOjeJgDMEqeRP7/JQ0Le/RPKutWNjA0OsnePlshzDgyzAEYJc/m\nnQcAaGtcHOGfRObT1a3O4T2lXQwOWkvAmDnmAIySxvd9ntvZT111GXXVhcxuPn8YHRnm7sd3MjA0\nCsBDG3u47eGtRKODOfY0jEMxB2CUNHv7RhgYmmDdirpFE/8HqKqOsLStiaqKML3RSaqqF8foJ2N2\nMQdglDSJ8M+65bOzQlIp4XkeS5qqGRmbYnjMJoQZM8ccgFHSJNb/Xbdi8TkAODjxrXdwosiWGKWI\nOQCjZPF9n007D9BQW8GSxsU1AijB0iaX+K5nwFJDGzPHHIBRcvi+z8DAAFs6uhkcnuC4ZTUMDUVh\nEQ6CaaqrpKI8RO+AtQCMmWMOwCg5otFBbrhrI394pNMV+HHufHQbY2OjxTWsCLh+gAjDYzH6o+YE\njJlhDsAoSSKRGvqGXMfn6mXNVFXXFNmi4tEehIESCfEMI1/MARglie/77O0bJVJZRl2kvNjmFJX2\nFtcRvKXT5gEYM8McgFGS9A9NMj4Zo70lsqjG/6ejqa6SyvIQm3dFbTawMSPMARglyd79Lt6/rMUm\nQHmex5LGSgZHJtmz3/ICGfljDsAoSbr6xoDFswBMLhLDYDfu6CuyJUYpYQ7AKDlicRf/r4uUU1u9\nuOP/CZYGifCe29FfZEuMUsIcgFFydPaMMDnlL5rVv/IhUlVGW2Mlm3b2MxWLF9sco0QwB2CUHJpY\n/tHi/4dwwsp6xiZibO+y0UBGfpgDMEqOLYts/d98kZX1AGzYZv0ARn6YAzBKiqlYnO1dQzTWllNd\nuTjy/+fLCavqKAt7PLW1t9imGCWCOQCjpNi2Z5CJqTjtzdXFNmXeUVke5sTVTezaN8S+fhsOauTG\nHIBRUiTW/13WUlVkS+Ynp61rBeCRjd1FtsQoBcwBGCXFcx39eEB7kzmAdJweOID1z+4tsiVGKWAO\nwCgZxidjPL9ngBVtESorwsU2Z17S0lDFqiW1PL21l9HxqWKbY8xzzAEYJcPWzgGmYj7HL8LlH3Ph\n+z7R6CCDgwOctKqWqVicRzfuttxARlbMARglQyL+f/xKcwCpjI4Mc/fjO7lvQxdTMVfz/8Mju4hG\nbU6AkRlzAEbJsKmjn5Dnceyy2mKbMi+pqo4QqaljxdJm6msq6D4wxfikLRZvZKagA6lF5OvAmbjF\n+i5T1UeTtlUBVwEnqepLC2mHUfqMjE2yvSvKMcvqqLL4f1Y8z0NWN/Hoc91s2H6AC1qai22SMU8p\nWAtARM4D1qnq2cAHgStTRK4A1hfq+MbCYsO2PuK+zwuPaym2KSWBrG4E4DG1WcFGZgoZAroAuB5A\nVTcBTSKS3Ha/HLixgMc3FhCJ2a2JYY5GdprqqmiqLWfzrkEGh22tYCM9hXQA7UDynPQeYFnii6oO\nA4t7KScjJ77v03/gAE8/30tjbTkNVTGi0UF8bHRLLlYvqSbuw/rnbFKYkZ65TKbiwdE/tW1tMxsB\nMhP5Quo2+SOTHxgY4MYHtjEyHmPVkghPbe+nt6ebmtoGWtsapuVGhysIhcqpqz04QSzkeeB502V1\ntVVp5dKXQU1NVR5yFXgT3rT+bHLJZTPRHwqVHzwfoKYmv3M4cU0jG7YP8sCz3bzrdSflXDqzFO6H\nUpGfT7Zko5AOYA+uFZBgOdCVIjNjh9DTE81btq2tLm/5mcia/NzJDw5G6dw/CcDKJfXEqSDuu9s2\nOjQ2LTc8PEEoFKOy+mBZ3PfxfCdXV1tFdGgsrVy6Mlc+llNueHgCP+xD6KA9+R4jX/2hUIzWNnc+\n+eqvq60iNjXFqcc28eTz/dz/+C5OWN2U5so7SuV+KAX5+WRLLgoZAroVeAeAiJwBdAZhn2QsBGTk\npKtvjLKwZ+mfj4BXnroEgNsf3V1kS4z5SMEcgKo+CDwmIvcD3wAuFZFLROQtACJyO3ALcIqIbBCR\n9xfKFqN02ds3ytBojGUtNYTDNm1lphzTXsOapXU8vqWH3gOjxTbHmGcUtA9AVS9PKdqQtO3CQh7b\nWBis37wfgLXLbPbvkeB5Hhe+ZCXf//1z3PFEJxefv67YJhnzCKtSGfOWWDzOo5v3U17msXqJzf6d\nKYn8QCetrKKuuoy7ntjN3n37LT+QMY05AGPesmFbH4MjU6xuq7bwzxGQyA/00MZu1rZHGJuIc+3N\nmyw/kDGNPVXGvOX+p92gsbVLrfP3SEnkB3rBcUupLA+zY98EI5Ym2ggwB2DMSwZHJnhyay/LW6pp\nrC0vtjklT3lZiFOObWYy5nPP0/uKbY4xTzAHYMxLbntkF7G4z5knteacwGTkxwmrGqkoC3HXU/sY\nGp0stjnGPMAcgDHvGBie4LZHd9FQW8HLT7LcP7NFeVmIE1fVMjYR46aHOoptjjEPMAdgzDt+98AO\nJibj/J+z11JRbrfobHLc8hoaa8u5/dHd9A2O5d7BWNDY02XMK3oPjHLXE520NVZx7mnLi23OgiMc\n8viTl61gKhbnN/duL7Y5RpExB2DMG2LxON//3TPE4j6vfUk7I8NRN2TRhq3PGr7vc8KyMpY1V3H/\nM11s3rGXwcEBmxuwSDEHYMwbbrp/K5t3R2lvqmR0bJz7NnRx56PbGBuzFAazxejIMPc+uYtjl0Xw\nffjRrdu47eGtDA7a3IDFyFymgzaMjHR0DXLjQ51Uloc49/SVVFe6W3NkeKjIli08qqojLGtpZcue\nUbr6RhleWVNsk4wiYS0Aoyj4vs/g4ACDgwPs6+3j3699iKmYz4vXNU6//I3C4XkeL5Y2ADbsGLQQ\n0CLFnjSjKESjg9z28FaqqiM89Fw/XfvHWNXs0VxjL6K5oq2pmtVLa9nZPcT653p40br23DsZCwpr\nARhFozpSQ0fPFJ37x1jeWsMpayzh21xzhrQR8uDHf9jKqKWIWHSYAzCKRv/QBI9rD9WVYV5z1prp\nJQ+NuaO+poITVtXSH53ghvttWOhiwxyAURTGJ2M8vKmfuA+veOEyaqos30+xOHFlHUuaqrjtkd3s\n7J6dpQaN0sAcgFEUfnv/boZGY5y8tonlrTYKpZiEwx6XvP544r7Pf9/wLGMTFgpaLJgDMOacjTv6\neGBjLw01ZbxILNfPfOC0dS285qWr6No/wo/+sNlGBS0SzAEYc8r4ZIwf3rIJz4OXHN9IOGS34Hzh\nHa86jmOX1/PQs93c/pgtIr8YsKfPmFN+c+82eg6Mcf7pS2mqqyi2OQZuTsbAwAAjw1H+4tVrqK0u\n42e3b+H29c9Pz9VI/FnLYGFh8wCMOWN71yC3PrKLJU3VvO6ly1n/XHexTTJw6SH+8ODzVFS6Ybhn\nndTEXU/28NM7OtjeNciK1uppuYvOXEd9fUMxzTVmEWsBGAXH9336+vu5+sZn8X145ytXMT46ZEne\n5hHV1TVEauqI1NSxfEkzLzs+QjgED2/qp38kRKSmjuqIddYvNMwBGAUnGh3kqhs30tU3yjHtEbr7\nhizJ2zynsaaMl66rxfM87nqik+6+kWKbZBQAcwBGwensHWFr1zjVlWWcecpyIjV1VFVbbXK+01pf\nznmnLyfu+9zxWCd90Ylim2TMMuYAjIIyNjHFD27dRtyHl79gKRXl4WKbZMyAlUtqOfe05UzF4tz3\nzH669lurbSFhDsAoKD++Vek5MM7xK2pY2Wa5fkqRte11nPWCdiamfL574xb6o+PFNsmYJcwBGAXj\nlod38sAze1m9JMIL19YX2xzjKDh+ZQMvPKaewZFJvnnd0zZbeIFgDsAoCLc8uINf3LmVprpK3vfa\nYwmFLNFbqSMranjZiS3s2Bvlyp8/SdzmBJQ8Ng/AmFXi8Tg3P/g8v753FzVVZfz1G9dRzrgN+VwA\neJ7Hxeetpn9oinuf7KQi5PFnFx2PZ1lcSxZrARizxuRUjKtu2MB19+6isiLEWSc1smV3vw35XECU\nhUP83dtPZe2yev74+G5+fc82mx1cwpgDMGYF3XWAz17zCOs37aeptpyLLzyBFUtbbMjnAsL3faLR\nQeKTI3zi3afQ2lDJ7x/s4KobN9piMiWKhYCMI8b3fTr29HDz+i4e2bwfDzjrxEaWNVdTF6kgOjRW\nbBONWWR0ZJi7H++jsbmF2ppKXnZCIw8828vDG7vZ3jXIO191HC86vs36e0oIcwDGEbGnd5ibH9zG\ngxt7iPvQUFPGGesa8Sb6mZywF8BCpao6QqSmjpraKlqp4PzTPHoGxrn/2X6+ff0ztNZX8pITmnn5\nC1eysq3W+gfmOeYAjLzpOTDKk1t6eVx72LzrAACRyjBnnNDG2mX1hDyP3n02W3QxMT42QhXjXPTi\nNrZ0DtPRPcItj3RxyyNdtNZX8sJjGzn9uCbOKF+K74fMIcwzzAEYh5GI9U5OxdnRPYzuGuS5XVF2\n9wxPyxy3vJaXrqtlIuZRW2dj/BczVdURmlubaW9r5szJGJue72RP3zh9wxPc+WQ3dz7ZTd1NWzj3\nhUt51RlraG2sLrbJRkBBHYCIfB04EzcI8DJVfTRp24XAvwMx4CZV/bdC2mLkJhaPs6MrypPaxUMb\nu+kfjhGPu20hD1prQ6xur2NZSxXVFWH29XQTqakHcwBGQEV5mOXN5axsraKhqZU9+0fYvmeQXfuG\nuGn9Hm5avwdZ2cDLX9DOS09cQsTWgi4qBXMAInIesE5VzxaRE4FrgLOTRP4LeA2wB7hbRK5T1ecK\nZY9xKPG4z96+EXZ0DbJ11352947Q0T3M+GR8WqaprpL25gjLWiI0VI0xORGnuXXJ9PaR4aFimG6U\nCOFwiFVLalm1pJbJyVHGRsZ4ZucoW3YPoLsH+PGtyrHL6zlpTRNr2+tZtaSW5vpKCxPNIYVsAVwA\nXA+gqptEpElEalV1SESOBfpUtRNARG4CXg2YAzhKfN9nfDLG0OgkPfsPMDw2Rf/QBP3RCfqi7n//\n0AQDQxNMxg4dv11bHWZFS4RIeIxlrXUsW9Y+vW10eJzJuT4ZY8EwNTFCdDDKace2cPyKCDv3jbBr\n3zBbOwfYsntgWi5SGWbV0jpOWNNMa10ly1trWNpcTY21FApCIR1AO/BY0veeoGxr8L8nads+4LgC\n2jKrTE7F6B8co29wjHjcJ+b77n/84P/pz7E4Md+nrneEgYFRQh7guVmVIdx/z0v6j/vfPzpFX/8w\nk5NxJqZiTEzGGZ+MMTI2xfDYJP2Dw4yOu+8j4zHGp+JERyYZGZ+aDttkorI8RENtOZGKMBXeGG1N\nNaxZ2T6dqbN3XxchW6vXmGUSI4giNdDa3Mjq5i6GR8aYCtdyYGiSgeFJ+qMT6M4DbN554JB9a6rC\ntDZU0VxXQXVlmEhlGY31Eaory/E8qKutYigYdpxoQXgehDyPifExQiH3ORTyCHkey5e1EB0ac8+h\n5xFKeQadAtyn4Hk8cGBkWq8r9g75HvI8vJDHWBwGDoxMP9ehQG8o5OF5HlUVYSrnSVbcuewEztau\nK5k23/hkjH/8zgMMjc6f+rAHlIWhLORTXx2mPOzhxyaorqygtraaSGWYSGWYyZEDVFeW0dLaSm1N\nJUPD4/T1dhMKTTI1McJUMIBnbHSYUKiMkeHo9DHGRkYYG4sdWpZOLigbHhpkZHg8p1yiLMREXnKJ\nsrIyiMW9rHJuhqrPyHCUEBOMDI/nf4wZnK9fEyfuh6bL8znGkVxPP+4X9Xoml83W9SwPl9HWXMWy\n5ioA+nq7GRmdIlLfQnf/GMNjcQ4MjTE25bOze5iO7oMDEY6OrbOkZ+ZUlIX44ofPorm+qmg2JCik\nA9iDq+knWA50BZ87U7atDMqy4llw0DgS3vqyOTnMD/nqnByHv3zT3BzHKBi/vqLYFjgK2c6/FXgH\ngIicAXSq6jCAqnYA9SKyRkTKgDcE8oZhGMYcUdAatYh8CXglbqjnpcAZwICq/kZEzgW+Eoj+SlX/\ns5C2GIZhGIZhGIZhGIZhGIZhGIZhGIZhLAbm7bBKEfln4KLgawhoV9UTUmTeA1wGxIGrgGbgPcAk\n8LfJuYcC+UngvqSim4A/yyKfrP/ZwJ7ng823qeoXs+hvB2pyyB9iv6peIyJLgU3Am1X1nhz2vxpo\nyyKfrP9nwOuASqAC+HtVXZ9FfzkQzSGfrP9q4FzgWNzw4k+o6v1Z9HtARw751N93G/Bz4AOq+ntS\nSHN9/hX43yzyyfq/D7wKWI0btPB+Vd2eRv9eIJH86EJVfSRp+2H5rXLkw9oB7Azkwd27rbgZ9P+p\nqt9OOX46/admkU+n/2PAObhr/iVVvT6H/iuyyKfq/yDwZWAJUAV8Ifm6p+oH/hP4QRb5w+xX1T0i\nUg08A/yrqv4wm/1BeSb5VP3fAr4XyAJ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       "text": [
        "<matplotlib.figure.Figure at 0x7f8124afd410>"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Prediction on Sammi's child"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The prediction of OLS Regression Model on Sammi is obtained by taking dot-product of the co-efficients obtained from the model with corresponding sammy's data."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"Prediction for Sammi's child according to OLS Regression is\", olsPrediction[-1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Prediction for Sammi's child according to OLS Regression is 7.49392724037\n"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Ridge Regression Model"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "It can be observed above that the co-efficients and intercept determined by the OLS model have insanely high/low values. Ridge regression penalizes such co-efficients."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf = linear_model.Ridge(alpha = 0.5)\n",
      "clf.fit(allRowsList, trainWeights)\n",
      "coeffArray = clf.coef_\n",
      "intercept = clf.intercept_"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"The co-efficients and the intercept determined by the Ridge Regression are:\"\n",
      "print \"-----------------Co-efficients----------------------\"\n",
      "print coeffArray\n",
      "print \"-----------------Intercept--------------------------\"\n",
      "print intercept"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The co-efficients and the intercept determined by the Ridge Regression are:\n",
        "-----------------Co-efficients----------------------\n",
        "[-0.27019736 -0.08045012 -0.03821463 -0.01453966  0.05324337 -0.0742844\n",
        "  0.27834685  0.0024808  -0.319014    0.12016997 -0.17691484 -0.36350541\n",
        " -0.65548501 -0.38365145  0.87710935 -0.45737314 -0.10154639  0.03719956\n",
        " -0.40866667 -0.01083832  0.1079103  -0.18551486  0.01380836 -0.10398397\n",
        " -0.02856255  0.31554274 -0.10449372 -0.00386799 -0.21947628 -0.11388111\n",
        " -0.15740975 -0.13670715 -0.20352929 -0.38579528 -0.29429244 -0.38068642\n",
        " -0.45009708  0.0276141  -0.01464623 -0.00109517  0.06526284 -0.00469074\n",
        "  0.02896953  0.02675315  0.05998394 -0.02261268 -0.05974978  0.71786181\n",
        " -0.1146464  -0.00715338  0.14555867]\n",
        "-----------------Intercept--------------------------\n",
        "7.94167665224\n"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ridgePrediction = clf.predict(testDF)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 39
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Evaluation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "testDFCopy['PREDICTED_WEIGHT_RIDGE'] = ridgePrediction[:-1]\n",
      "error_distribution(testDFCopy, testDFCopy['ACTUAL_WEIGHT'], testDFCopy['PREDICTED_WEIGHT_RIDGE'], 'Ridge Regression', 3)\n",
      "#plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The root mean square error for Ridge Regression is 1.06872084621\n",
        "The r2 score for Ridge Regression is 0.367957887007\n",
        "The mean absolute error for Ridge Regression is 0.829994989547\n",
        "The explained variance score for Ridge Regression is 0.368017074961\n",
        "\n",
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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wOmAs4S8EtGc4T1FmPPERQIvnVebZEsuCpgoANu7QUUDK9JG2BSAifwT+aIz5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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f8124b40e50>"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Prediction on Sammi's child"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As done for OLS Model, the prediction of Ridge Regression Model on Sammi is obtained by taking dot-product of the co-efficients obtained from the model with corresponding sammi's data."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"Prediction for Sammi's child according to Ridge Regression is\", ridgePrediction[-1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Prediction for Sammi's child according to Ridge Regression is 7.49385820357\n"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#K-nearest neighbors"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The below code is for the K-nearest neighbor model. Different values of k have been tried and the accuracy has been measured and there's no particular trend observed. Higher values of k tend to perform better, but only upto a certain limit. For example, a value of k = 10 yielded about the same RMSE as when k = 150. (But is RMSE the only indicator of model performance? How about other measures/error distribution variance with k?)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X, y = trainDF, trainWeights\n",
      "\n",
      "# create the model\n",
      "knn = neighbors.KNeighborsClassifier(n_neighbors=100)\n",
      "\n",
      "# fit the model\n",
      "knn.fit(X, y)\n",
      "\n",
      "knnPrediction = knn.predict(testDF)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#distances,indices = knn.kneighbors(testDF,5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 43
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Evaluation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "testDFCopy['K_NEAREST_NEIGHBOURS'] = knnPrediction[:-1]\n",
      "error_distribution(testDFCopy, testDFCopy['ACTUAL_WEIGHT'], testDFCopy['K_NEAREST_NEIGHBOURS'], 'KNN Regression', 4)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The root mean square error for KNN Regression is 1.18148042196\n",
        "The r2 score for KNN Regression is 0.227549757687\n",
        "The mean absolute error for KNN Regression is 0.912528175225\n",
        "The explained variance score for KNN Regression is 0.230179587161\n",
        "\n",
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Hq6/2qvXFxfDe9w5yzTXlOA6sWBHnzDOhrLKXh759BfVnlhAKudx/f1GBrTeM7JgDMIwM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\nIrJARIqBN/ryhmEYxgSR1xq1iHwFOBd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       "text": [
        "<matplotlib.figure.Figure at 0x7f811bc1c350>"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Prediction on Sammi's child"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"Prediction for Sammi's child according to k Nearest Neighbor Model is\", knnPrediction[-1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Prediction for Sammi's child according to k Nearest Neighbor Model is 6.8125\n"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Decision Tree Regression"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The below code is for the Decision Tree model. Different values of depth limits have been tried and the accuracy has been measured at each depth. A depth limit of 7 seemed to produce the most accurate results."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "resultsDict = {}\n",
      "optimalPrediction = []\n",
      "minRMS = 2.0\n",
      "#testDFCopy = testDF.iloc[:-1]\n",
      "\n",
      "for depth in range(1,15):\n",
      "    \n",
      "    clf_1 = DecisionTreeRegressor(max_depth=depth)\n",
      "    clf_1.fit(trainDF, trainWeights)\n",
      "\n",
      "    # Predict\n",
      "    decisionTreePrediction = clf_1.predict(testDF)\n",
      "    colName = str(depth) + '_DEC_TREE'\n",
      "\n",
      "    testDFCopy[colName] = decisionTreePrediction[:-1]\n",
      "    testDFCopy['ERROR_'+colName] = testDFCopy['ACTUAL_WEIGHT']  - testDFCopy[colName]   \n",
      "\n",
      "    testDFCopy['ERROR_'+colName+'_SQUARED'] = (testDFCopy['ERROR_'+colName] * testDFCopy['ERROR_'+colName])\n",
      "    rms = sqrt(np.mean(testDFCopy['ERROR_'+colName+'_SQUARED']))\n",
      "    if rms < minRMS:\n",
      "        minRMS = rms\n",
      "        optimalPrediction = decisionTreePrediction\n",
      "    print \"RMS for\", depth, \"-d model is\", rms\n",
      "    resultsDict[depth] = rms"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "RMS for 1 -d model is 1.17336714632\n",
        "RMS for"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 2 -d model is 1.10059187524\n",
        "RMS for"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 3 -d model is 1.07593546473\n",
        "RMS for"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 4 -d model is 1.06251270778\n",
        "RMS for"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 5 -d model is 1.05065455241\n",
        "RMS for"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 6 -d model is 1.04117410452\n",
        "RMS for"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 7 -d model is 1.03582802515\n",
        "RMS for"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 8 -d model is 1.0376539981\n",
        "RMS for"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 9 -d model is 1.04080059913\n",
        "RMS for"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 10 -d model is 1.04786271324\n",
        "RMS for"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 11 -d model is 1.05482649382\n",
        "RMS for"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 12 -d model is 1.06433843425\n",
        "RMS for"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 13 -d model is 1.07954523637\n",
        "RMS for"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 14 -d model is 1.09102766326\n"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The following plot shows the variation of error with depth for the decision tree regression model. It can be observed that depth = 8 produces the least RMSE value."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axes = plt.subplots(figsize=(12,10))\n",
      "axes.plot(resultsDict.keys(), resultsDict.values())\n",
      "print resultsDict\n",
      "plt.xlabel(\"Depth\")\n",
      "plt.xticks(range(1,20))\n",
      "plt.ylabel(\"RMS\")\n",
      "#axes.bar(preDict.keys(), preDict.values(), align=\"center\", width=0.5, alpha=0.5)\n",
      "axes.set_title(\"Decision Tree\")\n",
      "print type(optimalPrediction)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "{1: 1.173367146319418, 2: 1.1005918752398598, 3: 1.0759354647261548, 4: 1.0625127077794638, 5: 1.0506545524140338, 6: 1.0411741045210074, 7: 1.0358280251503298, 8: 1.0376539981011894, 9: 1.0408005991335643, 10: 1.0478627132376233, 11: 1.0548264938160352, 12: 1.0643384342502824, 13: 1.0795452363724263, 14: 1.0910276632552798}\n",
        "<type 'numpy.ndarray'>\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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FAAAAMgYl27H8vByVFecxLgIAAJBBKNk+EC0Pqa6pQz29MddRAAAAkASUbB+IhkOKx6UD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\njnL7z6KRS0S+voSEEGOGdOPkgV1ZsGwrr763kVf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RERERERERERERERERERGR44DWyRYR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\niIiIiIiIiIiIiIiIiIiIiEg8+F/abFK2eA/maAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f8124bada90>"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Evaluation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "error_distribution(testDFCopy, testDFCopy['ACTUAL_WEIGHT'], optimalPrediction[:-1], 'Decision Tree Regression', 1)\n",
      "#plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The root mean square error for Decision Tree Regression is 1.03582802515\n",
        "The r2 score for Decision Tree Regression is 0.406264838571\n",
        "The mean absolute error for Decision Tree Regression is 0.80086469335\n",
        "The explained variance score for Decision Tree Regression is 0.406353028241\n",
        "\n",
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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syU8d+dpgkFWYvWln9bomAMpLPDq79vbB7+4eIBSKU1rWR6wiQmdX3z77MskB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\nzMzMzMzMzMzMzMzMzMzMzMzMzGy5/gCvmB1y3R8pjgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f812397ccd0>"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Decision Tree with Boosting - AdaBoost Regressor"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rng = np.random.RandomState(1)\n",
      "clf_2 = AdaBoostRegressor(DecisionTreeRegressor(max_depth=8),\n",
      "                          n_estimators=30, random_state=rng)\n",
      "clf_2.fit(trainDF, trainWeights)\n",
      "\n",
      "# Predict\n",
      "adaBoostPrediction = clf_2.predict(testDF)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 49
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Evaluation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "testDFCopy['Adaboost'] = adaBoostPrediction[:-1]\n",
      "error_distribution(testDFCopy, testDFCopy['ACTUAL_WEIGHT'], testDFCopy['Adaboost'], 'AdaBoost Regression', 2)\n",
      "#plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The root mean square error for AdaBoost Regression is 1.036363108\n",
        "The r2 score for AdaBoost Regression is 0.40565126266\n",
        "The mean absolute error for AdaBoost Regression is 0.805922175468\n",
        "The explained variance score for AdaBoost Regression is 0.409128896885\n",
        "\n",
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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AG2pLY+hlc10lvg/t3SPFNsVYoEylBbAIuFBVtyU3iMjRqro16MTNiqp+Nm3T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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f811b845f90>"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Prediction on Sammi's child"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"Prediction for Sammi's child according to Ridge Regression is\", adaBoostPrediction[-1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Prediction for Sammi's child according to Ridge Regression is 7.70957738615\n"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Random Forest Regressor"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf_1 = RandomForestRegressor(max_depth=10)\n",
      "clf_1.fit(trainDF, trainWeights)\n",
      "\n",
      "# Predict\n",
      "randomForestPrediction = clf_1.predict(testDF)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 52
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Evaluation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "colName = 'RandomForest'\n",
      "testDFCopy[colName] = randomForestPrediction[:-1]\n",
      "error_distribution(testDFCopy, testDFCopy['ACTUAL_WEIGHT'], testDFCopy['RandomForest'], 'Random Forest Regressor', 3)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The root mean square error for Random Forest Regressor is 1.02129112803\n",
        "The r2 score for Random Forest Regressor is 0.422812956969\n",
        "The mean absolute error for Random Forest Regressor is 0.792014529883\n",
        "The explained variance score for Random Forest Regressor is 0.422864853214\n",
        "\n",
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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nkWF/oZh86eRnky35mMoooKXAIUANMAD8RkR+pao/ziF/PfBV4EIROQ5oV9XU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\ncBzQp6qXichJwLcD0b+qapGrT4ZhGIZ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       "text": [
        "<matplotlib.figure.Figure at 0x7f8124d4a910>"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Prediction on Sammi"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"Prediction for Sammi's child according to Random Forest Regression Model is\", randomForestPrediction[-1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Prediction for Sammi's child according to Random Forest Regression Model is 7.62628169171\n"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Results Summary"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.set_option('display.max_columns', 10)\n",
      "pd.set_option('display.width', 2000)\n",
      "newDF= pd.DataFrame(dictny)\n",
      "newDF"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>AdaBoost Regression</th>\n",
        "      <th>Baseline Model</th>\n",
        "      <th>Decision Tree Regression</th>\n",
        "      <th>KNN Regression</th>\n",
        "      <th>OLS Regression</th>\n",
        "      <th>Random Forest Regressor</th>\n",
        "      <th>Ridge Regression</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Explained Variance Score</th>\n",
        "      <td> 0.409129</td>\n",
        "      <td> 2.220446e-16</td>\n",
        "      <td> 0.406353</td>\n",
        "      <td> 0.230180</td>\n",
        "      <td> 0.368015</td>\n",
        "      <td> 0.422865</td>\n",
        "      <td> 0.368017</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Mean Absolute Error</th>\n",
        "      <td> 0.805922</td>\n",
        "      <td> 9.903592e-01</td>\n",
        "      <td> 0.800865</td>\n",
        "      <td> 0.912528</td>\n",
        "      <td> 0.829992</td>\n",
        "      <td> 0.792015</td>\n",
        "      <td> 0.829995</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>R2 Score</th>\n",
        "      <td> 0.405651</td>\n",
        "      <td> 0.000000e+00</td>\n",
        "      <td> 0.406265</td>\n",
        "      <td> 0.227550</td>\n",
        "      <td> 0.367956</td>\n",
        "      <td> 0.422813</td>\n",
        "      <td> 0.367958</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>RMS</th>\n",
        "      <td> 1.036363</td>\n",
        "      <td> 1.344285e+00</td>\n",
        "      <td> 1.035828</td>\n",
        "      <td> 1.181480</td>\n",
        "      <td> 1.068722</td>\n",
        "      <td> 1.021291</td>\n",
        "      <td> 1.068721</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>4 rows \u00d7 7 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 56,
       "text": [
        "                          AdaBoost Regression  Baseline Model  Decision Tree Regression  KNN Regression  OLS Regression  Random Forest Regressor  Ridge Regression\n",
        "Explained Variance Score             0.409129    2.220446e-16                  0.406353        0.230180        0.368015                 0.422865          0.368017\n",
        "Mean Absolute Error                  0.805922    9.903592e-01                  0.800865        0.912528        0.829992                 0.792015          0.829995\n",
        "R2 Score                             0.405651    0.000000e+00                  0.406265        0.227550        0.367956                 0.422813          0.367958\n",
        "RMS                                  1.036363    1.344285e+00                  1.035828        1.181480        1.068722                 1.021291          1.068721\n",
        "\n",
        "[4 rows x 7 columns]"
       ]
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image(filename='/home/nargis/Downloads/error_part1_final.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "IOError",
       "evalue": "[Errno 2] No such file or directory: u'/home/nargis/Downloads/error_part1_final.png'",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mIOError\u001b[0m                                   Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-57-ebae49275621>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mImage\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'/home/nargis/Downloads/error_part1_final.png'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/IPython/core/display.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data, url, filename, format, embed, width, height, retina)\u001b[0m\n\u001b[0;32m    644\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheight\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mheight\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    645\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mretina\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mretina\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 646\u001b[1;33m         \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mImage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    647\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    648\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mretina\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/IPython/core/display.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data, url, filename)\u001b[0m\n\u001b[0;32m    334\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilename\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mNone\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mfilename\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0municode_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    335\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 336\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    337\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    338\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/IPython/core/display.pyc\u001b[0m in \u001b[0;36mreload\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    666\u001b[0m         \u001b[1;34m\"\"\"Reload the raw data from file or URL.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    667\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0membed\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 668\u001b[1;33m             \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mImage\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    669\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mretina\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    670\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_retina_shape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/IPython/core/display.pyc\u001b[0m in \u001b[0;36mreload\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    344\u001b[0m         \u001b[1;34m\"\"\"Reload the raw data from file or URL.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    345\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilename\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 346\u001b[1;33m             \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_read_flags\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    347\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    348\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0murl\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mIOError\u001b[0m: [Errno 2] No such file or directory: u'/home/nargis/Downloads/error_part1_final.png'"
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image(filename='/home/nargis/Downloads/error_part_2_final.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
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OEQAA4Ibj6ekjb19/nU01WpMsXu5O6ta+ngIDA+Xt61/k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       "prompt_number": 90,
       "text": [
        "<IPython.core.display.Image at 0xade919cc>"
       ]
      }
     ],
     "prompt_number": 90
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Predicted Birth Weight of Sammi's Baby is 7lb 10oz at 41 weeks"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}